Pre-processing of scRNA-seq using Cellranger

Overview

Teaching: 15 min
Exercises: 5 min

Questions

• How do pre-process scRNA-seq data using cellranger?

Objectives

• Identify the available tools to preprocess cRNA-seq.

• Understand how to use Cell Ranger.

Dataset:

Expression data from scRNA-seq experiments represent thousands of reads for thousands of cells. Therefore, the data output can be very large and hard to store in your local machine. You will also need higher amounts of memory to analyse.

In this tutorial, We will use Human peripheral blood mononuclear cells (PBMCs) of a healthy female donor aged 25-30 were obtained by 10x Genomics. Libraries were generated from ~16,000 cells (11,996 cells recovered) as described in the Chromium Single Cell 3’ Reagent Kits User Guide (v3.1 Chemistry Dual Index) (CG000315 Rev C) using the Chromium X and sequenced on an Illumina NovaSeq 6000 to a read depth of approximately 40,000 mean reads per cell.

FIXME: Add an image.

Generation of count matrix:

In this part, we will generate the count matrix from the raw sequencing data. After sequencing, the output generated from the sequencing machine will be either output the raw sequencing data as BCL or FASTQ format. Using this raw sequence, we will generate the count matrix.

When using 10X Genomics library preparation method, then the Cell Ranger pipeline is most ideal way to pre-process the data. Make sure that you installed the Cell Ranger version 6.1 (see the instructions). Using cellranger, we can genome mapping, UMI filtering, UMI dedeplication and cell filtering.

FIXME: We need to add image cellranger pipeline where we shows different steps (e.g. extraction, filtering, ..) and also how to correct the naming of the input file

FIXME: Need to subset the dataset for the use only for this part of the lesson and refer to that at the beginning and the end.

In order to align the fastqs to the reference genome and count how many reads per gene per cell. We ca use cellranger count command.

cellranger count --id=bcl \
                   --transcriptome=/opt/refdata-cellranger-GRCh38-3.0.0 \
                   --fastqs=PATH_TO_FASTQ/ \
                   --sample=mysample\
                   --expect-cells=8000

Question

What do you do if your input from sequencing machine is bcl file?

Solution

cellranger wraps the illumina bcl2fastq command into cellranger mkfastq to convert it to fastq files for single-cell RNAseq data.

What is the output of cellranger count?

In the outs folder, and you will find the filtered_feature_bc_matrix folder, which contains 3 files. We can explore these files using ls command.

ls filtered_feature_bc_matrix/
  barcodes.tsv.gz   features.tsv.gz   9 matrix.mtx.gz

We can look into individual files using zcat and head command. The barcodes.tsv.gz should contains all the cell barcodes that passed the cellranger filter.

FIXME: Fix all the names/sequences when you subset the files

zcat barcodes.tsv.gz | head -5
AAAAAAACCCCCCCCC-1
AAAAAAACCCCCCCCC-1
AAAAAAACCCCCCCCC-1
AAAAAAACCCCCCCCC-1
AAAAAAAACCCCCCCCC-1

We can count how many cell/barcodes in barcodes.tsv.gz

zcat barcodes.tsv.gz | wc -l
11544

The second file is the features.tsv.gz, which contains the ENSEMBLE ID and gene symbol

zcat features.tsv.gz | head -5
ENSG00000222222 MPRRCCC     Gene Expression
ENSG00000222222 MPRRCCC Gene Expression
ENSG00000222222 MPRRCCC   Gene Expression
ENSG00000222222 MPRRCCC      Gene Expression
ENSG00000222222 MPRRCCC      Gene Expression

Question

How can you count the genes?

Solution

We can use zcat features.tsv.gz | wc -l to count the number of genes.

The third output is matrix.mtx.gz which is a sparse matrix which contains the non-zero counts. Sparse matrix efficiently save the disk space by only recording the non-zero entries.

FIXME: Add the dimension of the matrix and index of the row (gene) and column(cell) - Figure is very important here.

FIXME: We need a section explain alternative methods such as Alevin (Salmon Alevin + Alevin QC), STARsolo and Kallisto/ BUStools with images

Key Points

• scRNA-seq requires much pre-processing before analysis can be performed.