D-EE: Distributed software for visualizing intrinsic structure of large-scale single-cell data.

ABSTRACT:

Background

Dimensionality reduction and visualization play vital roles in single-cell RNA sequencing (scRNA-seq) data analysis. While they have been extensively studied, state-of-the-art dimensionality reduction algorithms are often unable to preserve the global structures underlying data. Elastic embedding (EE), a nonlinear dimensionality reduction method, has shown promise in revealing low-dimensional intrinsic local and global data structure. However, the current implementation of the EE algorithm lacks scalability to large-scale scRNA-seq data.

Results

We present a distributed optimization implementation of the EE algorithm, termed distributed elastic embedding (D-EE). D-EE reveals the low-dimensional intrinsic structures of data with accuracy equal to that of elastic em

SUBMITTER: An S 

PROVIDER: S-EPMC7657844 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature