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.
journal_name
Nat Methodsjournal_title
Nature methodsauthors
Skinnider MA,Squair JW,Foster LJdoi
10.1038/s41592-019-0372-4subject
Has Abstractpub_date
2019-05-01 00:00:00pages
381-386issue
5eissn
1548-7091issn
1548-7105pii
10.1038/s41592-019-0372-4journal_volume
16pub_type
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