SciGeneX: enhancing transcriptional analysis through gene module detection in single-cell and spatial transcriptomics data

TL;DR

SciGeneX is a new tool that improves analysis of single-cell and spatial transcriptomics data by identifying gene modules that reveal cell population diversity.

Contribution

SciGeneX introduces a novel method for detecting co-expressed gene modules to uncover rare and novel cell populations.

Findings

SciGeneX identifies gene modules that reflect cellular heterogeneity in single-cell and spatial transcriptomics data.

The method successfully uncovers rare and novel cell populations in human thymus spatial transcriptomics data.

SciGeneX outperforms existing methods on both artificial and experimental datasets.

Abstract

The standard pipeline to analyze single-cell RNA-seq or spatial transcriptomics data focuses on a gene-centric approach that overlooks the collective behavior of genes. However, understanding cell populations necessitates recognizing intricate combinations of activated and repressed pathways. Therefore, a broader view of gene behavior offers more accurate insights into cellular heterogeneity in single-cell or spatial transcriptomics data. Here, we describe SciGeneX (Single-cell informative Gene eXplorer), a R package implementing a neighborhood analysis and a graph partitioning method to generate co-expression gene modules. These modules, whether shared or restricted to cell populations, collectively reflect cellular heterogeneity. Their combinations are able to highlight specific cell populations, even rare ones. SciGeneX uncovers rare and novel cell populations that were not observed before in human thymus spatial transcriptomics data. We show that SciGeneX outperforms existing methods on both artificial and experimental datasets. Overall, SciGeneX will aid in unravelling cellular and molecular diversity in single-cell and spatial transcriptomics studies.

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