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opencc-by-4.0Nov 2021View details →

Dataset for the article "Can we use seismic reflection data to infer the interconnectivity of fracture networks?"

This package contains the effective stiffness coefficients of the fractured rock samples explored in the paper of Rubino et al. "Can we use seismic reflection data to infer the interconnectivity of fracture networks?".

opencc-by-4.0Jul 2022View details →

Supplementary table of PRIDE datasets analyzed for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"

Our proteomics dataset comes from The PRoteomics IDEntifications (PRIDE) database, the world’s largest data repository of mass spectrometry-based proteomics data. Specifically, we used 633 human proteomics project experiments with a total of 32,546 runs and reanalyzed them using ionbot with an FDR threshold of 0.01 [16], resulting in a total of 154,885,151 peptide spectrum matches for 18,846 proteins. Here is the full list of projects, runs, and general statistics.

opencc-by-4.0Aug 2022View details →

Evaluation of Phosphoproteomics data-driven signalling network inference

Data Each processed data set and its adjacency matrices representing gold standard networks are stored in one specific folder. The folders have the data set name. The process of obtaining the pairwise results and the evaluation metrics are divided into two main parts: 1. Pairwise results <ul> <li>First, all the data set folders have to be placed in one main folder.</li> <li>Then run the script: RunAll_Matrices.R</li> <li>This script: <ul> <li>Goes through each data set folder: <ul> <li>reads the data set</li> <li>calculates for each method the resulting matrix</li> <li>Save in the data folder one file with all resulting matrices: file type RData</li> </ul> </li> </ul> </li> </ul>     2. Evaluation     <ul> <li>After this first step, each data set folder has a file that contains all the resulting adjacency matrices.</li> <li>The second step is to run the script RunAll_Golds.R</li> <li>This script <ul> <li>Goes through each folder: <ul> <li>reads the data set</li> <li>reads the result adjacency matrices from RData files</li> <li>reads the gold networks</li> <li>Calculates all evaluation metrics for each result and each gold network type.</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Apr 2024View details →

Assessing Pairwise Ecological Association Inference using a novel Ecological Network Inference Simulation-Validation Framework - Data Repository

Accompanying data for manuscript entitled "A novel Network Inference Simulation-Validation Framework for Assessment of Ecological Network Inference Performance" whose submission is imminent.

opencc-by-4.0Apr 2023View details →

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). <a href="https://doi.org/10.1038/s41467-023-38637-9">https://doi.org/10.1038/s41467-023-38637-9</a> The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/scMTNI_sourcedata.tar.gz?versionId=a19f69c1-5bb2-436e-9a30-a7efbf1ab3da">scMTNI_sourcedata.tar.gz</a> contains the following 3 parts: 1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks. 2) Gold standard human and mouse datasets for evaluation. 3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx. The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/Buenrostro_Hematopoiesis.tar.gz">Buenrostro_Hematopoiesis.tar.gz</a> contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al. The source data <a href="../api/files/5c75876b-c08a-4186-a08d-da9db78f64f3/RawMotifFiles.tar.gz">RawMotifFiles.tar.gz</a> contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check <a href="https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md">https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md</a> for examples and scripts. The <a href="../api/records/11876980/draft/files/Buenrostro_priorNetwork_bamfiles.tar.gz/content">Buenrostro_priorNetwork_bamfiles.tar.gz</a> contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.

opencc-by-4.0Jan 2024View details →

Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks

Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks”  Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle  Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health Description:  This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include: <ul> <li> all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study.  </li> <li> all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration  </li> <li> precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction. </li> <ul> <li> The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds </li> <li> For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X.  </li> <li> We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds” </li> <li> The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables.  </li> </ul> <li> WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors.  </li> </ul>

opencc-by-4.0Sep 2024View details →

Dataset accompaning "Variational inference for correlated gravitational wave detector network noise"

Dataset accompaning the paper "Variational inference for correlated gravitational wave detector network noise"   <h3>Raw data files</h3> <ul> <li>ET_caseA_noise.h5   (correlated noise)</li> <li>ET_caseB_noise.h5   (uncorrelated noise)</li> </ul> These contain: <ul> <li>raw_XYZ (the XYZ channels of ET noise)</li> <li>time (time in seconds, corresponding to raw_XYZ)</li> <li>periodogram: <ul> <li>pdgrm (of the above channels, truncated to 5-128 Hz)</li> <li>freq (in Hz)</li> </ul> </li> <li>true_psd <ul> <li>psd </li> <li>freq</li> </ul> </li> </ul> Note: case C from the manuscript utilised the case B dataset, (but with a model that does not account for the cross-spectrum). It does not have a separate dataset.    <h3>Result file</h3> <ul> <li>ET-CaseA-SGVB-PSD.h5</li> <li>ET-CaseB-SGVB-PSD.h5</li> <li>ET-CaseC-SGVB-PSD.h5</li> </ul> These contain: <ul> <li>psd_quantiles (the lower 0.05, median 0.50, upper 0.95 quantiles of 500 PSD samples)</li> <li>freq (in Hz, associated with the psd_quantiles)</li> </ul>

opencc-by-4.0May 2023View details →

Repositiry for the article: "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain Mbebi & Zoran Nikoloski

