Regression on Latent Spaces for the Analysis of Multi-Condition Single-Cell RNA-Seq Data
Keywords: Geometric machine learning, single-cell, Grassmann manifold, geodesic regression
TL;DR: Cluster-free differential expression using regression on subspaces in multi-condition single-cell data.
Abstract: Multi-condition single-cell data reveals expression differences between corresponding cell subpopulations in different conditions. Here, we propose to use regression on latent spaces to simultaneously account for variance from known and latent factors. Our approach is built around multivariate regression on Grassmann manifolds. We use the method to analyze a drug treatment experiment on brain tumor biopsies. The method is a versatile new approach for identifying differentially expressed genes from single-cell data of heterogeneous cell subpopulations under arbitrary experimental designs without clustering.
Type Of Submission: Extended Abstract (4 pages, non-archival)
Submission Number: 69
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