scARMF: Association Rule Mining-based feature selection Framework for Single-Cell transcriptomics data

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Conference Article

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Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022


Single-cell RNA-sequencing (scRNA-seq) technologies have allowed researchers to investigate transcriptional regulation at a cellular resolution. One such analysis often involves extracting statistically significant groups of cells identified as clusters that enable cell-type identification, based on the presence or absence of canonical markers. However, it has been observed that cells with similar gene expression profiles, m ay sometimes represent variable transcriptional states. Identifying cell-type specific markers, is hence, not sufficient enough to understand the underlying molecular activity within a particular cell cluster. Rather, we should focus on finding key regulators within cell clusters. In order to assess cells' functionality beyond marker-based studies, genes driving or being driven by these key regulators need to be analysed against reference databases. In this work, we have developed an Association Rule Mining (ARM)-based feature selection Framework, called scARMF, which can identify major gene-gene interactions within a specific cell-cluster of interest in scRNA-seq data. These interaction networks have helped us identify key regulatory hubs (genes), some of which have been found to be relevant canonical markers when validated against a benchmarked reference database. The sub-networks formed by hub genes along with their neighbours, have been further assessed via Over Representation Analysis (ORA)-based pathway enrichment. This has revealed interesting functional characteristics that can be important for further downstream biological interpretations.

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