Question: Differential Expression Analysis on miRNA Normalized data
gravatar for asalimih
2.8 years ago by
asalimih30 wrote:

Hello, I have a normalized miRNA data matrix of 6 groups. every group has 3 samples.
density plot of each sample is: density plot 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) <- lmFit(miR.v, design) <-, contrasts=contr.matrix) <- eBayes(
dt.e <- decideTests(,lfc = 1)

the voom's plot:
Mean-variance trend

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:

  1. Is the matrix really normalized?(how can i know)
  2. if it is normalized can i use limma voom function on normalized data? if not what should i do?
  3. Am i doing anything wrong?

every help would be appreciated so much

ADD COMMENTlink modified 2.8 years ago • written 2.8 years ago by asalimih30
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