Directed Conservative Causal Core Gene Networks

Author(s):

Gokmen Altay

Keyword(s):

Gene Networks ◽

Large Scale ◽

Network Inference ◽

R Package ◽

Core Gene ◽

Supplementary File ◽

Core Network ◽

Large Scale Networks ◽

Direction Information

AbstractMotivation:Inferring large scale directional networks with higher accuracy has important applications such as gene regulatory network or finance.Results:We modified a well-established conservative causal core network inference algorithm, C3NET, to be able to infer very large scale networks with direction information. This advanced version is called Ac3net. We demonstrate that Ac3net outperforms C3NET and many other popular algorithms when considering the directional interaction information of gene/protein networks. We provide and R package and present performance results that are reproducible via the Supplementary file.Availability:Ac3net is available on CRAN and at github.com/altayg/Ac3netContact:[email protected] information:Supplementary file is available online.

corto: a lightweight R package for gene network inference and master regulator analysis

Bioinformatics ◽

10.1093/bioinformatics/btaa223 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3916-3917 ◽

Cited By ~ 6

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

Abstract Motivation Gene 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. Results We 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 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. Availability and implementation Cross-platform and multi-threaded R package on CRAN (stable version) https://cran.r-project.org/package=corto and Github (development release) https://github.com/federicogiorgi/corto. Supplementary information Supplementary data are available at Bioinformatics online.

FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

10.7287/peerj.preprints.27256v1 ◽

2018 ◽

Author(s):

Lisa-Katrin Turnhoff ◽

Ali Hadizadeh Esfahani ◽

Maryam Montazeri ◽

Nina Kusch ◽

Andreas Schuppert

Keyword(s):

Drug Response ◽

Drug Efficacy ◽

Response Prediction ◽

R Package ◽

Supplementary File ◽

Data Sets ◽

Training Algorithms ◽

Model Training

Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient data sets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art preprocessing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and Implementation: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE . Supplementary Information: Supplementary Files 1 and 2 provide detailed descriptions of the pipeline and the data preparation process, while Supplementary File 3 presents basic use cases of the package. Contact: [email protected]

MSCquartets 1.0: Quartet methods for species trees and networks under the multispecies coalescent model in R

Bioinformatics ◽

10.1093/bioinformatics/btaa868 ◽

2020 ◽

Author(s):

John A Rhodes ◽

Hector Baños ◽

Jonathan D Mitchell ◽

Elizabeth S Allman

Keyword(s):

Network Inference ◽

Incomplete Lineage Sorting ◽

R Package ◽

Species Tree ◽

Species Trees ◽

Lineage Sorting ◽

Coalescent Model ◽

Multispecies Coalescent ◽

Tree Inference

Abstract Summary MSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks under the Multispecies Coalescent model of incomplete lineage sorting and its network analog. Input for these analyses are collections of metric or topological locus trees which are then summarized by the quartets displayed on them. Results of hypothesis tests at user-supplied levels are displayed in a simplex plot by color-coded points. The package implements the QDC and WQDC algorithms for topological and metric species tree inference, and the NANUQ algorithm for level-1 topological species network inference, all of which give statistically consistent estimators under the model. Availability MSCquartets is available through the Comprehensive R Archive Network: https://CRAN.R-project.org/package=MSCquartets. Supplementary information Supplementary materials, including example data and analyses, are incorporated into the package.

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

10.1101/2020.02.10.942623 ◽

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]

RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

Bioinformatics ◽

10.1093/bioinformatics/btab683 ◽

2021 ◽

Author(s):

Zachary B Abrams ◽

Dwayne G Tally ◽

Lynne V Abruzzo ◽

Kevin R Coombes

Keyword(s):

Large Scale ◽

International System ◽

Genetic Data ◽

R Package ◽

Supplementary Data ◽

Computational Tools ◽

Text Format ◽

Karyotype Analyses ◽

Computational Analyses

Abstract Summary Cytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology. Availability and Implementation Freely available at https://CRAN.R-project.org/package=RCytoGPS. The code for the underlying CytoGPS software can be found at https://github.com/i2-wustl/CytoGPS. Supplementary information There is no supplementary data.

genuMet: distinguish genuine untargeted metabolic features without quality control samples

10.1101/837260 ◽

2019 ◽

Author(s):

L Cao ◽

C Clish ◽

FB Hu ◽

MA Martínez-González ◽

C Razquin ◽

...

Keyword(s):

Quality Control ◽

Large Scale ◽

Control Method ◽

R Package ◽

True Positive Rate ◽

True Negative ◽

True Negative Rate ◽

Positive Rate ◽

Measurement Artifacts

AbstractMotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.ResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.Availability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.ContactLiming Liang: [email protected] informationSupplementary data are available at ….

