15th June 2022

Single Cell RNAseq Analysis Workflow

10x overview

Not every droplet is useble

Quality Control overview

  • Aim of QC is …

    • To remove undetected genes
    • To remove empty droplets
    • To remove droplets with dead cells
    • To remove Doublet/multiplet
    • Ultimately To filter the data to only include true cells that are of high quality
  • Above is achieved by …

    • Applying hard cut-off or adaptive cut-off on …
      • Number of genes detected per cell
      • Percent of mitochondrial genes per cell
      • Number of UMIs/transcripts detected per cell

Quality Control

Bioconductor R packages:

  • scran: Collection functions for interpretation of single-cell RNA-seq data
  • scater: For focus on quality control and visualization.
  • DropletUtils: Handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics

Orchestrating Single-Cell Analysis with Bioconductor Robert Amezquita, Aaron Lun, Stephanie Hicks, Raphael Gottardo

http://bioconductor.org/books/release/OSCA/

Read CellRanger outputs into R

  • CellRanger outputs: gives two output folders raw and filtered

  • Each folder has three zipped files

    • features.tsv.gz, barcodes.tsv.gz and matrix.mtx.gz
    • raw_feature_bc_matrix
      • All valid barcodes from GEMs captured in the data
      • Contains about half a million to a million barcodes
      • Most barcodes do not actually contain cells
    • filtered_feature_bc_matrix
      • Excludes barcodes that correspond to this background
      • Contains valid cells according to 10x cell calling algorithm
      • Contains 100s to 1000s of barcodes

Single Cell Experiment Vocabulary alert

  • cell = Barcode = droplet
  • Transcript = UMI

The SingleCellExperiment object

The Counts Matrix

To access counts from sce object: counts(sce)

Feature metadata

To access gene metadata from sce object: rowData(sce)

Droplet annotation (Cell metadata)

To access cell metadata from sce object: colData(sce)

Properties of RNAseq data - Total UMIs

Properties of RNAseq data - Distribution of counts for a gene across cells

Properties of RNAseq data - Distribution of UMI counts

Properties of RNAseq data - Distribution of genes per cell

Properties of RNAseq data - Distribution of mitochondrial genes

Challenges

  • Selecting appropriate thresholds for filtering, so that high quality cells are kept without removing biologically relevant cell types
    • Differentiating poor quality cells from less complex ones
    • Differentiating transcriptionally active cell types from multiplets/doublets
    • Distinguishing dead cells from those cells that express a high proportion of mitochorial genome

Recommendations

  • Ensure that you know what types of cells you expect to be present before performing the QC.
  • Are you expecting to find low complexity cells in your sample or cells with higher levels of mitochondrial expression?
  • When assessing the quality of our data, we must take this biology into consideration