**0**wrote:

Dear All,

I'm working on a genomic plateform and I'm in charge of analysing biological data. A team of researchers asked me to compare two different methods to compute differentially expressed genes in a given dataset, from illumina human RNAseq data, aligned with Tophat2.

My dataset in composed of 12 samples, divided in 4 groups A, B, C and D (each of the groups containing 3 samples).

The first approach consists in using Cufflinks to compute FPKM values, calculate the mean FPKM value for each group, and then comparing the groups by performing a t-test (only if the coverage is > 1 in at least the 3 members of one of the 2 compared groups). So for each gene in each pairwise comparison, I get a p-value and a fold-change corresponding to the ratio of the means. A cutoff of p-value < 0.05 and FC > 1.5 was then applied.

The second approach consists in using the RUVseq method (http://www.bioconductor.org/packages/release/bioc/html/RUVSeq.html), based on a GLM approach. I'm using RUV-g with 11 housekeeping genes and I used the model :

set<-RUVg(set_raw_counts,row.names(controls),k=4)

where set_raw_counts is the SeqExpressionSet of the raw counts and controls contains housekeeping genes.

designAD<-model.matrix(~0+groups+W_1+W_2+W_3+W_4,data=pData(set))

yAD<-DGEList(counts=counts(set_raw_counts),group=groups)

yAD<-calcNormFactors(yAD,method="upperquartile")

yAD<-estimateGLMCommonDisp(yAD,designAD)

yAD<-estimateGLMTagwiseDisp(yAD,designAD)

fitAD<-glmFit(yAD,designAD)

Then for computing the DE genes in A vs B :

A_vs_**B**<-makeContrasts(groupsA-groups**B**,levels=designAD)

myContrasts<-makeContrasts(A_vs_**B**=groupsA-groupsB,levels=designAD)

lrt<-glmLRT(fitAD,contrast=myContrasts[,"A_vs_B"])

But I'm not sure to understand well the output in lrt$table. I got 3 columns, logFC, logCPM and p-value.

I'm sorry this is a recurrent question but how is calculated the logFC and the logCPM ? Is it possible to have the details of the calculation ? How can I make it comparable with the FC I got from the first approach (with FPKM ?) Because when I tried to convert the logFC to FC, this lead to FC with very different orders of magnitude from the FPKM ones.

Thank you very much for your time and help.