Expression is quantified to study cellular changes in response to external stimuli, differences between healthy and diseased states, and other research questions. Transcript levels are often used as a proxy for protein abundance, but these are often not equivalent due to post transcriptional events such as RNA interference and nonsense-mediated decay.
Expression is quantified by counting the number of reads that mapped to each locus in the transcriptome assembly step. Expression can be quantified for exons or genes using contigs or reference transcript annotations. These observed RNA-Seq read counts have been robustly validated against older technologies, including expression microarrays and qPCR. Tools that quantify counts are HTSeq, FeatureCounts, Rcount, maxcounts, FIXSEQ, and Cuffquant. These tools determine read counts from aligned RNA-Seq data, but alignment-free counts can also be obtained with Sailfish and Kallisto. The read counts are then converted into appropriate metrics for hypothesis testing, regressions, and other analyses. Parameters for this conversion are:
* Sequencing depth/coverage: Although depth is pre-specified when conducting multiple RNA-Seq experiments, it will still vary widely between experiments. Therefore, the total number of reads generated in a single experiment is typically normalized by converting counts to fragments, reads, or counts per million mapped reads (FPM, RPM, or CPM). The difference between RPM and FPM was historically derived during the evolution from single-end sequencing of fragments to paired-end sequencing. In single-end sequencing, there is only one read per fragment (i.e., RPM = FPM). In paired-end sequencing, there are two reads per fragment (i.e., RPM = 2 x FPM). Sequencing depth is sometimes referred to as library size, the number of intermediary cDNA molecules in the experiment.
* Gene length: Longer genes will have more fragments/reads/counts than shorter genes if transcript expression is the same. This is adjusted by dividing the FPM by the length of a feature (which can be a gene, transcript, or exon), resulting in the metric fragments per kilobase of feature per million mapped reads (FPKM). When looking at groups of features across samples, FPKM is converted to transcripts per million (TPM) by dividing each FPKM by the sum of FPKMs within a sample.
* Total sample RNA output: Because the same amount of RNA is extracted from each sample, samples with more total RNA will have less RNA per gene. These genes appear to have decreased expression, resulting in false positives in downstream analyses. Normalization strategies including quantile, DESeq2, TMM and Median Ratio attempt to account for this difference by comparing a set of non-differentially expressed genes between samples and scaling accordingly.
* Variance for each genes expression:' is modeled to account for sampling error (important for genes with low read counts), increase power, and decrease false positives. Variance can be estimated as a normal, Poisson, or negative binomial distribution and is frequently decomposed into technical and biological variance.