| Accession |
PRJCA012666 |
| Title |
Clustering Deviation Index (CDI): A robust and accurate internal measure for evaluating scRNA-seq data clustering |
| Relevance |
Medical |
| Data types |
Transcriptome or Gene expression
|
| Organisms |
Mus musculus
|
| Description |
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. |
| Sample scope |
Monoisolate |
| Release date |
2022-11-20 |
| Publication |
| PubMed ID |
Article title |
Journal name |
DOI |
Year |
| 36575517
|
Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering
|
Genome Biology
|
10.1186/s13059-022-02825-5
|
2022
|
|
|
| Grants |
| Agency |
program |
Grant ID |
Grant title |
| NIH
|
P30
|
AI064518
|
|
| NIH
|
U54
|
AG075936
|
|
| NIH
|
R33
|
CA225328
|
|
|
|
| Submitter |
Liuyang
Wang (wallacewly@gmail.com)
|
| Organization |
Duke University |
| Submission date |
2022-10-21 |
