ModularBoost: an efficient network inference algorithm based on module decomposition

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

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Evaluating the reproducibility of single-cell gene regulatory network inference algorithms

10.1101/2020.11.10.375923 ◽

2020 ◽

Author(s):

Yoonjee Kang ◽

Denis Thieffry ◽

Laura Cantini

Keyword(s):

Single Cell ◽

Network Inference ◽

Simulated Data ◽

Ground Truth ◽

Real Data ◽

Gene Regulatory Network Inference ◽

Sequencing Platform ◽

Cell Network ◽

Inference Algorithms ◽

Inference Methods

AbstractNetworks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth.Here, we benchmark four single-cell network inference methods based on their reproducibility, i.e. their ability to infer similar networks when applied to two independent datasets for the same biological condition. We tested each of these methods on real data from three biological conditions: human retina, T-cells in colorectal cancer, and human hematopoiesis.GENIE3 results to be the most reproducible algorithm, independently from the single-cell sequencing platform, the cell type annotation system, the number of cells constituting the dataset, or the thresholding applied to the links of the inferred networks. In order to ensure the reproducibility and ease extensions of this benchmark study, we implemented all the analyses in scNET, a Jupyter notebook available at https://github.com/ComputationalSystemsBiology/scNET.

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SimiC: A Single Cell Gene Regulatory Network Inference method with Similarity Constraints

10.1101/2020.04.03.023002 ◽

2020 ◽

Author(s):

Jianhao Peng ◽

Ullas V. Chembazhi ◽

Sushant Bangru ◽

Ian M. Traniello ◽

Auinash Kalsotra ◽

...

Keyword(s):

Single Cell ◽

Network Inference ◽

Regional Analysis ◽

Supplementary Information ◽

Inference Method ◽

Gene Regulatory Network Inference ◽

Inference Problem ◽

Cell State ◽

Gene Regulatory ◽

Inference Methods

AbstractMotivationWith the use of single-cell RNA sequencing (scRNA-Seq) technologies, it is now possible to acquire gene expression data for each individual cell in samples containing up to millions of cells. These cells can be further grouped into different states along an inferred cell differentiation path, which are potentially characterized by similar, but distinct enough, gene regulatory networks (GRNs). Hence, it would be desirable for scRNA-Seq GRN inference methods to capture the GRN dynamics across cell states. However, current GRN inference methods produce a unique GRN per input dataset (or independent GRNs per cell state), failing to capture these regulatory dynamics.ResultsWe propose a novel single-cell GRN inference method, named SimiC, that jointly infers the GRNs corresponding to each state. SimiC models the GRN inference problem as a LASSO optimization problem with an added similarity constraint, on the GRNs associated to contiguous cell states, that captures the inter-cell-state homogeneity. We show on a mouse hepatocyte single-cell data generated after partial hepatectomy that, contrary to previous GRN methods for scRNA-Seq data, SimiC is able to capture the transcription factor (TF) dynamics across liver regeneration, as well as the cell-level behavior for the regulatory program of each TF across cell states. In addition, on a honey bee scRNA-Seq experiment, SimiC is able to capture the increased heterogeneity of cells on whole-brain tissue with respect to a regional analysis tissue, and the TFs associated specifically to each sequenced tissue.AvailabilitySimiC is written in Python and includes an R API. It can be downloaded from https://github.com/jianhao2016/[email protected], [email protected] informationSupplementary data are available at the code repository.

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Gaining confidence in inferred networks

10.1101/2020.09.19.304980 ◽

2020 ◽

Author(s):

Léo P.M. Diaz ◽

Michael P.H. Stumpf

Keyword(s):

Biological Networks ◽

Regulatory Networks ◽

Network Inference ◽

False Negative ◽

Simulated Data ◽

Real Data ◽

Point Interactions ◽

Inference Algorithms ◽

Starting Point ◽

Inference Methods

AbstractNetwork inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and instead we often rely solely on the performance against simulated data. Gaining confidence in networks inferred from real data nevertheless thus requires establishing reliable validation methods. Here, we argue that the expectation of mixing patterns in biological networks such as gene regulatory networks offers a reasonable starting point: interactions are more likely to occur between nodes with similar biological functions. We can quantify this behaviour using the assortativity coefficient, and here we show that the resulting heuristic, functional assortativity, offers a reliable and informative route for comparing different inference algorithms.

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INFERENCE OF GENETIC NETWORKS USING LPMs: ASSESSMENT OF CONFIDENCE VALUES OF REGULATIONS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720010004859 ◽

2010 ◽

Vol 08 (04) ◽

pp. 661-677 ◽

Cited By ~ 9

Author(s):

SHUHEI KIMURA ◽

YUICHI SHIRAISHI ◽

MARIKO OKADA

Keyword(s):

False Positive ◽

Network Inference ◽

Bootstrap Method ◽

Genetic Network ◽

Repair System ◽

Inference Method ◽

Inference Problem ◽

Inference Problems ◽

Target Network ◽

Inference Methods

When we apply inference methods based on a set of differential equations into actual genetic network inference problems, we often end up with a large number of false-positive regulations. However, as we must check the inferred regulations through biochemical experiments, fewer false-positive regulations are preferable. In order to reduce the number of regulations checked, this study proposes a new method that assigns confidence values to all of the regulations contained in the target network. For this purpose, we combine a residual bootstrap method with the existing method, i.e. the inference method using linear programming machines (LPMs). Through numerical experiments on an artificial genetic network inference problem, we confirmed that most of the regulations with high confidence values are actually present in the target networks. We then used the proposed method to analyze the bacterial SOS DNA repair system, and succeeded in assigning reasonable confidence values to its regulations. Although this study combined the bootstrap method with the inference method using the LPMs, the proposed bootstrap approach could be combined with any method that has an ability to infer a genetic network from time-series of gene expression levels.

