17 “Ideal” scRNAseq pipeline (as of Oct 2019)

17.1 Experimental Design

• Avoid confounding biological and batch effects (Figure 17.1)
  • 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).

knitr::include_graphics("figures/Pipeline-batches.png")

Figure 17.1: Appropriate approaches to batch effects in scRNASeq. Red arrows indicate batch effects which are (pale) or are not (vibrant) correctable through batch-correction.

17.2 Processing Reads

• Read QC & Trimming
  • FASTQC, cutadapt
• Mapping
  • Small datasets or UMI datasets: align to genome/transcriptome using STAR
  • Large datasets: pseudo-alignment with Salmon or kallisto
• Quantification
  • Small dataset, no UMIs : featureCounts
  • Large datasets, no UMIs: Salmon, kallisto
  • UMI dataset : UMI-tools + featureCounts

17.3 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
  • scran
• Batch correction (if appropriate)
  • Replicates/Confounded RUVs
  • Unknown or unbalanced biological groups mnnCorrect
  • Balanced design ComBat

17.4 Biological Interpretation

• Feature Selection
  • M3Drop
• 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).