Mercator: An R Package for Visualization of Distance Matrices

10.1101/733261 ◽

2019 ◽

Author(s):

Zachary B. Abrams ◽

Caitlin E. Coombes ◽

Suli Li ◽

Kevin R. Coombes

Keyword(s):

Large Scale ◽

R Package ◽

Data Sets ◽

Data Types ◽

Vast Number ◽

Large Scale Data ◽

Scientific Disciplines ◽

Compare And Contrast ◽

Scale Data

AbstractSummaryUnsupervised data analysis in many scientific disciplines is based on calculating distances between observations and finding ways to visualize those distances. These kinds of unsupervised analyses help researchers uncover patterns in large-scale data sets. However, researchers can select from a vast number of different distance metrics, each designed to highlight different aspects of different data types. There are also numerous visualization methods with their own strengths and weaknesses. To help researchers perform unsupervised analyses, we developed the Mercator R package. Mercator enables users to see important patterns in their data by generating multiple visualizations using different standard algorithms, making it particularly easy to compare and contrast the results arising from different metrics. By allowing users to select the distance metric that best fits their needs, Mercator helps researchers perform unsupervised analyses that use pattern identification through computation and visual inspection.Availability and ImplementationMercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html)[email protected] informationSupplementary data are available at Bioinformatics online.

Identification of disease-associated loci using machine learning for genotype and network data integration

Bioinformatics ◽

10.1093/bioinformatics/btz310 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5182-5190 ◽

Cited By ~ 4

Author(s):

Luis G Leal ◽

Alessia David ◽

Marjo-Riita Jarvelin ◽

Sylvain Sebert ◽

Minna Männikkö ◽

...

Keyword(s):

Machine Learning ◽

Gene Networks ◽

Association Studies ◽

R Package ◽

Biological Data ◽

Machine Learning Algorithms ◽

Genome Wide Association Studies ◽

Omics Data ◽

Missing Heritability

Abstract Motivation Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritization of weak loci. Results We developed cNMTF (corrected non-negative matrix tri-factorization), an integrative algorithm based on clustering techniques of biological data. This method assesses the inter-relatedness between genotypes, phenotypes, the damaging effect of the variants and gene networks in order to identify loci-trait associations. cNMTF was used to prioritize genes associated with lipid traits in two population cohorts. We replicated 129 genes reported in GWAS world-wide and provided evidence that supports 85% of our findings (226 out of 265 genes), including recent associations in literature (NLGN1), regulators of lipid metabolism (DAB1) and pleiotropic genes for lipid traits (CARM1). Moreover, cNMTF performed efficiently against strong population structures by accounting for the individuals’ ancestry. As the method is flexible in the incorporation of diverse omics data sources, it can be easily adapted to the user’s research needs. Availability and implementation An R package (cnmtf) is available at https://lgl15.github.io/cnmtf_web/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

dbgap2x: An R package to explore and extract data from the database of Genotypes and Phenotypes (dbGaP)

Bioinformatics ◽

10.1093/bioinformatics/btz680 ◽

2019 ◽

Author(s):

Grégoire Versmée ◽

Laura Versmée ◽

Mikaël Dusenne ◽

Niloofar Jalali ◽

Paul Avillach

Keyword(s):

Data Sharing ◽

Large Scale ◽

Genomic Data ◽

R Package ◽

National Institutes Of Health ◽

Supplementary Data ◽

Complex Procedure ◽

Range Of Functions ◽

The Relationship

Abstract Summary Based on the Genomic Data Sharing Policy issued in August 2007, the National Institutes of Health (NIH) has supported several repositories such as the database of Genotypes and Phenotypes (dbGaP). dbGaP is an online repository that provides access to large-scale genetic and phenotypic datasets with more than 1,000 studies. However, navigating the website and understanding the relationship between the studies are not easy tasks. Moreover, the decryption of the files is a complex procedure. In this study we propose the dbgap2x R package that covers a broad range of functions for searching dbGaP studies, exploring the characteristics of a study and easily decrypting the files from dbGaP. Availability and implementation dbgap2x is an R package with the code available at https://github.com/gversmee/dbgap2x. A containerized version including the package, a Jupyter server and with a Notebook example is available at https://hub.docker.com/r/gversmee/dbgap2x. Supplementary information Supplementary data are available at Bioinformatics online.

simGWAS: a fast method for simulation of large scale case–control GWAS summary statistics

Bioinformatics ◽

10.1093/bioinformatics/bty898 ◽

2018 ◽

Vol 35 (11) ◽

pp. 1901-1906 ◽

Cited By ~ 4

Author(s):

Mary D Fortune ◽

Chris Wallace

Keyword(s):

Large Scale ◽

Simulated Data ◽

Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Intermediate Step ◽

Fast Method ◽

Summary Statistics ◽

Causal Variants

Abstract Motivation Methods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some ‘truth’ is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study. Results We have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis. Availability and implementation Our method is available under a GPL license as an R package from http://github.com/chr1swallace/simGWAS. Supplementary information Supplementary data are available at Bioinformatics online.