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Abstract
BackgroundRecent advances in single-cell RNA sequencing (scRNA-seq) technology have enabled the identification of individual cell types, such as epithelial cells, immune cells, and fibroblasts, in tissue samples containing complex cell populations. Cell typing is one of the key challenges in scRNA-seq data analysis that is usually achieved by estimating the expression of cell marker genes. However, there is no standard practice for cell typing, often resulting in variable and inaccurate outcomes.ResultsWe have developed a comprehensive and user-friendly R-based scRNA-seq analysis and cell typing package, scTyper. scTyper also provides a database of cell type markers, scTyper.db, which contains 213 cell marker sets collected from literature. These marker sets include but are not limited to markers for malignant cells, cancer-associated fibroblasts, and tumor-infiltrating T cells. Additionally, scTyper provides three customized methods for estimating cell-type marker expression, including nearest template prediction (NTP), gene set enrichment analysis (GSEA), and average expression values. DNA copy number inference method (inferCNV) has been implemented with an improved modification that can be used for malignant cell typing. The package also supports the data preprocessing pipelines by Cell Ranger from 10X Genomics and the Seurat package. A summary reporting system is also implemented, which may facilitate users to perform reproducible analyses.ConclusionsscTyper provides a comprehensive and user-friendly analysis pipeline for cell typing of scRNA-seq data with a curated cell marker database, scTyper.db.
Highlights
Recent advances in single-cell RNA sequencing technology have enabled the identification of individual cell types, such as epithelial cells, immune cells, and fibroblasts, in tissue samples containing complex cell populations
There is no standard practice for cell typing and use of different cell markers and cell typing algorithms often results in inconsistent cell type assignment
The expression of the cell marker sets can be estimated by three different methods, nearest template prediction (NTP) [15], pre-ranked gene set enrichment analysis (GSEA) [16], and average gene expression values
Summary
Recent advances in single-cell RNA sequencing (scRNA-seq) technology have enabled the identification of individual cell types, such as epithelial cells, immune cells, and fibroblasts, in tissue samples containing complex cell populations. Cell typing is one of the key challenges in scRNA-seq data analysis that is usually achieved by estimating the expression of cell marker genes. There is no standard practice for cell typing, often resulting in variable and inaccurate outcomes. Single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to profile transcriptomes at single-cell level [1, 2]. There are a number of challenges in the analysis of scRNA-seq data and its outcomes; one of the key challenges is the identification of cell types from the transcriptome data. With time, enriched resources for cell type markers that have been generated from different sources, including single cell sequencing and experimental studies, are becoming available [7, 8]. There is no standard practice for cell typing and use of different cell markers and cell typing algorithms often results in inconsistent cell type assignment
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