diff --git a/articles/Inference.html b/articles/Inference.html index f4a3e25..d3a4c7a 100644 --- a/articles/Inference.html +++ b/articles/Inference.html @@ -141,9 +141,9 @@

Fitting the model#> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C0 mutations #> ── [ VIBER ] My VIBER model n = 3 (w = 4 dimensions). Fit with k = 3 clusters. ─ -#> ℹ Starting clustering of clone C0 mutations• Clusters: π = 67% [C1] and 33% [C3], with π > 0. -#> ℹ Starting clustering of clone C0 mutations• Binomials: θ = <0.09, 0.19, 0.01, 0> [C1] and <0.01, 0.36, 0.03, 0.4> [C3]. -#> ℹ Starting clustering of clone C0 mutationsℹ Score(s): ELBO = -1461.827. Fit converged in 6 steps, ε = 1e-10. +#> ℹ Starting clustering of clone C0 mutations• Clusters: π = 67% [C3] and 33% [C1], with π > 0. +#> ℹ Starting clustering of clone C0 mutations• Binomials: θ = <0.09, 0.19, 0.01, 0> [C3] and <0.01, 0.36, 0.03, 0.4> [C1]. +#> ℹ Starting clustering of clone C0 mutationsℹ Score(s): ELBO = -1461.821. Fit converged in 6 steps, ε = 1e-10. #> ℹ Starting clustering of clone C0 mutations✔ Reduced to k = 2 (from 3) selecting VIBER cluster(s) with π > 0.166666666666667, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C0 mutations✔ Starting clustering of clone C0 mutations ... done #> @@ -153,8 +153,8 @@

Fitting the model#> # A tibble: 3 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> -#> 1 S1 0.0856 0.190 0 0 2 FALSE TRUE -#> 2 S2 0.00680 0.362 0.0287 0.396 1 FALSE FALSE +#> 1 S1 0.00684 0.362 0.0286 0.396 1 FALSE FALSE +#> 2 S2 0.0856 0.190 0 0 2 FALSE TRUE #> 3 C0 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 1, 2, 1, 1 #> ℹ Starting phylogeny inference of clone C0ℹ Total 2 tree structures - search is exahustive @@ -172,29 +172,31 @@

Fitting the model#> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C1 mutations #> ── [ VIBER ] My VIBER model n = 8 (w = 4 dimensions). Fit with k = 8 clusters. ─ -#> ℹ Starting clustering of clone C1 mutations• Clusters: π = 25% [C2], 25% [C3], 25% [C5], and 25% [C6], with π > 0. -#> ℹ Starting clustering of clone C1 mutations• Binomials: θ = <0, 0.14, 0.01, 0> [C2], <0, 0.08, 0.01, 0.15> [C3], <0.31, 0, -#> 0.01, 0.27> [C5], and <0.18, 0, 0.01, 0> [C6]. -#> ℹ Starting clustering of clone C1 mutationsℹ Score(s): ELBO = -4567.669. Fit converged in 8 steps, ε = 1e-10. -#> ℹ Starting clustering of clone C1 mutations✔ Reduced to k = 4 (from 8) selecting VIBER cluster(s) with π > 0.0625, and Binomial p > 0 in w > 0 dimension(s). +#> ℹ Starting clustering of clone C1 mutations• Clusters: π = 25% [C4], 25% [C5], 25% [C6], 13% [C2], and 13% [C8], with π > +#> 0. +#> ℹ Starting clustering of clone C1 mutations• Binomials: θ = <0, 0.08, 0.01, 0.15> [C4], <0, 0.14, 0.01, 0> [C5], <0.18, 0, +#> 0.01, 0> [C6], <0.2, 0.01, 0.02, 0.23> [C2], and <0.43, 0.01, 0.02, 0.32> [C8]. +#> ℹ Starting clustering of clone C1 mutationsℹ Score(s): ELBO = -4571.197. Fit converged in 9 steps, ε = 1e-10. +#> ℹ Starting clustering of clone C1 mutations✔ Reduced to k = 5 (from 8) selecting VIBER cluster(s) with π > 0.0625, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C1 mutations✔ Starting clustering of clone C1 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C1 #> [ ctree ~ clone trees generator for C1 ] #> -#> # A tibble: 5 × 8 -#> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> -#> 1 S1 0.00162 0.142 0 0 2 FALSE FALSE -#> 2 S2 0.00161 0.0756 0.0122 0.147 2 FALSE TRUE -#> 3 S3 0.314 0.00281 0.00870 0.271 2 FALSE FALSE -#> 4 S4 0.184 0.00279 0 0 2 FALSE FALSE -#> 5 C1 1 1 1 1 1 TRUE FALSE -#> ✔ Trees per region 2, 2, 1, 2 -#> ℹ Starting phylogeny inference of clone C1ℹ Total 6 tree structures - search is exahustive +#> # A tibble: 6 × 8 +#> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> +#> 1 S1 0.198 0.00560 0.0170 0.225 1 FALSE FALSE +#> 2 S2 0.00162 0.0756 0.0122 0.147 2 FALSE TRUE +#> 3 S3 0.00162 0.142 0 0 2 FALSE FALSE +#> 4 S4 0.184 0.00279 0 0 2 FALSE FALSE +#> 5 S5 0.430 0.00561 0.0170 0.318 1 FALSE FALSE +#> 6 C1 1 1 1 1 1 TRUE FALSE +#> ✔ Trees per region 6, 2, 5, 5 +#> ℹ Starting phylogeny inference of clone C1ℹ Total 48 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C1 #> ℹ Starting phylogeny inference of clone C1── Ranking trees -#> ℹ Starting phylogeny inference of clone C1✔ 6 trees with non-zero score, storing 6 +#> ℹ Starting phylogeny inference of clone C1✔ 33 trees with non-zero score, storing 33 #> ℹ Starting phylogeny inference of clone C1✔ Starting phylogeny inference of clone C1 ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting clustering of clone C4 mutations @@ -206,10 +208,10 @@

Fitting the model#> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C4 mutations #> ── [ VIBER ] My VIBER model n = 6 (w = 4 dimensions). Fit with k = 6 clusters. ─ -#> ℹ Starting clustering of clone C4 mutations• Clusters: π = 50% [C3], 17% [C2], 17% [C5], and 17% [C6], with π > 0. -#> ℹ Starting clustering of clone C4 mutations• Binomials: θ = <0.15, 0, 0.01, 0> [C3], <0.19, 0.22, 0.3, 0.2> [C2], <0, 0, -#> 0.02, 0.29> [C5], and <0.36, 0, 0.02, 0.28> [C6]. -#> ℹ Starting clustering of clone C4 mutationsℹ Score(s): ELBO = -3594.658. Fit converged in 6 steps, ε = 1e-10. +#> ℹ Starting clustering of clone C4 mutations• Clusters: π = 50% [C4], 17% [C1], 17% [C5], and 17% [C6], with π > 0. +#> ℹ Starting clustering of clone C4 mutations• Binomials: θ = <0.15, 0, 0.01, 0> [C4], <0.36, 0, 0.02, 0.28> [C1], <0, 0, +#> 0.02, 0.29> [C5], and <0.19, 0.22, 0.3, 0.2> [C6]. +#> ℹ Starting clustering of clone C4 mutationsℹ Score(s): ELBO = -3594.688. Fit converged in 7 steps, ε = 1e-10. #> ℹ Starting clustering of clone C4 mutations✔ Reduced to k = 4 (from 6) selecting VIBER cluster(s) with π > 0.0833333333333333, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C4 mutations✔ Starting clustering of clone C4 mutations ... done #> @@ -219,10 +221,10 @@

Fitting the model#> # A tibble: 5 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> -#> 1 S1 0.192 0.222 0.296 0.198 1 FALSE FALSE -#> 2 S2 0.146 0.00159 0 0 3 FALSE FALSE -#> 3 S3 0 0 0.0186 0.292 1 FALSE TRUE -#> 4 S4 0.358 0.00472 0.0171 0.275 1 FALSE FALSE +#> 1 S1 0.358 0.00474 0.0170 0.275 1 FALSE FALSE +#> 2 S2 0.146 0.00159 0 0 3 FALSE TRUE +#> 3 S3 0 0 0.0187 0.291 1 FALSE FALSE +#> 4 S4 0.192 0.222 0.296 0.198 1 FALSE FALSE #> 5 C4 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 6, 1, 6, 5 #> ℹ Starting phylogeny inference of clone C4ℹ Total 54 tree structures - search is exahustive @@ -242,7 +244,7 @@

Fitting the model#> ── [ VIBER ] My VIBER model n = 2 (w = 4 dimensions). Fit with k = 2 clusters. ─ #> ℹ Starting clustering of clone C7 mutations• Clusters: π = 50% [C1] and 50% [C2], with π > 0. #> ℹ Starting clustering of clone C7 mutations• Binomials: θ = <0.4, 0, 0.31, 0.35> [C1] and <0.01, 0.11, 0, 0> [C2]. -#> ℹ Starting clustering of clone C7 mutationsℹ Score(s): ELBO = -1584.513. Fit converged in 5 steps, ε = 1e-10. +#> ℹ Starting clustering of clone C7 mutationsℹ Score(s): ELBO = -1584.497. Fit converged in 5 steps, ε = 1e-10. #> ℹ Starting clustering of clone C7 mutations✔ Starting clustering of clone C7 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C7 @@ -265,415 +267,27 @@

Fitting the model#> #> ℹ Fitting model to estimate population growth rates #> ℹ Starting growth models inference of clone C0 -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 1.0000, 1.0000], -#> [104.0897, 82.3385], -#> [ 91.3039, 88.1951]], dtype=torch.float64) -#> $fitness -#> [1] 0.02802839 0.03648976 -#> -#> $sigma -#> [1] 0.3582631 0.7575347 1.0299374 -#> -#> $init_time -#> [1] 0 0 -#> -#> $parent_rate -#> NULL -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [ 8.9140, 0.0000], -#> [17.3552, 0.0000]], dtype=torch.float64) -#> $fitness -#> [1] -0.03116952 -0.56715947 -#> -#> $init_time -#> [1] 23.85784 102.79580 -#> -#> $sigma -#> [1] 0.9772817 0.8978677 0.9829488 -#> -#> $parent_rate -#> [1] 0.02802839 0.03648976 -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [ 0.7081, 2.3616], -#> [33.0660, 34.9500]], dtype=torch.float64) -#> $fitness -#> [1] 0.03869379 -0.14697628 -#> -#> $init_time -#> [1] 34.39790 35.26937 -#> -#> $sigma -#> [1] 0.9772817 0.7157080 0.3430361 -#> -#> $parent_rate -#> [1] 0.02802839 0.03648976 #> ✔ Starting growth models inference of clone C0 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C1 -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 1.0000, 1.0000], -#> [176.0420, 58.3042], -#> [ 67.4037, 68.3512]], dtype=torch.float64) -#> $fitness -#> [1] 0.02853957 0.03449172 -#> -#> $sigma -#> [1] 0.3582631 0.7572328 1.0310806 -#> -#> $init_time -#> [1] 0 0 -#> -#> $parent_rate -#> NULL -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[0.0000, 0.0000], -#> [0.2849, 0.0000], -#> [9.6035, 0.0000]], dtype=torch.float64) -#> $fitness -#> [1] -0.1007042 -0.5671707 -#> -#> $init_time -#> [1] 35.03398 102.79916 -#> -#> $sigma -#> [1] 0.9772817 0.8977652 0.9830980 -#> -#> $parent_rate -#> [1] 0.02853957 0.03449172 -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [ 0.2842, 0.7118], -#> [ 5.0968, 10.0482]], dtype=torch.float64) -#> $fitness -#> [1] -0.1296171 -0.3826614 -#> -#> $init_time -#> [1] 35.24030 40.42765 -#> -#> $sigma -#> [1] 0.9772817 0.8314688 0.8212854 -#> -#> $parent_rate -#> [1] 0.02853957 0.03449172 -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [55.2208, 0.5070], -#> [ 0.1892, 18.5386]], dtype=torch.float64) -#> $fitness -#> [1] -0.1509167 -0.2613391 -#> -#> $init_time -#> [1] 34.43733 40.59316 -#> -#> $sigma -#> [1] 0.9772817 0.9074182 0.9312515 -#> -#> $parent_rate -#> [1] 0.02853957 0.03449172 -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[0.0000e+00, 0.0000e+00], -#> [1.0154e+01, 0.0000e+00], -#> [5.2868e-04, 0.0000e+00]], dtype=torch.float64) -#> $fitness -#> [1] -0.1653602 -0.5670620 -#> -#> $init_time -#> [1] 35.36698 102.81580 -#> -#> $sigma -#> [1] 0.9772817 0.8979036 0.9834126 -#> -#> $parent_rate -#> [1] 0.02423247 0.02547768 #> ✔ Starting growth models inference of clone C1 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C2 -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 1.0000, 1.0000], -#> [ 0.8098, 2.8254], -#> [207.7979, 91.8297]], dtype=torch.float64) -#> $fitness -#> [1] 0.01534206 0.02682686 -#> -#> $sigma -#> [1] 0.3582631 0.7883580 1.0235894 -#> -#> $init_time -#> [1] 0 0 -#> -#> $parent_rate -#> NULL #> ✔ Starting growth models inference of clone C2 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C3 -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 1.0000, 1.0000], -#> [ 65.9970, 628.5097], -#> [ 41.4696, 177.6163]], dtype=torch.float64) -#> $fitness -#> [1] 0.02247947 0.04579011 -#> -#> $sigma -#> [1] 0.3582631 0.7660736 1.0343661 -#> -#> $init_time -#> [1] 0 0 -#> -#> $parent_rate -#> NULL -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [12.4054, 0.0000], -#> [ 0.0000, 0.0000]], dtype=torch.float64) -#> $fitness -#> [1] -0.1783549 -0.5669586 -#> -#> $init_time -#> [1] 35.59607 102.77967 -#> -#> $sigma -#> [1] 0.9772817 0.8979091 0.9824349 -#> -#> $parent_rate -#> [1] 0.02247947 0.04579011 #> ✔ Starting growth models inference of clone C3 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C4 -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 1.0000, 1.0000], -#> [204.4612, 131.5458], -#> [ 79.5126, 116.2736]], dtype=torch.float64) -#> $fitness -#> [1] 0.02988531 0.03910537 -#> -#> $sigma -#> [1] 0.3582631 0.7590958 1.0309155 -#> -#> $init_time -#> [1] 0 0 -#> -#> $parent_rate -#> NULL -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [39.2599, 38.9838], -#> [17.6310, 23.0672]], dtype=torch.float64) -#> $fitness -#> [1] -0.0450648 -0.2040258 -#> -#> $init_time -#> [1] 21.12701 28.01647 -#> -#> $sigma -#> [1] 0.9772817 0.8898020 0.7865362 -#> -#> $parent_rate -#> [1] 0.02988531 0.03910537 -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [ 0.0000, 2.4454], -#> [ 0.0000, 33.8940]], dtype=torch.float64) -#> $fitness -#> [1] -0.1616928 -0.1724282 -#> -#> $init_time -#> [1] 88.43205 35.94878 -#> -#> $sigma -#> [1] 0.9772817 0.8978503 0.9823709 -#> -#> $parent_rate -#> [1] 0.02988531 0.03910537 -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [73.2584, 2.2482], -#> [ 0.3757, 32.0111]], dtype=torch.float64) -#> $fitness -#> [1] -0.1466266 -0.1773972 -#> -#> $init_time -#> [1] 33.76333 37.11142 -#> -#> $sigma -#> [1] 0.9772817 0.9164603 0.9233754 -#> -#> $parent_rate -#> [1] 0.02988531 0.03910537 -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[0.0000e+00, 0.0000e+00], -#> [1.0686e+01, 0.0000e+00], -#> [5.9874e-04, 0.0000e+00]], dtype=torch.float64) -#> $fitness -#> [1] -0.1649059 -0.5672420 -#> -#> $init_time -#> [1] 35.35526 102.80495 -#> -#> $sigma -#> [1] 0.9772817 0.8979067 0.9825779 -#> -#> $parent_rate -#> [1] 0.02550333 0.03216819 #> ✔ Starting growth models inference of clone C4 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C5 -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 1.0000, 1.0000], -#> [ 0.3351, 2.3128], -#> [209.2974, 195.6723]], dtype=torch.float64) -#> $fitness -#> [1] 0.01271993 0.02931803 -#> -#> $sigma -#> [1] 0.3582631 0.7968826 1.0228082 -#> -#> $init_time -#> [1] 0 0 -#> -#> $parent_rate -#> NULL -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [ 0.0000, 0.0000], -#> [33.5705, 37.4506]], dtype=torch.float64) -#> $fitness -#> [1] 0.6730212 0.3223430 -#> -#> $init_time -#> [1] 61.44215 70.27547 -#> -#> $sigma -#> [1] 0.9772817 0.8978726 0.8595133 -#> -#> $parent_rate -#> [1] 0.01271993 0.02931803 #> ✔ Starting growth models inference of clone C5 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C6 -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 1.0000, 1.0000], -#> [236.6150, 44.2954], -#> [ 6.0231, 9.0474]], dtype=torch.float64) -#> $fitness -#> [1] 0.01856442 0.02701337 -#> -#> $sigma -#> [1] 0.3514583 0.7677312 1.0420123 -#> -#> $init_time -#> [1] 0 0 -#> -#> $parent_rate -#> NULL #> ✔ Starting growth models inference of clone C6 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C7 -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 1.0000, 1.0000], -#> [ 0.3376, 1.9955], -#> [161.7807, 46.7847]], dtype=torch.float64) -#> $fitness -#> [1] 0.01154934 0.02383878 -#> -#> $sigma -#> [1] 0.3548436 0.7959806 1.0250162 -#> -#> $init_time -#> [1] 0 0 -#> -#> $parent_rate -#> NULL -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[ 0.0000, 0.0000], -#> [ 0.1336, 0.6147], -#> [ 0.3783, 16.2700]], dtype=torch.float64) -#> $fitness -#> [1] -0.15434450 0.01245155 -#> -#> $init_time -#> [1] 35.57908 42.16349 -#> -#> $sigma -#> [1] 0.9837162 0.8694738 0.9149014 -#> -#> $parent_rate -#> [1] 0.01154934 0.02383878 -#> -#> tensor([[ 0], -#> [ 60], -#> [150]], dtype=torch.int32) -#> tensor([[0.0000e+00, 0.0000e+00], -#> [3.5230e-03, 0.0000e+00], -#> [1.8144e+01, 0.0000e+00]], dtype=torch.float64) -#> $fitness -#> [1] -0.01103162 -0.56685734 -#> -#> $init_time -#> [1] 35.57094 102.81567 -#> -#> $sigma -#> [1] 0.9772817 0.8977625 0.9846576 -#> -#> $parent_rate -#> [1] 0.01154934 0.02383878 #> ✔ Starting growth models inference of clone C7 ... done #> #> ℹ Fitting model to estimate population growth rates✔ Fitting model to estimate population growth rates ... done diff --git a/pkgdown.yml b/pkgdown.yml index ec7bff9..6fa4e32 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -7,7 +7,7 @@ articles: lineaGT: lineaGT.html articles/Plotting-functions: Plotting-functions.html articles/Utilities: Utilities.html -last_built: 2024-12-16T17:12Z +last_built: 2024-12-17T14:10Z urls: reference: caravagnalab.github.io/lineaGT/reference article: caravagnalab.github.io/lineaGT/articles diff --git a/search.json b/search.json index 0db8c39..62e4632 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"caravagnalab.github.io/lineaGT/articles/Inference.html","id":"fitting-the-model","dir":"Articles","previous_headings":"","what":"Fitting the model","title":"Lineage inference","text":"Printing fitted object information regarding data: lineages timpoints present data, number integration sites, number inferred clones ISs, estimated via model selection input range number clusters, clone, number assigned ISs mean coverage, per timepoint lineage.","code":"x = fit( cov.df = cov.example.filt, vaf.df = vaf.df.example, steps = 500, # n_runs = 1, k_interval = c(5, 15), timepoints_to_int = unlist(list(\"t1\"=60, \"t2\"=150)) ) #> ℹ Starting lineaGT model selection to retrieve the optimal number of clones #> ✔ Starting lineaGT model selection to retrieve the optimal number of clones ...