ReviewComputational and structural biotechnology journal2020
Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Thin Cells of Polymer-Modified Liquid Crystals Described by Voronoi Diagrams.Materials (Basel, Switzerland) · 2025Article
- Comprehensive analyses of brain cell communications based on multiple scRNA-seq and snRNA-seq datasets for revealing novel mechanism in neurodegenerative diseases.CNS neuroscience & therapeutics · 2023Review
- Novel Network Method Major Minor Variation Clustering Enables Identification of Poliovirus Clusters with High-Resolution Linkages.Journal of computational biology : a journal of computational molecular cell biology · 2023Article
- The Tsallis generalized entropy enhances the interpretation of transcriptomics datasets.PloS one · 2022Article
- Neurons and Astrocytes Elicit Brain Region Specific Transcriptional Responses to Prion Disease in the Murine CA1 and Thalamus.Frontiers in neuroscience · 2022Article
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Authors and funding
3 authors.
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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.
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