R-universe search: pathway

bioc

rWikiPathways:rWikiPathways - R client library for the WikiPathways API

Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions.

Maintained by Egon Willighagen. Last updated 5 months ago.

visualization graphandnetwork thirdpartyclient network metabolomics bioinformatics data-access pathways

96.7 match 15 stars 9.23 score 131 scripts 3 dependents

bioc

pathwayPCA:Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>; Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.

Maintained by Gabriel Odom. Last updated 5 months ago.

copynumbervariation dnamethylation geneexpression snp transcription geneprediction genesetenrichment genesignaling genetarget genomewideassociation genomicvariation cellbiology epigenetics functionalgenomics genetics lipidomics metabolomics proteomics systemsbiology transcriptomics classification dimensionreduction featureextraction principalcomponent regression survival multiplecomparison pathways

72.9 match 11 stars 7.74 score 42 scripts

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graphite:GRAPH Interaction from pathway Topological Environment

Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases.

Maintained by Gabriele Sales. Last updated 5 months ago.

pathways thirdpartyclient graphandnetwork network reactome kegg metabolomics bioinformatics mirror pathway-analysis

47.2 match 7 stars 10.17 score 122 scripts 21 dependents

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fgsea:Fast Gene Set Enrichment Analysis

The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction.

Maintained by Alexey Sergushichev. Last updated 3 months ago.

geneexpression differentialexpression genesetenrichment pathways cpp

26.8 match 387 stars 16.25 score 3.9k scripts 101 dependents

bioc

pathlinkR:Analyze and interpret RNA-Seq results

pathlinkR is an R package designed to facilitate analysis of RNA-Seq results. Specifically, our aim with pathlinkR was to provide a number of tools which take a list of DE genes and perform different analyses on them, aiding with the interpretation of results. Functions are included to perform pathway enrichment, with muliplte databases supported, and tools for visualizing these results. Genes can also be used to create and plot protein-protein interaction networks, all from inside of R.

Maintained by Travis Blimkie. Last updated 3 months ago.

genesetenrichment network pathways reactome rnaseq networkenrichment bioinformatics networks pathway-enrichment-analysis visualization

57.8 match 26 stars 6.62 score 2 scripts

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pathview:a tool set for pathway based data integration and visualization

Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.

Maintained by Weijun Luo. Last updated 5 months ago.

pathways graphandnetwork visualization genesetenrichment differentialexpression geneexpression microarray rnaseq genetics metabolomics proteomics systemsbiology sequencing

29.1 match 40 stars 11.24 score 1.6k scripts 10 dependents

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ReactomePA:Reactome Pathway Analysis

This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team.

Maintained by Guangchuang Yu. Last updated 5 months ago.

pathways visualization annotation multiplecomparison genesetenrichment reactome enrichment-analysis reactome-pathway-analysis reactomepa

26.4 match 40 stars 12.25 score 1.5k scripts 7 dependents

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SBGNview:"SBGNview: Data Analysis, Integration and Visualization on SBGN Pathways"

SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis.

Maintained by Weijun Luo. Last updated 5 months ago.

genetarget pathways graphandnetwork visualization genesetenrichment differentialexpression geneexpression microarray rnaseq genetics metabolomics proteomics systemsbiology sequencing

50.1 match 26 stars 6.23 score 22 scripts

jokergoo

CePa:Centrality-Based Pathway Enrichment

This package aims to find significant pathways through network topology information. It has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centrality measures are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system. CePa extends standard pathway enrichment methods, which include both over-representation analysis procedure and gene-set analysis procedure. <https://doi.org/10.1093/bioinformatics/btt008>.

Maintained by Zuguang Gu. Last updated 4 years ago.

46.1 match 3 stars 6.53 score 75 scripts

bioc

MOSClip:Multi Omics Survival Clip

Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself.

Maintained by Paolo Martini. Last updated 5 months ago.

software statisticalmethod graphandnetwork survival regression dimensionreduction pathways reactome

55.9 match 5.34 score 5 scripts

bioc

GSVA:Gene Set Variation Analysis for Microarray and RNA-Seq Data

Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.

Maintained by Robert Castelo. Last updated 3 days ago.

functionalgenomics microarray rnaseq pathways genesetenrichment gene-set-enrichment genomics pathway-enrichment-analysis

20.0 match 210 stars 14.72 score 1.6k scripts 19 dependents

egeulgen

pathfindR:Enrichment Analysis Utilizing Active Subnetworks

Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.

Maintained by Ege Ulgen. Last updated 26 days ago.

active-subnetworks enrichment pathway pathway-enrichment-analysis subnetwork

28.2 match 186 stars 10.13 score 138 scripts

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enrichplot:Visualization of Functional Enrichment Result

The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualization methods are developed based on 'ggplot2' graphics.

Maintained by Guangchuang Yu. Last updated 2 months ago.

annotation genesetenrichment go kegg pathways software visualization enrichment-analysis pathway-analysis

17.5 match 239 stars 15.71 score 3.1k scripts 58 dependents

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BioCor:Functional similarities

Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships...

Maintained by Lluís Revilla Sancho. Last updated 5 months ago.

statisticalmethod clustering geneexpression network pathways networkenrichment systemsbiology bioconductor-packages bioinformatics functional-similarity gene gene-sets pathway-analysis similarity similarity-measurement

41.5 match 14 stars 6.59 score

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ggkegg:Analyzing and visualizing KEGG information using the grammar of graphics

This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages.

Maintained by Noriaki Sato. Last updated 2 months ago.

pathways dataimport kegg ggplot2 ggraph pathway tidygraph visualization

31.6 match 224 stars 8.12 score 30 scripts 1 dependents

bioc

KEGGREST:Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG)

A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see <https://www.kegg.jp/kegg/rest/>). Note that KEGGREST is based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotation pathways thirdpartyclient kegg bioconductor-package core-package

17.4 match 9 stars 14.46 score 688 scripts 775 dependents

bioc

OmnipathR:OmniPath web service client and more

A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github).

Maintained by Denes Turei. Last updated 17 days ago.

graphandnetwork network pathways software thirdpartyclient dataimport datarepresentation genesignaling generegulation systemsbiology transcriptomics singlecell annotation kegg complexes enzyme-ptm networks networks-biology omnipath proteins quarto

24.2 match 126 stars 9.90 score 226 scripts 2 dependents

bioc

PanomiR:Detection of miRNAs that regulate interacting groups of pathways

PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data.