… #> #> ℹ Fitting model to cluster ISs #> ✔ Found 8 clones of ISs! #> #> ℹ Fitting model to cluster mutations #> ℹ Starting clustering of clone C0 mutations #> [ VIBER - variational fit ] #> #> ℹ Input n = 3, with k < 3. Dirichlet concentration α = 1e-06. #> ℹ Starting clustering of clone C0 mutationsℹ Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts. #> ℹ Starting clustering of clone C0 mutations #> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C0 mutations #> ── [ VIBER ] My VIBER model n = 3 (w = 4 dimensions). Fit with k = 3 clusters. ─ #> ℹ Starting clustering of clone C0 mutations• Clusters: π = 67% [C1] and 33% [C3], with π > 0. #> ℹ Starting clustering of clone C0 mutations• Binomials: θ = <0.09, 0.19, 0.01, 0> [C1] and <0.01, 0.36, 0.03, 0.4> [C3]. #> ℹ Starting clustering of clone C0 mutationsℹ Score(s): ELBO = -1461.827. Fit converged in 6 steps, ε = 1e-10. #> ℹ Starting clustering of clone C0 mutations✔ Reduced to k = 2 (from 3) selecting VIBER cluster(s) with π > 0.166666666666667, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C0 mutations✔ Starting clustering of clone C0 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C0 #> [ ctree ~ clone trees generator for C0 ] #> #> # A tibble: 3 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> #> 1 S1 0.0856 0.190 0 0 2 FALSE TRUE #> 2 S2 0.00680 0.362 0.0287 0.396 1 FALSE FALSE #> 3 C0 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 1, 2, 1, 1 #> ℹ Starting phylogeny inference of clone C0ℹ Total 2 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C0 #> ℹ Starting phylogeny inference of clone C0── Ranking trees #> ℹ Starting phylogeny inference of clone C0✔ 2 trees with non-zero score, storing 2 #> ℹ Starting phylogeny inference of clone C0✔ Starting phylogeny inference of clone C0 ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting clustering of clone C1 mutations #> [ VIBER - variational fit ] #> #> ℹ Input n = 8, with k < 8. Dirichlet concentration α = 1e-06. #> ℹ Starting clustering of clone C1 mutationsℹ Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts. #> ℹ Starting clustering of clone C1 mutations #> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C1 mutations #> ── [ VIBER ] My VIBER model n = 8 (w = 4 dimensions). Fit with k = 8 clusters. ─ #> ℹ Starting clustering of clone C1 mutations• Clusters: π = 25% [C2], 25% [C3], 25% [C5], and 25% [C6], with π > 0. #> ℹ Starting clustering of clone C1 mutations• Binomials: θ = <0, 0.14, 0.01, 0> [C2], <0, 0.08, 0.01, 0.15> [C3], <0.31, 0, #> 0.01, 0.27> [C5], and <0.18, 0, 0.01, 0> [C6]. #> ℹ Starting clustering of clone C1 mutationsℹ Score(s): ELBO = -4567.669. Fit converged in 8 steps, ε = 1e-10. #> ℹ Starting clustering of clone C1 mutations✔ Reduced to k = 4 (from 8) selecting VIBER cluster(s) with π > 0.0625, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C1 mutations✔ Starting clustering of clone C1 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C1 #> [ ctree ~ clone trees generator for C1 ] #> #> # A tibble: 5 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> #> 1 S1 0.00162 0.142 0 0 2 FALSE FALSE #> 2 S2 0.00161 0.0756 0.0122 0.147 2 FALSE TRUE #> 3 S3 0.314 0.00281 0.00870 0.271 2 FALSE FALSE #> 4 S4 0.184 0.00279 0 0 2 FALSE FALSE #> 5 C1 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 2, 2, 1, 2 #> ℹ Starting phylogeny inference of clone C1ℹ Total 6 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C1 #> ℹ Starting phylogeny inference of clone C1── Ranking trees #> ℹ Starting phylogeny inference of clone C1✔ 6 trees with non-zero score, storing 6 #> ℹ Starting phylogeny inference of clone C1✔ Starting phylogeny inference of clone C1 ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting clustering of clone C4 mutations #> [ VIBER - variational fit ] #> #> ℹ Input n = 6, with k < 6. Dirichlet concentration α = 1e-06. #> ℹ Starting clustering of clone C4 mutationsℹ Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts. #> ℹ Starting clustering of clone C4 mutations #> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C4 mutations #> ── [ VIBER ] My VIBER model n = 6 (w = 4 dimensions). Fit with k = 6 clusters. ─ #> ℹ Starting clustering of clone C4 mutations• Clusters: π = 50% [C3], 17% [C2], 17% [C5], and 17% [C6], with π > 0. #> ℹ Starting clustering of clone C4 mutations• Binomials: θ = <0.15, 0, 0.01, 0> [C3], <0.19, 0.22, 0.3, 0.2> [C2], <0, 0, #> 0.02, 0.29> [C5], and <0.36, 0, 0.02, 0.28> [C6]. #> ℹ Starting clustering of clone C4 mutationsℹ Score(s): ELBO = -3594.658. Fit converged in 6 steps, ε = 1e-10. #> ℹ Starting clustering of clone C4 mutations✔ Reduced to k = 4 (from 6) selecting VIBER cluster(s) with π > 0.0833333333333333, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C4 mutations✔ Starting clustering of clone C4 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C4 #> [ ctree ~ clone trees generator for C4 ] #> #> # A tibble: 5 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> #> 1 S1 0.192 0.222 0.296 0.198 1 FALSE FALSE #> 2 S2 0.146 0.00159 0 0 3 FALSE FALSE #> 3 S3 0 0 0.0186 0.292 1 FALSE TRUE #> 4 S4 0.358 0.00472 0.0171 0.275 1 FALSE FALSE #> 5 C4 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 6, 1, 6, 5 #> ℹ Starting phylogeny inference of clone C4ℹ Total 54 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C4 #> ℹ Starting phylogeny inference of clone C4── Ranking trees #> ℹ Starting phylogeny inference of clone C4✔ 33 trees with non-zero score, storing 33 #> ℹ Starting phylogeny inference of clone C4✔ Starting phylogeny inference of clone C4 ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting clustering of clone C7 mutations #> [ VIBER - variational fit ] #> #> ℹ Input n = 2, with k < 2. Dirichlet concentration α = 1e-06. #> ℹ Starting clustering of clone C7 mutationsℹ Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts. #> ℹ Starting clustering of clone C7 mutations #> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C7 mutations #> ── [ VIBER ] My VIBER model n = 2 (w = 4 dimensions). Fit with k = 2 clusters. ─ #> ℹ Starting clustering of clone C7 mutations• Clusters: π = 50% [C1] and 50% [C2], with π > 0. #> ℹ Starting clustering of clone C7 mutations• Binomials: θ = <0.4, 0, 0.31, 0.35> [C1] and <0.01, 0.11, 0, 0> [C2]. #> ℹ Starting clustering of clone C7 mutationsℹ Score(s): ELBO = -1584.513. Fit converged in 5 steps, ε = 1e-10. #> ℹ Starting clustering of clone C7 mutations✔ Starting clustering of clone C7 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C7 #> [ ctree ~ clone trees generator for C7 ] #> #> # A tibble: 3 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> #> 1 S1 0.396 0.00234 0.308 0.348 1 FALSE TRUE #> 2 S2 0.0104 0.112 0 0 1 FALSE FALSE #> 3 C7 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 2, 1, 1, 1 #> ℹ Starting phylogeny inference of clone C7ℹ Total 2 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C7 #> ℹ Starting phylogeny inference of clone C7── Ranking trees #> ℹ Starting phylogeny inference of clone C7✔ 2 trees with non-zero score, storing 2 #> ℹ Starting phylogeny inference of clone C7✔ Starting phylogeny inference of clone C7 ... done #> #> ℹ Fitting model to cluster mutations✔ Fitting model to cluster mutations ... done #> #> ℹ Fitting model to estimate population growth rates #> ℹ Starting growth models inference of clone C0 #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 1.0000, 1.0000], #> [104.0897, 82.3385], #> [ 91.3039, 88.1951]], dtype=torch.float64) #> $fitness #> [1] 0.02802839 0.03648976 #> #> $sigma #> [1] 0.3582631 0.7575347 1.0299374 #> #> $init_time #> [1] 0 0 #> #> $parent_rate #> NULL #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [ 8.9140, 0.0000], #> [17.3552, 0.0000]], dtype=torch.float64) #> $fitness #> [1] -0.03116952 -0.56715947 #> #> $init_time #> [1] 23.85784 102.79580 #> #> $sigma #> [1] 0.9772817 0.8978677 0.9829488 #> #> $parent_rate #> [1] 0.02802839 0.03648976 #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [ 0.7081, 2.3616], #> [33.0660, 34.9500]], dtype=torch.float64) #> $fitness #> [1] 0.03869379 -0.14697628 #> #> $init_time #> [1] 34.39790 35.26937 #> #> $sigma #> [1] 0.9772817 0.7157080 0.3430361 #> #> $parent_rate #> [1] 0.02802839 0.03648976 #> ✔ Starting growth models inference of clone C0 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C1 #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 1.0000, 1.0000], #> [176.0420, 58.3042], #> [ 67.4037, 68.3512]], dtype=torch.float64) #> $fitness #> [1] 0.02853957 0.03449172 #> #> $sigma #> [1] 0.3582631 0.7572328 1.0310806 #> #> $init_time #> [1] 0 0 #> #> $parent_rate #> NULL #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[0.0000, 0.0000], #> [0.2849, 0.0000], #> [9.6035, 0.0000]], dtype=torch.float64) #> $fitness #> [1] -0.1007042 -0.5671707 #> #> $init_time #> [1] 35.03398 102.79916 #> #> $sigma #> [1] 0.9772817 0.8977652 0.9830980 #> #> $parent_rate #> [1] 0.02853957 0.03449172 #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [ 0.2842, 0.7118], #> [ 5.0968, 10.0482]], dtype=torch.float64) #> $fitness #> [1] -0.1296171 -0.3826614 #> #> $init_time #> [1] 35.24030 40.42765 #> #> $sigma #> [1] 0.9772817 0.8314688 0.8212854 #> #> $parent_rate #> [1] 0.02853957 0.03449172 #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [55.2208, 0.5070], #> [ 0.1892, 18.5386]], dtype=torch.float64) #> $fitness #> [1] -0.1509167 -0.2613391 #> #> $init_time #> [1] 34.43733 40.59316 #> #> $sigma #> [1] 0.9772817 0.9074182 0.9312515 #> #> $parent_rate #> [1] 0.02853957 0.03449172 #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[0.0000e+00, 0.0000e+00], #> [1.0154e+01, 0.0000e+00], #> [5.2868e-04, 0.0000e+00]], dtype=torch.float64) #> $fitness #> [1] -0.1653602 -0.5670620 #> #> $init_time #> [1] 35.36698 102.81580 #> #> $sigma #> [1] 0.9772817 0.8979036 0.9834126 #> #> $parent_rate #> [1] 0.02423247 0.02547768 #> ✔ Starting growth models inference of clone C1 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C2 #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 1.0000, 1.0000], #> [ 0.8098, 2.8254], #> [207.7979, 91.8297]], dtype=torch.float64) #> $fitness #> [1] 0.01534206 0.02682686 #> #> $sigma #> [1] 0.3582631 0.7883580 1.0235894 #> #> $init_time #> [1] 0 0 #> #> $parent_rate #> NULL #> ✔ Starting growth models inference of clone C2 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C3 #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 1.0000, 1.0000], #> [ 65.9970, 628.5097], #> [ 41.4696, 177.6163]], dtype=torch.float64) #> $fitness #> [1] 0.02247947 0.04579011 #> #> $sigma #> [1] 0.3582631 0.7660736 1.0343661 #> #> $init_time #> [1] 0 0 #> #> $parent_rate #> NULL #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [12.4054, 0.0000], #> [ 0.0000, 0.0000]], dtype=torch.float64) #> $fitness #> [1] -0.1783549 -0.5669586 #> #> $init_time #> [1] 35.59607 102.77967 #> #> $sigma #> [1] 0.9772817 0.8979091 0.9824349 #> #> $parent_rate #> [1] 0.02247947 0.04579011 #> ✔ Starting growth models inference of clone C3 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C4 #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 1.0000, 1.0000], #> [204.4612, 131.5458], #> [ 79.5126, 116.2736]], dtype=torch.float64) #> $fitness #> [1] 0.02988531 0.03910537 #> #> $sigma #> [1] 0.3582631 0.7590958 1.0309155 #> #> $init_time #> [1] 0 0 #> #> $parent_rate #> NULL #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [39.2599, 38.9838], #> [17.6310, 23.0672]], dtype=torch.float64) #> $fitness #> [1] -0.0450648 -0.2040258 #> #> $init_time #> [1] 21.12701 28.01647 #> #> $sigma #> [1] 0.9772817 0.8898020 0.7865362 #> #> $parent_rate #> [1] 0.02988531 0.03910537 #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [ 0.0000, 2.4454], #> [ 0.0000, 33.8940]], dtype=torch.float64) #> $fitness #> [1] -0.1616928 -0.1724282 #> #> $init_time #> [1] 88.43205 35.94878 #> #> $sigma #> [1] 0.9772817 0.8978503 0.9823709 #> #> $parent_rate #> [1] 0.02988531 0.03910537 #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [73.2584, 2.2482], #> [ 0.3757, 32.0111]], dtype=torch.float64) #> $fitness #> [1] -0.1466266 -0.1773972 #> #> $init_time #> [1] 33.76333 37.11142 #> #> $sigma #> [1] 0.9772817 0.9164603 0.9233754 #> #> $parent_rate #> [1] 0.02988531 0.03910537 #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[0.0000e+00, 0.0000e+00], #> [1.0686e+01, 0.0000e+00], #> [5.9874e-04, 0.0000e+00]], dtype=torch.float64) #> $fitness #> [1] -0.1649059 -0.5672420 #> #> $init_time #> [1] 35.35526 102.80495 #> #> $sigma #> [1] 0.9772817 0.8979067 0.9825779 #> #> $parent_rate #> [1] 0.02550333 0.03216819 #> ✔ Starting growth models inference of clone C4 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C5 #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 1.0000, 1.0000], #> [ 0.3351, 2.3128], #> [209.2974, 195.6723]], dtype=torch.float64) #> $fitness #> [1] 0.01271993 0.02931803 #> #> $sigma #> [1] 0.3582631 0.7968826 1.0228082 #> #> $init_time #> [1] 0 0 #> #> $parent_rate #> NULL #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [ 0.0000, 0.0000], #> [33.5705, 37.4506]], dtype=torch.float64) #> $fitness #> [1] 0.6730212 0.3223430 #> #> $init_time #> [1] 61.44215 70.27547 #> #> $sigma #> [1] 0.9772817 0.8978726 0.8595133 #> #> $parent_rate #> [1] 0.01271993 0.02931803 #> ✔ Starting growth models inference of clone C5 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C6 #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 1.0000, 1.0000], #> [236.6150, 44.2954], #> [ 6.0231, 9.0474]], dtype=torch.float64) #> $fitness #> [1] 0.01856442 0.02701337 #> #> $sigma #> [1] 0.3514583 0.7677312 1.0420123 #> #> $init_time #> [1] 0 0 #> #> $parent_rate #> NULL #> ✔ Starting growth models inference of clone C6 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C7 #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 1.0000, 1.0000], #> [ 0.3376, 1.9955], #> [161.7807, 46.7847]], dtype=torch.float64) #> $fitness #> [1] 0.01154934 0.02383878 #> #> $sigma #> [1] 0.3548436 0.7959806 1.0250162 #> #> $init_time #> [1] 0 0 #> #> $parent_rate #> NULL #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[ 0.0000, 0.0000], #> [ 0.1336, 0.6147], #> [ 0.3783, 16.2700]], dtype=torch.float64) #> $fitness #> [1] -0.15434450 0.01245155 #> #> $init_time #> [1] 35.57908 42.16349 #> #> $sigma #> [1] 0.9837162 0.8694738 0.9149014 #> #> $parent_rate #> [1] 0.01154934 0.02383878 #> #> tensor([[ 0], #> [ 60], #> [150]], dtype=torch.int32) #> tensor([[0.0000e+00, 0.0000e+00], #> [3.5230e-03, 0.0000e+00], #> [1.8144e+01, 0.0000e+00]], dtype=torch.float64) #> $fitness #> [1] -0.01103162 -0.56685734 #> #> $init_time #> [1] 35.57094 102.81567 #> #> $sigma #> [1] 0.9772817 0.8977625 0.9846576 #> #> $parent_rate #> [1] 0.01154934 0.02383878 #> ✔ Starting growth models inference of clone C7 ... done #> #> ℹ Fitting model to estimate population growth rates✔ Fitting model to estimate population growth rates ... done data(x.example) x.example #> ── [ lineaGT ] ──── Python: /usr/share/miniconda/envs/lineagt-env/bin/python ── #> → Lineages: l1 and l2. #> → Timepoints: t1 and t2. #> → Number of Insertion Sites: 66. #> #> ── Optimal IS model with k = 8. #> #> C4 (19 ISs) : l1 [285, 209]; l2 [ 51, 492] #> C1 (15 ISs) : l1 [245, 177]; l2 [ 23, 289] #> C0 (6 ISs) : l1 [145, 240]; l2 [ 32, 373] #> C2 (6 ISs) : l1 [ 1, 547]; l2 [ 1, 388] #> C3 (6 ISs) : l1 [ 92, 109]; l2 [245, 751] #> C5 (6 ISs) : l1 [ 0, 551]; l2 [ 1, 828] #> C6 (4 ISs) : l1 [330, 16]; l2 [ 17, 38] #> C7 (4 ISs) : l1 [ 0, 426]; l2 [ 1, 198]"},{"path":"caravagnalab.github.io/lineaGT/articles/Input-formats.html","id":"coverage-dataframe","dir":"Articles","previous_headings":"","what":"Coverage Dataframe","title":"Input formats","text":"first dataframe requires following columns: : integration site ID, timepoints: longitunal timepoint, lineage: cell lineage name, coverage number reads assigned ISs. lineage timepoints columns present, single longitunal observation single lineage assumed. dataset example following:","code":"data(cov.df.example) cov.df.example #> # A tibble: 428 × 4 #> IS timepoints lineage coverage #> #> 1 IS1 t1 l1 124 #> 2 IS1 t2 l1 190 #> 3 IS1 t1 l2 2 #> 4 IS1 t2 l2 6 #> 5 IS10 t1 l1 4 #> 6 IS10 t2 l1 14 #> 7 IS10 t1 l2 0 #> 8 IS10 t2 l2 12 #> 9 IS100 t1 l1 0 #> 10 IS100 t2 l1 418 #> # ℹ 418 more rows"},{"path":"caravagnalab.github.io/lineaGT/articles/Input-formats.html","id":"mutations-dataframe","dir":"Articles","previous_headings":"","what":"Mutations Dataframe","title":"Input formats","text":"second dataframe requires following columns: : integration site ID, mutation: mutation ID, timepoints: longitunal timepoint, lineage: cell lineage name, alt: per-locus variant allele number reads, dp: per-locus total number reads, hence per-locus depth. dataframe example following:","code":"data(vaf.df.example) vaf.df.example #> # A tibble: 116 × 6 #> IS mutation timepoints lineage alt dp #> #> 1 IS1 mut1 t1 l1 58 134 #> 2 IS1 mut1 t2 l1 53 173 #> 3 IS1 mut1 t1 l2 0 26 #> 4 IS1 mut1 t2 l2 90 322 #> 5 IS10 mut12 t1 l1 0 372 #> 6 IS10 mut12 t2 l1 0 146 #> 7 IS10 mut12 t1 l2 0 57 #> 8 IS10 mut12 t2 l2 0 482 #> 9 IS11 mut13 t1 l1 160 372 #> 10 IS11 mut13 t2 l1 0 146 #> # ℹ 106 more rows"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"mixture-weights","dir":"Articles","previous_headings":"","what":"Mixture weights","title":"Plotting functions","text":"mixture weights number ISs per cluster can visualized function plot_mixture_weights() .","code":"plot_mixture_weights(x.example)"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"scatterplot","dir":"Articles","previous_headings":"","what":"Scatterplot","title":"Plotting functions","text":"function plot_scatter_density() returns list 2D multivariate densities estimated model. argument highlight can used show subset clusters argument min_frac show clusters specified frequency least one dimension. Note observed coverage values across lineages time modeled independent, therefore dimension corresponds combination time-point lineage.","code":"plots = plot_scatter_density(x.example) plots$`cov.t2.l1:cov.t1.l2` # to visualize a single plot"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"marginal-distributions","dir":"Articles","previous_headings":"","what":"Marginal distributions","title":"Plotting functions","text":"function plot_marginal() returns plot marginal estimated densities cluster, time-point lineage. option single_plot returns density whole mixture grouped lineage time-point.","code":"marginals = plot_marginal(x.example) marginals_mixture = plot_marginal(x.example, single_plot=T) patchwork::wrap_plots(marginals / marginals_mixture)"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"mullerplot","dir":"Articles","previous_headings":"","what":"Mullerplot","title":"Plotting functions","text":"function plot_mullerplot() shows expansion identified populations time. supports options =c(\"frac\",\"pop\") corresponding absolule population abundance relative fraction, respectively. option mutations set TRUE, subclones originated within population reported well mullerplot. function supports also visualization single clone monitor growth subpopulations, argument single_clone. Moreover, identified clusters (showing low coverage dimensions) represents poly-clonal populations, since uniquely identified mixture model. Therefore, estimated abundance values might readjusted according estimated number populations clusters.","code":"mp1 = plot_mullerplot(x.example, which=\"frac\") mp2 = plot_mullerplot(x.example, which=\"pop\") patchwork::wrap_plots(mp1, mp2, ncol=1) mp1 = plot_mullerplot(x.example, which=\"frac\", mutations=T) mp2 = plot_mullerplot(x.example, which=\"pop\", mutations=T) patchwork::wrap_plots(mp1, mp2, ncol=1) plot_mullerplot(x.example, highlight=\"C4\", mutations=T, single_clone=T) estimate_n_pops(x.example) #> C0 C1 C2 C3 C4 C5 C6 C7 #> 1 2 1 1 2 1 1 1 