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

SummaryGene regulatory networks (GRNs) provide a powerful framework for studying cellular differentiation. However, it is less clear how GRNs encode cellular responses to everyday microenvironmental cues. Macrophages can be polarized and potentially repolarized based on environmental signaling. In order to identify the GRNs that drive macrophage polarization and the heterogeneous single-cell subpopulations that are present in the process, we used a high-resolution time course of bulk and single-cell RNA-seq and ATAC-seq assays of HL-60-derived macrophages polarized towards M1 or M2 over 24 hours. We identified transient M1 and M2 markers, including the main transcription factors that underlie polarization, and subpopulations of naive, transitional, and terminally polarized macrophages. We built bulk and single-cell polarization GRNs to compare the recovered interactions and found that each technology recovered only a subset of known interactions. Our data provide a resource to study the GRN of cellular maturation in response to microenvironmental stimuli in a variety of contexts in homeostasis and disease.

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scTenifoldNet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data

10.1101/2020.02.12.931469 ◽

2020 ◽

Author(s):

Daniel Osorio ◽

Yan Zhong ◽

Guanxun Li ◽

Jianhua Z. Huang ◽

James J. Cai

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Principal Component Regression ◽

Principal Component ◽

Real Data ◽

Low Rank ◽

Gene Regulatory

AbstractConstructing and comparing gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNAseq) data has the potential to reveal critical components in the underlying regulatory networks regulating different cellular transcriptional activities. Here, we present a robust and powerful machine learning workflow—scTenifoldNet—for comparative GRN analysis of single cells. The scTenifoldNet workflow, consisting of principal component regression, low-rank tensor approximation, and manifold alignment, constructs and compares transcriptome-wide single-cell GRNs (scGRNs) from different samples to identify gene expression signatures shifting with cellular activity changes such as those associated with pathophysiological processes and responses to environmental perturbations. We used simulated data to benchmark scTenifoldNet’s performance, and then applied scTenifoldNet to several real data sets. In real-data applications, scTenifoldNet identified highly specific changes in gene regulation in response to acute morphine treatment, an antibody anticancer drug, gene knockout, double-stranded RNA stimulus, and amyloid-beta plaques in various types of mouse and human cells. We anticipate that scTenifoldNet can help achieve breakthroughs through constructing and comparing scGRNs in poorly characterized biological systems, by deciphering the full cellular and molecular complexity of the data.HighlightsscTenifoldNet is a machine learning workflow built upon principal component regression, low-rank tensor approximation, and manifold alignmentscTenifoldNet uses single-cell RNA sequencing (scRNAseq) data to construct single-cell gene regulatory networks (scGRNs)scTenifoldNet compares scGRNs of different samples to identify differentially regulated genesReal-data applications demonstrate that scTenifoldNet accurately detects specific signatures of gene expression relevant to the cellular systems tested.Short abstractWe present scTenifoldNet—a machine learning workflow built upon principal component regression, low-rank tensor approximation, and manifold alignment—for constructing and comparing single-cell gene regulatory networks (scGRNs) using data from single-cell RNA sequencing (scRNAseq). scTenifoldNet reveals regulatory changes in gene expression between samples by comparing the constructed scGRNs. With real data, scTenifoldNet identifies specific gene expression programs associated with different biological processes, providing critical insights into the underlying mechanism of regulatory networks governing cellular transcriptional activities.

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Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments

Patterns ◽

10.1016/j.patter.2021.100332 ◽

2021 ◽

Vol 2 (9) ◽

pp. 100332

Author(s):

N. Alexia Raharinirina ◽

Felix Peppert ◽

Max von Kleist ◽

Christof Schütte ◽

Vikram Sunkara

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Higher Order ◽

Rna Seq ◽

Higher Order Moments ◽

Gene Regulatory

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A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis

Frontiers in Plant Science ◽

10.3389/fpls.2016.01936 ◽

2016 ◽

Vol 7 ◽

Cited By ~ 19

Author(s):

Ying Ni ◽

Delasa Aghamirzaie ◽

Haitham Elmarakeby ◽

Eva Collakova ◽

Song Li ◽

...

Keyword(s):

Machine Learning ◽

Seed Development ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Learning Approach ◽

Machine Learning Approach ◽

Gene Regulatory

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Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application

10.1101/2020.08.30.273839 ◽

2020 ◽

Author(s):

Turki Turki ◽

Y-h. Taguchi

Keyword(s):

Deep Learning ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Metabolic Diseases ◽

Large Data ◽

Data Repositories ◽

Cell Gene Expression ◽

Gene Regulatory ◽

Cell Gene

AbstractAnalyzing single-cell pancreatic data would play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, analyzing various functions related to the inference of gene regulatory networks, derived from single-cell data, remains difficult, thereby posing a barrier to the deepening of understanding of cellular metabolism. Since recent studies have led to the reliable inference of single-cell gene regulatory networks (SCGRNs), the challenge of discriminating between SCGRNs has now arisen. By accurately discriminating between SCGRNs (e.g., distinguishing SCGRNs of healthy pancreas from those of T2D pancreas), biologists would be able to annotate, organize, visualize, and identify common patterns of SCGRNs for metabolic diseases. Such annotated SCGRNs could play an important role in speeding up the process of building large data repositories. In this study, we aimed to contribute to the development of a novel deep learning (DL) application. First, we generated a dataset consisting of 224 SCGRNs belonging to both T2D and healthy pancreas and made it freely available. Next, we chose seven DL architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, DenseNet121, and DenseNet169, trained each of them on the dataset, and checked prediction based on a test set. We evaluated the DL architectures on an HP workstation platform with a single NVIDIA GeForce RTX 2080Ti GPU. Experimental results on the whole dataset, using several performance measures, demonstrated the superiority of VGG19 DL model in the automatic classification of SCGRNs, derived from the single-cell pancreatic data.

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