Maintained by Pourya Naderi. Last updated 5 months ago.

geneexpression genesetenrichment genetarget mirna pathways

48.4 match 3 stars 4.89 score 13 scripts

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maftools:Summarize, Analyze and Visualize MAF Files

Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort.

Maintained by Anand Mayakonda. Last updated 5 months ago.

datarepresentation dnaseq visualization drivermutation variantannotation featureextraction classification somaticmutation sequencing functionalgenomics survival bioinformatics cancer-genome-atlas cancer-genomics genomics maf-files tcga curl bzip2 xz-utils zlib

15.5 match 459 stars 14.63 score 948 scripts 18 dependents

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KEGGgraph:KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor

KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc.

Maintained by Jitao David Zhang. Last updated 5 months ago.

pathways graphandnetwork visualization kegg

28.2 match 7.76 score 114 scripts 23 dependents

tinnlab

RCPA:Consensus Pathway Analysis

Provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.

Maintained by Ha Nguyen. Last updated 4 months ago.

biobase deseq2 geoquery edger limma rcyjs fgsea browserviz summarizedexperiment annotationdbi rontotools

38.4 match 1 stars 5.50 score 70 scripts

igordot

msigdbr:MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format

Provides the 'Molecular Signatures Database' (MSigDB) gene sets typically used with the 'Gene Set Enrichment Analysis' (GSEA) software (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>, Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>, Castanza et al. 2023 <doi:10.1038/s41592-023-02014-7>) as an R data frame. The package includes the human genes as listed in MSigDB as well as the corresponding symbols and IDs for frequently studied model organisms such as mouse, rat, pig, fly, and yeast.

Maintained by Igor Dolgalev. Last updated 3 days ago.

enrichment-analysis gene-sets genomics gsea msigdb pathway-analysis pathways

17.5 match 72 stars 12.01 score 3.6k scripts 21 dependents

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hipathia:HiPathia: High-throughput Pathway Analysis

Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway.

Maintained by Marta R. Hidalgo. Last updated 5 months ago.

pathways graphandnetwork geneexpression genesignaling go

55.8 match 3.62 score 42 scripts

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clusterProfiler:A universal enrichment tool for interpreting omics data

This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions.

Maintained by Guangchuang Yu. Last updated 4 months ago.

annotation clustering genesetenrichment go kegg multiplecomparison pathways reactome visualization enrichment-analysis gsea

11.3 match 1.1k stars 17.03 score 11k scripts 48 dependents

bioc

TCGAbiolinks:TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.

Maintained by Tiago Chedraoui Silva. Last updated 25 days ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network sequencing survival software bioc bioconductor gdc integrative-analysis tcga tcga-data tcgabiolinks

13.3 match 305 stars 14.45 score 1.6k scripts 6 dependents

bioc

gage:Generally Applicable Gene-set Enrichment for Pathway Analysis

GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods.

Maintained by Weijun Luo. Last updated 5 months ago.

pathways go differentialexpression microarray onechannel twochannel rnaseq genetics multiplecomparison genesetenrichment geneexpression systemsbiology sequencing

21.1 match 5 stars 8.71 score 784 scripts 1 dependents

bioc

ASSIGN:Adaptive Signature Selection and InteGratioN (ASSIGN)

ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature.

Maintained by Ying Shen. Last updated 5 months ago.

software geneexpression pathways bayesian

23.6 match 2 stars 7.37 score 65 scripts 1 dependents

bioc

BioCartaImage:BioCarta Pathway Images

The core functionality of the package is to provide coordinates of genes on the BioCarta pathway images and to provide methods to add self-defined graphics to the genes of interest.

Maintained by Zuguang Gu. Last updated 5 months ago.

software pathways biocarta visualization

34.2 match 11 stars 5.04 score 6 scripts

bioc

multiGSEA:Combining GSEA-based pathway enrichment with multi omics data integration

Extracted features from pathways derived from 8 different databases (KEGG, Reactome, Biocarta, etc.) can be used on transcriptomic, proteomic, and/or metabolomic level to calculate a combined GSEA-based enrichment score.

Maintained by Sebastian Canzler. Last updated 2 months ago.

genesetenrichment pathways reactome biocarta

22.8 match 18 stars 7.06 score 32 scripts

bioc

gep2pep:Creation and Analysis of Pathway Expression Profiles (PEPs)

Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively.

Maintained by Francesco Napolitano. Last updated 5 months ago.

geneexpression differentialexpression genesetenrichment dimensionreduction pathways go

35.8 match 4.48 score 4 scripts

wolski

sigora:Signature Overrepresentation Analysis

Pathway Analysis is statistically linking observations on the molecular level to biological processes or pathways on the systems(i.e., organism, organ, tissue, cell) level. Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative. However, this can lead to identifying spurious pathways as statistically significant since components are often shared amongst pathways. SIGORA seeks to avoid this pitfall by focusing on genes or gene pairs that are (as a combination) specific to a single pathway. In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. The current version allows for pathway analysis of human and mouse datasets. In addition, it contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories and mechanisms for extracting GPS for user-supplied repositories.

Maintained by Witold Wolski. Last updated 3 years ago.

genesetenrichment go software pathways kegg

36.0 match 4.43 score 18 scripts 1 dependents

ikwak2

aSPU:Adaptive Sum of Powered Score Test

R codes for the (adaptive) Sum of Powered Score ('SPU' and 'aSPU') tests, inverse variance weighted Sum of Powered score ('SPUw' and 'aSPUw') tests and gene-based and some pathway based association tests (Pathway based Sum of Powered Score tests ('SPUpath'), adaptive 'SPUpath' ('aSPUpath') test, 'GEEaSPU' test for multiple traits - single 'SNP' (single nucleotide polymorphism) association in generalized estimation equations, 'MTaSPUs' test for multiple traits - single 'SNP' association with Genome Wide Association Studies ('GWAS') summary statistics, Gene-based Association Test that uses an extended 'Simes' procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version of 'GATES' test for pathway-based association testing ('GATES-Simes'). ). The tests can be used with genetic and other data sets with covariates. The response variable is binary or quantitative. Summary; (1) Single trait-'SNP' set association with individual-level data ('aSPU', 'aSPUw', 'aSPUr'), (2) Single trait-'SNP' set association with summary statistics ('aSPUs'), (3) Single trait-pathway association with individual-level data ('aSPUpath'), (4) Single trait-pathway association with summary statistics ('aSPUsPath'), (5) Multiple traits-single 'SNP' association with individual-level data ('GEEaSPU'), (6) Multiple traits- single 'SNP' association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP' set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath').