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Seidr: a gene meta-network calculation toolkit

10.1101/250696 ◽

2018 ◽

Cited By ~ 3

Author(s):

Bastian Schiffthaler ◽

Alonso Serrano ◽

Nathaniel Street ◽

Nicolas Delhomme

Keyword(s):

Gene Network ◽

Network Inference ◽

Differential Expression Analysis ◽

Gene Interaction ◽

Data Sets ◽

Multiple Gene ◽

Gene Network Inference ◽

Inference Algorithms ◽

Gene Network Analysis ◽

Inference Methods

AbstractSummaryGene network analysis is a powerful tool to identify and prioritize candidate genes, especially from data sets where experimental design renders other approaches, such as differential expression analysis, limiting or infeasible. Numerous gene network inference algorithms have been published and are commonly individually applied in transcriptomics studies. It has, however, been shown that every algorithm is biased towards identifying specific types of gene interaction and that an ensemble of inference methods can reconstruct more accurate networks. This approach has been hindered by lack of an implementation to run and combine such combinations of inference algorithms. Here, we present Seidr: a toolkit to perform multiple gene network inferences and combine their results into a unified meta-network.Availability and implementationSeidr code is open-source, available from GitHub and also compiled in docker and singularity containers. It is implemented in C++ for fast computation and supports massive parallelisation through MPI. Documentation, tutorials and exemplary use are available from https://[email protected], [email protected]

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Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

Scientific Reports ◽

10.1038/s41598-021-87234-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sk Md Mosaddek Hossain ◽

Aanzil Akram Halsana ◽

Lutfunnesa Khatun ◽

Sumanta Ray ◽

Anirban Mukhopadhyay

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Mixture Model ◽

Cancer Progression ◽

Dirichlet Process ◽

Network Inference ◽

Correlation Method ◽

Gaussian Mixture ◽

Ductal Adenocarcinoma ◽

Gene Regulatory Network Inference ◽

Gene Modules

AbstractPancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.

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BRANE Cut: Biologically-Related A priori Network Enhancement with Graph cuts for Gene Regulatory Network Inference

10.1101/032383 ◽

2015 ◽

Author(s):

Aurélie Pirayre ◽

Camille Couprie ◽

Frédérique Bidard ◽

Laurent Duval ◽

Jean-Christophe Pesquet

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Networks ◽

Network Inference ◽

State Of The Art ◽

A Priori ◽

Graph Cuts ◽

Gene Regulatory Network Inference ◽

Gene Regulatory ◽

Inference Methods

Background: Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions. Methods: Weighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs. We enforce biologically plausible coupling between groups and types of genes by minimizing an edge labeling functional coding for a priori structures. The optimization is carried out with Graph cuts, an approach popular in image processing and computer vision. We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge. Results: Our BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6% to 11%). On a real Escherichia coli compendium, an improvement of 11.8% compared to CLR and 3% compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-cut.html Conclusions: BRANE Cut is a weighted graph thresholding method. Using biologically sound penalties and data-driven parameters, it improves three state-of-the-art GRN inference methods. It is applicable as a generic network inference post-processing, due its computational efficiency.

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Improved High Dimensional Discrete Bayesian Network Inference using Triplet Region Construction

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12198 ◽

2020 ◽

Vol 69 ◽

pp. 231-295

Author(s):

Peng Lin ◽

Martin Neil ◽

Norman Fenton

Keyword(s):

Network Inference ◽

General Purpose ◽

Space Complexity ◽

High Dimensional ◽

Choice Problem ◽

Worst Case ◽

Exact Inference ◽

Tree Width ◽

Inference Methods ◽

Bayesian Network Inference

Performing efficient inference on high dimensional discrete Bayesian Networks (BNs) is challenging. When using exact inference methods the space complexity can grow exponentially with the tree-width, thus making computation intractable. This paper presents a general purpose approximate inference algorithm, based on a new region belief approximation method, called Triplet Region Construction (TRC). TRC reduces the cluster space complexity for factorized models from worst-case exponential to polynomial by performing graph factorization and producing clusters of limited size. Unlike previous generations of region-based algorithms, TRC is guaranteed to converge and effectively addresses the region choice problem that bedevils other region-based algorithms used for BN inference. Our experiments demonstrate that it also achieves significantly more accurate results than competing algorithms.

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Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

10.1101/786798 ◽

2019 ◽

Author(s):

Hongzhu Cui ◽

Suhas Srinivasan ◽

Dmitry Korkin

Keyword(s):

Biological Networks ◽

Molecular Mechanisms ◽

Association Studies ◽

Genetic Diseases ◽

Detection Methods ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Human Interactome ◽

Module Detection ◽

Disease Module

AbstractProgress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, DIMSUM, which enables the integration of genome-wide association studies (GWAS), functional effects of mutations, and protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for disease module analysis, facilitating discovery of new disease markers.

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