plot_mullerplot(x.example, which=\"frac\", mutations=T, estimate_npops=T)"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"vaf","dir":"Articles","previous_headings":"","what":"VAF","title":"Plotting functions","text":"function plot_vaf_time() can used visualize behaviour mutations variant allele frequencies time subclone.","code":"plot_vaf_time(x.example)"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"phylogenetic-evolution","dir":"Articles","previous_headings":"","what":"Phylogenetic evolution","title":"Plotting functions","text":"cluster ISs, function plot_phylogeny() reports estimated phylogenetic tree.","code":"plot_phylogeny(x.example) #> This graph was created by an old(er) igraph version. #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version. #> For now we convert it on the fly... #> This graph was created by an old(er) igraph version. #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version. #> For now we convert it on the fly... #> This graph was created by an old(er) igraph version. #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version. #> For now we convert it on the fly... #> This graph was created by an old(er) igraph version. #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version. #> For now we convert it on the fly... #> $C0 #> #> $C1 #> #> $C4 #> #> $C7"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"clonal-growth","dir":"Articles","previous_headings":"","what":"Clonal Growth","title":"Plotting functions","text":"fitted exponential logistic growth regressions shown plot_growth_regression() , reporting default fit best model, selected one highest likelihood. regressions can inspected setting show_best=F . function can used show growth regressions subclones identified somatic mutations. alternative way visualising differences growth rates plot_growth_rates() function, reporting values estimated growth rates (sub)population. Disabling show_best option, model lowest likelihood shown dashed line.","code":"plot_growth_regression(x.example, show_best=F) #> Scale for colour is already present. #> Adding another scale for colour, which will replace the existing scale. plot_growth_regression(x.example, highlight=\"C4\", mutations=T) plot_growth_rates(x.example, show_best=F)"},{"path":"caravagnalab.github.io/lineaGT/articles/lineaGT.html","id":"installation-of-the-package","dir":"Articles","previous_headings":"","what":"Installation of the package","title":"Get started","text":"can install LineaGT GitHub using devtools. Load package.","code":"devtools::install_github(\"caravagnalab/lineaGT\") library(lineaGT)"},{"path":"caravagnalab.github.io/lineaGT/articles/lineaGT.html","id":"python-dependencies-installation","dir":"Articles","previous_headings":"","what":"Python dependencies installation","title":"Get started","text":"package loaded, package automatically check whether: version Anaconda Miniconda available, otherwise Miniconda installation started, conda environment loaded, package check lineagt-env present, otherwise created, use existing conda environment, can loaded loading package, either reticulate function reticulate::use_condaenv() using lineaGT function load_conda_env(): eventually, required Python dependencies installed loaded environment, installed.","code":"reticulate::use_condaenv(\"env-name\", required=TRUE) load_conda_env(envname=\"env-name\")"},{"path":"caravagnalab.github.io/lineaGT/articles/lineaGT.html","id":"functions-to-manually-configure-an-environment","dir":"Articles","previous_headings":"","what":"Functions to manually configure an environment","title":"Get started","text":"function pylineaGT can also used interactively manually configure existing environment, create one scratch. function first check Anaconda Miniconda installation available, otherwise prompt Miniconda installation. input name environment either name existing environment name environment created. environment loaded created, required Python dependencies installed.","code":"configure_environment(env_name=\"lineaget-env\", use_default=F)"},{"path":"caravagnalab.github.io/lineaGT/articles/lineaGT.html","id":"check-the-loaded-python-version-and-environment","dir":"Articles","previous_headings":"","what":"Check the loaded Python version and environment","title":"Get started","text":"package provides also set helper functions check environment loaded. have_loaded_env() check environment already loaded, which_conda_env() check environment loaded. have_python_deps() check Python packages list installed specified environment. load_conda_env() load specified environment.","code":""},{"path":"caravagnalab.github.io/lineaGT/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Elena Buscaroli. Author, maintainer. Giulio Caravagna. Author.","code":""},{"path":"caravagnalab.github.io/lineaGT/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Buscaroli E, Caravagna G (2024). lineaGT: lineaGT. R package version 0.1.0, https://caravagnalab.github.io/lineaGT/, https://github.com/caravagnalab/lineaGT.","code":"@Manual{, title = {lineaGT: lineaGT}, author = {Elena Buscaroli and Giulio Caravagna}, year = {2024}, note = {R package version 0.1.0, https://caravagnalab.github.io/lineaGT/}, url = {https://github.com/caravagnalab/lineaGT}, }"},{"path":"caravagnalab.github.io/lineaGT/index.html","id":"lineagt-","dir":"","previous_headings":"","what":"lineaGT","title":"lineaGT","text":"package implements algorithm determine lineage inference gene therapy assays based insertion sites, accounting also somatic mutations accumulation. specifically, starting coverage values ISs identified gene therapy assays associated somatic mutations, lineaGT can: cluster ISs observed multi-lineage longitudinal coverage identify populations cells originated Haematopoietic Stem Cell estimate abundances sample; cluster somatic mutations observed multi-lineage longitudinal variant allele frequency within clone, identify subpopulations; infer population genetics parameters, .e., growth rates, population supporting exponential logistic growth models selecting optimal one. R package provides R interface Python algorithms developed pyLineaGT package, uses Pyro probabilistic programming language infer lineage histories.","code":""},{"path":"caravagnalab.github.io/lineaGT/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"lineaGT","text":"use lineaGT, please cite: E. Buscaroli, S. Milite, R. Bergamin, N. Calonaci, F. Gazzo, . Calabria, G. Caravagna. Bayesian multi-lineage tracing gene therapy assays. preparation.","code":""},{"path":[]},{"path":"caravagnalab.github.io/lineaGT/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"lineaGT","text":"can install released version lineaGT GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"caravagnalab/lineaGT\")"},{"path":"caravagnalab.github.io/lineaGT/index.html","id":"copyright-and-contacts","dir":"","previous_headings":"Installation","what":"Copyright and contacts","title":"lineaGT","text":"Elena Buscaroli, Giulio Caravagna. Cancer Data Science (CDS) Laboratory, University Trieste, Italy.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/configure_environment.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure the reticulate environment — configure_environment","title":"Configure the reticulate environment — configure_environment","text":"Function configure Python dependencies R. Python environment available, function check version conda miniconda, otherwise install miniconda, install Python package pylineaGT.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/configure_environment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure the reticulate environment — configure_environment","text":"","code":"configure_environment(envname = \"lineagt-env\", use_default = F)"},{"path":"caravagnalab.github.io/lineaGT/reference/configure_environment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure the reticulate environment — configure_environment","text":"env_name name conda environment use, available.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/cov.df.example.html","id":null,"dir":"Reference","previous_headings":"","what":"Example coverage data — cov.df.example","title":"Example coverage data — cov.df.example","text":"Example coverage data","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/cov.df.example.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example coverage data — cov.df.example","text":"","code":"data(cov.df.example)"},{"path":"caravagnalab.github.io/lineaGT/reference/cov.df.example.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example coverage data — cov.df.example","text":"object class tbl_df (inherits tbl, data.frame) 428 rows 4 columns.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/cov.df.example.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example coverage data — cov.df.example","text":"","code":"data(cov.df.example) head(cov.df.example) #> # A tibble: 6 × 4 #> IS timepoints lineage coverage #> #> 1 IS1 t1 l1 124 #> 2 IS1 t2 l1 190 #> 3 IS1 t1 l2 2 #> 4 IS1 t2 l2 6 #> 5 IS10 t1 l1 4 #> 6 IS10 t2 l1 14"},{"path":"caravagnalab.github.io/lineaGT/reference/estimate_n_pops.html","id":null,"dir":"Reference","previous_headings":"","what":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","title":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","text":"Function implemented estimate real number clones cluster.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/estimate_n_pops.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","text":"","code":"estimate_n_pops(x, highlight = c())"},{"path":"caravagnalab.github.io/lineaGT/reference/estimate_n_pops.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","text":"x mvnmm object highlight clusters show","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/estimate_n_pops.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","text":"named array reporting cluster estimated true number populations.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/filter_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Filters the input dataset. — filter_dataset","title":"Filters the input dataset. — filter_dataset","text":"Function used filter observations, .e. ISs, input dataframe coverage values.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/filter_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filters the input dataset. — filter_dataset","text":"","code":"filter_dataset( cov.df, min_cov = 5, min_frac = 0.05, k_interval = c(10, 20), metric = \"calinski_harabasz_score\", seed = 5 )"},{"path":"caravagnalab.github.io/lineaGT/reference/filter_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filters the input dataset. — filter_dataset","text":"cov.df Input coverage dataset. must least columns coverage, timepoints, lineage, , coverage values, timepoint, lineage , respectively. min_cov add min_frac add k_interval add metric add seed add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/filter_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filters the input dataset. — filter_dataset","text":"dataset shape input one, filtered observations.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an object of class mvnmm. — fit","title":"Creates an object of class mvnmm. — fit","text":"Function fit input data.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an object of class mvnmm. — fit","text":"","code":"fit( cov.df, vaf.df = NULL, infer_phylogenies = TRUE, infer_growth = TRUE, k_interval = c(5, 15), n_runs = 1, steps = 500, min_steps = 20, lr = 0.005, p = 1, min_frac = 0, max_IS = NULL, check_conv = TRUE, covariance = \"full\", hyperparams = list(), default_lm = TRUE, timepoints_to_int = list(), show_progr = FALSE, store_grads = TRUE, store_losses = TRUE, store_params = FALSE, seed_optim = TRUE, seed = 6, seed_init = reticulate::py_none(), sample_id = \"\" )"},{"path":"caravagnalab.github.io/lineaGT/reference/fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an object of class mvnmm. — fit","text":"cov.df Input coverage dataset. must least columns coverage, , additional columns timepoints lineage, added missing, assuming single timepoint lineage. vaf.df Input VAF dataset. NULL, mutations clustering performed. must least columns mutation, , alt, dp, additional vaf, timepoints, lineage, , mutation, number reads mutated allele, overall depth, vaf values, timepoint, lineage, mutation, respectively. infer_phylogenies Boolean. set TRUE, function also compute attach returned object phylogenetic trees cluster. k_interval Interval K values test. n_runs Number runs perform K. steps Maximum number steps inference. lr Learning rate used inference. p Numeric value used check convergence parameters. min_frac add max_IS add check_conv Boolean. set TRUE, function check early convergence, otherwise perform steps iterations. covariance Covariance type Multivariate Gaussian. hyperparams add default_lm add timepoints_to_int add show_progr Boolean. TRUE, progression bar shown inference. store_grads Booolean. TRUE, gradient norms parameters iteration stored. store_losses Boolean. TRUE, computed losses parameters iteration stored. store_params Boolean. TRUE, estimated parameters iteration stored. seed_optim add seed Value seed. sample_id add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates an object of class mvnmm. — fit","text":"mvnmm object, containing input dataset, annotated IS_values, N, K, T specific dataset, input column names, list params contain inferred parameters, python object","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_growth_rates.html","id":null,"dir":"Reference","previous_headings":"","what":"Infer growth rates for each clone and subclone. — fit_growth_rates","title":"Infer growth rates for each clone and subclone. — fit_growth_rates","text":"Infer growth rates clone subclone.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_growth_rates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Infer growth rates for each clone and subclone. — fit_growth_rates","text":"","code":"fit_growth_rates( x, steps = 500, highlight = c(), timepoints_to_int = c(), growth_model = \"exp.log\", force = T, tree_score = 1, py_pkg = NULL, mutations = F )"},{"path":"caravagnalab.github.io/lineaGT/reference/fit_growth_rates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Infer growth rates for each clone and subclone. — fit_growth_rates","text":"x mvnmm object. steps maximum number steps inference. highlight set clusters run inference . specified, run clusters. timepoints_to_int provided timepoints integers timepoints--int list stored x, list mapping values integers required. growth_model string specifying type growth model use, exp log corresponding exponential logistic models, respectively. force model already fitted, setting force FALSE keep computed rates. Setting force TRUE fit model specified clusters.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_growth_rates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Infer growth rates for each clone and subclone. — fit_growth_rates","text":"mvnmm object additional tibble growth.rates containing estimated population genetics parameters.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_mutations.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the mutations clustering — fit_mutations","title":"Fit the mutations clustering — fit_mutations","text":"Fit mutations clustering","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_mutations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the mutations clustering — fit_mutations","text":"","code":"fit_mutations( x, vaf.df = NULL, infer_phylo = TRUE, min_frac = 0, max_IS = NULL, highlight = list() )"},{"path":"caravagnalab.github.io/lineaGT/reference/fit_mutations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the mutations clustering — fit_mutations","text":"x mvnmm object. vaf.df dataframe mutations data. infer_phylo Boolean indicating whether infer also phylogenetic evolution per cluster ISs. min_frac add max_IS add highlight add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_mutations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the mutations clustering — fit_mutations","text":"mvnmm object additional list x.muts containing estimated subclones somatic mutations.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_phylogenies.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the phylogenetic trees — fit_phylogenies","title":"Fit the phylogenetic trees — fit_phylogenies","text":"Fit phylogenetic trees","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_phylogenies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the phylogenetic trees — fit_phylogenies","text":"","code":"fit_phylogenies( x, vaf.df = NULL, min_frac = 0, highlight = list(), fit_muts = FALSE )"},{"path":"caravagnalab.github.io/lineaGT/reference/fit_phylogenies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the phylogenetic trees — fit_phylogenies","text":"x add vaf.df add min_frac add highlight add do_filter add label add fit_viber add lineages add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_phylogenies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the phylogenetic trees — fit_phylogenies","text":"mvnmm object additional list x.trees containing estimated clone trees.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_ISs.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the number of ISs per cluster. — get_ISs","title":"Get the number of ISs per cluster. — get_ISs","text":"Get number ISs per cluster.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_ISs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the number of ISs per cluster. — get_ISs","text":"","code":"get_ISs(x, highlight = c())"},{"path":"caravagnalab.github.io/lineaGT/reference/get_ISs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the number of ISs per cluster. — get_ISs","text":"x fitted object highlight clusters retrieve","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_ISs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the number of ISs per cluster. — get_ISs","text":"array names clusters highlight values number ISs assigned cluster","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the coverage dataframe. — get_cov_dataframe","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"Function retrieve coverage dataframe used initialize object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"","code":"get_cov_dataframe(x, verbose = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"coverage dataset used fit model.