Recognizes the set of necessary and sufficient marker genes from an sc/snRNAseq experiment. NSforest is based on a random forest of decision trees machine learning approach. It creates standard cell type definitions. The result of this method can serve as a reference knowledgebase to support interoperability of information about the role of cellular phenotypes in human health and disease.
J. Craig Venter Institute, La Jolla, CA, USA; Allen Institute for Brain Science, Seattle, WA, USA; Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA; European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK; Department of Pathology, University of California San Diego, La Jolla, CA, USA
NSforest funding source(s)
Supported by the Allen Institute for Brain Science, the JCVI Innovation Fund, the U.S. National Institutes of Health R21-AI122100 and U19-AI118626, and the California Institute for Regenerative Medicine GC1R-06673-B.