spatial single cell models
https://www.mattpeeples.net/modules/PointPattern.html
WangLab
Methods and applications for single-cell and spatial multi-omics
In silico tissue generation and power analysis for spatial omics
Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope $$log p ({{{{{{{{\bf{X}}}}}}}}}^{(t)} {{{{{{{\bf{y}}}}}}}},, {k}{1},, {k}{2}),=,\log p({{{{{{{\bf{y}}}}}}}}{{{{{{{{\bf{X}}}}}}}}}^{(t)},, {k}{1},, {k}{2}),+\log p({{{{{{{{\bf{x}}}}}}}}}{1}^{(t)}{k}{1})+\logp({{{{{{{{\bf{x}}}}}}}}}{2}^{(t)}| {k}{2})-\log p({{{{{{{\bf{y}}}}}}}} {k}{1},, {k}{2}}$$
SODB facilitates comprehensive exploration of spatial omics data
Zero-preserving imputation of single-cell RNA-seq data
ambient RNA remover , https://github.com/marcalva/diem
Spatially resolved epigenomic profiling of single cells in complex tissues
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data https://www.biorxiv.org/content/10.1101/2022.01.28.478202v2.full.pdf
Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction https://github.com/welch-lab/MultiVelo
Generalizing RNA velocity to transient cell states through dynamical modeling https://github.com/theislab/scvelo
a scalable deep neural network that locally infers velocity for each cell from its neighbors https://github.com/GuangyuWangLab2021/cellDancer