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Contrastive Learning-Based Method for Single-Cell Multi-omics Data Clustering
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DOI：10.1007/978-981-95-0698-9_29.png)
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Abstract
En 中文
The rapid development of joint profiling methods makes it possible to simultaneously measure multi-modal data from the same cell. It enables the more comprehensive insight of cellular heterogeneity. However, due to the heterogeneity of features across different modalities and the complexity of each modal data, effectively integrating multi-modal data to achieve more accurate cell heterogeneity analysis remains challenging. Here, we propose a novel contrastive learning-based approach for single-cell multi-omics data clustering, named scCLC. Taking contrastive learning as backbone, scCLC integrates scRNA-seq data and scATAC-seq data for cell presentation learning with dedicated data augmentation strategy and self-supervised learning model. scCLC leverages the topological structure derived from single-cell multi-omics data to determine positive pairs, which not only increases the training sample size but also provides basic label priors for the model to learn more cluster-friendly cell representations. The experimental results on single-cell multi-omics datasets show the superior clustering performance of scCLC. When applied to visualization, scCLC can effectively separate the cell subtypes.
Keywords:
Single-cell Multi-omics
Cell Heterogeneity
Self-supervised Learning
Journal
B
IF:
0
Papers:
25
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Cited Papers
High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell.png)
NATURE BIOTECHNOLOGY
OAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveA deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq dataGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveShareSaveJoint profiling of chromatin accessibility and gene expression in thousands of single cellsScienceOAAI
ScienceIF0
OAAIShareSaveNEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cellsNATURE METHODS
OAAI
NATURE METHODSIF32.1
OAAIShareSaveA mixture-of-experts deep generative model for integrated analysis of single-cell multiomics dataCELL REPORTS METHODSOAAI
CELL REPORTS METHODSIF4.5
OAAIShareSaveShareSaveShareSave
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