Critical Assessment of RNA-Seq Differential Expression

Warden and Wu Preprint: v1 In general, this primarily focuses on the following types of comparisons: Cell line experiments with over-expression or knock-down to define a known causal gene, with processing starting with public reads. 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). Differential expression methods include the following: edgeR (GLM) edgeR-robust (GLM) edgeR (QL) edgeR-robust (QL) DESeq1 DESeq2 limma-voom limma-trend (CPM) limma-trend (FPKM/RPKM) ANOVA (log2 FRPKM/RPKM) The most common preprocessing strategies include STAR, TopHat2, and Salmon. However, a limited amount of additional processing with HISAT2, kallisto, Bowtie2 (+eXpress), and Bowtie1 (+RSEM) is also provided. Most STAR and TopHat2 alignments use htseq-count for quantification, as well as running cuffdiff (for single variable 2-group comparisons). However, a limited amount of additional processing with featureCounts is also provided. Most STAR and TopHat2 alignments start with the public forward reads, even if paired-end data was available.