Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering is a research paper published in Genome biology (2020). On theSindex it has a DataRank of 0. It has been cited 46 times.
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National Institutes of Health
Grant: 1R01GM122083-01
Statistical Methods for Single-Cell RNA-Seq
National Institutes of Health
Grant: 2P20GM109035-06
COBRE: Center for Computational Biology of Human Disease
National Institutes of Health
Grant: 1P01NS097206-01
Epigenetic regulation of neurogenesis
National Science Foundation
Grant: 1054905
CAREER: Statistical and Computational Methods for RNA-seq data
FWCI
1.94
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering
Additional file 1 of Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering
Additional file 2 of Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering
Additional file 2 of Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering
Additional file 1 of CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data
Additional file 1 of CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data
Additional file 2 of CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data
Additional file 2 of CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data