scholarly journals Mapping gene regulatory networks from single-cell omics data

2018 ◽  
Vol 17 (4) ◽  
pp. 246-254 ◽  
Author(s):  
Mark W E J Fiers ◽  
Liesbeth Minnoye ◽  
Sara Aibar ◽  
Carmen Bravo González-Blas ◽  
Zeynep Kalender Atak ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Omics Data ◽  
Mapping Gene ◽  
Gene Regulatory

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

Patterns ◽  
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

scholarly journals Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease

2020 ◽  
Author(s):  
Mufang Ying ◽  
Peter Rehani ◽  
Panagiotis Roussos ◽  
Daifeng Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Disease Genes ◽  
Omics Data ◽  
Cell Type ◽  
Cellular Resolution ◽  
Gene Regulatory

AbstractStrong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer’s disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.

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

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.

scholarly journals Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks

Cell ◽  
2019 ◽  
Vol 176 (1-2) ◽  
pp. 361-376.e17 ◽  
Author(s):  
Adam J. Rubin ◽  
Kevin R. Parker ◽  
Ansuman T. Satpathy ◽  
Yanyan Qi ◽  
Beijing Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Causal Gene ◽  
Gene Regulatory

scholarly journals Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference

Cell Reports ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. 2290-2303 ◽  
Author(s):  
Delphine Potier ◽  
Kristofer Davie ◽  
Gert Hulselmans ◽  
Marina Naval Sanchez ◽  
Lotte Haagen ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Eye Development ◽  
Drosophila Eye ◽  
Mapping Gene ◽  
Gene Regulatory

scholarly journals SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

2017 ◽  
Vol 34 (2) ◽  
pp. 258-266 ◽  
Author(s):  
Nan Papili Gao ◽  
S M Minhaz Ud-Dean ◽  
Olivier Gandrillon ◽  
Rudiyanto Gunawan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Transcriptional Expression ◽  
Gene Regulatory

scholarly journals Mapping gene regulatory networks of primary CD4+T cells using single-cell genomics and genome engineering

2019 ◽  
Author(s):  
Rachel E. Gate ◽  
Min Cheol Kim ◽  
Andrew Lu ◽  
David Lee ◽  
Eric Shifrut ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Genome Engineering ◽  
Regulatory Elements ◽  
Mapping Gene ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractGene regulatory programs controlling the activation and polarization of CD4+T cells are incompletely mapped and the interindividual variability in these programs remain unknown. We sequenced the transcriptomes of ~160k CD4+T cells from 9 donors following pooled CRISPR perturbation targeting 140 regulators. We identified 134 regulators that affect T cell functionalization, includingIRF2as a positive regulator of Th2polarization. Leveraging correlation patterns between cells, we mapped 194 pairs of interacting regulators, including known (e.g.BATFandJUN) and novel interactions (e.g.ETS1andSTAT6). Finally, we identified 80 natural genetic variants with effects on gene expression, 48 of which are modified by a perturbation. In CD4+T cells, CRISPR perturbations can influencein vitropolarization and modify the effects oftransandcisregulatory elements on gene expression.

scholarly journals Single cell and single nucleus RNA-Seq reveal cellular heterogeneity and homeostatic regulatory networks in adult mouse stria vascularis

2019 ◽  
Author(s):  
Soumya Korrapati ◽  
Ian Taukulis ◽  
Rafal Olszewski ◽  
Madeline Pyle ◽  
Shoujun Gu ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Stria Vascularis ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Genetic Mechanisms ◽  
Single Nucleus ◽  
Gene Regulatory

AbstractThe stria vascularis (SV) generates the endocochlear potential (EP) in the inner ear and is necessary for proper hair cell mechanotransduction and hearing. While channels belonging to SV cell types are known to play crucial roles in EP generation, relatively little is known about gene regulatory networks that underlie the ability of the SV to generate and maintain the EP. Using single cell and single nucleus RNA-sequencing, we identify and validate known and rare cell populations in the SV. Furthermore, we establish a basis for understanding molecular mechanisms underlying SV function by identifying potential gene regulatory networks as well as druggable gene targets. Finally, we associate known deafness genes with adult SV cell types. This work establishes a basis for dissecting the genetic mechanisms underlying the role of the SV in hearing and will serve as a basis for designing therapeutic approaches to hearing loss related to SV dysfunction.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

scholarly journals Multiview Graph Learning for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Abdullah Karaaslanli ◽  
SATABDI SAHA ◽  
Selin Aviyente ◽  
Tapabrata Maiti
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Joint Estimation ◽  
Embryonic Stem Cell Differentiation ◽  
Graph Learning ◽  
Gene Regulatory ◽  
Cell Gene

Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

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