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"","code":"if (FALSE) get_cov_dataframe(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"Returns list K dataframes, dimension TxT, corresponding covariance matrices estimated clone k","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"","code":"get_covariance_Cholesky(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"list estimated covariance matrices.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"","code":"if (FALSE) get_covariance_Cholesky(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated covariance matrices. — get_covariance_Sigma","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"Returns list K dataframes, dimension TxT, corresponding covariance matrices estimated clone k","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"","code":"get_covariance_Sigma(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"list estimated covariance matrices.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"","code":"if (FALSE) get_covariance_Sigma(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the model dimensions. — get_dimensions","title":"Extract the model dimensions. — get_dimensions","text":"Returns vector dimensions model.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the model dimensions. — get_dimensions","text":"","code":"get_dimensions(x, verbose = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the model dimensions. — get_dimensions","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the model dimensions. — get_dimensions","text":"vector model dimensions.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the model dimensions. — get_dimensions","text":"","code":"if (FALSE) get_dimensions(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the observations labels. — get_labels","title":"Extract the observations labels. — get_labels","text":"Returns list N elements, corresponding labels observation.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the observations labels. — get_labels","text":"","code":"get_labels(x, init = F)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the observations labels. — get_labels","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the observations labels. — get_labels","text":"list observations labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the observations labels. — get_labels","text":"","code":"if (FALSE) get_labels(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the data lineages. — get_lineages","title":"Extract the data lineages. — get_lineages","text":"Returns vector lineages input data.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the data lineages. — get_lineages","text":"","code":"get_lineages(x, verbose = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the data lineages. — get_lineages","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the data lineages. — get_lineages","text":"vector data lineages.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the data lineages. — get_lineages","text":"","code":"if (FALSE) get_lineages(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated mean parameters. — get_mean","title":"Extract the estimated mean parameters. — get_mean","text":"Returns dataframe KxT estimated mean paramaters mu_kt per clone k dimension t.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated mean parameters. — get_mean","text":"","code":"get_mean(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated mean parameters. — get_mean","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated mean parameters. — get_mean","text":"dataframe mean parameters.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated mean parameters. — get_mean","text":"","code":"if (FALSE) get_mean(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated variance parameters. — get_sigma","title":"Extract the estimated variance parameters. — get_sigma","text":"Returns dataframe KxT estimated variance paramaters sigma_kt per clone k dimension t.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated variance parameters. — get_sigma","text":"","code":"get_sigma(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated variance parameters. — get_sigma","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated variance parameters. — get_sigma","text":"dataframe variance parameters.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated variance parameters. — get_sigma","text":"","code":"if (FALSE) get_sigma(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the data timepoints. — get_timepoints","title":"Extract the data timepoints. — get_timepoints","text":"Returns vector timepoints input data.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the data timepoints. — get_timepoints","text":"","code":"get_timepoints(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the data timepoints. — get_timepoints","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the data timepoints. — get_timepoints","text":"vector data timepoints.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the data timepoints. — get_timepoints","text":"","code":"if (FALSE) get_timepoints(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the list of unique observations labels. — get_unique_labels","title":"Extract the list of unique observations labels. — get_unique_labels","text":"Returns list K elements, corresponding unique labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the list of unique observations labels. — get_unique_labels","text":"","code":"get_unique_labels(x, init = FALSE)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the list of unique observations labels. — get_unique_labels","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the list of unique observations labels. — get_unique_labels","text":"list unique labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the list of unique observations labels. — get_unique_labels","text":"","code":"if (FALSE) get_unique_labels(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"Function retrieve list unique labels mutations clusters, form C_c1.Cm1, c1 clone identifier m1 subclone identifier.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"","code":"get_unique_muts_labels(x, clusters = c(), verbose = F)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"x mvnmm object. clusters vector-like variable, identifiers clones want retrieve subclone labels . empty, labels returned.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"vector mutations labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"","code":"if(FALSE) get_unique_muts_labels(x, c(\"C_0\"))"},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the mutations dataframe. — get_vaf_dataframe","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"Function retrieve mutations dataframe used initialize object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"","code":"get_vaf_dataframe(x, verbose = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"mutations dataset.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"","code":"if (FALSE) get_vaf_dataframe(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated mixing proportions. — get_weights","title":"Extract the estimated mixing proportions. — get_weights","text":"Returns list dimension K estimated mixing proportions.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated mixing proportions. — get_weights","text":"","code":"get_weights(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated mixing proportions. — get_weights","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated mixing proportions. — get_weights","text":"list estimated mixing proportions.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated mixing proportions. — get_weights","text":"","code":"if (FALSE) get_weights(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated posterior probabilities. — get_z_probs","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"Returns dataframe shape NxK posterior distribution p(k|n) observation n belong cluster k.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"","code":"get_z_probs(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"dataframe posterior distributions.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"","code":"if (FALSE) get_z_probs(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/have_loaded_env.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if there is a loaded conda environment. — have_loaded_env","title":"Check if there is a loaded conda environment. — have_loaded_env","text":"Function check conda environment already loaded.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/have_loaded_env.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if there is a loaded conda environment. — have_loaded_env","text":"","code":"have_loaded_env()"},{"path":"caravagnalab.github.io/lineaGT/reference/have_loaded_env.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if there is a loaded conda environment. — have_loaded_env","text":"Boolean, TRUE environment loaded, FALSE otherwise.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/have_python_deps.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if Python packages are installed in the environment. — have_python_deps","title":"Check if Python packages are installed in the environment. — have_python_deps","text":"Function check one Python packages present conda environment.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/have_python_deps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if Python packages are installed in the environment. — have_python_deps","text":"","code":"have_python_deps(envname = \"\", py_pkgs = c(\"pylineagt\"))"},{"path":"caravagnalab.github.io/lineaGT/reference/have_python_deps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if Python packages are installed in the environment. — have_python_deps","text":"envname name environment check. empty, function check currently loaded environment. py_pkgs list vector Python packages.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/have_python_deps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if Python packages are installed in the environment. — have_python_deps","text":"list Boolean. input package, TRUE package installed, FALSE otherwise.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/load_conda_env.html","id":null,"dir":"Reference","previous_headings":"","what":"Load the input conda environment. — load_conda_env","title":"Load the input conda environment. — load_conda_env","text":"Function load input conda environment. function raise error Python version already attached reticulate package. case, necessary restart R session load desired environment **** calling lineaGT function interfacing Python - filter_dataset() fit().","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/load_conda_env.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load the input conda environment. — load_conda_env","text":"","code":"load_conda_env(envname = \"lineagt-env\")"},{"path":"caravagnalab.github.io/lineaGT/reference/plot.mvnmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Mullerplot — plot.mvnmm","title":"Mullerplot — plot.mvnmm","text":"Mullerplot showing longitudinal clonal evolution per lineage.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot.mvnmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mullerplot — plot.mvnmm","text":"","code":"# S3 method for class 'mvnmm' plot(x, ...)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot.mvnmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mullerplot — plot.mvnmm","text":"x object class mvnmm ... Default extra parameters","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot.mvnmm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mullerplot — plot.mvnmm","text":"ggplot object mullerplot fitted object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_IC.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the Information Criteria computed during model selection. — plot_IC","title":"Function to plot the Information Criteria computed during model selection. — plot_IC","text":"Function plot Information Criteria computed model selection.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_IC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the Information Criteria computed during model selection. — plot_IC","text":"","code":"plot_IC(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_IC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the Information Criteria computed during model selection. — plot_IC","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_differentiation_tree.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","title":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","text":"Visualize number subclones differentiation tree","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_differentiation_tree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","text":"","code":"plot_differentiation_tree( x, edges = differentiation_tree(), highlight = c(), single_tree = T, clonal = T, wrap = T, timepoints = c() )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_differentiation_tree.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","text":"x add edges add highlight add single_tree add wrap add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_differentiation_tree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","text":"ggplot object showing identified subclones hematopoietic tree.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_gradient_norms.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the gradients norms. — plot_gradient_norms","title":"Function to plot the gradients norms. — plot_gradient_norms","text":"gradient norms parameters per iteration, computed input K used model selection.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_gradient_norms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the gradients norms. — plot_gradient_norms","text":"","code":"plot_gradient_norms(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_gradient_norms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the gradients norms. — plot_gradient_norms","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_rates.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize the infered growth rates. — plot_growth_rates","title":"Visualize the infered growth rates. — plot_growth_rates","text":"Function visualize growth coefficients clone lineage.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_rates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize the infered growth rates. — plot_growth_rates","text":"","code":"plot_growth_rates( x, highlight = c(), min_frac = 0, mutations = F, timepoints_to_int = list(), fit = F, show_best = T )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_rates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize the infered growth rates. — plot_growth_rates","text":"x mvnmm object. highlight vector clusters IDs highlight plot. min_frac min_frac numeric value [0,1] representing minimum abundance highlight clone. mutations Boolean. set TRUE, growth visualize cluster mutations. timepoints_to_int list map timepoint value integer. fit add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_rates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize the infered growth rates. — plot_growth_rates","text":"","code":"if (FALSE) plot_growth_rates(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize the regression given the infered growth rates. — plot_growth_regression","title":"Visualize the regression given the infered growth rates. — plot_growth_regression","text":"Function visualize growht clone lineage.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize the regression given the infered growth rates. — plot_growth_regression","text":"","code":"plot_growth_regression( x, highlight = c(), min_frac = 0, mutations = F, timepoints_to_int = list(), fit = F, show_best = T, ratio = NULL )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize the regression given the infered growth rates. — plot_growth_regression","text":"x mvnmm object. highlight vector clusters IDs highlight plot. min_frac min_frac numeric value [0,1] representing minimum abundance highlight clone. mutations Boolean. set TRUE, growth visualize cluster mutations. timepoints_to_int list map timepoint value integer. fit add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize the regression given the infered growth rates. — plot_growth_regression","text":"","code":"if (FALSE) plot_exp_fit(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_losses.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the training losses. — plot_losses","title":"Function to plot the training losses. — plot_losses","text":"Function plot training losses.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_losses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the training losses. — plot_losses","text":"","code":"plot_losses(x, train = FALSE)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_losses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the training losses. — plot_losses","text":"x mvnmm object. train Boolean. set TRUE, losses computed model selection visualized.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_marginal.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of the marginal distribution of each dimension — plot_marginal","title":"Histogram of the marginal distribution of each dimension — plot_marginal","text":"Function plot marginal distribution coverage, timepoint, colored cluster.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_marginal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of the marginal distribution of each dimension — plot_marginal","text":"","code":"plot_marginal( x, min_frac = 0, highlight = c(), binwidth = 10, show_dens = T, timepoints_to_int = list(), facet_lin = F, single_plot = F )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_marginal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of the marginal distribution of each dimension — plot_marginal","text":"x mvnmm object. highlight vector clusters IDs highlight plot. binwidth numeric value representing histogram binwidth.