Critical Assessment of RNA-Seq Differential Expression

<p><strong>Warden and Wu Preprint</strong>: <a href="https://www.biorxiv.org/content/10.1101/2024.02.10.579728v1">v1</a></p> <p>In general, this primarily focuses on the following types of comparisons:</p> <ol> <li>Cell line experiments with over-expression or knock-down to define a known causal gene, with processing starting with public reads.</li> <li>Processed TCGA (The Cancer Genome Atlas) data for breast cancer (BRCA) to compare gene expression by immunohistochemistry status (ER/ESR1, PR/PGR, or HER2/ERBB2).</li> </ol> <p>Differential expression methods include the following:</p> <ul> <li><em>edgeR (GLM)</em></li> <li><em>edgeR-robust (GLM)</em></li> <li><em>edgeR (QL)</em></li> <li><em>edgeR-robust (QL)</em></li> <li><em>DESeq1</em></li> <li><em>DESeq2</em></li> <li><em>limma-voom</em></li> <li><em>limma-trend (CPM)</em></li> <li><em>limma-trend (FPKM/RPKM)</em></li> <li><em>ANOVA (log2 FRPKM/RPKM)</em></li> </ul> <p>The most common preprocessing strategies include STAR, TopHat2, and Salmon. &nbsp;However, a limited amount of additional processing with HISAT2, kallisto, Bowtie2 (+eXpress), and Bowtie1 (+RSEM) is also provided.</p> <p>Most STAR and TopHat2 alignments use htseq-count for quantification, as well as running cuffdiff (for single variable 2-group comparisons). &nbsp;However, a limited amount of additional processing with featureCounts is also provided.</p> <p>Most STAR and TopHat2 alignments start with the public <strong>forward</strong> reads, even if paired-end data was available.</p>