Hello, I'm very sorry for posting again a question regarding RNA-seq with no replicates. I've read most of the posts and still don't know what's the best way to proceed. My experimental design was: two industrial fermenters, one as control, the other as the treatment and we took samples every 24 hours for a total of 4 samples. Due to a fail on the fermenter I have replicates only for the first time points. So My first approach was to use the last two time points as one (replicates). I've used edge R, and make contrast for each pairwise comparison I want to test. My BCV is really high (2.06) and I've used glmQLFit. So I got the FC for each of the contrasts. But my main problem is that the replicates are very very different from each other so my supervisor suggest to redo the analysis by individual fermenter run (which means without replicates). Does this seems sensible? If so, I've been checking edgeR on how to proceed but as soon as I get to calculate the dispersion it fails, I get this error: "Warning message:
In estimateGLMCommonDisp.default(y = y$counts, design = design, :
No residual df: setting dispersion to NA".
count_cols <- c("Gene","GLU24H","GLU48H","GLU72H","GLU.FA24H","GLU.FA48H","GLU.FA72H")
x<-read.delim('~/Desktop/PhDProject/3rd_Year_RESULTS/Counts/counts_run1.csv',skip=0, sep=",", check.names=FALSE, colClasses='character', na.strings=c())
x[,count_cols] <- apply(x[,count_cols], 2, function(v) as.numeric(v))
counts <- x[, count_cols]
keepMin <- apply(counts, 1, max) >= 1
keepCpm <- rowSums(cpm(counts)> 1) >= 1
keep <- keepMin & keepCpm
x <- x[keep,]
counts <- counts[keep,]
y <- DGEList(counts=counts, group = group)
y <- calcNormFactors(y, method="TMM")
PGRA_rpkm<-rpkm(counts, log=FALSE, gene.length = genes)
plotMD(cpm(PGRA_rpkm, log=TRUE), column=6) > abline(h=0, col="red", lty=2, lwd=2)
design <- model.matrix(~0+group)
y <- estimateGLMCommonDisp(y,design)
Any help will be really useful as I'm currently stuck at this.
Thanks in advance!!