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_marginal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of the marginal distribution of each dimension — plot_marginal","text":"","code":"if (FALSE) plot_marginal(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mixture_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","title":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","text":"add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mixture_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","text":"","code":"plot_mixture_weights(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mixture_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mixture_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","text":"","code":"if (FALSE) plot_mixture_weights(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mullerplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Muller plot — plot_mullerplot","title":"Muller plot — plot_mullerplot","text":"Function visualize mullerplot fitted object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mullerplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Muller plot — plot_mullerplot","text":"","code":"plot_mullerplot( x, which = \"frac\", highlight = c(), min_frac = 0, estimate_npops = FALSE, rm_mixt = FALSE, timepoints_to_int = c(), mutations = F, single_clone = T, tree_score = 1, legend.pos = \"right\" )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mullerplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Muller plot — plot_mullerplot","text":"x mvnmm object. string among \"frac\",\"pop\",\"fitness\" determining whether plot coverage normalized [0,1], absolute clone abundance, clone colored growth rate, computed assuming exponential growth. highlight vector clusters IDs highlight plot. min_frac min_frac numeric value [0,1] representing minimum abundance highlight clone. timepoints_to_int list map timepoint value integer. mutations Boolean. set TRUE, also clusters mutations visualized. single_clone Boolean. mutations single_clone set TRUE, clones reported highlight respective subclones visualised. tree_score add legend.pos add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mullerplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Muller plot — plot_mullerplot","text":"","code":"if (FALSE) plot_mullerplot(x, wrap=T)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_phylogeny.html","id":null,"dir":"Reference","previous_headings":"","what":"Clonal evolution trees — plot_phylogeny","title":"Clonal evolution trees — plot_phylogeny","text":"Clonal evolution trees","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_phylogeny.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clonal evolution trees — plot_phylogeny","text":"","code":"plot_phylogeny(x, show_best = 1, min_frac = 0, highlight = c())"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_phylogeny.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clonal evolution trees — plot_phylogeny","text":"x mvnmm object. show_best number trees visualize based computed score. min_frac value [0,1] representing minimum abundance show clusters. highlight list labels ID show.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_phylogeny.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clonal evolution trees — plot_phylogeny","text":"list ggplot objects estimated clone trees.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":null,"dir":"Reference","previous_headings":"","what":"2D scatterplot and density — plot_scatter_density","title":"2D scatterplot and density — plot_scatter_density","text":"Function plot scatterplots coverage, one timepoint , together Gaussian density.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D scatterplot and density — plot_scatter_density","text":"","code":"plot_scatter_density(x, plot_density = T, highlight = c(), min_frac = 0)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D scatterplot and density — plot_scatter_density","text":"x mvnmm object. plot_density Boolean. set FALSE, Gaussian density displayed. highlight vector clusters IDs highlight plot. facet Boolean. set TRUE, plot faceted labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D scatterplot and density — plot_scatter_density","text":"list plots, one timepoints combination.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"2D scatterplot and density — plot_scatter_density","text":"","code":"if (FALSE) plots = plot_scatter_density(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":null,"dir":"Reference","previous_headings":"","what":"VAF 2D scatterplot — plot_vaf","title":"VAF 2D scatterplot — plot_vaf","text":"Function plot VAFs mutations one timepoint ","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VAF 2D scatterplot — plot_vaf","text":"","code":"plot_vaf(x, min_frac = 0, highlight = c(), wrap = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"VAF 2D scatterplot — plot_vaf","text":"x mvnmm object. min_frac value [0,1] representing minimum abundance show clusters. highlight list labels ID show. wrap Boolean wrap scatterplots multiple lineages unique plot.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"VAF 2D scatterplot — plot_vaf","text":"list VAF scatterplots.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"VAF 2D scatterplot — plot_vaf","text":"","code":"if (FALSE) plot_vaf(x, min_frac=0.1)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":null,"dir":"Reference","previous_headings":"","what":"VAF over time — plot_vaf_time","title":"VAF over time — plot_vaf_time","text":"Function plot VAFs mutations along time","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VAF over time — plot_vaf_time","text":"","code":"plot_vaf_time(x, min_frac = 0, highlight = c(), timepoints_to_int = list())"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"VAF over time — plot_vaf_time","text":"x mvnmm object. min_frac value [0,1] representing minimum abundance show clusters. highlight list labels ID show. timepoints_to_int list map timepoint value integer.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"VAF over time — plot_vaf_time","text":"ggplot object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"VAF over time — plot_vaf_time","text":"","code":"if (FALSE) plot_vaf_time(x, min_frac=0.1)"},{"path":"caravagnalab.github.io/lineaGT/reference/print.mvnmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method — print.mvnmm","title":"Print method — print.mvnmm","text":"add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/print.mvnmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method — print.mvnmm","text":"","code":"# S3 method for class 'mvnmm' print(x, ...)"},{"path":"caravagnalab.github.io/lineaGT/reference/print.mvnmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method — print.mvnmm","text":"x object class mvnmm ... Default extra paramaters","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/print.mvnmm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method — print.mvnmm","text":"Prints screen information regarding fitted object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/vaf.df.example.html","id":null,"dir":"Reference","previous_headings":"","what":"Example mutation data — vaf.df.example","title":"Example mutation data — vaf.df.example","text":"Example mutation data","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/vaf.df.example.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example mutation data — vaf.df.example","text":"","code":"data(vaf.df.example)"},{"path":"caravagnalab.github.io/lineaGT/reference/vaf.df.example.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example mutation data — vaf.df.example","text":"object class tbl_df (inherits tbl, data.frame) 116 rows 6 columns.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/vaf.df.example.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example mutation data — vaf.df.example","text":"","code":"data(vaf.df.example) head(vaf.df.example) #> # A tibble: 6 × 6 #> IS mutation timepoints lineage alt dp #> #> 1 IS1 mut1 t1 l1 58 134 #> 2 IS1 mut1 t2 l1 53 173 #> 3 IS1 mut1 t1 l2 0 26 #> 4 IS1 mut1 t2 l2 90 322 #> 5 IS10 mut12 t1 l1 0 372 #> 6 IS10 mut12 t2 l1 0 146"},{"path":"caravagnalab.github.io/lineaGT/reference/which_conda_env.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the name of the currently loaded environment. — which_conda_env","title":"Retrieve the name of the currently loaded environment. — which_conda_env","text":"Function retrieve name currently loaded conda environment.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/which_conda_env.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the name of the currently loaded environment. — which_conda_env","text":"","code":"which_conda_env()"},{"path":"caravagnalab.github.io/lineaGT/reference/which_conda_env.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the name of the currently loaded environment. — which_conda_env","text":"character corresponding name loaded environment.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/x.example.html","id":null,"dir":"Reference","previous_headings":"","what":"Example mutation data — x.example","title":"Example mutation data — x.example","text":"Example mutation data","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/x.example.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example mutation data — x.example","text":"","code":"data(vaf.df.example)"},{"path":"caravagnalab.github.io/lineaGT/reference/x.example.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example mutation data — x.example","text":"object class mvnmm length 20.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/x.example.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example mutation data — x.example","text":"","code":"data(vaf.df.example) head(vaf.df.example) #> # A tibble: 6 × 6 #> IS mutation timepoints lineage alt dp #> #> 1 IS1 mut1 t1 l1 58 134 #> 2 IS1 mut1 t2 l1 53 173 #> 3 IS1 mut1 t1 l2 0 26 #> 4 IS1 mut1 t2 l2 90 322 #> 5 IS10 mut12 t1 l1 0 372 #> 6 IS10 mut12 t2 l1 0 146"}] +[{"path":"caravagnalab.github.io/lineaGT/articles/Inference.html","id":"fitting-the-model","dir":"Articles","previous_headings":"","what":"Fitting the model","title":"Lineage inference","text":"Printing fitted object information regarding data: lineages timpoints present data, number integration sites, number inferred clones ISs, estimated via model selection input range number clusters, clone, number assigned ISs mean coverage, per timepoint lineage.","code":"x = fit( cov.df = cov.example.filt, vaf.df = vaf.df.example, steps = 500, # n_runs = 1, k_interval = c(5, 15), timepoints_to_int = unlist(list(\"t1\"=60, \"t2\"=150)) ) #> ℹ Starting lineaGT model selection to retrieve the optimal number of clones #> ✔ Starting lineaGT model selection to retrieve the optimal number of clones ...… #> #> ℹ Fitting model to cluster ISs #> ✔ Found 8 clones of ISs! #> #> ℹ Fitting model to cluster mutations #> ℹ Starting clustering of clone C0 mutations #> [ VIBER - variational fit ] #> #> ℹ Input n = 3, with k < 3. Dirichlet concentration α = 1e-06. #> ℹ Starting clustering of clone C0 mutationsℹ Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts. #> ℹ Starting clustering of clone C0 mutations #> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C0 mutations #> ── [ VIBER ] My VIBER model n = 3 (w = 4 dimensions). Fit with k = 3 clusters. ─ #> ℹ Starting clustering of clone C0 mutations• Clusters: π = 67% [C3] and 33% [C1], with π > 0. #> ℹ Starting clustering of clone C0 mutations• Binomials: θ = <0.09, 0.19, 0.01, 0> [C3] and <0.01, 0.36, 0.03, 0.4> [C1]. #> ℹ Starting clustering of clone C0 mutationsℹ Score(s): ELBO = -1461.821. Fit converged in 6 steps, ε = 1e-10. #> ℹ Starting clustering of clone C0 mutations✔ Reduced to k = 2 (from 3) selecting VIBER cluster(s) with π > 0.166666666666667, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C0 mutations✔ Starting clustering of clone C0 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C0 #> [ ctree ~ clone trees generator for C0 ] #> #> # A tibble: 3 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> #> 1 S1 0.00684 0.362 0.0286 0.396 1 FALSE FALSE #> 2 S2 0.0856 0.190 0 0 2 FALSE TRUE #> 3 C0 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 1, 2, 1, 1 #> ℹ Starting phylogeny inference of clone C0ℹ Total 2 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C0 #> ℹ Starting phylogeny inference of clone C0── Ranking trees #> ℹ Starting phylogeny inference of clone C0✔ 2 trees with non-zero score, storing 2 #> ℹ Starting phylogeny inference of clone C0✔ Starting phylogeny inference of clone C0 ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting clustering of clone C1 mutations #> [ VIBER - variational fit ] #> #> ℹ Input n = 8, with k < 8. Dirichlet concentration α = 1e-06. #> ℹ Starting clustering of clone C1 mutationsℹ Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts. #> ℹ Starting clustering of clone C1 mutations #> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C1 mutations #> ── [ VIBER ] My VIBER model n = 8 (w = 4 dimensions). Fit with k = 8 clusters. ─ #> ℹ Starting clustering of clone C1 mutations• Clusters: π = 25% [C4], 25% [C5], 25% [C6], 13% [C2], and 13% [C8], with π > #> 0. #> ℹ Starting clustering of clone C1 mutations• Binomials: θ = <0, 0.08, 0.01, 0.15> [C4], <0, 0.14, 0.01, 0> [C5], <0.18, 0, #> 0.01, 0> [C6], <0.2, 0.01, 0.02, 0.23> [C2], and <0.43, 0.01, 0.02, 0.32> [C8]. #> ℹ Starting clustering of clone C1 mutationsℹ Score(s): ELBO = -4571.197. Fit converged in 9 steps, ε = 1e-10. #> ℹ Starting clustering of clone C1 mutations✔ Reduced to k = 5 (from 8) selecting VIBER cluster(s) with π > 0.0625, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C1 mutations✔ Starting clustering of clone C1 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C1 #> [ ctree ~ clone trees generator for C1 ] #> #> # A tibble: 6 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> #> 1 S1 0.198 0.00560 0.0170 0.225 1 FALSE FALSE #> 2 S2 0.00162 0.0756 0.0122 0.147 2 FALSE TRUE #> 3 S3 0.00162 0.142 0 0 2 FALSE FALSE #> 4 S4 0.184 0.00279 0 0 2 FALSE FALSE #> 5 S5 0.430 0.00561 0.0170 0.318 1 FALSE FALSE #> 6 C1 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 6, 2, 5, 5 #> ℹ Starting phylogeny inference of clone C1ℹ Total 48 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C1 #> ℹ Starting phylogeny inference of clone C1── Ranking trees #> ℹ Starting phylogeny inference of clone C1✔ 33 trees with non-zero score, storing 33 #> ℹ Starting phylogeny inference of clone C1✔ Starting phylogeny inference of clone C1 ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting clustering of clone C4 mutations #> [ VIBER - variational fit ] #> #> ℹ Input n = 6, with k < 6. Dirichlet concentration α = 1e-06. #> ℹ Starting clustering of clone C4 mutationsℹ Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts. #> ℹ Starting clustering of clone C4 mutations #> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C4 mutations #> ── [ VIBER ] My VIBER model n = 6 (w = 4 dimensions). Fit with k = 6 clusters. ─ #> ℹ Starting clustering of clone C4 mutations• Clusters: π = 50% [C4], 17% [C1], 17% [C5], and 17% [C6], with π > 0. #> ℹ Starting clustering of clone C4 mutations• Binomials: θ = <0.15, 0, 0.01, 0> [C4], <0.36, 0, 0.02, 0.28> [C1], <0, 0, #> 0.02, 0.29> [C5], and <0.19, 0.22, 0.3, 0.2> [C6]. #> ℹ Starting clustering of clone C4 mutationsℹ Score(s): ELBO = -3594.688. Fit converged in 7 steps, ε = 1e-10. #> ℹ Starting clustering of clone C4 mutations✔ Reduced to k = 4 (from 6) selecting VIBER cluster(s) with π > 0.0833333333333333, and Binomial p > 0 in w > 0 dimension(s). #> ℹ Starting clustering of clone C4 mutations✔ Starting clustering of clone C4 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C4 #> [ ctree ~ clone trees generator for C4 ] #> #> # A tibble: 5 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> #> 1 S1 0.358 0.00474 0.0170 0.275 1 FALSE FALSE #> 2 S2 0.146 0.00159 0 0 3 FALSE TRUE #> 3 S3 0 0 0.0187 0.291 1 FALSE FALSE #> 4 S4 0.192 0.222 0.296 0.198 1 FALSE FALSE #> 5 C4 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 6, 1, 6, 5 #> ℹ Starting phylogeny inference of clone C4ℹ Total 54 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C4 #> ℹ Starting phylogeny inference of clone C4── Ranking trees #> ℹ Starting phylogeny inference of clone C4✔ 33 trees with non-zero score, storing 33 #> ℹ Starting phylogeny inference of clone C4✔ Starting phylogeny inference of clone C4 ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting clustering of clone C7 mutations #> [ VIBER - variational fit ] #> #> ℹ Input n = 2, with k < 2. Dirichlet concentration α = 1e-06. #> ℹ Starting clustering of clone C7 mutationsℹ Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts. #> ℹ Starting clustering of clone C7 mutations #> ✔ VIBER fit completed in 0.03 mins (status: converged) #> ℹ Starting clustering of clone C7 mutations #> ── [ VIBER ] My VIBER model n = 2 (w = 4 dimensions). Fit with k = 2 clusters. ─ #> ℹ Starting clustering of clone C7 mutations• Clusters: π = 50% [C1] and 50% [C2], with π > 0. #> ℹ Starting clustering of clone C7 mutations• Binomials: θ = <0.4, 0, 0.31, 0.35> [C1] and <0.01, 0.11, 0, 0> [C2]. #> ℹ Starting clustering of clone C7 mutationsℹ Score(s): ELBO = -1584.497. Fit converged in 5 steps, ε = 1e-10. #> ℹ Starting clustering of clone C7 mutations✔ Starting clustering of clone C7 mutations ... done #> #> ℹ Fitting model to cluster mutationsℹ Starting phylogeny inference of clone C7 #> [ ctree ~ clone trees generator for C7 ] #> #> # A tibble: 3 × 8 #> cluster t1.l1 t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver #> #> 1 S1 0.396 0.00234 0.308 0.348 1 FALSE TRUE #> 2 S2 0.0104 0.112 0 0 1 FALSE FALSE #> 3 C7 1 1 1 1 1 TRUE FALSE #> ✔ Trees per region 2, 1, 1, 1 #> ℹ Starting phylogeny inference of clone C7ℹ Total 2 tree structures - search is exahustive #> ℹ Starting phylogeny inference of clone C7 #> ℹ Starting phylogeny inference of clone C7── Ranking trees #> ℹ Starting phylogeny inference of clone C7✔ 2 trees with non-zero score, storing 2 #> ℹ Starting phylogeny inference of clone C7✔ Starting phylogeny inference of clone C7 ... done #> #> ℹ Fitting model to cluster mutations✔ Fitting model to cluster mutations ... done #> #> ℹ Fitting model to estimate population growth rates #> ℹ Starting growth models inference of clone C0 #> ✔ Starting growth models inference of clone C0 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C1 #> ✔ Starting growth models inference of clone C1 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C2 #> ✔ Starting growth models inference of clone C2 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C3 #> ✔ Starting growth models inference of clone C3 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C4 #> ✔ Starting growth models inference of clone C4 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C5 #> ✔ Starting growth models inference of clone C5 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C6 #> ✔ Starting growth models inference of clone C6 ... done #> #> ℹ Fitting model to estimate population growth ratesℹ Starting growth models inference of clone C7 #> ✔ Starting growth models inference of clone C7 ... done #> #> ℹ Fitting model to estimate population growth rates✔ Fitting model to estimate population growth rates ... done data(x.example) x.example #> ── [ lineaGT ] ──── Python: /usr/share/miniconda/envs/lineagt-env/bin/python ── #> → Lineages: l1 and l2. #> → Timepoints: t1 and t2. #> → Number of Insertion Sites: 66. #> #> ── Optimal IS model with k = 8. #> #> C4 (19 ISs) : l1 [285, 209]; l2 [ 51, 492] #> C1 (15 ISs) : l1 [245, 177]; l2 [ 23, 289] #> C0 (6 ISs) : l1 [145, 240]; l2 [ 32, 373] #> C2 (6 ISs) : l1 [ 1, 547]; l2 [ 1, 388] #> C3 (6 ISs) : l1 [ 92, 109]; l2 [245, 751] #> C5 (6 ISs) : l1 [ 0, 551]; l2 [ 1, 828] #> C6 (4 ISs) : l1 [330, 16]; l2 [ 17, 38] #> C7 (4 ISs) : l1 [ 0, 426]; l2 [ 1, 198]"},{"path":"caravagnalab.github.io/lineaGT/articles/Input-formats.html","id":"coverage-dataframe","dir":"Articles","previous_headings":"","what":"Coverage Dataframe","title":"Input formats","text":"first dataframe requires following columns: : integration site ID, timepoints: longitunal timepoint, lineage: cell lineage name, coverage number reads assigned ISs. lineage timepoints columns present, single longitunal observation single lineage assumed. dataset example following:","code":"data(cov.df.example) cov.df.example #> # A tibble: 428 × 4 #> IS timepoints lineage coverage #> #> 1 IS1 t1 l1 124 #> 2 IS1 t2 l1 190 #> 3 IS1 t1 l2 2 #> 4 IS1 t2 l2 6 #> 5 IS10 t1 l1 4 #> 6 IS10 t2 l1 14 #> 7 IS10 t1 l2 0 #> 8 IS10 t2 l2 12 #> 9 IS100 t1 l1 0 #> 10 IS100 t2 l1 418 #> # ℹ 418 more rows"},{"path":"caravagnalab.github.io/lineaGT/articles/Input-formats.html","id":"mutations-dataframe","dir":"Articles","previous_headings":"","what":"Mutations Dataframe","title":"Input formats","text":"second dataframe requires following columns: : integration site ID, mutation: mutation ID, timepoints: longitunal timepoint, lineage: cell lineage name, alt: per-locus variant allele number reads, dp: per-locus total number reads, hence per-locus depth. dataframe example following:","code":"data(vaf.df.example) vaf.df.example #> # A tibble: 116 × 6 #> IS mutation timepoints lineage alt dp #> #> 1 IS1 mut1 t1 l1 58 134 #> 2 IS1 mut1 t2 l1 53 173 #> 3 IS1 mut1 t1 l2 0 26 #> 4 IS1 mut1 t2 l2 90 322 #> 5 IS10 mut12 t1 l1 0 372 #> 6 IS10 mut12 t2 l1 0 146 #> 7 IS10 mut12 t1 l2 0 57 #> 8 IS10 mut12 t2 l2 0 482 #> 9 IS11 mut13 t1 l1 160 372 #> 10 IS11 mut13 t2 l1 0 146 #> # ℹ 106 more rows"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"mixture-weights","dir":"Articles","previous_headings":"","what":"Mixture weights","title":"Plotting functions","text":"mixture weights number ISs per cluster can visualized function plot_mixture_weights() .","code":"plot_mixture_weights(x.example)"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"scatterplot","dir":"Articles","previous_headings":"","what":"Scatterplot","title":"Plotting functions","text":"function plot_scatter_density() returns list 2D multivariate densities estimated model. argument highlight can used show subset clusters argument min_frac show clusters specified frequency least one dimension. Note observed coverage values across lineages time modeled independent, therefore dimension corresponds combination time-point lineage.","code":"plots = plot_scatter_density(x.example) plots$`cov.t2.l1:cov.t1.l2` # to visualize a single plot"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"marginal-distributions","dir":"Articles","previous_headings":"","what":"Marginal distributions","title":"Plotting functions","text":"function plot_marginal() returns plot marginal estimated densities cluster, time-point lineage. option single_plot returns density whole mixture grouped lineage time-point.","code":"marginals = plot_marginal(x.example) marginals_mixture = plot_marginal(x.example, single_plot=T) patchwork::wrap_plots(marginals / marginals_mixture)"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"mullerplot","dir":"Articles","previous_headings":"","what":"Mullerplot","title":"Plotting functions","text":"function plot_mullerplot() shows expansion identified populations time. supports options =c(\"frac\",\"pop\") corresponding absolule population abundance relative fraction, respectively. option mutations set TRUE, subclones originated within population reported well mullerplot. function supports also visualization single clone monitor growth subpopulations, argument single_clone. Moreover, identified clusters (showing low coverage dimensions) represents poly-clonal populations, since uniquely identified mixture model. Therefore, estimated abundance values might readjusted according estimated number populations clusters.","code":"mp1 = plot_mullerplot(x.example, which=\"frac\") mp2 = plot_mullerplot(x.example, which=\"pop\") patchwork::wrap_plots(mp1, mp2, ncol=1) mp1 = plot_mullerplot(x.example, which=\"frac\", mutations=T) mp2 = plot_mullerplot(x.example, which=\"pop\", mutations=T) patchwork::wrap_plots(mp1, mp2, ncol=1) plot_mullerplot(x.example, highlight=\"C4\", mutations=T, single_clone=T) estimate_n_pops(x.example) #> C0 C1 C2 C3 C4 C5 C6 C7 #> 1 2 1 1 2 1 1 1 plot_mullerplot(x.example, which=\"frac\", mutations=T, estimate_npops=T)"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"vaf","dir":"Articles","previous_headings":"","what":"VAF","title":"Plotting functions","text":"function plot_vaf_time() can used visualize behaviour mutations variant allele frequencies time subclone.","code":"plot_vaf_time(x.example)"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"phylogenetic-evolution","dir":"Articles","previous_headings":"","what":"Phylogenetic evolution","title":"Plotting functions","text":"cluster ISs, function plot_phylogeny() reports estimated phylogenetic tree.","code":"plot_phylogeny(x.example) #> This graph was created by an old(er) igraph version. #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version. #> For now we convert it on the fly... #> This graph was created by an old(er) igraph version. #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version. #> For now we convert it on the fly... #> This graph was created by an old(er) igraph version. #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version. #> For now we convert it on the fly... #> This graph was created by an old(er) igraph version. #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version. #> For now we convert it on the fly... #> $C0 #> #> $C1 #> #> $C4 #> #> $C7"},{"path":"caravagnalab.github.io/lineaGT/articles/Plotting-functions.html","id":"clonal-growth","dir":"Articles","previous_headings":"","what":"Clonal Growth","title":"Plotting functions","text":"fitted exponential logistic growth regressions shown plot_growth_regression() , reporting default fit best model, selected one highest likelihood. regressions can inspected setting show_best=F . function can used show growth regressions subclones identified somatic mutations. alternative way visualising differences growth rates plot_growth_rates() function, reporting values estimated growth rates (sub)population. Disabling show_best option, model lowest likelihood shown dashed line.","code":"plot_growth_regression(x.example, show_best=F) #> Scale for colour is already present. #> Adding another scale for colour, which will replace the existing scale. plot_growth_regression(x.example, highlight=\"C4\", mutations=T) plot_growth_rates(x.example, show_best=F)"},{"path":"caravagnalab.github.io/lineaGT/articles/lineaGT.html","id":"installation-of-the-package","dir":"Articles","previous_headings":"","what":"Installation of the package","title":"Get started","text":"can install LineaGT GitHub using devtools. Load package.","code":"devtools::install_github(\"caravagnalab/lineaGT\") library(lineaGT)"},{"path":"caravagnalab.github.io/lineaGT/articles/lineaGT.html","id":"python-dependencies-installation","dir":"Articles","previous_headings":"","what":"Python dependencies installation","title":"Get started","text":"package loaded, package automatically check whether: version Anaconda Miniconda available, otherwise Miniconda installation started, conda environment loaded, package check lineagt-env present, otherwise created, use existing conda environment, can loaded loading package, either reticulate function reticulate::use_condaenv() using lineaGT function load_conda_env(): eventually, required Python dependencies installed loaded environment, installed.","code":"reticulate::use_condaenv(\"env-name\", required=TRUE) load_conda_env(envname=\"env-name\")"},{"path":"caravagnalab.github.io/lineaGT/articles/lineaGT.html","id":"functions-to-manually-configure-an-environment","dir":"Articles","previous_headings":"","what":"Functions to manually configure an environment","title":"Get started","text":"function pylineaGT can also used interactively manually configure existing environment, create one scratch. function first check Anaconda Miniconda installation available, otherwise prompt Miniconda installation. input name environment either name existing environment name environment created. environment loaded created, required Python dependencies installed.","code":"configure_environment(env_name=\"lineaget-env\", use_default=F)"},{"path":"caravagnalab.github.io/lineaGT/articles/lineaGT.html","id":"check-the-loaded-python-version-and-environment","dir":"Articles","previous_headings":"","what":"Check the loaded Python version and environment","title":"Get started","text":"package provides also set helper functions check environment loaded. have_loaded_env() check environment already loaded, which_conda_env() check environment loaded. have_python_deps() check Python packages list installed specified environment. load_conda_env() load specified environment.","code":""},{"path":"caravagnalab.github.io/lineaGT/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Elena Buscaroli. Author, maintainer. Giulio Caravagna. Author.","code":""},{"path":"caravagnalab.github.io/lineaGT/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Buscaroli E, Caravagna G (2024). lineaGT: lineaGT. R package version 0.1.0, https://caravagnalab.github.io/lineaGT/, https://github.com/caravagnalab/lineaGT.","code":"@Manual{, title = {lineaGT: lineaGT}, author = {Elena Buscaroli and Giulio Caravagna}, year = {2024}, note = {R package version 0.1.0, https://caravagnalab.github.io/lineaGT/}, url = {https://github.com/caravagnalab/lineaGT}, }"},{"path":"caravagnalab.github.io/lineaGT/index.html","id":"lineagt-","dir":"","previous_headings":"","what":"lineaGT","title":"lineaGT","text":"package implements algorithm determine lineage inference gene therapy assays based insertion sites, accounting also somatic mutations accumulation. specifically, starting coverage values ISs identified gene therapy assays associated somatic mutations, lineaGT can: cluster ISs observed multi-lineage longitudinal coverage identify populations cells originated Haematopoietic Stem Cell estimate abundances sample; cluster somatic mutations observed multi-lineage longitudinal variant allele frequency within clone, identify subpopulations; infer population genetics parameters, .e., growth rates, population supporting exponential logistic growth models selecting optimal one. R package provides R interface Python algorithms developed pyLineaGT package, uses Pyro probabilistic programming language infer lineage histories.","code":""},{"path":"caravagnalab.github.io/lineaGT/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"lineaGT","text":"use lineaGT, please cite: E. Buscaroli, S. Milite, R. Bergamin, N. Calonaci, F. Gazzo, . Calabria, G. Caravagna. Bayesian multi-lineage tracing gene therapy assays. preparation.","code":""},{"path":[]},{"path":"caravagnalab.github.io/lineaGT/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"lineaGT","text":"can install released version lineaGT GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"caravagnalab/lineaGT\")"},{"path":"caravagnalab.github.io/lineaGT/index.html","id":"copyright-and-contacts","dir":"","previous_headings":"Installation","what":"Copyright and contacts","title":"lineaGT","text":"Elena Buscaroli, Giulio Caravagna. Cancer Data Science (CDS) Laboratory, University Trieste, Italy.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/configure_environment.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure the reticulate environment — configure_environment","title":"Configure the reticulate environment — configure_environment","text":"Function configure Python dependencies R. Python environment available, function check version conda miniconda, otherwise install miniconda, install Python package pylineaGT.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/configure_environment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure the reticulate environment — configure_environment","text":"","code":"configure_environment(envname = \"lineagt-env\", use_default = F)"},{"path":"caravagnalab.github.io/lineaGT/reference/configure_environment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure the reticulate environment — configure_environment","text":"env_name name conda environment use, available.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/cov.df.example.html","id":null,"dir":"Reference","previous_headings":"","what":"Example coverage data — cov.df.example","title":"Example coverage data — cov.df.example","text":"Example coverage data","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/cov.df.example.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example coverage data — cov.df.example","text":"","code":"data(cov.df.example)"},{"path":"caravagnalab.github.io/lineaGT/reference/cov.df.example.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example coverage data — cov.df.example","text":"object class tbl_df (inherits tbl, data.frame) 428 rows 4 columns.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/cov.df.example.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example coverage data — cov.df.example","text":"","code":"data(cov.df.example) head(cov.df.example) #> # A tibble: 6 × 4 #> IS timepoints lineage coverage #> #> 1 IS1 t1 l1 124 #> 2 IS1 t2 l1 190 #> 3 IS1 t1 l2 2 #> 4 IS1 t2 l2 6 #> 5 IS10 t1 l1 4 #> 6 IS10 t2 l1 14"},{"path":"caravagnalab.github.io/lineaGT/reference/estimate_n_pops.html","id":null,"dir":"Reference","previous_headings":"","what":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","title":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","text":"Function implemented estimate real number clones cluster.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/estimate_n_pops.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","text":"","code":"estimate_n_pops(x, highlight = c())"},{"path":"caravagnalab.github.io/lineaGT/reference/estimate_n_pops.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","text":"x mvnmm object highlight clusters show","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/estimate_n_pops.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function implemented to estimate the real number of clones in each cluster. — estimate_n_pops","text":"named array reporting cluster estimated true number populations.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/filter_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Filters the input dataset. — filter_dataset","title":"Filters the input dataset. — filter_dataset","text":"Function used filter observations, .e. ISs, input dataframe coverage values.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/filter_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filters the input dataset. — filter_dataset","text":"","code":"filter_dataset( cov.df, min_cov = 5, min_frac = 0.05, k_interval = c(10, 20), metric = \"calinski_harabasz_score\", seed = 5 )"},{"path":"caravagnalab.github.io/lineaGT/reference/filter_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filters the input dataset. — filter_dataset","text":"cov.df Input coverage dataset. must least columns coverage, timepoints, lineage, , coverage values, timepoint, lineage , respectively. min_cov add min_frac add k_interval add metric add seed add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/filter_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filters the input dataset. — filter_dataset","text":"dataset shape input one, filtered observations.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an object of class mvnmm. — fit","title":"Creates an object of class mvnmm. — fit","text":"Function fit input data.