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

• Differential expression - Differences in expression between sample group within a cluster

• Differential abundance - Differences in cell numbers between sample groups within a cluster

Replicates are samples not cells:

• single cells within a sample are not independent of each other,
• using cells as replicates amounts to studying variation inside 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

Workflow:

• compute pseudo-bulk count by summing across cells,

  • per cluster and per sample

• perform bulk analysis with fewer 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

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

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.