Skip to main content

Optimal marker gene selection for cell type discrimination in single cell analyses

Author(s): Dumitrascu, Bianca; Villar, Soledad; Mixon, Dustin G; Engelhardt, Barbara E

Download
To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1j54s
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDumitrascu, Bianca-
dc.contributor.authorVillar, Soledad-
dc.contributor.authorMixon, Dustin G-
dc.contributor.authorEngelhardt, Barbara E-
dc.date.accessioned2021-10-08T19:50:48Z-
dc.date.available2021-10-08T19:50:48Z-
dc.date.issued2021en_US
dc.identifier.citationDumitrascu, Bianca, Soledad Villar, Dustin G. Mixon, and Barbara E. Engelhardt. "Optimal marker gene selection for cell type discrimination in single cell analyses." Nature Communications 12, no. 1 (2021). doi:10.1038/s41467-021-21453-4en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1j54s-
dc.description.abstractSingle-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization.en_US
dc.languageengen_US
dc.language.isoen_USen_US
dc.relation.ispartofNature Communicationsen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleOptimal marker gene selection for cell type discrimination in single cell analysesen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1038/s41467-021-21453-4-
dc.identifier.eissn2041-1723-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

Files in This Item:
File Description SizeFormat 
MarkerSelect.pdf1.24 MBAdobe PDFView/Download


Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.