Identification of Distinct Topological Structures From High-Dimensional Data

Abstract

Single-cell RNA sequencing allows the direct measurement of the expression of tens of thousands of genes, providing an unprecedented view of the transcriptomic state of a cell. Within each cell, different biological processes such as differentiation or cell cycle take place simultaneously, each contributing a different characterization of cell state. To identify gene sets that govern these processes for the purpose of disentangling convolved biological processes, we present "Identification of Distinct topological structures" (ID). ID works by constructing an alternative low-dimensional parametrization of the high-dimensional system, applying a finite perturbation to this alternative parametrization, and looking for genes that respond similarly. With this approach, we demonstrate that ID is capable of identifying structures within the data that will otherwise be missed. We further demonstrate the utility of ID in scRNA-seq datasets collected under various conditions, delineating cellular differentiation, characterizing cellular response to external perturbation, and dissecting the effect of genetic knock-outs.