scholarly journals Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Annamalai Muthiah ◽  
Susanna R. Keller ◽  
Jae K. Lee
Keyword(s):  
Time Series ◽  
Human Disease ◽  
Gene Networks ◽  
Gene Network ◽  
Time Course ◽  
Network Inference ◽  
Building Blocks ◽  
Superior Performance ◽  
Expression Data ◽  
Genomic Expression

Different computational approaches have been examined and compared for inferring network relationships from time-series genomic data on human disease mechanisms under the recent Dialogue on Reverse Engineering Assessment and Methods (DREAM) challenge. Many of these approaches infer all possible relationships among all candidate genes, often resulting in extremely crowded candidate network relationships with many more False Positives than True Positives. To overcome this limitation, we introduce a novel approach, Module Anchored Network Inference (MANI), that constructs networks by analyzing sequentially small adjacent building blocks (modules). Using MANI, we inferred a 7-gene adipogenesis network based on time-series gene expression data during adipocyte differentiation. MANI was also applied to infer two 10-gene networks based on time-course perturbation datasets from DREAM3 and DREAM4 challenges. MANI well inferred and distinguished serial, parallel, and time-dependent gene interactions and network cascades in these applications showing a superior performance to other in silico network inference techniques for discovering and reconstructing gene network relationships.

IMPROVING GENE NETWORK INFERENCE BY COMPARING EXPRESSION TIME-SERIES ACROSS SPECIES, DEVELOPMENTAL STAGES OR TISSUES

2004 ◽  
Vol 02 (04) ◽  
pp. 765-783 ◽  
Author(s):  
GUILLAUME BOURQUE ◽  
DAVID SANKOFF
Keyword(s):  
Time Series ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Interaction Coefficients ◽  
Gene Network Inference ◽  
Linear Differential

We present a method for gene network inference and revision based on time-series data. Gene networks are modeled using linear differential equations and a generalized stepwise multiple linear regression procedure is used to recover the interaction coefficients. Our system is designed for the recovery of gene interactions concurrently in many gene regulatory networks related by a tree or a more general graph. We show how this comparative framework can facilitate the recovery of the networks and improve the quality of the solutions inferred.

scholarly journals corto: a lightweight R package for Gene Network Inference and Master Regulator Analysis

2020 ◽  
Author(s):  
Daniele Mercatelli ◽  
Gonzalo Lopez-Garcia ◽  
Federico M. Giorgi
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Network Inference ◽  
Human Tumor ◽  
R Package ◽  
Specific Gene ◽  
Master Regulator ◽  
Gene Network Inference ◽  
Link Type

AbstractMotivationGene Network Inference and Master Regulator Analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses, but most require a computer cluster or large amounts of RAM to be executed.ResultsWe developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for Copy Number Variations (CNVs) and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets.AvailabilityCross-platform and multi-threaded R package on CRAN (stable version) https://cran.rproject.org/package=corto and Github (development release) https://github.com/federicogiorgi/[email protected]

scholarly journals Biological network inference from single-cell multi-omics data using heterogeneous graph transformer

2021 ◽  
Author(s):  
Anjun Ma ◽  
Xiaoying Wang ◽  
Cankun Wang ◽  
Jingxian Li ◽  
Tong Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Gene Networks ◽  
Network Inference ◽  
Lung Tumor ◽  
Superior Performance ◽  
Omics Data ◽  
Learning Platform ◽  
Cell Clustering ◽  
Cell Cell

We present DeepMAPS, a deep learning platform for cell-type-specific biological gene network inference from single-cell multi-omics (scMulti-omics). DeepMAPS includes both cells and genes in a heterogeneous graph to infer cell-cell, cell-gene, and gene-gene relations simultaneously. The graph attention neural network considers a cell and a gene with both local and global information, making DeepMAPS more robust to data noises. We benchmarked DeepMAPS on 18 datasets for cell clustering and network inference, and the results showed that our method outperforms various existing tools. We further applied DeepMAPS on a case study of lung tumor leukocyte CITE-seq data and observed superior performance in cell clustering, and predicted biologically meaningful cell-cell communication pathways based on the inferred gene networks. To improve the feasibility and ensure the reproducibility of analyzing scMulti-omics data, we deployed a webserver with multi-functions and various visualizations. Overall, we valued DeepMAPS as a novel platform of the state-of-the-art deep learning model in the single-cell study and can promote the use of scMulti-omics data in the community.

Inferring Gene Networks using Robust Statistical Techniques

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Venkat R. Nadadoor ◽  
Amos Ben-Zvi ◽  
Sirish L. Shah
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Information Criterion ◽  
Statistical Techniques ◽  
Connectivity Matrix ◽  
Expression Data ◽  
Statistical Tools ◽  
Linear Ordinary Differential Equations ◽  
Leave One Out ◽  
Novel Algorithm

Inference of gene networks is an important step in understanding cellular dynamics. In this work, a novel algorithm is proposed for inferring gene networks from gene expression data using linear ordinary differential equations. Under the proposed method, a combination of known statistical tools including partial least squares (PLS), leave-one-out jackknifing, and the Akaike information criterion (AIC) are used for robust estimation of gene connectivity matrix. The proposed approach is tested and validated using a computer simulated gene network model and an experimental data on a nine gene network in Eschericia coli.

Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference

2019 ◽  
Vol 14 (3) ◽  
pp. 255-268 ◽  
Author(s):  
Wei Zhang ◽  
Wenchao Li ◽  
Jianming Zhang ◽  
Ning Wang
Keyword(s):  
Single Cell ◽  
In Silico ◽  
Gene Networks ◽  
Network Inference ◽  
Performance Metrics ◽  
Information Source ◽  
Inference Algorithm ◽  
Expression Data ◽  
Complementary Information ◽  
Cell Expression

Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference. Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution. Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list. Results: Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source. Conclusion: In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

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