This is the repository for the manuscript "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain J. Mbebi & Zoran Nikoloski. Organisation <ol> <li>The folder Codes contains the following R scripts with the K-folds cross-validation option to learn the hyperparameters:</li> </ol> <ul> <li>Mixed_L1L21_GRN.R which computes L1L21-solution</li> <li>Mixed_L1L21G_GRN.R which computes L1L21G-solution</li> <li>Mixed_L2L21_GRN.R which computes L2L21-solution</li> <li>Mixed_L2L21G_GRN.R which computes L2L21G-solution</li> <li>L1L21_Dream5_Scerevisiae_example_run.R is an example run using the L1L21-solution with S. cerevisiae data (Network 4 in DREAM5 challenge) All files needed to successfully run "L1L21_Dream5_Scerevisiae_example_run" are locaded in the folder Codes.</li> </ul> 2. The folder Figures contains all figures in the manuscript. 3. The folder Inferred-networks contains all network objects for each dataset and each inference methods in the comparative analysis. Dependencies and required packages The following packages are required for the contending approaches in the comparative analysis: "devtools", "foreach", "plyr", "glmnet" and "randomForest". GENIE3 The GENIE3 package can be installed from: <a href="http://bioconductor.org/packages/release/bioc/html/GENIE3.html">http://bioconductor.org/packages/release/bioc/html/GENIE3.html</a> TIGRESS The TIGRESS repository can be obtained from: <a href="https://github.com/jpvert/tigress">https://github.com/jpvert/tigress</a> ENNET The ENNET repository can be obtained from: <a href="https://github.com/slawekj/ennet">https://github.com/slawekj/ennet</a> PLSNET The Matlab source code of PLSNET can be obtained from: <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17</a> PORTIA The PORTIA repository can be obtained from: <a href="https://github.com/AntoinePassemiers/PORTIA">https://github.com/AntoinePassemiers/PORTIA</a> D3GRN The Matlab source code of D3GRN can be obtained from: <a href="https://github.com/chenxofhit/D3GRN">https://github.com/chenxofhit/D3GRN</a> Fused-LASSO The fused-LASSO repository can be obtained from: <a href="https://github.com/omranian/inference-of-GRN-using-Fused-LASSO">https://github.com/omranian/inference-of-GRN-using-Fused-LASSO</a> ANOVerence Because of some technical issues (e.g code's accessibility: <a href="http://www2.bio.ifi.lmu.de/%CB%9Ckueffner/anova.tar.gz">http://www2.bio.ifi.lmu.de/&tilde;kueffner/anova.tar.gz</a>), we were not able to reproduce ANOVerence results and used the inferred network from DREAM5 challenge instead. 4. Although the codes here were tested on Fedora 29 (Workstation Edition) using R (version 4.2.2), they can run under any Linux or Windows OS distributions, as long as all the required packages are compatible with the desired R version.

opencc-by-4.0Sep 2022View details →

Inferring microbial co-occurrence network from amplicon data: a systematic evaluation

Supporting data for the manuscript "Inferring microbial co-occurrence network from amplicon data: a systematic evaluation".

dryad36/100

Data from: Ecological network inference from long-term presence-absence data

Open the record for dataset details and reuse information.

publicJul 2018View details →

dryad36/100

Massively scalable inference of level-1 phylogenetic networks

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publicNov 2025View details →

dryad36/100

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold Networks

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publicApr 2025View details →

opencc-by-nc-4.0Jun 2019View details →

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

This repository contains input files from the synthetic, curated, and processed experimental single-cell gene expression datasets used in BEELINE. New in version 3: 1) Ground-truth networks used for analysis of experimental scRNA-seq datasets for mouse and human datasets 2) Changed license to CC BY-NC 4.0 from GPL v3.0 to account for the non-commercial clause for the network data

opencc-zeroAug 2020View details →

Data from: Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks

Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their non-independence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P1,P2),P3),Out) and a matrix of pairwise nucleotide divergence (dXY) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe-CNN, was able to accurately perform model selection for hybridization scenarios across a wide-breath of parameter space. We then used HyDe-CNN to test models of admixture in Heliconius butterflies, as well as comparing it to a random forest classifier trained on introgression-based statistics. Given the flexibility of our approach, the dropping cost of long-read sequencing, and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.

opencc-by-4.0Jul 2020View details →

Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR <= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della

opencc-zeroJul 2021View details →

Data from: How to make methodological decisions when inferring social networks.

