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

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

doi:10.1016/j.patter.2020.100139

Cited by 32 publications

(35 citation statements)

References 89 publications

(161 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 1: Constructing scGRN with scRNAseq data from WT samples With the scRNAseq data from a WT sample, scTenifoldKnk first constructs an scGRN using a pipeline we proposed previously, namely, scTenifoldNet. 9 This network construction step contains three sub-steps (Figure 1A):…”

Section: Results 1: the Sctenifoldknk Workflowmentioning

confidence: 99%

“…We have previously shown that scRNAseq information can be leveraged to fuel the machine learning algorithms for reliable scGRN construction. 9 In a GRN, the regulatory effect manifests as observable synchronized patterns of expression between genes. These genes are associated with the same biological process, pathway, or under the control of the same set of TFs.…”

Section: Discussionmentioning

confidence: 99%

“…To construct scGRNs, scTenifoldKnk uses the method we previously proposed for scTenifoldNet. 9 The procedure of scGRN construction consists of three steps: cell subsampling, principal component (PC) regression, and tensor decomposition.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

Osorio

Zhong

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping, providing unprecedented insights into cell-type-specific gene function. However, the use of single-cell technology in large-scale, systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk, a machine learning workflow that performs virtual KO experiments using single-cell RNA sequencing (scRNA-seq) data. scTenifoldKnk first uses data from wild-type (WT) samples to construct a single-cell gene regulatory network (scGRN). Then, a gene is knocked out from the constructed scGRN by setting weights of the gene's outward edges to zeros. ScTenifoldKnk then compares this "pseudo-KO" scGRN with the original scGRN to identify differentially regulated (DR) genes. These DR genes, also called virtual-KO perturbed genes, are used to assess the impact of the gene KO and reveal the gene's function in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk analysis recapitulates the main findings of three real-animal KO experiments and confirms the functions of genes underlying three Mendelian diseases. We show the power of scTenifoldKnk as a predictive method to successfully predict the outcomes of two KO experiments that involve intestinal enterocytes in Ahr-/- mice and pancreatic islet cells in Malat1-/- mice, respectively. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are virtually deleted, allowing gene functions to be revealed in a cell type-specific manner.

show abstract

Section: Results 1: the Sctenifoldknk Workflowmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

Osorio

Zhong

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To systematically compare macrophage transcriptomes between M and S patient groups, we employed a machine learning workflow-named scTenifoldNet (Fig. 1d), which we developed to construct and compare single-cell gene regulatory networks (scGRNs) [14]. The most important advantage of this network-based comparative analytical framework is its sensitivity.…”

Section: Main Textmentioning

confidence: 99%

“…The most important advantage of this network-based comparative analytical framework is its sensitivity. We have shown that scTenifoldNet can detect differential regulatory patterns between highly similar scRNA-seq samples to reveal gene regulatory changes, which are undetectable otherwise [14]. scTenifoldNet is built upon several machine learning methods including principal component regression, low-rank tensor approximation, and manifold alignment (Fig.…”

Section: Main Textmentioning

confidence: 99%

Single-Cell Gene Regulatory Network Analysis Reveals Potential Mechanisms of Action of Antimalarials Against SARS-CoV-2

Cai

Osorio

2021

Computational Advances in Bio and Medical Sciences

Self Cite

View full text Add to dashboard Cite

The efficiency of antimalarials, chloroquine (CQ) and hydroxychloroquine (HCQ), in the prevention and treatment of coronavirus disease 2019 (COVID-19) is under intense debate. The mechanisms of action of antimalarials against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have not been fully elucidated. Here, we applied a network-based comparative analysis, implemented in our machine learning workflow-scTenifoldNet, to scRNA-seq data from COVID-19 patients with different levels of severity. We found that genes of the Malaria pathway expressed in macrophages are significantly differentially regulated between patients with moderate and severe symptoms. Our findings help reveal the mechanisms of action of CQ and HCQ during SARS-CoV-2 infection, providing new evidence to support the use of these antimalarial drugs in the treatment of COVID-19, especially for patients who are mildly affected or in the early stage of the infection.

show abstract

Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence

Cao,

Chen

2024

Methods in Molecular Biology

View full text Add to dashboard Cite

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

Cited by 32 publications

References 89 publications

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

Single-Cell Gene Regulatory Network Analysis Reveals Potential Mechanisms of Action of Antimalarials Against SARS-CoV-2

Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence

Contact Info

Product

Resources

About