Using Regulatory Networks to Enhance Single-Cell Clustering

The clustering of single-cell RNA-sequencing data has been established as an important first step in single-cell gene expression data analysis for scientists to identify cell type based on RNA level expression. This is important because once a cell type has been identified, the phenotype association, as well as the spatiotemporal dynamics of specific cell types, can be characterized, which could lead to identifying cells associated with cancers and other diseases. However, the high-dimensionality of the data poses computational challenges, while drop-outs (genes that are not identified despite being expressed) hamper the reliability of inference. Since established knowledge on transcriptional regulatory networks provide information on the regulatory relationships between genes, we hypothesize that regulatory networks can help remedy missing data, while also reducing dimensionality. To test this hypothesis, we use a previously existing regulatory network, modern clustering methods, and network propagation together to help enhance clustering performance, which enhances accurate identification of cell types.