Researchers have developed scMINER, a new computational tool designed for comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data. This tool facilitates data preprocessing, clustering, network inference, and visualization through a user-friendly interface. ScMINER aims to help researchers identify key genes and signaling pathways within individual cells, offering insights into cellular mechanisms.
ScMINER uses mutual information to measure the distance between cells, enabling accurate clustering and reverse-engineering of gene networks. Unlike other methods, scMINER does not rely on binding motifs, allowing for activity assessment of over 6,000 transcription and signaling drivers from scRNA-seq experiments. The tool also includes a web-based portal for exploring and sharing scRNA-seq data.
Benchmarking has demonstrated that scMINER outperforms existing algorithms in distinguishing between different types of T cells and accurately reconstructing transcription factor regulatory networks and signaling networks. This makes scMINER a valuable asset for researchers aiming to dissect complex cellular processes and identify hidden drivers in single-cell omics data. The software is publicly accessible.