Social network analyses allow studying the processes underlying the associations between individuals and the consequences of those associations. Constructing and analysing social networks can be challenging, especially when designing new studies as researchers are confronted with decisions about how to collect data and construct networks, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. Here, we suggest an approach for making decisions when starting social network research in a new study system that avoids the pitfall of multiple hypotheses testing. We argue that best edge definition for a network is a decision that can be made using a priori knowledge about the species, and that is independent from the hypotheses that the network will ultimately be used to evaluate. We illustrate this approach with a study conducted on a colonial cooperatively breeding bird, the sociable weaver. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then evaluated which combination of data collection and association definition maximised (i) the assortment of individuals into previously known 'breeding groups' (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). This evaluation of different methods based on a priori knowledge of the study species can be implemented in a diverse array of study systems and makes the case for using existing, biologically meaningful knowledge about a system to help navigate the myriad of methodological decisions about data collection and network inference.

opencc-zeroDec 2010View details →

Data from: Inferring species networks from gene trees in high-polyploid North American and Hawaiian violets (Viola, Violaceae)

The phylogenies of allopolyploids take the shape of networks and cannot be adequately represented as bifurcating trees. Especially for high-polyploids (i.e., organisms with more than six sets of nuclear chromosomes), the signatures of gene homoeolog loss, deep coalescence and polyploidy may become confounded, with the result that gene trees may be congruent with more than one species network. Herein, we obtained the most parsimonious species network by objective comparison of competing scenarios involving polyploidization and homoeolog loss in a high-polyploid lineage of violets (Viola, Violaceae) mostly or entirely restricted to North America, Central America, or Hawaii. We amplified homoeologs of the low-copy nuclear gene GPI by single-molecule PCR and the chloroplast trnL-F region by conventional PCR for 51 species and subspecies. Topological incongruence among GPI homoeolog subclades, owing to deep coalescence and two instances of putative loss (or lack of detection) of homoeologs, were reconciled by applying the maximum tree topology for each subclade. The most parsimonious species network and the fossil-based calibration of the homoeolog tree favored monophyly of the high-polyploids, which has resulted from allodecaploidization 9–14 Ma ago, involving sympatric ancestors from the extant Viola sections Chamaemelanium (diploid), Plagiostigma (paleotetraploid), and Viola (paleotetraploid). While two of the high-polyploid lineages (Boreali-Americanae, Pedatae) remained decaploid, recurrent polyploidization with tetraploids of section Plagiostigma within the last 5 Ma has resulted in two 14-ploid lineages (Mexicanae, Nosphinium) and one 18-ploid lineage (Langsdorffianae). This implies a more complex phylogenetic and biogeographic origin of the Hawaiian violets (Nosphinium) than that previously inferred from rDNA data and illustrates the necessity of considering polyploidy in phylogenetic and biogeographic reconstruction.

opencc-zeroDec 2016View details →

Data from: Does detection range matter for inferring social networks in a benthic shark using acoustic telemetry?

Accurately estimating contacts between animals can be critical in ecological studies such as examining social structure, predator–prey interactions or transmission of information and disease. While biotelemetry has been used successfully for such studies in terrestrial systems, it is still under development in the aquatic environment. Acoustic telemetry represents an attractive tool to investigate spatio-temporal behaviour of marine fish and has recently been suggested for monitoring underwater animal interactions. To evaluate the effectiveness of acoustic telemetry in recording interindividual contacts, we compared co-occurrence matrices deduced from three types of acoustic receivers varying in detection range in a benthic shark species. Our results demonstrate that (i) associations produced by acoustic receivers with a large detection range (i.e. Vemco VR2W) were significantly different from those produced by receivers with smaller ranges (i.e. Sonotronics miniSUR receivers and proximity loggers) and (ii) the position of individuals within their network, or centrality, also differed. These findings suggest that acoustic receivers with a large detection range may not be the best option to represent true social networks in the case of a benthic marine animal. While acoustic receivers are increasingly used by marine ecologists, we recommend users first evaluate the influence of detection range to depict accurate individual interactions before using these receivers for social or predator–prey studies. We also advocate for combining multiple receiver types depending on the ecological question being asked and the development of multi-sensor tags or testing of new automated proximity loggers, such as the Encounternet system, to improve the precision and accuracy of social and predator–prey interaction studies.

opencc-by-4.0Oct 2021View details →

Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference [Artefact Evaluation]

Source code of paper "Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference" submitted to FPGA'22 for artefact evaluation.