Calculation of Log2 fold change in DEXSeq
1
0
Entering edit mode
tmvarsha • 0
@tmvarsha-23248
Last seen 3 months ago

Hello,

I'd like to know how the log2 fold change is calculated between target and comparison population in DEXSeq. Going over the estimateExonFoldChanges function in an older version (0.12.1) of the package, I realize the interaction coefficient is taken from the model: count ~ condition * exon and fold change is calculated by applying a variance stabilizing transformation and then transformed to a log2 scale:

alleffects <- do.call(rbind, alleffects)
alleffects <- vst(exp(alleffects), object)
alleffects <- log2(alleffects/alleffects[, denoCol]).  ###foldChange <- effects[,"target"] - effects[,"comparison"]

alleffects data frame looks like the following:

Gene target comparison
Ptma 5.36425504487572   5.10532234811512
Ptma 4.43604234783272   4.7521435893567
Ptma 4.30355887270297   4.72294913039353
Tmpo 2.12202872975088   1.08346386248873
Msn 1.86941999824138    2.34083780006062

However, in a newer version that I am currently using (1.28.3), looks like the target and comparison values being used for fold change calculation are not vst transformed. Also, being divided by log(2).

alleffects <- rbind(alleffectsBM, alleffectsSM)
alleffectsVst <- vst(exp(alleffects), object)
alleffects <- alleffects/log(2)
alleffects <- alleffects - alleffects[, denoCol] ###foldChange

The calculation in the older version makes more sense for a log2 fold change than in version 1.28.3.

An explanation for this discrepancy in fold change calculation would be appreciated.

Thanks in advance!

DEXSeq log2foldchange Tutorial • 119 views
ADD COMMENT
0
Entering edit mode
Alejandro Reyes ★ 1.8k
@alejandro-reyes-5124
Last seen 8 days ago
Novartis Institutes for BioMedical Reseā€¦

Hi @tmvarsha,

Thanks for your detailed report. The first version actually had a bug: vst data is already log-like, so the code was wrongly calculating a log2 fold change from a log-like data. In the second version, you are right that it is not variance-stabilized transformed. The appropiate approach would be to use the shrinkage approaches of DESeq2: I've been thinking on how to implement this but I need to find the time to do this.

Alejandro

ADD COMMENT

Login before adding your answer.

Traffic: 359 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6