mastR: Marker Automated Screening Tool for multi-omics data

Abstract

Motivation: Biomarker discovery and utilization is important and offers insight into potential underlying mechanisms of disease. Existing marker identification methods primarily focus on single cell RNA sequencing (scRNA-seq) data, with no specific automated methods designed to learn from the bulk RNA-seq data. Furthermore, when adapting scRNA-seq methods to bulk RNA-seq, the background expressions of non-targeted cell types are not accounted for. Here we bridge this gap with an automated marker identification method that works for bulk RNA sequencing data. Results: We developed mastR, a novel computational tool for accurate marker identification from omics data. It leverages robust pipelines from edgeR and limma R/Bioconductor packages, performing pairwise comparisons between groups, and aggregating the results through rank-product-based permutation test. A signal-to-noise ratio approach is implemented to minimize background signals. We assess the performance of a mastR-derived NK cell signature against curated published signatures and find our derived signature performs as well if not better than published signatures. We also demonstrate the utility of mastR on simulated scRNA sequencing data and provide examples of mastR outperforming Seurat in marker selection. Availability and implementation: All statistical analyses were carried out using R (version 4.3.0 or higher) and Bioconductor (version 3.17 and higher). MastR is available as an R/Bioconductor package with a comprehensive vignette for ease of use (https://bioconductor.org/packages/release/bioc/html/mastR.html) and a guide hosted on GitHub: https://davislaboratory.github.io/mastR/.