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an object of class mvnmm. — fit","text":"","code":"fit( cov.df, vaf.df = NULL, infer_phylogenies = TRUE, infer_growth = TRUE, k_interval = c(5, 15), n_runs = 1, steps = 500, min_steps = 20, lr = 0.005, p = 1, min_frac = 0, max_IS = NULL, check_conv = TRUE, covariance = \"full\", hyperparams = list(), default_lm = TRUE, timepoints_to_int = list(), show_progr = FALSE, store_grads = TRUE, store_losses = TRUE, store_params = FALSE, seed_optim = TRUE, seed = 6, seed_init = reticulate::py_none(), sample_id = \"\" )"},{"path":"caravagnalab.github.io/lineaGT/reference/fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an object of class mvnmm. — fit","text":"cov.df Input coverage dataset. must least columns coverage, , additional columns timepoints lineage, added missing, assuming single timepoint lineage. vaf.df Input VAF dataset. NULL, mutations clustering performed. must least columns mutation, , alt, dp, additional vaf, timepoints, lineage, , mutation, number reads mutated allele, overall depth, vaf values, timepoint, lineage, mutation, respectively. infer_phylogenies Boolean. set TRUE, function also compute attach returned object phylogenetic trees cluster. k_interval Interval K values test. n_runs Number runs perform K. steps Maximum number steps inference. lr Learning rate used inference. p Numeric value used check convergence parameters. min_frac add max_IS add check_conv Boolean. set TRUE, function check early convergence, otherwise perform steps iterations. covariance Covariance type Multivariate Gaussian. hyperparams add default_lm add timepoints_to_int add show_progr Boolean. TRUE, progression bar shown inference. store_grads Booolean. TRUE, gradient norms parameters iteration stored. store_losses Boolean. TRUE, computed losses parameters iteration stored. store_params Boolean. TRUE, estimated parameters iteration stored. seed_optim add seed Value seed. sample_id add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates an object of class mvnmm. — fit","text":"mvnmm object, containing input dataset, annotated IS_values, N, K, T specific dataset, input column names, list params contain inferred parameters, python object","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_growth_rates.html","id":null,"dir":"Reference","previous_headings":"","what":"Infer growth rates for each clone and subclone. — fit_growth_rates","title":"Infer growth rates for each clone and subclone. — fit_growth_rates","text":"Infer growth rates clone subclone.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_growth_rates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Infer growth rates for each clone and subclone. — fit_growth_rates","text":"","code":"fit_growth_rates( x, steps = 500, highlight = c(), timepoints_to_int = c(), growth_model = \"exp.log\", force = T, tree_score = 1, py_pkg = NULL, mutations = F )"},{"path":"caravagnalab.github.io/lineaGT/reference/fit_growth_rates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Infer growth rates for each clone and subclone. — fit_growth_rates","text":"x mvnmm object. steps maximum number steps inference. highlight set clusters run inference . specified, run clusters. timepoints_to_int provided timepoints integers timepoints--int list stored x, list mapping values integers required. growth_model string specifying type growth model use, exp log corresponding exponential logistic models, respectively. force model already fitted, setting force FALSE keep computed rates. Setting force TRUE fit model specified clusters.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_growth_rates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Infer growth rates for each clone and subclone. — fit_growth_rates","text":"mvnmm object additional tibble growth.rates containing estimated population genetics parameters.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_mutations.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the mutations clustering — fit_mutations","title":"Fit the mutations clustering — fit_mutations","text":"Fit mutations clustering","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_mutations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the mutations clustering — fit_mutations","text":"","code":"fit_mutations( x, vaf.df = NULL, infer_phylo = TRUE, min_frac = 0, max_IS = NULL, highlight = list() )"},{"path":"caravagnalab.github.io/lineaGT/reference/fit_mutations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the mutations clustering — fit_mutations","text":"x mvnmm object. vaf.df dataframe mutations data. infer_phylo Boolean indicating whether infer also phylogenetic evolution per cluster ISs. min_frac add max_IS add highlight add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_mutations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the mutations clustering — fit_mutations","text":"mvnmm object additional list x.muts containing estimated subclones somatic mutations.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_phylogenies.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the phylogenetic trees — fit_phylogenies","title":"Fit the phylogenetic trees — fit_phylogenies","text":"Fit phylogenetic trees","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_phylogenies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the phylogenetic trees — fit_phylogenies","text":"","code":"fit_phylogenies( x, vaf.df = NULL, min_frac = 0, highlight = list(), fit_muts = FALSE )"},{"path":"caravagnalab.github.io/lineaGT/reference/fit_phylogenies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the phylogenetic trees — fit_phylogenies","text":"x add vaf.df add min_frac add highlight add do_filter add label add fit_viber add lineages add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/fit_phylogenies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the phylogenetic trees — fit_phylogenies","text":"mvnmm object additional list x.trees containing estimated clone trees.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_ISs.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the number of ISs per cluster. — get_ISs","title":"Get the number of ISs per cluster. — get_ISs","text":"Get number ISs per cluster.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_ISs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the number of ISs per cluster. — get_ISs","text":"","code":"get_ISs(x, highlight = c())"},{"path":"caravagnalab.github.io/lineaGT/reference/get_ISs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the number of ISs per cluster. — get_ISs","text":"x fitted object highlight clusters retrieve","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_ISs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the number of ISs per cluster. — get_ISs","text":"array names clusters highlight values number ISs assigned cluster","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the coverage dataframe. — get_cov_dataframe","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"Function retrieve coverage dataframe used initialize object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"","code":"get_cov_dataframe(x, verbose = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"coverage dataset used fit model.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_cov_dataframe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve the coverage dataframe. — get_cov_dataframe","text":"","code":"if (FALSE) get_cov_dataframe(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"Returns list K dataframes, dimension TxT, corresponding covariance matrices estimated clone k","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"","code":"get_covariance_Cholesky(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"list estimated covariance matrices.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Cholesky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated Cholesky matrices, used to factorise the covariance matrix. — get_covariance_Cholesky","text":"","code":"if (FALSE) get_covariance_Cholesky(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated covariance matrices. — get_covariance_Sigma","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"Returns list K dataframes, dimension TxT, corresponding covariance matrices estimated clone k","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"","code":"get_covariance_Sigma(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"list estimated covariance matrices.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_covariance_Sigma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated covariance matrices. — get_covariance_Sigma","text":"","code":"if (FALSE) get_covariance_Sigma(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the model dimensions. — get_dimensions","title":"Extract the model dimensions. — get_dimensions","text":"Returns vector dimensions model.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the model dimensions. — get_dimensions","text":"","code":"get_dimensions(x, verbose = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the model dimensions. — get_dimensions","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the model dimensions. — get_dimensions","text":"vector model dimensions.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_dimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the model dimensions. — get_dimensions","text":"","code":"if (FALSE) get_dimensions(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the observations labels. — get_labels","title":"Extract the observations labels. — get_labels","text":"Returns list N elements, corresponding labels observation.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the observations labels. — get_labels","text":"","code":"get_labels(x, init = F)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the observations labels. — get_labels","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the observations labels. — get_labels","text":"list observations labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the observations labels. — get_labels","text":"","code":"if (FALSE) get_labels(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the data lineages. — get_lineages","title":"Extract the data lineages. — get_lineages","text":"Returns vector lineages input data.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the data lineages. — get_lineages","text":"","code":"get_lineages(x, verbose = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the data lineages. — get_lineages","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the data lineages. — get_lineages","text":"vector data lineages.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_lineages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the data lineages. — get_lineages","text":"","code":"if (FALSE) get_lineages(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated mean parameters. — get_mean","title":"Extract the estimated mean parameters. — get_mean","text":"Returns dataframe KxT estimated mean paramaters mu_kt per clone k dimension t.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated mean parameters. — get_mean","text":"","code":"get_mean(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated mean parameters. — get_mean","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated mean parameters. — get_mean","text":"dataframe mean parameters.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated mean parameters. — get_mean","text":"","code":"if (FALSE) get_mean(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated variance parameters. — get_sigma","title":"Extract the estimated variance parameters. — get_sigma","text":"Returns dataframe KxT estimated variance paramaters sigma_kt per clone k dimension t.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated variance parameters. — get_sigma","text":"","code":"get_sigma(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated variance parameters. — get_sigma","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated variance parameters. — get_sigma","text":"dataframe variance parameters.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_sigma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated variance parameters. — get_sigma","text":"","code":"if (FALSE) get_sigma(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the data timepoints. — get_timepoints","title":"Extract the data timepoints. — get_timepoints","text":"Returns vector timepoints input data.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the data timepoints. — get_timepoints","text":"","code":"get_timepoints(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the data timepoints. — get_timepoints","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the data timepoints. — get_timepoints","text":"vector data timepoints.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_timepoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the data timepoints. — get_timepoints","text":"","code":"if (FALSE) get_timepoints(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the list of unique observations labels. — get_unique_labels","title":"Extract the list of unique observations labels. — get_unique_labels","text":"Returns list K elements, corresponding unique labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the list of unique observations labels. — get_unique_labels","text":"","code":"get_unique_labels(x, init = FALSE)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the list of unique observations labels. — get_unique_labels","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the list of unique observations labels. — get_unique_labels","text":"list unique labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the list of unique observations labels. — get_unique_labels","text":"","code":"if (FALSE) get_unique_labels(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"Function retrieve list unique labels mutations clusters, form C_c1.Cm1, c1 clone identifier m1 subclone identifier.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"","code":"get_unique_muts_labels(x, clusters = c(), verbose = F)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"x mvnmm object. clusters vector-like variable, identifiers clones want retrieve subclone labels . empty, labels returned.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"vector mutations labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_unique_muts_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve the list of unique labels of mutation clusters. — get_unique_muts_labels","text":"","code":"if(FALSE) get_unique_muts_labels(x, c(\"C_0\"))"},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the mutations dataframe. — get_vaf_dataframe","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"Function retrieve mutations dataframe used initialize object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"","code":"get_vaf_dataframe(x, verbose = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"mutations dataset.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_vaf_dataframe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve the mutations dataframe. — get_vaf_dataframe","text":"","code":"if (FALSE) get_vaf_dataframe(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated mixing proportions. — get_weights","title":"Extract the estimated mixing proportions. — get_weights","text":"Returns list dimension K estimated mixing proportions.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated mixing proportions. — get_weights","text":"","code":"get_weights(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated mixing proportions. — get_weights","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated mixing proportions. — get_weights","text":"list estimated mixing proportions.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated mixing proportions. — get_weights","text":"","code":"if (FALSE) get_weights(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the estimated posterior probabilities. — get_z_probs","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"Returns dataframe shape NxK posterior distribution p(k|n) observation n belong cluster k.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"","code":"get_z_probs(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"dataframe posterior distributions.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/get_z_probs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the estimated posterior probabilities. — get_z_probs","text":"","code":"if (FALSE) get_z_probs(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/have_loaded_env.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if there is a loaded conda environment. — have_loaded_env","title":"Check if there is a loaded conda environment. — have_loaded_env","text":"Function check conda environment already loaded.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/have_loaded_env.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if there is a loaded conda environment. — have_loaded_env","text":"","code":"have_loaded_env()"},{"path":"caravagnalab.github.io/lineaGT/reference/have_loaded_env.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if there is a loaded conda environment. — have_loaded_env","text":"Boolean, TRUE environment loaded, FALSE otherwise.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/have_python_deps.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if Python packages are installed in the environment. — have_python_deps","title":"Check if Python packages are installed in the environment. — have_python_deps","text":"Function check one Python packages present conda environment.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/have_python_deps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if Python packages are installed in the environment. — have_python_deps","text":"","code":"have_python_deps(envname = \"\", py_pkgs = c(\"pylineagt\"))"},{"path":"caravagnalab.github.io/lineaGT/reference/have_python_deps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if Python packages are installed in the environment. — have_python_deps","text":"envname name environment check. empty, function check currently loaded environment. py_pkgs list vector Python packages.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/have_python_deps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if Python packages are installed in the environment. — have_python_deps","text":"list Boolean. input package, TRUE package installed, FALSE otherwise.