scTyper: a comprehensive pipeline for the cell typing analysis of single-cell RNA-seq data

1. Overview

scTyper is a comprehensive pipeline for the cell typing and scRNA-Seq data analysis. It has been equipped with both database of cell type markers such as scTyper.db, CellMarker. Of note, markers for malignant cells, cancer-associated fibroblasts, and tumor-infiltrating T cells were collected in this database, which will be helpful in analyzing data from cancer tissues. In addition, scTyper provides three customized methods for estimating cell type marker expression, including the nearest template prediction (NTP), gene set enrichment analysis (GSEA), and average expression values. DNA copy number inference method (inferCNV), with improved modification, was also implemented in scTyper, which can be used for the typing of malignant cells. The package also supports the data preprocessing pipelines of Cell Ranger from 10X Genomics. Reporting system for analysis summary is also implemented which may facilitate users to perform reproducible analyses.

2. Workflow

scTyper is comprised of the modularized processes of ‘QC’, ‘Cell Ranger’, ‘Seurat processing’, ‘cell typing’, and ‘malignant cell typing’. These processes can be customized by manipulating the parameters for each process. If users want to perform only the cell typing process and a preprocessed input file with Seurat object is already prepared, the processing steps of ‘QC’, ‘Cell Ranger’ and ‘Seurat processing’ can be skipped by setting the parameters ‘qc’, ‘run.cellranger’ and ‘norm.seurat’ to ‘FALSE’. The processes and their parameters implemented in scTyper are summarized.

3. Getting Started

3.1. External dependency

3.1.1 Program

scTyper uses FastQC and Cell Ranger to process scRNA-seq dataset, based on reference data. If raw data processing is required, it should be installed (no need to install if not needed).

The Program and reference data can be found in the links below:
FastQC : http://www.bioinformatics.babraham.ac.uk/projects/fastqc
Cell Ranger : https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest#loupe

3.1.2 Required libraries

scTyper relies on the following dependencies which should be downloaded/updated.The current version of scTyper supports Linux operating system, due to the compatibility of the required software packages. Parallel computations on a multi-core machines can be used by calling the ‘parallel’ flag in R package.

Processing	library
Quality Check	fastqcr, parallel
Cell Ranger	parallel
Seurat processing	Seurat
Cell typing	Seurat, parallel
malignant.celltyping	infercnv, perm, reshape2, gProfileR, GenomicRanges, grDevices, parallel
Report	rmarkdown, ComplexHeatmap, reshape2, pander, png, ggplot2, grid, gridExtra, circlize, knitr, kableExtra, colorspace

3.2 Installation and loading

Source codes for scTyper are available at : https://github.com/omicsCore/scTyper

3.2.1 Installation package

scTyper runs in the R statistical computing environment. R version 3.5 or higher is required.

# install BiocManager if necessary
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# install devtools if necessary
BiocManager::install("devtools")
library(devtools)

# install the scTyper package
devtools::install_github("omicsCore/scTyper")

3.2.2 Loading package and documentation

# load
library("scTyper") 
library(help="scTyper")

omicsCore/scTyper