compare between samples. Do you want to do differential expression analysis? If so, the most appropriate tools are DESeq2 and edgeR. These will also deal with normalisation and provide the most sophisticated method so you don't have to worry about absolute vs relative quantification.

From literature (I dig a lot into blogs, papers, etc.. ) and essentially I've summed up the following: Both RPKM and FPKM measures shouldn't be used anymore since they contain an essentially arbitrary scaling factor which is dependent on the average effective length of the transcripts in the underlying sample. Not reproducible, not comparable... TPM measure seems to be more appropriate in dealing with this issue since the sums of normalized reads of each sample are the same across all samples, making it "more suitable" to compare samples. However, its calculation (specifically the denominator term) is also sample dependent and this would be the main reason why I shouldn't use it to directly compare expression values between samples. CPM seems to be a less-normalized measure since it takes into account only library size. On the opposite hand, estimated read count don't normalize samples at all, making it useless to my goal (unless I use some between-sample normalization method). My point is that TPM seems to be the most reliable expression measurement to compare different samples. Still, TMP performs within-sample normalization (although there's a lot of papers comparing samples based on TPM values). Do you think TPM is suitable to compare between-samples expression values?

TPM is not suitable for between-sample normalization because it doesn't account for differences in library composition. It is also very dependent on a few highly expressed genes that may not be the same between your samples. Instead, you could use the TMM-normalized counts or the median of ratio normalization used in DESeq2.

255 genes found only in cell lines and not tissues 1220 genes found only in tissues and not cell lines

(Uhlén M et al, 2015). The cell lines have been harvested during log phase of growth and extracted high quality mRNA was used as input material for library construction and subsequent sequencing. The expression level of gene-specific transcripts is given as Transcript Per Million (TPM) values. Genes with a TPM value ≥1 are considered as detected. Altogether the transcriptome of 64 cell lines have been analyzed to form a basis of different expression categories.

Polyester: simulating RNA-seq datasets with differential transcript expression

Transcriptomics technologies

Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap