Apologies, I should have been more expansive in my first post. I understand that 1 vs 1 cannot give you e.g. reliable p-values for differences but my goal here is simply to create a heatmap of pre vs post treatment logFC across individual patients. My question about cmps is linked to this snippet of code from the tximport vignette:

cts <- txi$counts
normMat <- txi$length

# Obtaining per-observation scaling factors for length, adjusted to avoid
# changing the magnitude of the counts.
normMat <- normMat/exp(rowMeans(log(normMat)))
normCts <- cts/normMat

# Computing effective library sizes from scaled counts, to account for
# composition biases between samples.
library(edgeR)
eff.lib <- calcNormFactors(normCts) * colSums(normCts)

# Combining effective library sizes with the length factors, and calculating
# offsets for a log-link GLM.
normMat <- sweep(normMat, 2, eff.lib, "*")
normMat <- log(normMat)

# Creating a DGEList object for use in edgeR.
y <- DGEList(cts)
y <- scaleOffset(y, normMat)
# filtering
keep <- filterByExpr(y)
## Warning in filterByExpr.DGEList(y): All samples appear to belong to the same
## group.
y <- y[keep, ]
# y is now ready for estimate dispersion functions see edgeR User's Guide
For creating a matrix of CPMs within edgeR, the following code chunk can be used:

cpms <- edgeR::cpm(y, offset = y$offset, log = FALSE)

where the calcNormFactors function is used to calculate the TMM effective library size given that the default 'method' argument in calcNormFactor appears to be "TMM". In fact from what I understand TMM counts can be obtained by simply running:

y <- calcNormFactors(y)
y <- edgeR:cpm(y)

where y is a DGEList object. If what I just wrote is correct (which might not be) then the 'cpms' calculated in the tximport vignette are not the classic CPMs, but rather more akin to TMMs. Also I don't really understand why not using simply the two lines of code above (calcNormFactors+cpm) to get from tximport$counts (run with the option countsFromAbundance="no") to a y object that can be used in downstream analysis.

My impression is that for what I want (that is, a heatmap of pre vs post treatment logFC where columns are patients and rows are genes from a list of genes of interest, I could just run the following:

cts <- txi$counts
colnames(cts) <- colnames(txi$length)
cts_df <- as.data.frame(cts)
y <- DGEList(cts)
y <- calcNormFactors(y, method = "TMM")
TMM_log_counts <- cpm(y, log = "TRUE")

and then take the difference between the log values in the TMM_log_counts matrix.

Does this sound reasonable or am I (very possibly) missing something?

Thanks for your help! Marco