Module 8 - Pathway Enrichment Analysis

Veronique Voisin

This module will cover how to use differential expression output to generate pathway enrichment results.

Lecture

Presentation of the dataset

  • This module is a continuation of the transcriptomics module 6 and we will use the same dataset. This dataset contains bulk RNA-seq data of a model of breast cancer. The gene counts were deposited to the Gene Expression Omnibus (GEO) database under accession GSE222367 and are publicly available.

  • RNA was extracted from MCF7, a breast cancer cell line under different conditions: parental cell line and cell line samples resistant to the drugs palbociclib or abemaciclib.

  • Biological replicates were available for each condition.

  • RNA-seq library was prepared and sequenced using an Illumina HiSeq 2500 sequencer.

  • Sequence reads were mapped to the human reference genome (Gencode GRCh38) using TopHat as aligner.

  • The BAM files with mapped reads for each sample were sorted and then used as input for HTSeq 0.11.0 in order to generate read count data at the gene level.

  • We retrieved the gene counts and performed differential expression analysis using a negative binomial generalized linear model, followed by pathway enrichment analysis.

GEO dataset

Figure 1: GEO dataset