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

BMC Bioinformatics
Archit Verma, Barbara E Engelhardt

Abstract

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-dimensional nonlinear structure in scRNA-seq data. Gene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Student's t-distribution to estimate a manifold that is robust to technical and biological noise found in normalized scRNA-seq data. We compare our approach to commo...Continue Reading

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Methods Mentioned

BETA
scRNA-seq
PCA
single-cell sequencing
RNA-seq

Software Mentioned

tGPLVM
scLVM
Edward
SNE
scVI
PAGODA
UMAP
tGPVLM
BBVI
PCA

Related Concepts

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