Thank you a lot, I tried to repeat with the d3 with 34% of the initial number of genes (it was obtained after the strong filtering), and with d2 (25%), but had the same problem( Possibly, should I filter more and more strictly? I think, leaving 34% and 25% - it's very strict?
> d <- dmDSdata(counts=counts, samples=samps)
> d
An object of class dmDSdata
with 38577 genes and 7 samples
* data accessors: counts(), samples()
design()
mean_expression(), common_precision(), genewise_precision()
proportions(), coefficients()
> d2 <- dmFilter(d,
+ min_samps_feature_expr=n.small, min_feature_expr=10,
+ min_samps_feature_prop=n.small, min_feature_prop=0.1,
+ min_samps_gene_expr=n, min_gene_expr=10)
> d2
An object of class dmDSdata
with 9587 genes and 7 samples
* data accessors: counts(), samples()
> d3 <- dmFilter(d,
+ min_samps_feature_expr=n.small, min_feature_expr=10,
+ min_samps_gene_expr=n, min_gene_expr=10)
> d3
An object of class dmDSdata
with 13222 genes and 7 samples
* data accessors: counts(), samples()
Yes, I saw DEXSeq. Thank you very much, I am the beginner and I haven't know other programs, in addition to those discussed in details (with commands) in this article. I will try to use IsoformSwitchAnalyzeR too.