Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data

Single-cell RNA sequencing (scRNA-Seq) technology has emerged as a powerful tool to investigate cellular heterogeneity within tissues, organs, and organisms. One fundamental question pertaining to single-cell gene expression data analysis revolves around the identification of cell types, which const...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2023-11, Vol.20 (6), p.3737-3747
Main Authors: Liu, Jing-Xing, Zhang, Dai-Jun, Zhao, Jing-Xiu, Zheng, Chun-Hou, Gao, Ying-Lian
Format: Article
Language:English
Subjects:
Algorithms
Cellular structure
Cluster Analysis
Clustering
Computer science
Data analysis
Data models
Data processing
Gene expression
Gene Expression Profiling - methods
gene marker
Gene sequencing
Hash based algorithms
Heterogeneity
locality-sensitive hashing
low-rank representation
Manifold learning
Manifolds (mathematics)
RNA
Sequence Analysis, RNA - methods
Sequential analysis
Similarity
Single-Cell Analysis - methods
Single-Cell Gene Expression Analysis
Single-cell RNA sequencing data
Workflow
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Summary:Single-cell RNA sequencing (scRNA-Seq) technology has emerged as a powerful tool to investigate cellular heterogeneity within tissues, organs, and organisms. One fundamental question pertaining to single-cell gene expression data analysis revolves around the identification of cell types, which constitutes a critical step within the data processing workflow. However, existing methods for cell type identification through learning low-dimensional latent embeddings often overlook the intercellular structural relationships. In this paper, we present a novel non-negative low-rank similarity correction model (NLRSIM) that leverages subspace clustering to preserve the global structure among cells. This model introduces a novel manifold learning process to address the issue of imbalanced neighbourhood spatial density in cells, thereby effectively preserving local geometric structures. This procedure utilizes a position-sensitive hashing algorithm to construct the graph structure of the data. The experimental results demonstrate that the NLRSIM surpasses other advanced models in terms of clustering effects and visualization experiments. The validated effectiveness of gene expression information after calibration by the NLRSIM model has been duly ascertained in the realm of relevant biological studies. The NLRSIM model offers unprecedented insights into gene expression, states, and structures at the individual cellular level, thereby contributing novel perspectives to the field.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2023.3319375