Maintained by Il-Youp Kwak. Last updated 4 years ago.

21.5 match 12 stars 7.18 score 42 scripts 1 dependents

bioc

annotate:Annotation for microarrays

Using R enviroments for annotation.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotation pathways go

13.4 match 11.41 score 812 scripts 243 dependents

bioc

fedup:Fisher's Test for Enrichment and Depletion of User-Defined Pathways

An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results.

Maintained by Catherine Ross. Last updated 5 months ago.

genesetenrichment pathways networkenrichment network bioconductor enrichment

28.5 match 7 stars 5.32 score 10 scripts

bioc

DOSE:Disease Ontology Semantic and Enrichment analysis

This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data.

Maintained by Guangchuang Yu. Last updated 5 months ago.

annotation visualization multiplecomparison genesetenrichment pathways software disease-ontology enrichment-analysis semantic-similarity

10.0 match 119 stars 14.97 score 2.0k scripts 61 dependents

bioc

graph:graph: A package to handle graph data structures

A package that implements some simple graph handling capabilities.

Maintained by Bioconductor Package Maintainer. Last updated 9 days ago.

graphandnetwork

12.4 match 11.78 score 764 scripts 342 dependents

bioc

EnrichmentBrowser:Seamless navigation through combined results of set-based and network-based enrichment analysis

The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

immunooncology microarray rnaseq geneexpression differentialexpression pathways graphandnetwork network genesetenrichment networkenrichment visualization reportwriting

15.5 match 20 stars 9.37 score 164 scripts 3 dependents

bioc

GeneTonic:Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis

This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries.

Maintained by Federico Marini. Last updated 2 months ago.

gui geneexpression software transcription transcriptomics visualization differentialexpression pathways reportwriting genesetenrichment annotation go shinyapps bioconductor bioconductor-package data-exploration data-visualization functional-enrichment-analysis gene-expression pathway-analysis reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

17.5 match 77 stars 8.28 score 37 scripts 1 dependents

bioc

GOSemSim:GO-terms Semantic Similarity Measures

The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively.

Maintained by Guangchuang Yu. Last updated 5 months ago.

annotation go clustering pathways network software bioinformatics gene-ontology semantic-similarity cpp

10.0 match 63 stars 14.12 score 708 scripts 68 dependents

brockk

trialr:Clinical Trial Designs in 'rstan'

A collection of clinical trial designs and methods, implemented in 'rstan' and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch.

Maintained by Kristian Brock. Last updated 1 years ago.

cpp

16.1 match 41 stars 8.55 score 106 scripts 3 dependents

bioc

PROPS:PRObabilistic Pathway Score (PROPS)

This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways.

Maintained by Lichy Han. Last updated 4 months ago.

classification bayesian geneexpression

21.8 match 6.19 score 518 scripts

asa12138

ReporterScore:Generalized Reporter Score-Based Enrichment Analysis for Omics Data

Inspired by the classic 'RSA', we developed the improved 'Generalized Reporter Score-based Analysis (GRSA)' method, implemented in the R package 'ReporterScore', along with comprehensive visualization methods and pathway databases. 'GRSA' is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the 'GRSA' supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods.

Maintained by Chen Peng. Last updated 2 months ago.

19.8 match 67 stars 6.79 score 13 scripts

bioc

edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 4 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

10.0 match 13.40 score 17k scripts 255 dependents

bioc

rBiopaxParser:Parses BioPax files and represents them in R

Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported.

Maintained by Frank Kramer. Last updated 5 months ago.

datarepresentation

22.0 match 10 stars 5.85 score 7 scripts

egeulgen

pathfindR.data:Data Package for 'pathfindR'

This is a data-only package, containing data needed to run the CRAN package 'pathfindR', a package for enrichment analysis utilizing active subnetworks. This package contains protein-protein interaction network data, data related to gene sets and example input/output data.

Maintained by Ege Ulgen. Last updated 11 months ago.

30.4 match 4.21 score 1 scripts 1 dependents

bioc

ROntoTools:R Onto-Tools suite

Suite of tools for functional analysis.

Maintained by Sorin Draghici. Last updated 5 months ago.

networkanalysis microarray graphsandnetworks

24.9 match 5.10 score 15 scripts 2 dependents

bioc

coRdon:Codon Usage Analysis and Prediction of Gene Expressivity

Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results.

Maintained by Anamaria Elek. Last updated 5 months ago.

software metagenomics geneexpression genesetenrichment geneprediction visualization kegg pathways genetics cellbiology biomedicalinformatics immunooncology

16.4 match 20 stars 7.71 score 48 scripts 1 dependents

jmanitz

kangar00:Kernel Approaches for Nonlinear Genetic Association Regression

Methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel).

Maintained by Juliane Manitz. Last updated 6 months ago.

34.7 match 2 stars 3.62 score 21 scripts

reimandlab

ActivePathways:Integrative Pathway Enrichment Analysis of Multivariate Omics Data

Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) <doi:10.1038/s41467-024-49986-4>.

Maintained by Juri Reimand. Last updated 8 months ago.

14.1 match 107 stars 8.61 score 35 scripts 2 dependents

bioc

cogena:co-expressed gene-set enrichment analysis

cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning.

Maintained by Zhilong Jia. Last updated 5 months ago.

clustering genesetenrichment geneexpression visualization pathways kegg go microarray sequencing systemsbiology datarepresentation dataimport bioconductor bioinformatics

16.4 match 12 stars 7.36 score 32 scripts

bioc

mirIntegrator:Integrating microRNA expression into signaling pathways for pathway analysis

Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels.

Maintained by Diana Diaz. Last updated 5 months ago.

network microarray graphandnetwork pathways kegg

36.4 match 1 stars 3.30 score 2 scripts

bioc

MIRit:Integrate microRNA and gene expression to decipher pathway complexity

MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization.

