Exponential-Family Embedding With Application to Cell Developmental Trajectories for Single-Cell RNA-Seq Data.

ABSTRACT: Scientists often embed cells into a lower-dimensional space when studying single-cell RNA-seq data for improved downstream analyses such as developmental trajectory analyses, but the statistical properties of such nonlinear embedding methods are often not well understood. In this article, we develop the exponential-family SVD (eSVD), a nonlinear embedding method for both cells and genes jointly with respect to a random dot product model using exponential-family distributions. Our estimator uses alternating minimization, which enables us to have a computationally efficient method, prove the identifiability conditions and consistency of our method, and provide statistically principled procedures to tune our method. All these qualities help advance the single-cell embedding literature, and we

SUBMITTER: Lin KZ 

PROVIDER: S-EPMC8336573 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature