Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm - Chen, Liang; Wang, Weinan; Zhai, Yuyao; Deng, Minghua (Frontiers in Genetics, 2020)
Single-cell RNA sequencing technologies have enabled us to study tissue heterogeneity at cellular resolution. Fast-developing sequencing platforms like droplet-based sequencing make it feasible to parallel process thousands of single cells effectively. Although unique molecular identifier(UMI) can remove bias from amplification noise to a certain extent, clustering for such sparse and high-dimensional large-scale discrete data remains intractable and challenging. Most existing deep learning-based clustering methods utilize the mean square error or negative binomial distribution with or without zero inflation to denoise single-cell UMI count data, which may underfit or overfit the gene expression profiles. In addition, neglecting molecule sampling mechanism and extracting representation by simple linear dimension reduction with hard clustering algorithm may distort data structure and lead to spurious analytical results. In this paper, we combine the deep autoencoder technique with statistical modeling and develop a novel and effective clustering method, scDMFK, for single-cell transcriptome UMI count data. ScDMFK utilizes multinomial distribution to characterize data structure and draw support from neural network to facilitate model parameter estimation. In the learned low-dimensional latent space, we propose an adaptive fuzzy k-means algorithm with entropy regularization to perform soft clustering. Various simulation scenarios and the analysis of ten real datasets show that scDMFK outperforms other state-of-the-art methods with respect to data modeling and clustering algorithm. Besides, scDMFK has excellent scalability on large-scale single-cell datasets.