Hi Steve,

Thanks for you reply. I have tried what you suggested and checked the limma
guide sessions. Now I am getting the results from only one treatment which
is great but the problem is all my adjusted Pvalues (adj.P.Val) are >0.05.

 Don't really know how to solve this, I know my data works because I used
DESeq and got around 200 differentially expressed genes FDR < 0.05.

Here is the desing, targets and the script:

> design
  co c2
1  1  0
2  1  0
3  1  0
4  1  0
5  0  1
6  0  1
7  0  1
8  0  1
9  0  1

> targets
   Individual condition
B1          1        co
B3          3        co
B4          4        co
B5          5        co
D1          1        c2
D2          2        c2
D3          3        c2
D4          4        c2
D5          5        c2



>co=as.matrix(read.table("2014_04_02_6h_PB.csv",header=T, sep=",",
row.names=1))

>keep <- rowSums(co)>=9
>nkeep <- sum(keep)
>co2 <- co[keep,]

>nf = calcNormFactors (co2)

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

>targets

>ind <- factor (targets$Individual)

>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=ind)

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

>cm <- makeContrasts (co2Vsco=c2-co, levels=design)

>fit2 <- eBayes(contrasts.fit(fit,cm))

>res <- topTable (fit2,coef="co2Vsco", n=nrow (y), sort.by=“none”)
>res <- data.frame (res)
>res2 <- subset (res, adj.P.Val < 0.1)

> res2
[1] ID        logFC     AveExpr   t         P.Value   adj.P.Val B
<0 rows> (or 0-length row.names)

Thanks again,

Catalina

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