SCMarker: Ab initio marker selection for single cell transcriptome profiling.

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) technologies like Drop-seq and SMART-seq provide high-resolution gene expression data.
• Identifying informative genes and cell-type markers is crucial for dimensionality reduction and accurate cell typing in scRNA-seq analysis.

Purpose of the Study:

• To develop an ab initio method for unsupervised marker selection in scRNA-seq data.
• To identify genes with subpopulation-discriminative expression and co- or mutually-exclusive expression patterns.

Main Methods:

• The study presents SCMarker, a novel computational method for unsupervised marker gene selection.
• SCMarker identifies genes with distinct expression patterns across cell subpopulations.
• The method was applied to various scRNA-seq datasets from multiple tissue types, followed by clustering algorithms.

Main Results:

• SCMarker achieved consistent improvements in cell-type classification accuracy.
• The method successfully identified biologically meaningful marker genes.
• Application across diverse datasets demonstrated robust performance.

Conclusions:

• SCMarker is an effective tool for unsupervised marker selection in scRNA-seq data.
• The method enhances cell-type classification and facilitates the discovery of novel cell-type-discriminative markers.
• The source code is publicly available for broader research application.