Differential expression
Created by Olga Dethlefsen & Ståle Nygård. Parts of the code were adapted from https://nbisweden.github.io/workshop-scRNAseq/, courtesy of Åsa Björklund
Learning objectives
- define differential expression DE
- identify properties of scRNA-seq data influencing DE
- assess data distributions
What is differential expression testing
- taking read count data &
- performing statistical analysis to discover quantitative changes in expression levels between experimental groups
- i.e. to decide whether, for a given gene, an observed difference in read counts is significant (greater than what would be expected just due to natural random variation)
DE is an “old problem”
- known from bulk RNA-seq and microarray studies
- in fact building on one of the most common statistical problems, i.e comparing groups for statistical differences
Single-cell vs bulk RNA-seq count matrices
Characteristics of scRNA-seq data
- high noise levels (technical and biological factors)
- low library sizes
- low amount of available mRNAs results in amplification biases and “dropout events”
- 3’ bias, partial coverage and uneven depth (technical)
- stochastic nature of transcription (biological)
- multimodality in gene expression; presence of multiple possible cell states within a cell population (biological)
Example distributions
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