Enhancing Rare Cell Type Identification in Single-Cell Data: An Innovative Gene Filtering Approach using Bipartite Cell-Gene Relation Graph

Maziyar Baran pouyan; Krishnaprasad Thirunarayan; Lingwei Chen; Hossein Mohammadi; Hojjat Torabi Goudarzi

Abstract

Single cell technology refers to a set of scientific techniques that allows researchers to study cellular structures in depth.The behaviors and properties of individual cells may be hidden by traditional biological experiments, which frequently measure averages over a large number of cells. With the aid of single cell technology, scientists may examine each cell separately and gain a much more in-depth understanding of biological processes. Hence, a useful tool for examining cellular diversity is single cell RNA sequencing (scRNA-seq). However, the high dimensionality and technical noise of scRNA-seq data make analysis difficult. To address this issue, gene filtering has been widely adopted to minimize single cell data noise and enhance the quality of subsequent analyses. Nonetheless, existing gene filtering techniques may inadvertently omit critical but rare genes which are necessary for identifying rare cell types that play a pivotal role in comprehending many biological processes. A novel graph-based gene selection technique is suggested in this study with the aim of preserving the informative genes to better identify rare cell types. Our findings demonstrate that this technique enhances the identification of rare cell populations, providing a refined approach for scRNA-seq data analysis and potentially enabling earlier and more reliable disease detection.

Keywords: Single Cell RNA-seq data; Rare cell type identification; Gene filtering

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