RNA-Seq workflow: gene-level exploratory analysis and differential expression

By Michael I. Love, Simon Anders, Vladislav Kim, Wolfgang Huber. F1000Research, 2015

405 citations recorded by the source corpus.

Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f456a6e973e391d39f4985e2d9c741f240253338

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Citations in the corpus

B cells and tertiary lymphoid structures promote immunotherapy response (2020, positive): “6) 46 software to identify differentially expressed genes (DEGs) between two response (R versus NR) groups.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Figure 7: A) A meandispersion plot of the RNAseq experiment from [126] , as processed in [127] .”