SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species

Single cell RNA-Seq has emerged as a powerful tool in diverse applications, ranging from determining the cell-type composition of tissues to uncovering the 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 finding cells that express combinations of marker genes that had 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, and it is notably able to make sensitive and accurate classifications across platforms and species. We demonstrate how SingleCellNet can be used to classify previously undetermined cells, and how it can be used to assess the outcome of cell fate engineering experiments.\n\nHighlightO_LISingleCellNet (SCN) enables the classification of scRNA-Seq data across platforms and species\nC_LIO_LISCN is open source and extendible\nC_LIO_LIWe illustrate the utility of SCN with three example applications\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC=\"FIGDIR/small/508085_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (47K):\norg.highwire.dtl.DTLVardef@1750006org.highwire.dtl.DTLVardef@548dbdorg.highwire.dtl.DTLVardef@1257cb1org.highwire.dtl.DTLVardef@1d8072_HPS_FORMAT_FIGEXP M_FIG C_FIG