Hello, I have a normalized miRNA data matrix of 6 groups. every group has 3 samples.
density plot of each sample is: I have no access to the raw count data. So for analysing this matrix I used limma package. But results I get are not as it must be (based on the corresponding paper).
my R code:
gr <- factor(rep(c("DC1","DC2","DC3","DC4","DC5","DC6"),each=3)) miR$samples$group <- gr; design <- model.matrix(~0+gr) colnames(design) <- gsub("gr", "", colnames(design)) contr.matrix <- makeContrasts( DC1.DC2 = DC2-DC1, DC2.DC3 = DC3-DC2, DC3.DC4 = DC4-DC3, DC4.DC5 = DC5-DC4, DC5.DC6 = DC6-DC5, levels = colnames(design)) miR.v <- voom(miR, design, plot=TRUE) miR.v.fit <- lmFit(miR.v, design) miR.v.fit <- contrasts.fit(miR.v.fit, contrasts=contr.matrix) miR.e.fit <- eBayes(miR.v.fit) dt.e <- decideTests(miR.e.fit,lfc = 1) summary(dt.e)
the voom's plot:
output of summary(dt.e) function:
> summary(dt.e) DC1.DC2 DC2.DC3 DC3.DC4 DC4.DC5 DC5.DC6 Down 4 0 1 1 4 NotSig 1970 1983 1975 1982 1978 Up 9 0 7 0 1
As you can see in the code, i want to know upregulation and downregulation of miRNAs between every two consecutive groups.
so here are my questions:
- Is the matrix really normalized?(how can i know)
- if it is normalized can i use limma voom function on normalized data? if not what should i do?
- Am i doing anything wrong?
every help would be appreciated so much