1 Aignment and feature counting

1.1 Introduction

We will use two sets of Bone Marrow Mononuclear Cells (BMMC):

‘CaronBourque2020’: pediatric samples
‘Hca’: HCA Census of Immune Cells for adult BMMCs

Fastq files were retrieved from publicly available archive (SRA and HCA).

Sequencing quality was assessed and visualised using fastQC and MultiQC.

Reads were aligned against GRCh38 and features counted using cellranger (v3.1.0).

#wrkDir <- "/mnt/scratchb/bioinformatics/baller01/20200511_FernandesM_ME_crukBiSs2020/CaronBourque2020/grch38300"
#setwd(wrkDir)

#cellrangerDir <- sprintf("%s/%s/grch38300", projDir, "CaronBourque2020")
#projDirOsx <- "/Users/baller01/MyMount/clust1b/20200511_FernandesM_ME_crukBiSs2020"
#cellrangerDirOsx <- sprintf("%s/%s/grch38300", projDirOsx, "CaronBourque2020")
projDir <- "/home/ubuntu/Course_Materials/scRNAseq"
cellrangerDir <- sprintf("%s/%s/grch38300", projDir, "CaronBourque2020")

outDirBit <- params$outDirBit # "AnaWiSeurat/Attempt1"
plotDir <- "QcPlots"

1.2 10X cellranger pipeline in brief

Each sample was analysed separately with cellranger. This pipeline “is a set of analysis pipelines that process Chromium single-cell RNA-seq output to align reads, generate feature-barcode matrices and perform clustering and gene expression analysis.”

See code to call cellranger cellranger count

1.3 10X cellranger reports

# CaronBourque2020
cb_sampleSheetFn <- file.path(projDir, "Data/CaronBourque2020/SraRunTable.txt")
# Human Cell Atlas
hca_sampleSheetFn <- file.path(projDir, "Data/Hca/accList_Hca.txt")

# read sample sheet in:
splShtColToKeep <- c("Run", "Sample.Name", "source_name")

cb_sampleSheet <- read.table(cb_sampleSheetFn, header=T, sep=",")
hca_sampleSheet <- read.table(hca_sampleSheetFn, header=F, sep=",")
colnames(hca_sampleSheet) <- "Sample.Name"
hca_sampleSheet$Run <- hca_sampleSheet$Sample.Name
hca_sampleSheet$source_name <- "ABMMC" # adult BMMC

sampleSheet <- rbind(cb_sampleSheet[,splShtColToKeep], hca_sampleSheet[,splShtColToKeep])

# make dir name for each sample of interest
# with 'Run' column

htmlVec <- sprintf("%s/%s/%s/outs/web_summary.html", cellrangerDir, sampleSheet$Run, sampleSheet$Run)
names(htmlVec) <- sampleSheet$Run

for(i in 1:length(htmlVec)){
    cat("[", names(htmlVec)[i], "](", htmlVec[i],")\n\n")
}

# TODO: add links to sample sheet and show with DT::datatable

CRUK CI Summer School 2020 - introduction to single-cell RNA-seq analysis

cellranger

Stephane Ballereau, Zeynep Kalender Atak, Katarzyna Kania

1 Aignment and feature counting

1.1 Introduction

1.2 10X cellranger pipeline in brief

1.3 10X cellranger reports