Mcadet: a feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection

Abstract

Single-cell RNA sequencing (scRNA-seq) data analysis faces numerous challenges, including high sparsity, a high-dimensional feature space, technical biases, and biological noise. These challenges hinder downstream analysis, necessitating the use of feature selection methods to address technical biases, identify informative genes, and reduce data dimensionality. However, existing methods for selecting highly variable genes (HVGs) exhibit limited overlap and inconsistent clustering performance across benchmark datasets. Moreover, these methods often struggle to accurately select HVGs from fine-resolution scRNA-seq datasets and rare cell types, raising concerns about the reliability of their results. To overcome these limitations, we propose a novel feature selection framework for unique molecular identifiers (UMIs) scRNA-seq data called Mcadet. Mcadet integrates Multiple Correspondence Analysis (MCA), graph-based community detection, and a novel statistical testing approach. To assess the effectiveness of Mcadet, we conducted extensive evaluations using both simulated and real-world data, employing unbiased metrics for comparison. Our results demonstrate the superior performance of Mcadet in the selection of HVGs in scenarios involving fine-resolution scRNA-seq datasets and datasets containing rare cell populations. By addressing the challenges of feature selection in scRNA-seq analysis, Mcadet provides a valuable tool for improving the reliability and accuracy of downstream analyses in single-cell transcriptomics.