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• W3027765392 abstract "Single-cell transcriptomics offers a powerful way to reveal the heterogeneity of individual cells. To date, many information theoretical approaches have been proposed to assess diversity and similarity, and characterize the latent heterogeneity in transcriptome data. Diversity implies gene expression variations and can facilitate the identification of signature genes; while, similarity unravels co-expression patterns for cell type clustering. In this review, we summarized 16 measures of information theory used for evaluating diversity and similarity in single-cell transcriptomic data, provide references and shed light on selected theoretical properties when there is a need to select proper measurements in general cases. We further provide an R package assembling discussed approaches to improve the researchers own single-cell transcriptome study. At last, we prospected further applications of diversity and similarity measures in support of depicting heterogeneity in single-cell multi-omics data." @default.
• W3027765392 created "2020-05-29" @default.
• W3027765392 creator A5003544186 @default.
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• W3027765392 date "2020-01-01" @default.
• W3027765392 modified "2023-10-14" @default.
• W3027765392 title "Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics" @default.
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• W3027765392 doi "https://doi.org/10.1016/j.csbj.2020.05.005" @default.
• W3027765392 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7371753" @default.
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