scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

Daniel Osorio; Yan Zhong; Guanxun Li; Jianhua Z. Huang; James J. Cai

doi:10.1016/j.patter.2020.100139

scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

Daniel Osorio
, Yan Zhong
, Guanxun Li
, Jianhua Z. Huang^*
, James J. Cai^*

^*Corresponding author for this work

Texas A&M University

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

Abstract

scTenifoldNet is a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment. It uses single-cell RNA sequencing data to construct single-cell gene regulatory networks (scGRNs) and compares scGRNs of different samples to identify differentially regulated genes. Real-data applications demonstrate that scTenifoldNet accurately detects specific signatures of gene expression relevant to the cellular systems tested.

Original language	English
Article number	100139
Journal	Patterns
Volume	1
Issue number	9
DOIs	https://doi.org/10.1016/j.patter.2020.100139
State	Published - 11 Dec 2020
Externally published	Yes

Keywords

DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem
gene regulatory network
machine learning
manifold alignment
principal-component regression
scRNA-seq
scTenifoldNet
single-cell RNA sequencing
tensor decomposition

Access to Document

10.1016/j.patter.2020.100139

Cite this

@article{e9d982e694af4a5085ecc7a981d0c9cb,

title = "scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data",

abstract = "scTenifoldNet is a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment. It uses single-cell RNA sequencing data to construct single-cell gene regulatory networks (scGRNs) and compares scGRNs of different samples to identify differentially regulated genes. Real-data applications demonstrate that scTenifoldNet accurately detects specific signatures of gene expression relevant to the cellular systems tested.",

keywords = "DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem, gene regulatory network, machine learning, manifold alignment, principal-component regression, scRNA-seq, scTenifoldNet, single-cell RNA sequencing, tensor decomposition",

author = "Daniel Osorio and Yan Zhong and Guanxun Li and Huang, \{Jianhua Z.\} and Cai, \{James J.\}",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors",

year = "2020",

month = dec,

day = "11",

doi = "10.1016/j.patter.2020.100139",

language = "英语",

volume = "1",

journal = "Patterns",

issn = "2666-3899",

publisher = "Cell Press",

number = "9",

}

TY - JOUR

T1 - scTenifoldNet

T2 - A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

AU - Osorio, Daniel

AU - Zhong, Yan

AU - Li, Guanxun

AU - Huang, Jianhua Z.

AU - Cai, James J.

PY - 2020/12/11

Y1 - 2020/12/11

N2 - scTenifoldNet is a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment. It uses single-cell RNA sequencing data to construct single-cell gene regulatory networks (scGRNs) and compares scGRNs of different samples to identify differentially regulated genes. Real-data applications demonstrate that scTenifoldNet accurately detects specific signatures of gene expression relevant to the cellular systems tested.

AB - scTenifoldNet is a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment. It uses single-cell RNA sequencing data to construct single-cell gene regulatory networks (scGRNs) and compares scGRNs of different samples to identify differentially regulated genes. Real-data applications demonstrate that scTenifoldNet accurately detects specific signatures of gene expression relevant to the cellular systems tested.

KW - DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem

KW - gene regulatory network

KW - machine learning

KW - manifold alignment

KW - principal-component regression

KW - scRNA-seq

KW - scTenifoldNet

KW - single-cell RNA sequencing

KW - tensor decomposition

UR - https://www.scopus.com/pages/publications/85096558897

U2 - 10.1016/j.patter.2020.100139

DO - 10.1016/j.patter.2020.100139

M3 - 文章

AN - SCOPUS:85096558897

SN - 2666-3899

VL - 1

JO - Patterns

JF - Patterns

IS - 9

M1 - 100139

ER -

scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this