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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.
Index Terms—Cell clustering analysis, Data denoising, Boolean
matrix factorization, Cancer microenvirionment, Metastasis.
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