PMID- 32859930 OWN - NLM STAT- MEDLINE VI - 11 IP - 1 TI - A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data. PG - 4318 LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't PL - England TA - Nat Commun JT - Nature communications JID - 101528555 IS - 2041-1723 (Electronic) LID - 10.1038/s41467-020-17900-3 [doi] FAU - Vandenbon, Alexis AU - Vandenbon A AUID- ORCID: http://orcid.org/0000-0003-2180-5732 AD - Institute for Frontier Life and Medical Sciences, Kyoto University, 53 Shougoin Kawara-cho, Sakyo-ku, Kyoto, 606-8507, Japan. alexisvdb@infront.kyoto-u.ac.jp. AD - Institute for Liberal Arts and Sciences, Kyoto University, Yoshidanihonmatsu-cho, Sakyo-ku, Kyoto, 606-8501, Japan. alexisvdb@infront.kyoto-u.ac.jp. FAU - Diez, Diego AU - Diez D AUID- ORCID: http://orcid.org/0000-0002-2325-4893 AD - Immunology Frontier Research Center, Osaka University, 3-1 Yamada-oka, Suita, Osaka, 565-0871, Japan. IS - 2041-1723 (Linking) SB - IM MH - Bone Marrow MH - Cluster Analysis MH - Computational Biology/*methods MH - Data Mining MH - Gene Expression MH - Gene Expression Profiling/*methods MH - Gene Regulatory Networks MH - Single-Cell Analysis/methods MH - Software MH - *Transcriptome PMC - PMC7455704 DCOM- 20200917 LR - 20210828 DP - 20200828 DEP - 20200828 AB - A common analysis of single-cell sequencing data includes clustering of cells and identifying differentially expressed genes (DEGs). How cell clusters are defined has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. To address this difficulty, we present singleCellHaystack, a method that enables the prediction of DEGs without relying on explicit clustering of cells. Our method uses Kullback-Leibler divergence to find genes that are expressed in subsets of cells that are non-randomly positioned in a multidimensional space. Comparisons with existing DEG prediction approaches on artificial datasets show that singleCellHaystack has higher accuracy. We illustrate the usage of singleCellHaystack through applications on 136 real transcriptome datasets and a spatial transcriptomics dataset. We demonstrate that our method is a fast and accurate approach for DEG prediction in single-cell data. singleCellHaystack is implemented as an R package and is available from CRAN and GitHub.