Evaluating measures of association for single-cell transcriptomics. View Full Text


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Article Info

DATE

2019-04-08

AUTHORS

Michael A Skinnider, Jordan W Squair, Leonard J Foster

ABSTRACT

Single-cell transcriptomics provides an opportunity to characterize cell-type-specific transcriptional networks, intercellular signaling pathways and cellular diversity with unprecedented resolution by profiling thousands of cells in a single experiment. However, owing to the unique statistical properties of scRNA-seq data, the optimal measures of association for identifying gene-gene and cell-cell relationships from single-cell transcriptomics remain unclear. Here, we conducted a large-scale evaluation of 17 measures of association for their ability to reconstruct cellular networks, cluster cells of the same type and link cell-type-specific transcriptional programs to disease. Measures of proportionality were consistently among the best-performing methods across datasets and tasks. Our analysis provides data-driven guidance for gene and cell network analysis in single-cell transcriptomics. More... »

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41592-019-0372-4

    DOI

    http://dx.doi.org/10.1038/s41592-019-0372-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1113300829

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30962620


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