Maintained by Jacopo Ronchi. Last updated 18 hours ago.

software generegulation networkenrichment networkinference epigenetics functionalgenomics systemsbiology network pathways geneexpression differentialexpression mirna mirna-mrna-interaction mirna-seq mirnaseq-analysis cpp

29.8 match 4.00 score 2 scripts

bioc

rGREAT:GREAT Analysis - Functional Enrichment on Genomic Regions

GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions.

Maintained by Zuguang Gu. Last updated 2 days ago.

genesetenrichment go pathways software sequencing wholegenome genomeannotation coverage cpp

11.8 match 86 stars 9.96 score 320 scripts 1 dependents

bioc

famat:Functional analysis of metabolic and transcriptomic data

Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process.

Maintained by Mathieu Charles. Last updated 5 months ago.

functionalprediction genesetenrichment pathways go reactome kegg compound gene-ontology genes shiny

31.1 match 1 stars 3.78 score 2 scripts

bioc

PAST:Pathway Association Study Tool (PAST)

PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways.

Maintained by Thrash Adam. Last updated 5 months ago.

pathways genesetenrichment

20.6 match 5 stars 5.70 score 7 scripts

bioc

NoRCE:NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment

While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast.

Maintained by Gulden Olgun. Last updated 5 months ago.

biologicalquestion differentialexpression genomeannotation genesetenrichment genetarget genomeassembly go

25.4 match 1 stars 4.60 score 6 scripts

bioc

iSEEpathways:iSEE extension for panels related to pathway analysis

This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of pathway analysis results. This package does not perform pathway analysis. Instead, it provides methods to embed precomputed pathway analysis results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

software infrastructure differentialexpression geneexpression gui visualization pathways genesetenrichment go shinyapps bioconductor hacktoberfest isee iseeu

23.5 match 1 stars 4.95 score 10 scripts

bioc

CHRONOS:CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis

A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs.

Maintained by Panos Balomenos. Last updated 5 months ago.

systemsbiology graphandnetwork pathways kegg openjdk

30.2 match 3.86 score 12 scripts

bioc

GenomicSuperSignature:Interpretation of RNA-seq experiments through robust, efficient comparison to public databases

This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package.

Maintained by Sehyun Oh. Last updated 5 months ago.

transcriptomics systemsbiology principalcomponent rnaseq sequencing pathways clustering bioconductor-package exploratory-data-analysis gsea mesh principal-component-analysis rna-sequencing-profiles transferlearning

16.7 match 16 stars 6.97 score 59 scripts

bioc

sSNAPPY:Single Sample directioNAl Pathway Perturbation analYsis

A single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential direction of change. Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels.

Maintained by Wenjun Liu. Last updated 5 months ago.

software geneexpression genesetenrichment genesignaling

23.6 match 1 stars 4.83 score 15 scripts

tbates

umx:Structural Equation Modeling and Twin Modeling in R

Quickly create, run, and report structural equation models, and twin models. See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.

Maintained by Timothy C. Bates. Last updated 5 hours ago.

behavior-genetics genetics openmx psychology sem statistics structural-equation-modeling tutorials twin-models umx

12.0 match 44 stars 9.45 score 472 scripts

bioc

Category:Category Analysis

A collection of tools for performing category (gene set enrichment) analysis.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotation go pathways genesetenrichment

13.5 match 7.93 score 183 scripts 16 dependents

sssydysss

TransProR:Analysis and Visualization of Multi-Omics Data

A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.

Maintained by Dongyue Yu. Last updated 18 days ago.

14.1 match 174 stars 7.55 score 34 scripts

bioc

ReactomeGSA:Client for the Reactome Analysis Service for comparative multi-omics gene set analysis

The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited.

Maintained by Johannes Griss. Last updated 4 months ago.

genesetenrichment proteomics transcriptomics systemsbiology geneexpression reactome

13.1 match 23 stars 8.05 score 67 scripts

bioc

KEGGlincs:Visualize all edges within a KEGG pathway and overlay LINCS data

See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files.

Maintained by Shana White. Last updated 5 months ago.

networkinference geneexpression datarepresentation thirdpartyclient cellbiology graphandnetwork pathways kegg network

25.9 match 4.00 score 3 scripts

bioc

padma:Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis

Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles.

Maintained by Andrea Rau. Last updated 5 months ago.

software statisticalmethod principalcomponent geneexpression pathways rnaseq biocarta methylseq

20.5 match 3 stars 4.95 score 2 scripts

bioc

MPAC:Multi-omic Pathway Analysis of Cells

Multi-omic Pathway Analysis of Cells (MPAC), integrates multi-omic data for understanding cellular mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying key pathway proteins with potential clinical associations. From CNA and RNA-seq data, it determines genes’ DNA and RNA states (i.e., repressed, normal, or activated), which serve as the input for PARADIGM to calculate Inferred Pathway Levels (IPLs). It also permutes DNA and RNA states to create a background distribution to filter IPLs as a way to remove events observed by chance. It provides multiple methods for downstream analysis and visualization.

Maintained by Peng Liu. Last updated 15 hours ago.

software technology sequencing rnaseq survival clustering immunooncology

23.8 match 4.20 score 1 scripts

bioc

MetaboSignal:MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways

MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes.

Maintained by Andrea Rodriguez-Martinez. Last updated 5 months ago.

graphandnetwork genesignaling genetarget network pathways kegg reactome software

20.3 match 4.90 score 8 scripts

bioc

goseq:Gene Ontology analyser for RNA-seq and other length biased data

Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data.

Maintained by Federico Marini. Last updated 5 months ago.

immunooncology sequencing go geneexpression transcription rnaseq differentialexpression annotation genesetenrichment kegg pathways software

10.0 match 1 stars 9.67 score 636 scripts 9 dependents

bioc

NCIgraph:Pathways from the NCI Pathways Database

Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them.

Maintained by Laurent Jacob. Last updated 5 months ago.

pathways graphandnetwork

22.5 match 4.26 score 10 scripts 1 dependents

bioc

BulkSignalR:Infer Ligand-Receptor Interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics

Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results.

Maintained by Jean-Philippe Villemin. Last updated 3 months ago.

network rnaseq software proteomics transcriptomics networkinference spatial

18.3 match 5.22 score 15 scripts

bioc

metapone:Conducts pathway test of metabolomics data using a weighted permutation test

The package conducts pathway testing from untargetted metabolomics data. It requires the user to supply feature-level test results, from case-control testing, regression, or other suitable feature-level tests for the study design. Weights are given to metabolic features based on how many metabolites they could potentially match to. The package can combine positive and negative mode results in pathway tests.

