Hi,
BooteJTK is working really well for some of my data: For example, it deals really well with data that has three replicates per ZT.
However, when I use it on data, which has a different number of replicates for two different ZT, it seems that issues occur. An example would be a time series in which ZT01, ZT04, ZT07, ZT10, ZT13, ZT16, ZT19 all have 3 replicates each and ZT22 has 4 replicates. This is what happens:
-
In the the output file XYZ_Ns_postVash it is indicated that all ZT have 4 replicates (N = 4).
-
The distribution of the GammaBH is weird: There are "clusters" of p-values, for example, 500 genes have a p-value of 0.005, 500 genes have a p-value of 0.01, 250 genes have a p-value of 0.02 and so on.
If I am not mistaken, there is an issue with how differences in the number of replicates between different ZT are handled.
Is there a way to solve this?
Thank you!
Christian
Hi,
BooteJTK is working really well for some of my data: For example, it deals really well with data that has three replicates per ZT.
However, when I use it on data, which has a different number of replicates for two different ZT, it seems that issues occur. An example would be a time series in which ZT01, ZT04, ZT07, ZT10, ZT13, ZT16, ZT19 all have 3 replicates each and ZT22 has 4 replicates. This is what happens:
In the the output file XYZ_Ns_postVash it is indicated that all ZT have 4 replicates (N = 4).
The distribution of the GammaBH is weird: There are "clusters" of p-values, for example, 500 genes have a p-value of 0.005, 500 genes have a p-value of 0.01, 250 genes have a p-value of 0.02 and so on.
If I am not mistaken, there is an issue with how differences in the number of replicates between different ZT are handled.
Is there a way to solve this?
Thank you!
Christian