TY - JOUR T1 - CellHarmony: Cell-level matching and comparison of single-cell transcriptomes JF - bioRxiv M3 - 10.1101/412080 AU - DePasquale, Erica AK AU - Ferchen, Kyle AU - Hay, Stuart AU - Muench, David E AU - Grimes, H. Leighton AU - Salomonis, Nathan Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/09/08/412080.abstract N2 - To understand the molecular etiology of human disease, precision analyses of individual cell populations and their molecular alternations are desperately needed. Single-cell genomics represents an ideal platform to enable to the quantification of specific cell types, the discovery of transcriptional cell states and underlying molecular differences that can be compared across specimens. We present a new computational approach called cellHarmony, to consistently classify individual cells from a query (i.e., mutant) against a reference (i.e., wild-type) dataset to discover crucial differences in discrete or transitional cell-populations. CellHarmony performs a supervised classification of new scRNA-Seq data against a priori delineated cell populations and assoicated genes to visualize the combined datasets and derive consistent annotations in a platform-independent manner. Such analyses enable the comparison of results from distinct single-cell platforms against well-curated references, such as those from emerging cell atlases, or against orthogonal profiles from a related experiment. In addition, cellHarmony produces differential expression results from non-confounded aligned cell populations to explore the impact of chemical, genetic, environmental and temporal perturbations. This approach works seamlessly with the unsupervised classification and annotation of cell-states using the software ICGS in AltAnalyze. Using cellHarmony, we demonstrate consistent molecular and population insights in human disease scRNA-Seq data across technological platforms. ER -