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

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
.