GSE161465
Quantifying the effect of experimental perturbations in single-cell3RNA-sequencing data using graph signal processing
Current methods for quantification of differences between scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space such as clusters of cells. Here, we describe a continuous measure of the effect of a perturbation across the transcriptomic space with single cell resolution. We describe the cellular state space as manifold, use a heat filter to estimate the density of each sample over the manifold, and normalize these densities to obtain the relative likelihood of observing each cell in each of the observed conditions. We develop vertex frequency clustering to identify populations of affected cells at the appropriate level of granularity. Using these selected populations we can derive gene signatures of affected populations of cells. We demonstrate both algorithms using a combination of biological and synthetic datasets. Overall design: Nondiabetic human islets obtained from Prodo Laboratories were allowed to rest for 1 day and were treated or were not treated with 25ng/ml IFNg for 24 hrs