TY - JOUR TI - Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq AU - Kotliar, Dylan AU - Veres, Adrian AU - Nagy, M Aurel AU - Tabrizi, Shervin AU - Hodis, Eran AU - Melton, Douglas A AU - Sabeti, Pardis C A2 - Valencia, Alfonso A2 - Barkai, Naama A2 - Mereu, Elisabetta A2 - Göttgens, Berthold VL - 8 PY - 2019 DA - 2019/07/08 SP - e43803 C1 - eLife 2019;8:e43803 DO - 10.7554/eLife.43803 UR - https://doi.org/10.7554/eLife.43803 AB - Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis. KW - gene expression programs KW - single-cell Rna-Seq KW - matrix factorization KW - visual cortex KW - brain organoids KW - synaptogenesis JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -