Entering edit mode
6.2 years ago
amitunited0532
▴
40
Hello,
I have performed an RNAseq experiment, for which I used HISAT2 for the alingment, Stringtie for the assembly and the R package Ballgown for the Differential Expression (DE) analysis. After generation of differential expression table, I found q value is mostly same for all genes which is given below.
Command which I run:
results_genes = stattest(bacteria_filter, feature = "gene", covariate = "sample", getFC = TRUE, meas = "FPKM")
results_genes = merge(results_genes,bacteria_gene_names,by.x=c("id"),by.y=c("gene_id"))
differential_genes = subset(results_genes, results_genes$pval<0.05)
write.csv(differential_genes, "differential_genes.csv", row.names = FALSE)
Output:
qval
0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586 0.999988586
Thank you
Please use the code rather than the blockquote buttom to indicate code :)
you probably don't have any significant hits even at non-FDR-adjusted levels. FDR-adjustment does lead to chunking quite often, though it's rarely a concern: if you need to rank by reproducibility use the untransformed levels, if you need to test at a certain threshold, use the FDR-adjusted levels