SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data / 基因组蛋白质组与生物信息学报·英文版
Genomics, Proteomics & Bioinformatics
; (4): 201-210, 2019.
Article
Dans En
| WPRIM
| ID: wpr-772939
Responsable en Bibliothèque :
WPRO
ABSTRACT
Clustering is a prevalent analytical means to analyze single cell RNA sequencing (scRNA-seq) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of pressing need. Here we proposed Spearman subsampling-clustering-classification (SSCC), a new clustering framework based on random projection and feature construction, for large-scale scRNA-seq data. SSCC greatly improves clustering accuracy, robustness, and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, SSCC achieved 20% improvement for clustering accuracy and 50-fold acceleration, but only consumed 66% memory usage, compared to the widelyused software package SC3. Compared to k-means, the accuracy improvement of SSCC can reach 3-fold. An R implementation of SSCC is available at https//github.com/Japrin/sscClust.
Texte intégral:
1
Indice:
WPRIM
Sujet Principal:
Algorithmes
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Logiciel
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Analyse de regroupements
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Analyse de séquence d'ARN
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Statistique non paramétrique
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Biologie informatique
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Bases de données comme sujet
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Analyse de profil d'expression de gènes
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Analyse sur cellule unique
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Méthodes
Limites du sujet:
Animals
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Humans
langue:
En
Texte intégral:
Genomics, Proteomics & Bioinformatics
Année:
2019
Type:
Article