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/load_conda_env.html","id":null,"dir":"Reference","previous_headings":"","what":"Load the input conda environment. — load_conda_env","title":"Load the input conda environment. — load_conda_env","text":"Function load input conda environment. function raise error Python version already attached reticulate package. case, necessary restart R session load desired environment **** calling lineaGT function interfacing Python - filter_dataset() fit().","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/load_conda_env.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load the input conda environment. — load_conda_env","text":"","code":"load_conda_env(envname = \"lineagt-env\")"},{"path":"caravagnalab.github.io/lineaGT/reference/plot.mvnmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Mullerplot — plot.mvnmm","title":"Mullerplot — plot.mvnmm","text":"Mullerplot showing longitudinal clonal evolution per lineage.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot.mvnmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mullerplot — plot.mvnmm","text":"","code":"# S3 method for class 'mvnmm' plot(x, ...)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot.mvnmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mullerplot — plot.mvnmm","text":"x object class mvnmm ... Default extra parameters","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot.mvnmm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mullerplot — plot.mvnmm","text":"ggplot object mullerplot fitted object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_IC.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the Information Criteria computed during model selection. — plot_IC","title":"Function to plot the Information Criteria computed during model selection. — plot_IC","text":"Function plot Information Criteria computed model selection.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_IC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the Information Criteria computed during model selection. — plot_IC","text":"","code":"plot_IC(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_IC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the Information Criteria computed during model selection. — plot_IC","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_differentiation_tree.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","title":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","text":"Visualize number subclones differentiation tree","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_differentiation_tree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","text":"","code":"plot_differentiation_tree( x, edges = differentiation_tree(), highlight = c(), single_tree = T, clonal = T, wrap = T, timepoints = c() )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_differentiation_tree.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","text":"x add edges add highlight add single_tree add wrap add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_differentiation_tree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize the number of subclones on the differentiation tree — plot_differentiation_tree","text":"ggplot object showing identified subclones hematopoietic tree.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_gradient_norms.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the gradients norms. — plot_gradient_norms","title":"Function to plot the gradients norms. — plot_gradient_norms","text":"gradient norms parameters per iteration, computed input K used model selection.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_gradient_norms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the gradients norms. — plot_gradient_norms","text":"","code":"plot_gradient_norms(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_gradient_norms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the gradients norms. — plot_gradient_norms","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_rates.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize the infered growth rates. — plot_growth_rates","title":"Visualize the infered growth rates. — plot_growth_rates","text":"Function visualize growth coefficients clone lineage.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_rates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize the infered growth rates. — plot_growth_rates","text":"","code":"plot_growth_rates( x, highlight = c(), min_frac = 0, mutations = F, timepoints_to_int = list(), fit = F, show_best = T )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_rates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize the infered growth rates. — plot_growth_rates","text":"x mvnmm object. highlight vector clusters IDs highlight plot. min_frac min_frac numeric value [0,1] representing minimum abundance highlight clone. mutations Boolean. set TRUE, growth visualize cluster mutations. timepoints_to_int list map timepoint value integer. fit add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_rates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize the infered growth rates. — plot_growth_rates","text":"","code":"if (FALSE) plot_growth_rates(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize the regression given the infered growth rates. — plot_growth_regression","title":"Visualize the regression given the infered growth rates. — plot_growth_regression","text":"Function visualize growht clone lineage.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize the regression given the infered growth rates. — plot_growth_regression","text":"","code":"plot_growth_regression( x, highlight = c(), min_frac = 0, mutations = F, timepoints_to_int = list(), fit = F, show_best = T, ratio = NULL )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize the regression given the infered growth rates. — plot_growth_regression","text":"x mvnmm object. highlight vector clusters IDs highlight plot. min_frac min_frac numeric value [0,1] representing minimum abundance highlight clone. mutations Boolean. set TRUE, growth visualize cluster mutations. timepoints_to_int list map timepoint value integer. fit add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_growth_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize the regression given the infered growth rates. — plot_growth_regression","text":"","code":"if (FALSE) plot_exp_fit(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_losses.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the training losses. — plot_losses","title":"Function to plot the training losses. — plot_losses","text":"Function plot training losses.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_losses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the training losses. — plot_losses","text":"","code":"plot_losses(x, train = FALSE)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_losses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the training losses. — plot_losses","text":"x mvnmm object. train Boolean. set TRUE, losses computed model selection visualized.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_marginal.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of the marginal distribution of each dimension — plot_marginal","title":"Histogram of the marginal distribution of each dimension — plot_marginal","text":"Function plot marginal distribution coverage, timepoint, colored cluster.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_marginal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of the marginal distribution of each dimension — plot_marginal","text":"","code":"plot_marginal( x, min_frac = 0, highlight = c(), binwidth = 10, show_dens = T, timepoints_to_int = list(), facet_lin = F, single_plot = F )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_marginal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of the marginal distribution of each dimension — plot_marginal","text":"x mvnmm object. highlight vector clusters IDs highlight plot. binwidth numeric value representing histogram binwidth.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_marginal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of the marginal distribution of each dimension — plot_marginal","text":"","code":"if (FALSE) plot_marginal(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mixture_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","title":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","text":"add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mixture_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","text":"","code":"plot_mixture_weights(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mixture_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","text":"x mvnmm object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mixture_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barplot of the per-cluster mixture weights and number of ISs. — plot_mixture_weights","text":"","code":"if (FALSE) plot_mixture_weights(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mullerplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Muller plot — plot_mullerplot","title":"Muller plot — plot_mullerplot","text":"Function visualize mullerplot fitted object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mullerplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Muller plot — plot_mullerplot","text":"","code":"plot_mullerplot( x, which = \"frac\", highlight = c(), min_frac = 0, estimate_npops = FALSE, rm_mixt = FALSE, timepoints_to_int = c(), mutations = F, single_clone = T, tree_score = 1, legend.pos = \"right\" )"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mullerplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Muller plot — plot_mullerplot","text":"x mvnmm object. string among \"frac\",\"pop\",\"fitness\" determining whether plot coverage normalized [0,1], absolute clone abundance, clone colored growth rate, computed assuming exponential growth. highlight vector clusters IDs highlight plot. min_frac min_frac numeric value [0,1] representing minimum abundance highlight clone. timepoints_to_int list map timepoint value integer. mutations Boolean. set TRUE, also clusters mutations visualized. single_clone Boolean. mutations single_clone set TRUE, clones reported highlight respective subclones visualised. tree_score add legend.pos add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_mullerplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Muller plot — plot_mullerplot","text":"","code":"if (FALSE) plot_mullerplot(x, wrap=T)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_phylogeny.html","id":null,"dir":"Reference","previous_headings":"","what":"Clonal evolution trees — plot_phylogeny","title":"Clonal evolution trees — plot_phylogeny","text":"Clonal evolution trees","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_phylogeny.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clonal evolution trees — plot_phylogeny","text":"","code":"plot_phylogeny(x, show_best = 1, min_frac = 0, highlight = c())"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_phylogeny.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clonal evolution trees — plot_phylogeny","text":"x mvnmm object. show_best number trees visualize based computed score. min_frac value [0,1] representing minimum abundance show clusters. highlight list labels ID show.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_phylogeny.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clonal evolution trees — plot_phylogeny","text":"list ggplot objects estimated clone trees.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":null,"dir":"Reference","previous_headings":"","what":"2D scatterplot and density — plot_scatter_density","title":"2D scatterplot and density — plot_scatter_density","text":"Function plot scatterplots coverage, one timepoint , together Gaussian density.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D scatterplot and density — plot_scatter_density","text":"","code":"plot_scatter_density(x, plot_density = T, highlight = c(), min_frac = 0)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D scatterplot and density — plot_scatter_density","text":"x mvnmm object. plot_density Boolean. set FALSE, Gaussian density displayed. highlight vector clusters IDs highlight plot. facet Boolean. set TRUE, plot faceted labels.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D scatterplot and density — plot_scatter_density","text":"list plots, one timepoints combination.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_scatter_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"2D scatterplot and density — plot_scatter_density","text":"","code":"if (FALSE) plots = plot_scatter_density(x)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":null,"dir":"Reference","previous_headings":"","what":"VAF 2D scatterplot — plot_vaf","title":"VAF 2D scatterplot — plot_vaf","text":"Function plot VAFs mutations one timepoint ","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VAF 2D scatterplot — plot_vaf","text":"","code":"plot_vaf(x, min_frac = 0, highlight = c(), wrap = T)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"VAF 2D scatterplot — plot_vaf","text":"x mvnmm object. min_frac value [0,1] representing minimum abundance show clusters. highlight list labels ID show. wrap Boolean wrap scatterplots multiple lineages unique plot.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"VAF 2D scatterplot — plot_vaf","text":"list VAF scatterplots.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"VAF 2D scatterplot — plot_vaf","text":"","code":"if (FALSE) plot_vaf(x, min_frac=0.1)"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":null,"dir":"Reference","previous_headings":"","what":"VAF over time — plot_vaf_time","title":"VAF over time — plot_vaf_time","text":"Function plot VAFs mutations along time","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VAF over time — plot_vaf_time","text":"","code":"plot_vaf_time(x, min_frac = 0, highlight = c(), timepoints_to_int = list())"},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"VAF over time — plot_vaf_time","text":"x mvnmm object. min_frac value [0,1] representing minimum abundance show clusters. highlight list labels ID show. timepoints_to_int list map timepoint value integer.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"VAF over time — plot_vaf_time","text":"ggplot object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/plot_vaf_time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"VAF over time — plot_vaf_time","text":"","code":"if (FALSE) plot_vaf_time(x, min_frac=0.1)"},{"path":"caravagnalab.github.io/lineaGT/reference/print.mvnmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method — print.mvnmm","title":"Print method — print.mvnmm","text":"add","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/print.mvnmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method — print.mvnmm","text":"","code":"# S3 method for class 'mvnmm' print(x, ...)"},{"path":"caravagnalab.github.io/lineaGT/reference/print.mvnmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method — print.mvnmm","text":"x object class mvnmm ... Default extra paramaters","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/print.mvnmm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method — print.mvnmm","text":"Prints screen information regarding fitted object.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/vaf.df.example.html","id":null,"dir":"Reference","previous_headings":"","what":"Example mutation data — vaf.df.example","title":"Example mutation data — vaf.df.example","text":"Example mutation data","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/vaf.df.example.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example mutation data — vaf.df.example","text":"","code":"data(vaf.df.example)"},{"path":"caravagnalab.github.io/lineaGT/reference/vaf.df.example.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example mutation data — vaf.df.example","text":"object class tbl_df (inherits tbl, data.frame) 116 rows 6 columns.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/vaf.df.example.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example mutation data — vaf.df.example","text":"","code":"data(vaf.df.example) head(vaf.df.example) #> # A tibble: 6 × 6 #> IS mutation timepoints lineage alt dp #> #> 1 IS1 mut1 t1 l1 58 134 #> 2 IS1 mut1 t2 l1 53 173 #> 3 IS1 mut1 t1 l2 0 26 #> 4 IS1 mut1 t2 l2 90 322 #> 5 IS10 mut12 t1 l1 0 372 #> 6 IS10 mut12 t2 l1 0 146"},{"path":"caravagnalab.github.io/lineaGT/reference/which_conda_env.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the name of the currently loaded environment. — which_conda_env","title":"Retrieve the name of the currently loaded environment. — which_conda_env","text":"Function retrieve name currently loaded conda environment.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/which_conda_env.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the name of the currently loaded environment. — which_conda_env","text":"","code":"which_conda_env()"},{"path":"caravagnalab.github.io/lineaGT/reference/which_conda_env.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the name of the currently loaded environment. — which_conda_env","text":"character corresponding name loaded environment.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/x.example.html","id":null,"dir":"Reference","previous_headings":"","what":"Example mutation data — x.example","title":"Example mutation data — x.example","text":"Example mutation data","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/x.example.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example mutation data — x.example","text":"","code":"data(vaf.df.example)"},{"path":"caravagnalab.github.io/lineaGT/reference/x.example.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example mutation data — x.example","text":"object class mvnmm length 20.","code":""},{"path":"caravagnalab.github.io/lineaGT/reference/x.example.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example mutation data — x.example","text":"","code":"data(vaf.df.example) head(vaf.df.example) #> # A tibble: 6 × 6 #> IS mutation timepoints lineage alt dp #> #> 1 IS1 mut1 t1 l1 58 134 #> 2 IS1 mut1 t2 l1 53 173 #> 3 IS1 mut1 t1 l2 0 26 #> 4 IS1 mut1 t2 l2 90 322 #> 5 IS10 mut12 t1 l1 0 372 #> 6 IS10 mut12 t2 l1 0 146"}]