A robust nonlinear low-dimensional manifold for single cell RNA-seq data.

ABSTRACT:

Background

Modern developments in single-cell sequencing technologies enable broad insights into cellular state. Single-cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden our understanding of cellular heterogeneity in tissues and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single-cell data. However, methods have yet to be developed for unfiltered and unnormalized count data that estimate uncertainty in the low-dimensional space. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensiona

SUBMITTER: Verma A 

PROVIDER: S-EPMC7374962 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature