Identification of cell types from single cell data using stable clustering.
Sci Rep
; 10(1): 12349, 2020 07 23.
Article
in En
| MEDLINE
| ID: mdl-32703984
ABSTRACT
Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues and organs at different stages. Identifying the cell types present in the sample from the single cell transcriptome data is a common goal in many single-cell experiments. Several methods have been developed to do this. However, correctly identifying the true cell types remains a challenge. We present a framework that addresses this problem. Our hypothesis is that the meaningful characteristics of the data will remain despite small perturbations of data. We validate the performance of the proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five simulation datasets with different degrees of the cluster separability. We compare the proposed method with five other existing methods:
RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Sequence Analysis, RNA
/
Single-Cell Analysis
/
High-Throughput Nucleotide Sequencing
/
Transcriptome
Type of study:
Diagnostic_studies
/
Prognostic_studies
Language:
En
Journal:
Sci Rep
Year:
2020
Type:
Article
Affiliation country:
United States