Maintained by Tianwei Yu. Last updated 5 months ago.

technology massspectrometry metabolomics pathways

23.4 match 4.00 score 9 scripts

bioc

BiGGR:Constraint based modeling in R using metabolic reconstruction databases

This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs.

Maintained by Anand K. Gavai. Last updated 5 months ago.

systems biology pathway network graphandnetwork visualization metabolomics

20.0 match 4.67 score 58 scripts

bioc

progeny:Pathway RespOnsive GENes for activity inference from gene expression

PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics.

Maintained by Aurélien Dugourd. Last updated 5 months ago.

systemsbiology geneexpression functionalprediction generegulation

10.4 match 99 stars 8.90 score 221 scripts 1 dependents

t-grimes

dnapath:Differential Network Analysis using Gene Pathways

Integrates pathway information into the differential network analysis of two gene expression datasets as described in Grimes, Potter, and Datta (2019) <doi:10.1038/s41598-019-41918-3>. Provides summary functions to break down the results at the pathway, gene, or individual connection level. The differential networks for each pathway of interest can be plotted, and the visualization will highlight any differentially expressed genes and all of the gene-gene associations that are significantly differentially connected.

Maintained by Tyler Grimes. Last updated 22 days ago.

cpp

40.1 match 2.30 score 5 scripts

bioc

mitch:Multi-Contrast Gene Set Enrichment Analysis

mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments.

Maintained by Mark Ziemann. Last updated 4 months ago.

geneexpression genesetenrichment singlecell transcriptomics epigenetics proteomics differentialexpression reactome dnamethylation methylationarray gene-regulation gene-seq-analysis pathway-analysis

12.9 match 16 stars 7.06 score 15 scripts

bioc

NetPathMiner:NetPathMiner for Biological Network Construction, Path Mining and Visualization

NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation.

Maintained by Ahmed Mohamed. Last updated 4 months ago.

graphandnetwork pathways network clustering classification libsbml libxml2 openblas cpp

13.6 match 9 stars 6.56 score 9 scripts

hanjunwei-lab

MiRSEA:'MicroRNA' Set Enrichment Analysis

The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.

Maintained by Junwei Han. Last updated 5 years ago.

statistics pathways microrna enrichment analysis

19.5 match 4.51 score 16 scripts

bioc

miRNApath:miRNApath: Pathway Enrichment for miRNA Expression Data

This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes.

Maintained by James M. Ward. Last updated 5 months ago.

annotation pathways differentialexpression networkenrichment mirna

20.2 match 4.30 score 3 scripts

bioc

decoupleR:decoupleR: Ensemble of computational methods to infer biological activities from omics data

Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase.

Maintained by Pau Badia-i-Mompel. Last updated 5 months ago.

differentialexpression functionalgenomics geneexpression generegulation network software statisticalmethod transcription

7.7 match 230 stars 11.27 score 316 scripts 3 dependents

bioc

PCAN:Phenotype Consensus ANalysis (PCAN)

Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors).

Maintained by Matthew Page. Last updated 5 months ago.

annotation sequencing genetics functionalprediction variantannotation pathways network

20.5 match 4.15 score 7 scripts

bioc

MetaPhOR:Metabolic Pathway Analysis of RNA

MetaPhOR was developed to enable users to assess metabolic dysregulation using transcriptomic-level data (RNA-sequencing and Microarray data) and produce publication-quality figures. A list of differentially expressed genes (DEGs), which includes fold change and p value, from DESeq2 or limma, can be used as input, with sample size for MetaPhOR, and will produce a data frame of scores for each KEGG pathway. These scores represent the magnitude and direction of transcriptional change within the pathway, along with estimated p-values.MetaPhOR then uses these scores to visualize metabolic profiles within and between samples through a variety of mechanisms, including: bubble plots, heatmaps, and pathway models.

Maintained by Emily Isenhart. Last updated 5 months ago.

metabolomics rnaseq pathways geneexpression differentialexpression kegg sequencing microarray

20.9 match 4.00 score 1 scripts

moosa-r

rbioapi:User-Friendly R Interface to Biologic Web Services' API

Currently fully supports Enrichr, JASPAR, miEAA, PANTHER, Reactome, STRING, and UniProt! The goal of rbioapi is to provide a user-friendly and consistent interface to biological databases and services. In a way that insulates the user from the technicalities of using web services API and creates a unified and easy-to-use interface to biological and medical web services. This is an ongoing project; New databases and services will be added periodically. Feel free to suggest any databases or services you often use.

Maintained by Moosa Rezwani. Last updated 1 months ago.

api-client bioinformatics biology enrichment enrichment-analysis enrichr jaspar mieaa over-representation-analysis panther reactome string uniprot

11.0 match 20 stars 7.60 score 55 scripts

bioc

piano:Platform for integrative analysis of omics data

Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses.

Maintained by Leif Varemo Wigge. Last updated 5 months ago.

microarray preprocessing qualitycontrol differentialexpression visualization geneexpression genesetenrichment pathways bioconductor bioconductor-package bioinformatics gene-set-enrichment transcriptomics

10.0 match 13 stars 8.30 score 183 scripts 7 dependents

bioc

hypeR:An R Package For Geneset Enrichment Workflows

An R Package for Geneset Enrichment Workflows.

Maintained by Anthony Federico. Last updated 5 months ago.

genesetenrichment annotation pathways bioinformatics computational-biology geneset-enrichment-analysis

10.0 match 76 stars 8.22 score 145 scripts

bioc

ReactomeGraph4R:Interface for the Reactome Graph Database

Pathways, reactions, and biological entities in Reactome knowledge are systematically represented as an ordered network. Instances are represented as nodes and relationships between instances as edges; they are all stored in the Reactome Graph Database. This package serves as an interface to query the interconnected data from a local Neo4j database, with the aim of minimizing the usage of Neo4j Cypher queries.

Maintained by Chi-Lam Poon. Last updated 5 months ago.

dataimport pathways reactome network graphandnetwork

15.5 match 6 stars 5.26 score 6 scripts

bioc

pathRender:Render molecular pathways

build graphs from pathway databases, render them by Rgraphviz.

