%0 Journal Article
%T Evaluating measures of association for single-cell transcriptomics
%J -
%D 2019
%R https://doi.org/10.1038/s41592-019-0372-4
%X 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¨Cgene and cell¨Ccell 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
%U https://www.nature.com/articles/s41592-019-0372-4