mastR: an R package for automated identification of tissue-specific gene signatures in multi-group differential expression analysis

Chen, J; Mohamed, A; Bhuva, DD; Davis, MJ; Tan, CW

Author(s): Chen, J; Mohamed, A; Bhuva, DD; Davis, MJ; Tan, CW;
Details: Publication Year 2025-03-04,Volume 41,Issue #3,Page btaf114
Journal Title: Bioinformatics
Abstract: MOTIVATION: Biomarker discovery is important and offers insight into potential underlying mechanisms of disease. While existing biomarker identification methods primarily focus on single cell RNA sequencing (scRNA-seq) data, there remains a need for automated methods designed for labeled bulk RNA-seq data from sorted cell populations or experiments. Current methods require curation of results or statistical thresholds and may not account for tissue background expression. Here we bridge these limitations with an automated marker identification method for labeled bulk RNA-seq data that explicitly considers background expressions. RESULTS: We developed mastR, a novel tool for accurate marker identification using transcriptomic data. It leverages robust statistical pipelines like edgeR and limma to perform pairwise comparisons between groups, and aggregates results using rank-product-based permutation test. A signal-to-noise ratio approach is implemented to minimize background signals. We assessed the performance of mastR-derived NK cell signatures against published curated signatures and found that the mastR-derived signature performs as well, if not better than the published signatures. We further demonstrated the utility of mastR on simulated scRNA-seq data and in comparison with Seurat in terms of marker selection performance. AVAILABILITY AND IMPLEMENTATION: mastR is freely available from https://bioconductor.org/packages/release/bioc/html/mastR.html. A vignette and guide are available at https://davislaboratory.github.io/mastR. All statistical analyses were carried out using R (version >/=4.3.0) and Bioconductor (version >/=3.17).
Publisher: Oxford Academic
Keywords: *Software; Humans; *Gene Expression Profiling/methods; Sequence Analysis, RNA/methods; Transcriptome; Single-Cell Analysis/methods; Killer Cells, Natural/metabolism; Algorithms; Computational Biology/methods; Organ Specificity
Research Division(s): Bioinformatics and Computational Biology
PubMed ID: 40098239
Publisher's Version: https://doi.org/10.1093/bioinformatics/btaf114
Open Access at Publisher's Site: https://doi.org/10.1093/bioinformatics/btaf114
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2025-04-08 02:51:02

Last Modified: 2025-04-08 03:12:51