期刊
NATURE METHODS
卷 16, 期 5, 页码 381-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41592-019-0372-4
关键词
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资金
- Genome Canada [214PRO]
- Genome British Columbia [214PRO]
- WestGrid
- Compute Canada
- CIHR Vanier Canada Graduate Scholarship
- Izaak Walton Killam Memorial Pre-Doctoral Fellowship
- UBC Four Year Fellowship
- Vancouver Coastal Health-CIHR-UBC MD/PhD Studentship
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.
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