Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework.
IEEE/ACM Trans Comput Biol Bioinform
; 19(2): 1154-1164, 2022.
Article
de En
| MEDLINE
| ID: mdl-33026977
ABSTRACT
The rapid development of single-cell RNA sequencing (scRNA-seq)technology reveals the gene expression status and gene structure of individual cells, reflecting the heterogeneity and diversity of cells. The traditional methods of scRNA-seq data analysis treat data as the same subspace, and hide structural information in other subspaces. In this paper, we propose a low-rank subspace ensemble clustering framework (LRSEC)to analyze scRNA-seq data. Assuming that the scRNA-seq data exist in multiple subspaces, the low-rank model is used to find the lowest rank representation of the data in the subspace. It is worth noting that the penalty factor of the low-rank kernel function is uncertain, and different penalty factors correspond to different low-rank structures. Moreover, the single cluster model is difficult to find the cellular structure of all datasets. To strengthen the correlation between model solutions, we construct a new ensemble clustering framework LRSEC by using the low-rank model as the basic learner. The LRSEC framework captures the global structure of data through low-rank subspaces, which has better clustering performance than a single clustering model. We validate the performance of the LRSEC framework on seven small datasets and one large dataset and obtain satisfactory results.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Algorithmes
/
Analyse sur cellule unique
Type d'étude:
Prognostic_studies
Langue:
En
Journal:
ACM Trans Comput Biol Bioinform
Sujet du journal:
BIOLOGIA
/
INFORMATICA MEDICA
Année:
2022
Type de document:
Article