21/09/2022

Workflow

Data set

  • Data set: CaronBourque2020: Data from Childhood acute lymphoblastic leukemia (cALL)
  • cells: Bone Marrow Mononuclear cells (BMMCs)
    • 12 samples
    • 4 Sample groups
      • HHD: The high hyper diploid cases.
        • Two replicates.
      • PBMMC: healthy pediatric BMMC.
        • Four replicates.
        • There are two PBMMC_1 samples. These are two libraries from the same sample material.
      • ETV6-RUNX1: ETV6/RUNX1 rearrangement
        • Four replicates
      • Pre-T: Pre-T ALL
        • Two replicates
  • Aim: characterize the heterogeneity of gene expression at the cell level, within and between patients

10x library file structure

The 10x library contains four pieces of information, in the form of DNA sequences, for each “read”.

  • sample index - identifies the library, with one or two indexes per sample
  • 10x barcode - identifies the droplet in the library
  • UMI - identifies the transcript molecule within a cell and gene
  • insert - the transcript molecule

Cell Ranger

  • 10x Cell Ranger - This not only carries out the alignment and feature counting, but will also:
    • Call cells
    • Generate a summary report in html format
    • Generate a “cloupe” file

Cell Ranger references

cellranger mkref
–fasta={GENOME FASTA}
–genes={ANNOTATION GTF}
–genome={OUTPUT FOLDER FOR INDEX}
–nthreads={CPUS}

Running cellranger count

cellranger count –id={OUTPUT_SAMPLE_NAME}
–transcriptome={DIRECTORY_WITH_REFERENCE}
–fastqs={DIRECTORY_WITH_FASTQ_FILES}
–sample={NAME_OF_SAMPLE_IN_FASTQ_FILES}
–localcores={NUMBER_OF_CPUS}
–localmem={RAM_MEMORY}

Cell Ranger outputs

The contents of the outs directory are:

knitr::include_graphics("Images/CellRangerOutputOuts.png")

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

The SingleCellExperiment object

Useful commands in QC

  • access counts: counts(sce)

  • access gene metadata: rowData(sce)

  • access cell metadata: colData(sce)

QC parameters

  • The library size
  • Number of expressed genes in each cell
  • proportion of UMIs mapped to genes in the mitochondrial genome