Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning

Author:

Lee Junseok1ORCID,Kim Sungwon1,Hyun Dongmin2,Lee Namkyeong1,Kim Yejin3ORCID,Park Chanyoung1

Affiliation:

1. Department of Industrial and Systems Engineering, KAIST , Daejeon 34141, Republic of Korea

2. Institute of Artificial Intelligence, POSTECH , Pohang 37673, Republic of Korea

3. Center for Safe Artificial Intelligence for Healthcare, School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, TX 77030, United States

Abstract

Abstract Motivation Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types of cells with clustering techniques becomes an important task for downstream analysis. However, challenges of scRNA-seq data such as pervasive dropout phenomena hinder obtaining robust clustering outputs. Although existing studies try to alleviate these problems, they fall short of fully leveraging the relationship information and mainly rely on reconstruction-based losses that highly depend on the data quality, which is sometimes noisy. Results This work proposes a graph-based prototypical contrastive learning method, named scGPCL. Specifically, scGPCL encodes the cell representations using Graph Neural Networks on cell–gene graph that captures the relational information inherent in scRNA-seq data and introduces prototypical contrastive learning to learn cell representations by pushing apart semantically dissimilar pairs and pulling together similar ones. Through extensive experiments on both simulated and real scRNA-seq data, we demonstrate the effectiveness and efficiency of scGPCL. Availability and implementation Code is available at https://github.com/Junseok0207/scGPCL.

Funder

National Research Foundation of Korea

Institute of Information & communications Technology Planning & Evaluation

Korea government

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference32 articles.

1. Deep soft k-means clustering with self-training for single-cell RNA sequence data;Chen;NAR Genomics Bioinf,2020

2. Debiased contrastive learning;Chuang;Adv Neural Inf Process Syst,2020

3. Contrastive self-supervised clustering of scRNA-seq data;Ciortan;BMC Bioinformatics,2021

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