  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.

Genomics;Artificial Intelligence;Machine Learning