Maintained by Vince Carey. Last updated 5 months ago.

graphandnetwork pathways visualization

22.4 match 3.60 score 2 scripts

kharchenkolab

pagoda2:Single Cell Analysis and Differential Expression

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

scrna-seq single-cell single-cell-rna-seq transcriptomics openblas cpp openmp

10.0 match 222 stars 8.00 score 282 scripts

bioc

ChromSCape:Analysis of single-cell epigenomics datasets with a Shiny App

ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis.

Maintained by Pacome Prompsy. Last updated 5 months ago.

shinyapps software singlecell chipseq atacseq methylseq classification clustering epigenetics principalcomponent annotation batcheffect multiplecomparison normalization pathways preprocessing qualitycontrol reportwriting visualization genesetenrichment differentialpeakcalling epigenomics shiny single-cell cpp

13.7 match 14 stars 5.83 score 16 scripts

interstellar-consultation-services

covid19dbcand:Selected 'Drugbank' Drugs for COVID-19 Treatment Related Data in R Format

Provides different datasets parsed from 'Drugbank' <https://www.drugbank.ca/covid-19> database using 'dbparser' package. It is a smaller version from 'dbdataset' package. It contains only information about COVID-19 possible treatment.

Maintained by Mohammed Ali. Last updated 11 months ago.

dataset dbparser drugbank drugbank-database

17.6 match 3 stars 4.48 score 6 scripts

bioc

funOmics:Aggregating Omics Data into Higher-Level Functional Representations

The 'funOmics' package ggregates or summarizes omics data into higher level functional representations such as GO terms gene sets or KEGG metabolic pathways. The aggregated data matrix represents functional activity scores that facilitate the analysis of functional molecular sets while allowing to reduce dimensionality and provide easier and faster biological interpretations. Coordinated functional activity scores can be as informative as single molecules!

Maintained by Elisa Gomez de Lope. Last updated 5 months ago.

software transcriptomics metabolomics proteomics pathways go kegg

15.4 match 5 stars 5.10 score 3 scripts

bioc

iPath:iPath pipeline for detecting perturbed pathways at individual level

iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes.

Maintained by Kenong Su. Last updated 5 months ago.

pathways software geneexpression survival cpp

16.9 match 2 stars 4.60 score 3 scripts

bioc

rrvgo:Reduce + Visualize GO

Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity.

Maintained by Sergi Sayols. Last updated 5 months ago.

annotation clustering go network pathways software

10.0 match 24 stars 7.74 score 190 scripts

eltebioinformatics

mulea:Enrichment Analysis Using Multiple Ontologies and False Discovery Rate

Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Maintained by Tamas Stirling. Last updated 3 months ago.

annotation differentialexpression geneexpression genesetenrichment go graphandnetwork multiplecomparison pathways reactome software transcription visualization enrichment enrichment-analysis functional-enrichment-analysis gene-set-enrichment ontologies transcriptomics cpp

10.5 match 28 stars 7.36 score 34 scripts

bioc

sparrow:Take command of set enrichment analyses through a unified interface

Provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package.

Maintained by Steve Lianoglou. Last updated 3 months ago.

genesetenrichment pathways bioinformatics gsea

11.7 match 21 stars 6.58 score 13 scripts

bioc

annaffy:Annotation tools for Affymetrix biological metadata

Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria.

Maintained by Colin A. Smith. Last updated 5 months ago.

onechannel microarray annotation go pathways reportwriting

13.5 match 5.64 score 60 scripts 3 dependents

bioc

GlobalAncova:Global test for groups of variables via model comparisons

The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.

Maintained by Manuela Hummel. Last updated 5 months ago.

microarray onechannel differentialexpression pathways regression

14.0 match 5.32 score 9 scripts 1 dependents

bioc

epiNEM:epiNEM

epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens.

Maintained by Martin Pirkl. Last updated 5 months ago.

pathways systemsbiology networkinference network

12.8 match 1 stars 5.83 score 1 scripts 3 dependents

pyanglab

directPA:Direction Analysis for Pathways and Kinases

Direction analysis is a set of tools designed to identify combinatorial effects of multiple treatments/conditions on pathways and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics data. See Yang P et al (2014) <doi:10.1093/bioinformatics/btt616>; and Yang P et al. (2016) <doi:10.1002/pmic.201600068>.

Maintained by Pengyi Yang. Last updated 1 years ago.

17.5 match 4.14 score 31 scripts 1 dependents

bioc

cosmosR:COSMOS (Causal Oriented Search of Multi-Omic Space)

COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets.

Maintained by Attila Gabor. Last updated 5 months ago.

cellbiology pathways network proteomics metabolomics transcriptomics genesignaling data-integration metabolomic-data network-modelling phosphoproteomics

10.0 match 59 stars 7.22 score 35 scripts

bioc

limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 4 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

5.2 match 13.81 score 16k scripts 585 dependents

bioc

CellNOptR:Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data

This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network.

Maintained by Attila Gabor. Last updated 5 months ago.

cellbasedassays cellbiology proteomics pathways network timecourse immunooncology

10.5 match 6.72 score 98 scripts 6 dependents

pyanglab

ClueR:Cluster Evaluation

CLUster Evaluation (CLUE) is a computational method for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms and subsequently identify key kinases or pathways from each cluster. Its implementation in R is called ClueR. See README on <https://github.com/PYangLab/ClueR> for more details. P Yang et al. (2015) <doi:10.1371/journal.pcbi.1004403>.

Maintained by Pengyi Yang. Last updated 1 years ago.

16.5 match 10 stars 4.23 score 17 scripts

bioc

globaltest:Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing

The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms.

Maintained by Jelle Goeman. Last updated 5 months ago.

microarray onechannel bioinformatics differentialexpression go pathways

10.0 match 6.96 score 79 scripts 7 dependents

thermostats

RVA:RNAseq Visualization Automation

Automate downstream visualization & pathway analysis in RNAseq analysis. 'RVA' is a collection of functions that efficiently visualize RNAseq differential expression analysis result from summary statistics tables. It also utilize the Fisher's exact test to evaluate gene set or pathway enrichment in a convenient and efficient manner.

Maintained by Xingpeng Li. Last updated 3 years ago.

