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Mcadet: A feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection
Journal article   Open access   Peer reviewed

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

Saishi Cui, Sina Nassiri and Issa Zakeri
PLoS computational biology, v 20(10), 1012560
01 Oct 2024
DOI: https://doi.org/10.1371/journal.pcbi.1012560
PMID: 39466833
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1371/journal.pcbi.1012560View
Published, Version of Record (VoR) Open CC BY V4.0

Abstract

Cluster Analysis Computational Biology - methods Gene Expression Profiling - methods Gene Expression Profiling - statistics & numerical data Humans Reproducibility of Results RNA-Seq - methods Sequence Analysis, RNA - methods Sequence Analysis, RNA - statistics & numerical data Single-Cell Analysis - methods Single-Cell Analysis - statistics & numerical data Single-Cell Gene Expression Analysis Algorithms Software
Single-cell RNA sequencing (scRNA-seq) data analysis faces numerous challenges, including high sparsity, a high-dimensional feature space, and biological noise. These challenges hinder downstream analysis, necessitating the use of feature selection methods to 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 minority cell types, which are more difficult to distinguish, raising concerns about the reliability of their results. To overcome these limitations, we propose a novel feature selection framework for 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 minority cell populations. Overall, we demonstrate that Mcadet enhances the reliability of selected HVGs, although the impact of HVG selection on various downstream analyses varies and needs to be further investigated.

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Collaboration types
Industry collaboration
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Web of Science research areas
Biochemical Research Methods
Mathematical & Computational Biology
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