Hi,

I think I followed the correct steps to find significant unchanged genes between the conditions, but it returns no significant genes.

What does that mean? Isn't the basic assumption of the analysis that most of the genes are unchanged?

Version: DESeq2_1.20.0     

Thanks!

Tirza Doniger

======================================================

dataset <- DESeqDataSetFromMatrix(countData = merged_counts,
                                  colData = colData,
                                  design= ~Treatment)

ddsNoPrior <- DESeq(dataset, betaPrior=FALSE)
resLA <- results(ddsNoPrior, lfcThreshold=0.1, altHypothesis="lessAbs")

=============================================================

> summary(resLA)

out of 21068 with nonzero total read count
adjusted p-value < 0.1
LFC > 0.10 (up)    : 0, 0%
LFC < -0.10 (down) : 0, 0%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

==================================================

hi Tirza,

The short answer is that you don't have enough precision on the LFCs to say that any are within (-0.1, 0.1). If you expanded the range, you could probably reject some of the null hypotheses at your current sample size, or if you had more samples you could get more precision on the LFC estimates.