Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the biological states of a single cell at high resolution. For computational efficiency and easy visualization, dimensionality reduction is necessary to capture gene expression patterns in low-dimensional space. Here we propose an ensemble method for simultaneous dimensionality reduction and feature gene extraction (EDGE) of scRNA-seq data. Different from existing dimensionality reduction techniques, the proposed method implements an ensemble learning scheme that utilizes massive weak learners for an accurate similarity search. Based on the similarity matrix constructed by those weak learners, the low-dimensional embedding of the data is estimated and optimized through spectral embedding and stochastic gradien

SUBMITTER: Sun X 

PROVIDER: S-EPMC7673125 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature