Abstract：
:Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.
journal_name
Cell Systjournal_title
Cell systemsauthors
Tan Y,Cahan Pdoi
10.1016/j.cels.2019.06.004subject
Has Abstractpub_date
2019-08-28 00:00:00pages
207-213.e2issue
2eissn
2405-4712issn
2405-4720pii
S2405-4712(19)30199-1journal_volume
9pub_type
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