Hello,
I am comparing two biological conditions each condition has 2 biological replicates. The design matrix is:
samples = data.frame( + condition = c("high","high","low","low"), + replicate = c(1:2,1:2), + row.names = c("high1","high2","low1","low2"), + stringsAsFactors = TRUE, + check.names = FALSE + )
After creating my exon count set, I make a BA plot and color all the significant hits "padjust<0.05" in red. The vast majority of my sig-regulated exons are higher in my high sample with almost none in the low sample. A plot is attached, FPKM=read counts.
I have normalized my samples in a number of ways including the technique included with DEXSeq. I think my problem is that one pair replicates has a large variance and therefore the exons that are regulated in this direction are not detected?
I would like to test/demonstrate this by plotting the variance for EACH biological sample against the mean. Can someone show me how this might be accomplished.
TIA
I am comparing two biological conditions each condition has 2 biological replicates. The design matrix is:
samples = data.frame( + condition = c("high","high","low","low"), + replicate = c(1:2,1:2), + row.names = c("high1","high2","low1","low2"), + stringsAsFactors = TRUE, + check.names = FALSE + )
After creating my exon count set, I make a BA plot and color all the significant hits "padjust<0.05" in red. The vast majority of my sig-regulated exons are higher in my high sample with almost none in the low sample. A plot is attached, FPKM=read counts.
I have normalized my samples in a number of ways including the technique included with DEXSeq. I think my problem is that one pair replicates has a large variance and therefore the exons that are regulated in this direction are not detected?
I would like to test/demonstrate this by plotting the variance for EACH biological sample against the mean. Can someone show me how this might be accomplished.
TIA
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