Hi everybody,
I am struggling trying to calculate log2FC manually with an RNA-seq experiment that has no replicates. I know this question has been posted but hadn't been able to transfer the answers to my data.
So I have 3 conditions, let's say HpA, SpA and Empty. I would like to look at the fold change values in the genes since I can't do a DEG using the function's that need replicates to fit a model (what's logical).
Here's my code:
group <- as.factor(c(1,2,3))
rawc <- DGEList(rawcounts2, group = group)
I remove low counts
keep <- filterByExpr(rawc)
filt.count <- rawc[keep, , keep.lib.sizes=F]
Normalize taking account for the library size
norm.count <- calcNormFactors(filt.count)
norm.count$samples
Calculate CPM which are log2 transformed
logcpm <- cpm(norm.count,log=TRUE)
And here's where I'm stuck. Whilst my data is already log2 transformed, I thought that just doing the ratio between the conditions I want to compare should be ok.
logFC_HpAEmpty <- (logcpm[,1]/logcpm[,3])
logFC_SpAEmpty <- logcpm[,2]/logcpm[,3]
I don't know if I am right and what's next. I will appreciate your advise and help as I have never done this type of analysis manually and I think it's a good exercise to further understand the fold changes in expression values.
I thank you in advance for your time.
Lea
Hi,
First, thank you for your advise. I read the edgeR's vignette and that's why I was wondering to do a descriptive analysis that includes an analysis of fold changes, as said in section 2.12. I thought I was going this way. I am maybe misunderstanding the log fold change concept? Are my logFC (obtained by subtraction) results correct? If it is, should I compare them in a plot or can I retrieve a table?
Lea