Clustering and visualization of single-cell RNA-seq data using path metrics

Andriana Manousidaki, Anna Little, Yuying Xie

Abstract: Author summary Advancements in single-cell technologies with the ability to measure gene expression at the cellular level have provided unprecedented opportunity to investigate the cell type (T cells, B cells, etc) and cell state diversity (active T cells and exhausted T cells) within tissues and cancers. However, analyzing this complex high-dimensional data when the noise level is high requires sophisticated tools to effectively extract useful biological information and faithfully visualize the data in a low-dimensional space (2- or 3-D). Existing computational methods such as dimension reduction and clustering (group similar cells together) for single-cell data struggle to simultaneously preserve local group structure and global data geometry (developmental relationship between cell types). To tackle this problem, we’ve developed a new analysis framework called scPMP (Single-Cell Path Metrics Profiling) based on a unique approach to measure distances between cells which takes into account both the density of cells (common vs rare cell types) and the overall structure of the data. We have demonstrated the ability of scPMP to better preserve the natural grouping of cells and the relationships between different groups over existing methods in numerous real and simulated data sets. This improvement could lead to more accurate identification of cell types and states.