Regulatory Driven Clustering of Single-Cell Data; Clustering of single-cell RNA sequencing from glioblastoma with a novel mathematical method
Abstract
Cancer is a leading cause of death worldwide. Single-cell RNA sequencing has arisen as an important
method to explore the gene expression of biological cells, including cancer cells. In this study,
we deployed a computational algorithm known as ScRegClust to dissect single-cell RNA-sequencing
(scRNA-seq) data from brain tumors. This method uncovers modules of co-expressed genes, and
identifies corresponding regulators, such as transcription factors and kinases. We sought to discern
whether distinct scRNA-seq datasets could mutually inform each other by examining the patterns
of gene clustering and regulatory mechanisms. The goal was to leverage this knowledge to guide
the algorithm in a subsequent run, thereby enhancing its performance. Although the preliminary
findings from simulated data offered promising prospects, transitioning to real-world data consisting
of glioblastomas presented considerable hurdles. While our results shed light on the intricacies
of reconstructing regulatory programs, the overall performance did not meet our initial projections.
These findings underscore the complexity of and challenges associated with scRNA-seq analysis,
underscoring the necessity for further exploration and refinement of current methodologies. This
research enriches the field of data integration in cancer genomics and lays a foundation for future
efforts aimed at refining regulatory-driven clustering of single-cell data.
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