Name
Title | ||
|---|---|---|
A data denoising approach to optimize functional clustering of single cell RNA-sequencing data |
Abstract | ||
|---|---|---|
Single cell RNA-sequencing (scRNA-seq) technology enables comprehensive transcriptomic profiling of thousands of cells with distinct phenotypic and physiological states in a complex tissue. Substantial efforts have been made to characterize single cells of distinct identities from scRNA-seq data, including various cell clustering techniques. While existing approaches can handle single cells in terms of different cell (sub)types at a high resolution, identification of the functional variability within the same cell type remains unsolved. In addition, there is a lack of robust method to handle the inter-subject variation that often brings severe confounding effects for the functional clustering of single cells. In this study, we developed a novel data denoising and cell clustering approach, namely CIBS, to provide biologically explainable functional classification for scRNA-seq data. CIBS is based on a systems biology model of transcriptional regulation that assumes a multi-modality distribution of the cells' activation status, and it utilizes a Boolean matrix factorization approach on the discretized expression status to robustly derive functional modules. CIBS is empowered by a novel fast Boolean Matrix Factorization method, namely PFAST, to increase the computational feasibility on large scale scRNA-seq data. Application of CIBS on two scRNA-seq datasets collected from cancer tumor micro-environment successfully identified subgroups of cancer cells with distinct expression patterns of epithelial-mesenchymal transition and extracellular matrix marker genes, which was not revealed by the existing cell clustering analysis tools. The identified cell groups were significantly associated with the clinically confirmed lymph-node invasion and metastasis events across different patients. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BIBM49941.2020.9313483 | 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | DocType | ISBN |
Cell clustering analysis,Data denoising,Boolean matrix factorization,Cancer microenvirionment,Metastasis | Conference | 978-1-7281-6216-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changlin Wan | 1 | 3 | 3.79 |
| Dongya Jia | 2 | 2 | 1.08 |
| Yue Zhao | 3 | 186 | 33.54 |
| Wennan Chang | 4 | 13 | 4.10 |
| Sha Cao | 5 | 3 | 4.85 |
| Xiao Wang | 6 | 5 | 4.61 |
| Zhang Chi | 7 | 10 | 7.64 |