Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering.
Genome Biol
; 23(1): 269, 2022 12 27.
Article
en En
| MEDLINE
| ID: mdl-36575517
ABSTRACT
Most single-cell RNA sequencing (scRNA-seq) analyses begin with cell clustering; thus, the clustering accuracy considerably impacts the validity of downstream analyses. In contrast with the abundance of clustering methods, the tools to assess the clustering accuracy are limited. We propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. As a result, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.
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Base de datos:
MEDLINE
Asunto principal:
Algoritmos
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Perfilación de la Expresión Génica
Idioma:
En
Revista:
Genome Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA
Año:
2022
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Article