12.3 match 9 stars 5.65 score 6 scripts

olink-proteomics

OlinkAnalyze:Facilitate Analysis of Proteomic Data from Olink

A collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.

Maintained by Kathleen Nevola. Last updated 19 days ago.

olink proteomics proteomics-data-analysis

7.2 match 104 stars 9.72 score 61 scripts

bioc

gatom:Finding an Active Metabolic Module in Atom Transition Network

This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.

Maintained by Alexey Sergushichev. Last updated 5 months ago.

geneexpression differentialexpression pathways network

13.2 match 6 stars 5.26 score 8 scripts

bioc

GOstats:Tools for manipulating GO and microarrays

A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotation go multiplecomparison geneexpression microarray pathways genesetenrichment graphandnetwork

10.0 match 6.93 score 528 scripts 12 dependents

bioc

TRONCO:TRONCO, an R package for TRanslational ONCOlogy

The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC).

Maintained by Luca De Sano. Last updated 5 months ago.

biomedicalinformatics bayesian graphandnetwork somaticmutation networkinference network clustering dataimport singlecell immunooncology algorithms cancer-inference tumors

10.5 match 30 stars 6.50 score 38 scripts

bioc

CBNplot:plot bayesian network inferred from gene expression data based on enrichment analysis results

This package provides the visualization of bayesian network inferred from gene expression data. The networks are based on enrichment analysis results inferred from packages including clusterProfiler and ReactomePA. The networks between pathways and genes inside the pathways can be inferred and visualized.

Maintained by Noriaki Sato. Last updated 5 months ago.

visualization bayesian geneexpression networkinference pathways reactome network networkenrichment genesetenrichment

10.8 match 62 stars 6.27 score 9 scripts

bioc

GOexpress:Visualise microarray and RNAseq data using gene ontology annotations

The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

software geneexpression transcription differentialexpression genesetenrichment datarepresentation clustering timecourse microarray sequencing rnaseq annotation multiplecomparison pathways go visualization immunooncology bioconductor bioconductor-package bioconductor-stats geneontology geneset-enrichment

10.0 match 9 stars 6.75 score 31 scripts

bioc

pairkat:PaIRKAT

PaIRKAT is model framework for assessing statistical relationships between networks of metabolites (pathways) and an outcome of interest (phenotype). PaIRKAT queries the KEGG database to determine interactions between metabolites from which network connectivity is constructed. This model framework improves testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. PaIRKAT uses known biological connections between high dimensional variables by representing them as edges of ‘graphs’ or ‘networks.’ It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or ‘smoothing’ approach for managing this issue.

Maintained by Max McGrath. Last updated 5 months ago.

software metabolomics kegg pathways network graphandnetwork regression

16.8 match 4.00 score 1 scripts

bioc

PathNet:An R package for pathway analysis using topological information

PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10.

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

pathways differentialexpression multiplecomparison kegg networkenrichment network

15.6 match 4.30 score 5 scripts

bioc

categoryCompare:Meta-analysis of high-throughput experiments using feature annotations

Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).

Maintained by Robert M. Flight. Last updated 5 months ago.

annotation go multiplecomparison pathways geneexpression bioconductor

10.0 match 6 stars 6.68 score

bioc

bioCancer:Interactive Multi-Omics Cancers Data Visualization and Analysis

This package is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data.

Maintained by Karim Mezhoud. Last updated 5 months ago.

gui datarepresentation network multiplecomparison pathways reactome visualization geneexpression genetarget analysis biocancer-interface cancer cancer-studies rmarkdown

11.2 match 20 stars 5.95 score 7 scripts

plangfelder

WGCNA:Weighted Correlation Network Analysis

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

Maintained by Peter Langfelder. Last updated 6 months ago.

cpp

6.9 match 54 stars 9.65 score 5.3k scripts 32 dependents

bioc

ADAM:ADAM: Activity and Diversity Analysis Module

ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes).

Maintained by Jose Luiz Rybarczyk Filho. Last updated 5 months ago.

genesetenrichment pathways kegg geneexpression microarray cpp

13.9 match 4.78 score 8 scripts 1 dependents

bioc

Moonlight2R:Identify oncogenes and tumor suppressor genes from omics data

The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes.

Maintained by Matteo Tiberti. Last updated 2 months ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network survival genesetenrichment networkenrichment

10.0 match 5 stars 6.59 score 43 scripts

bioc

MoonlightR:Identify oncogenes and tumor suppressor genes from omics data

Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.

Maintained by Matteo Tiberti. Last updated 5 months ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network survival genesetenrichment networkenrichment

10.0 match 17 stars 6.57 score

bioc

GSEABenchmarkeR:Reproducible GSEA Benchmarking

The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated.

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

immunooncology microarray rnaseq geneexpression differentialexpression pathways graphandnetwork network genesetenrichment networkenrichment visualization reportwriting bioconductor-package u24ca289073

10.0 match 13 stars 6.55 score 23 scripts

bioc

SiPSiC:Calculate Pathway Scores for Each Cell in scRNA-Seq Data

Infer biological pathway activity of cells from single-cell RNA-sequencing data by calculating a pathway score for each cell (pathway genes are specified by the user). It is recommended to have the data in Transcripts-Per-Million (TPM) or Counts-Per-Million (CPM) units for best results. Scores may change when adding cells to or removing cells off the data. SiPSiC stands for Single Pathway analysis in Single Cells.

Maintained by Daniel Davis. Last updated 5 months ago.

software differentialexpression genesetenrichment biomedicalinformatics cellbiology transcriptomics rnaseq singlecell transcription sequencing immunooncology dataimport

12.6 match 6 stars 5.18 score 3 scripts

bioc

ndexr:NDEx R client library

This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation.

Maintained by Florian Auer. Last updated 5 months ago.

pathways dataimport network

10.0 match 9 stars 6.44 score 38 scripts

bioc

pathifier:Quantify deregulation of pathways in cancer

Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample.

Maintained by Assif Yitzhaky. Last updated 5 months ago.

network

12.7 match 5.07 score 28 scripts 1 dependents

bioc

ssPATHS:ssPATHS: Single Sample PATHway Score

This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples.

Maintained by Natalie R. Davidson. Last updated 5 months ago.

software geneexpression biomedicalinformatics rnaseq pathways transcriptomics dimensionreduction classification

15.8 match 4.00 score 1 scripts

bioc

RCX:R package implementing the Cytoscape Exchange (CX) format

Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape.

