Clustering Single-Cell RNA Sequence Data Using Information Maximized and Noise-Invariant Representations

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 20; no. 3; pp. 1983 - 1994
• Main Authors: Mondal, Arnab Kumar, Joshi, Indu, Singh, Pravendra, AP, Prathosh
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Algorithms; Artificial neural networks; Base Sequence; Cluster Analysis; Clustering; Clustering algorithms; Clustering methods; clustering of scRNA-seq data; Data models; Deep learning; Dimensionality reduction of scRNA-seq data; Gene Expression Profiling - methods; Gene sequencing; Genomes; Invariants; Machine learning; Neural networks; noise-invariant representation learning; Nucleotide sequence; Representation learning; Representations; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Training; Transcriptomes
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2022.3227202

Single-cell RNA sequencing (scRNA-seq) is a revolutionary methodology that helps to analyze transcriptome or genome information from a single cell. However, high dimensionality and sparsity in data due to dropout events pose computational challenges for existing state-of-the-art scRNA-seq clustering methods. Learning efficient representations becomes even more challenging due to the presence of noise in scRNA-seq data. To overcome the effect of noise and learn effective representations, this paper proposes sc-INDC ( S ingle- C ell I nformation Maximized N oise-Invariant D eep C lustering), a deep neural network that facilitates learning of informative and noise-invariant representations of scRNA-seq data. Furthermore, the time complexity of the proposed sc-INDC is significantly lower compared to state-of-the-art scRNA-seq clustering methods. Extensive experimentation on fourteen publicly available scRNA-seq datasets illustrates the efficacy of the proposed model. Additionally, visualizations of t-SNE plots and several ablation studies are also conducted to provide insights into the improved representation ability of sc-INDC. Code of the proposed sc-INDC will be available at: https://github.com/arnabkmondal/sc-INDC .