scTree: An R package to generate antibody-compatible classifiers from single-cell sequencing data

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) measures cell transcriptomes for population identification.
• Identifying marker genes is crucial but often yields excessive gene lists.
• Selecting genes for experimental follow-up (e.g., FACS) from these lists is challenging.

Purpose of the Study:

• To present scTree, an R package for identifying a minimal gene set for downstream experiments.
• To aid biologists in selecting key genes from scRNA-seq data for experimental validation.
• To provide an open-source tool for efficient marker gene selection.

Main Methods:

• scTree utilizes scRNA-seq data to cluster heterogeneous cell transcriptomes.
• The tool identifies a minimal set of marker genes differentiating cell populations.
• The package is implemented in the R programming language.

Main Results:

• scTree provides a concise list of genes for experimental follow-up.
• The tool simplifies the selection of genes for bulk population analysis.
• It enables efficient experimental validation of cell populations identified via scRNA-seq.

Conclusions:

• scTree offers a practical solution for reducing gene lists from scRNA-seq analysis.
• The package facilitates experimental validation by providing a minimal gene set.
• It is a valuable, free, and open-source resource for the R and scRNA-seq community.