November 2021

Outline

  • Motivation

  • Differential expression

  • Differential abundance

Single Cell RNAseq Analysis Workflow

Motivation

Clusters and/or cell types have been identified, we now want to compare sample groups:

  • which genes vary in expression ?
  • which clusters differ in abundance ?

Replicates are samples not cells:

  • single cells within a sample are not independent of each other,
  • using cells as replicates amounts to studying variation among an individual
  • while we want to study variation across a population

Pseudo-bulk:

  • gene expression levels for each cluster in each sample
  • are obtained by summing across cells

Differential expression between conditions

An example. TSNE plots showing clusters and sample groups (left) and samples (right):

Differential expression between conditions

Workflow:

  • compute pseudo-bulk count by summing across cells,

    • per cluster and per sample
  • perform bulk analysis with few replicates,

    • for each cluster separately

Method:

  • quasi-likelihood (QL) methods from the edgeR package

  • negative binomial generalized linear model (NB GLM)

    • to handle overdispersed count data
    • in experiments with limited replication

Differential expression between conditions

Steps:

  • Remove samples with very low library sizes, e.g. < 20 cells

    • better normalisation
  • Remove genes that are lowly expressed,

    • reduces computational work,
    • improves the accuracy of mean-variance trend modelling
    • decreases the severity of the multiple testing correction
    • filter: log-CPM threshold in a minimum number of samples, smallest sample group
  • Correct for composition biases

    • by computing normalization factors with the trimmed mean of M-values method
  • Test whether the log-fold change between sample groups is significantly different from zero

    • estimate the negative binomial (NB) dispersions
    • estimate the quasi-likelihood dispersions, uncertainty and variability of the per-gene variance

Single Cell RNAseq Analysis Workflow

Differential abundance between conditions

Aim:

  • test for significant changes in per-cluster cell abundance across conditions

Example:

  • which cell types are depleted or enriched upon treatment?

Steps:

  • Count cells assigned to each label, i.e. cluster or cell type

  • Same workflow as for differential expression above,

    • except counts are of cells per label, not of reads per gene
  • Share information across labels

    • to improve our estimates of the biological variability in cell abundance between replicates.