Artikel ; Online: Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq.
2024 Band 15, Heft 1, Seite(n) 699
Abstract: While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous ... ...
Abstract | While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using real and synthetic datasets, we find that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to millions of cells. |
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Mesh-Begriff(e) | Single-Cell Gene Expression Analysis ; Algorithms ; Cluster Analysis |
Sprache | Englisch |
Erscheinungsdatum | 2024-01-24 |
Erscheinungsland | England |
Dokumenttyp | Journal Article |
ZDB-ID | 2553671-0 |
ISSN | 2041-1723 ; 2041-1723 |
ISSN (online) | 2041-1723 |
ISSN | 2041-1723 |
DOI | 10.1038/s41467-023-43406-9 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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