Affiliation:
1. School of Mathematical Sciences, Peking University , Beijing 100871, China
2. Center for Quantitative Biology, Peking University , Beijing 100871, China
3. Center for Statistical Science, Peking university , Beijing 100871, China
Abstract
Abstract
Motivation
The rapid development of single-cell RNA sequencing (scRNA-seq) makes it possible to study the heterogeneity of individual cell characteristics. Cell clustering is a vital procedure in scRNA-seq analysis, providing insight into complex biological phenomena. However, the noisy, high-dimensional and large-scale nature of scRNA-seq data introduces challenges in clustering analysis. Up to now, many deep learning-based methods have emerged to learn underlying feature representations while clustering. However, these methods are inefficient when it comes to rare cell type identification and barely able to fully utilize gene dependencies or cell similarity integrally. As a result, they cannot detect a clear cell type structure which is required for clustering accuracy as well as downstream analysis.
Results
Here, we propose a novel scRNA-seq clustering algorithm called scNAME which incorporates a mask estimation task for gene pertinence mining and a neighborhood contrastive learning framework for cell intrinsic structure exploitation. The learned pattern through mask estimation helps reveal uncorrupted data structure and denoise the original single-cell data. In addition, the randomly created augmented data introduced in contrastive learning not only helps improve robustness of clustering, but also increases sample size in each cluster for better data capacity. Beyond this, we also introduce a neighborhood contrastive paradigm with an offline memory bank, global in scope, which can inspire discriminative feature representation and achieve intra-cluster compactness, yet inter-cluster separation. The combination of mask estimation task, neighborhood contrastive learning and global memory bank designed in scNAME is conductive to rare cell type detection. The experimental results of both simulations and real data confirm that our method is accurate, robust and scalable. We also implement biological analysis, including marker gene identification, gene ontology and pathway enrichment analysis, to validate the biological significance of our method. To the best of our knowledge, we are among the first to introduce a gene relationship exploration strategy, as well as a global cellular similarity repository, in the single-cell field.
Availability and implementation
An implementation of scNAME is available from https://github.com/aster-ww/scNAME.
Supplementary information
Supplementary data are available at Bioinformatics online.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
16 articles.
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