10 “Ideal” scRNAseq pipeline (as of Oct 2017)

10.1 Experimental Design

  • Avoid confounding biological and batch effects (Figure 10.1)
    • Multiple conditions should be captured on the same chip if possible
    • Perform multiple replicates of each condition where replicates of different conditions should be performed together if possible
    • Statistics cannot correct a completely confounded experiment!
  • Unique molecular identifiers
    • Greatly reduce noise in data
    • May reduce gene detection rates (unclear if it is UMIs or other protocol differences)
    • Lose splicing information
    • Use longer UMIs (~10bp)
    • Correct for sequencing errors in UMIs using UMI-tools
  • Spike-ins
    • Useful for quality control
    • May be useful for normalizing read counts
    • Can be used to approximate cell-size/RNA content (if relevant to biological question)
    • Often exhibit higher noise than endogenous genes (pipetting errors, mixture quality)
    • Requires more sequencing to get enough endogenous reads per cell
  • Cell number vs Read depth
    • Gene detection plateaus starting from 1 million reads per cell
    • Transcription factor detection (regulatory networks) require high read depth and most sensitive protocols (i.e. Fluidigm C1)
    • Cell clustering & cell-type identification benefits from large number of cells and doesn’t requireas high sequencing depth (~100,000 reads per cell).
Appropriate approaches to batch effects in scRNASeq. Red arrows indicate batch effects which are (pale) or are not (vibrant) correctable through batch-correction.

Figure 10.1: Appropriate approaches to batch effects in scRNASeq. Red arrows indicate batch effects which are (pale) or are not (vibrant) correctable through batch-correction.

10.2 Processing Reads

10.3 Preparing Expression Matrix

  • Cell QC
    • scater
    • consider: mtRNA, rRNA, spike-ins (if available), number of detected genes per cell, total reads/molecules per cell
  • Library Size Normalization
  • Batch correction (if appropriate)
    • Replicates/Confounded RUVs
    • Unknown or unbalanced biological groups mnnCorrect
    • Balanced design ComBat

10.4 Biological Interpretation

  • Feature Selection
  • Clustering and Marker Gene Identification
    • \(\le 5000\) cells : SC3
    • \(>5000\) cells : Seurat
  • Pseudotime
    • distinct timepoints: TSCAN
    • small dataset/unknown number of branches: Monocle2
    • large continuous dataset: destiny
  • Differential Expression
    • Small number of cells and few groups : scde
    • Replicates with batch effects : mixture/linear models
    • Balanced batches: edgeR or MAST
    • Large datasets: Kruskal-Wallis test (all groups at once), or Wilcox-test (compare 2-groups at a time).