Network-community analysis of cellular senescence
arxiv(2024)
Abstract
Most cellular phenotypes are genetically complex. Identifying the set of
genes that are most closely associated with a specific cellular state is still
an open question in many cases. Here we study the transcriptional profile of
cellular senescence using a combination of network-based approaches, which
include eigenvector centrality feature selection and community detection. We
apply our method to cell-type-resolved RNA sequencing data obtained from
injured muscle tissue in mice. The analysis identifies some genetic markers
consistent with previous findings, and other previously unidentified ones,
which are validated with previously published single-cell RNA sequencing data
in a different type of tissue. The key identified genes, both those previously
known and the newly identified ones, are transcriptional targets of factors
known to be associated with established hallmarks of senescence, and can thus
be interpreted as molecular correlates of such hallmarks. The method proposed
here could be applied to any complex cellular phenotype even when only bulk RNA
sequencing is available, provided the data is resolved by cell type.
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