scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

• URL: http://arxiv.org/abs/2312.16600v1
• Date: Wed, 27 Dec 2023 14:50:59 GMT
• Title: scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning
• Authors: Weikang Jiang, Jinxian Wang, Jihong Guan and Shuigeng Zhou
• Abstract summary: Single-cell RNA sequencing (scRNA-seq) enables researchers to analyze gene expression at single-cell level. One important task in scRNA-seq data analysis is unsupervised clustering. We propose Cluster-aware Iterative Contrastive Learning (CICL) for scRNA-seq data clustering.
• Score: 29.199004624757233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-cell RNA sequencing (scRNA-seq) enables researchers to analyze gene expression at single-cell level. One important task in scRNA-seq data analysis is unsupervised clustering, which helps identify distinct cell types, laying down the foundation for other downstream analysis tasks. In this paper, we propose a novel method called Cluster-aware Iterative Contrastive Learning (CICL in short) for scRNA-seq data clustering, which utilizes an iterative representation learning and clustering framework to progressively learn the clustering structure of scRNA-seq data with a cluster-aware contrastive loss. CICL consists of a Transformer encoder, a clustering head, a projection head and a contrastive loss module. First, CICL extracts the feature vectors of the original and augmented data by the Transformer encoder. Then, it computes the clustering centroids by K-means and employs the student t-distribution to assign pseudo-labels to all cells in the clustering head. The projection-head uses a Multi-Layer Perceptron (MLP) to obtain projections of the augmented data. At last, both pseudo-labels and projections are used in the contrastive loss to guide the model training. Such a process goes iteratively so that the clustering result becomes better and better. Extensive experiments on 25 real world scRNA-seq datasets show that CICL outperforms the SOTA methods. Concretely, CICL surpasses the existing methods by from 14% to 280%, and from 5% to 133% on average in terms of performance metrics ARI and NMI respectively.

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