Hi all,

I have a RNAseq data to analyse were I have a control and a one treatment
for different individuals. I need to block the effects of the individual,
but I am having several troubles to get the data that I need. I am using
voom because my data is very heterogeneous and voom seams to do a good job
normalising my reads.

I am having the following issues:

   1.

   I want to get the differentially expressed genes (DEGs) of my treatment
   not of my control. I don't understand after the eBayes analysis why I get
   the coefficients for both. I have tried a > makeContrasts (TreatvsCont=
   c2-co, levels = design) to subtract the control effect but then I get 0
   DEGs.
   2.

   I am not sure when to include the 0 (null model) in the model formula, I
   have read examples for both types of models.

This are my targets, with my column names of my counts, individual and
condition

>targets

Individual condition

A1 1 co

A2 3 co

A4 4 co

A5 5 co

E1 1 c2

E2 2 c2

E3 3 c2

E4 4 c2

E5 5 c2

This is the code I have been trying:

>co2=as.matrix(read.table("2014_04_02_1h_PB.csv",header=T, sep=",",
row.names=1))

>nf = calcNormFactors (co2)

>targets= read.table ("targets.csv", header = T, sep=",",row.names=1)

>treat <- factor (targets$condition, levels= c("co", "c2"))

>design <- model.matrix(~0+treat)

>colnames (design) <- levels (treat)

>y <- voom(co2,design,lib.size=colSums(co2)*nf)

>corfit <- duplicateCorrelation(y,design,block=targets$Individual)

>fit <-
lmFit(y,design,block=targets$Individual,correlation=corfit$consensus)

>fit2<- eBayes (fit)

>results_trt <- topTable (fit2, coef="c2", n=nrow (y), sort.by="none")

>From which gives me 18,000 genes with adj.P.Val < 0.01 out of 22,000 genes
that I have in total. Which makes no sense..

Thanks in advance for the help.

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