Maintained by Florian Auer. Last updated 5 months ago.

pathways dataimport network

10.0 match 8 stars 6.28 score 10 scripts 1 dependents

bioc

scde:Single Cell Differential Expression

The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).

Maintained by Evan Biederstedt. Last updated 5 months ago.

immunooncology rnaseq statisticalmethod differentialexpression bayesian transcription software analysis bioinformatics heterogenity ngs single-cell transcriptomics openblas cpp openmp

8.2 match 173 stars 7.53 score 141 scripts

bioc

MLP:Mean Log P Analysis

Pathway analysis based on p-values associated to genes from a genes expression analysis of interest. Utility functions enable to extract pathways from the Gene Ontology Biological Process (GOBP), Molecular Function (GOMF) and Cellular Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG) and Reactome databases. Methodology, and helper functions to display the results as a table, barplot of pathway significance, Gene Ontology graph and pathway significance are available.

Maintained by Tobias Verbeke. Last updated 5 months ago.

genetics geneexpression pathways reactome kegg go

12.8 match 4.78 score 4 scripts 1 dependents

bioc

mgsa:Model-based gene set analysis

Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology.

Maintained by Sebastian Bauer. Last updated 5 months ago.

pathways go genesetenrichment openmp

10.0 match 5 stars 6.08 score 12 scripts

bioc

CEMiTool:Co-expression Modules identification Tool

The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network.

Maintained by Helder Nakaya. Last updated 5 months ago.

geneexpression transcriptomics graphandnetwork mrnamicroarray rnaseq network networkenrichment pathways immunooncology

10.5 match 5.76 score 38 scripts

bioc

CARNIVAL:A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming

An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated.

Maintained by Attila Gabor. Last updated 5 months ago.

transcriptomics geneexpression network causal-models footprints integer-linear-programming pathway-enrichment-analysis

6.7 match 57 stars 9.03 score 90 scripts 1 dependents

mrcieu

epigraphdb:Interface Package for the 'EpiGraphDB' Platform

The interface package to access data from the 'EpiGraphDB' <https://epigraphdb.org> platform. It provides easy access to the 'EpiGraphDB' platform with functions that query the corresponding REST endpoints on the API <https://api.epigraphdb.org> and return the response data in the 'tibble' data frame format.

Maintained by Yi Liu. Last updated 3 years ago.

api-client bioinformatics epidemiology graph-database mendelian-randomization phenotypes

10.0 match 27 stars 6.02 score 13 scripts

bioc

oppar:Outlier profile and pathway analysis in R

The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis.

Maintained by Soroor Hediyeh zadeh. Last updated 5 months ago.

pathways genesetenrichment systemsbiology geneexpression software

18.2 match 3.30 score 3 scripts

cran

TPEA:A Novel Topology-Based Pathway Enrichment Analysis Approach

We described a novel Topology-based pathway enrichment analysis, which integrated the global position of the nodes and the topological property of the pathways in Kyoto Encyclopedia of Genes and Genomes Database. We also provide some functions to obtain the latest information about pathways to finish pathway enrichment analysis using this method.

Maintained by Wei Jiang. Last updated 8 years ago.

40.5 match 1 stars 1.48 score 1 dependents

bioc

escape:Easy single cell analysis platform for enrichment

A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells.

Maintained by Nick Borcherding. Last updated 2 months ago.

software singlecell classification annotation genesetenrichment sequencing genesignaling pathways

10.0 match 5.92 score 138 scripts

bioc

BLMA:BLMA: A package for bi-level meta-analysis

Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis.

Maintained by Van-Dung Pham. Last updated 5 months ago.

genesetenrichment pathways differentialexpression microarray

14.1 match 4.18 score 51 scripts

bioc

dce:Pathway Enrichment Based on Differential Causal Effects

Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.

Maintained by Kim Philipp Jablonski. Last updated 3 months ago.

software statisticalmethod graphandnetwork regression geneexpression differentialexpression networkenrichment network kegg bioconductor causality

12.8 match 13 stars 4.59 score 4 scripts

bioc

bnem:Training of logical models from indirect measurements of perturbation experiments

bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate).

Maintained by Martin Pirkl. Last updated 5 months ago.

pathways systemsbiology networkinference network geneexpression generegulation preprocessing

12.7 match 2 stars 4.60 score 5 scripts

hanjunwei-lab

LncPath:Identifying the Pathways Regulated by LncRNA Sets of Interest

Identifies pathways synergisticly regulated by the interested lncRNA(long non-coding RNA) sets based on a lncRNA-mRNA(messenger RNA) interaction network. 1) The lncRNA-mRNA interaction network was built from the protein-protein interactions and the lncRNA-mRNA co-expression relationships in 28 RNA-Seq data sets. 2) The interested lncRNAs can be mapped into networks as seed nodes and a random walk strategy will be performed to evaluate the rate of each coding genes influenced by the seed lncRNAs. 3) Pathways regulated by the lncRNA set will be evaluated by a weighted Kolmogorov-Smirnov statistic as an ES Score. 4) The p value and false discovery rate value will also be calculated through a permutation analysis. 5) The running score of each pathway can be plotted and the heat map of each pathway can also be plotted if an expression profile is provided. 6) The rank and scores of the gene list of each pathway can be printed.

Maintained by Junwei Han. Last updated 5 years ago.

12.9 match 1 stars 4.52 score 11 scripts

bioc

EGSEA:Ensemble of Gene Set Enrichment Analyses

This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set.

Maintained by Monther Alhamdoosh. Last updated 5 months ago.

immunooncology differentialexpression go geneexpression genesetenrichment genetics microarray multiplecomparison onechannel pathways rnaseq sequencing software systemsbiology twochannel metabolomics proteomics kegg graphandnetwork genesignaling genetarget networkenrichment network classification

10.0 match 5.81 score 64 scripts

bioc

GWENA:Pipeline for augmented co-expression analysis

The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions.

Maintained by Gwenaëlle Lemoine. Last updated 5 months ago.

software geneexpression network clustering graphandnetwork genesetenrichment pathways visualization rnaseq transcriptomics mrnamicroarray microarray networkenrichment sequencing go co-expression enrichment-analysis gene network-analysis pipeline

10.0 match 24 stars 5.76 score 12 scripts

bioc

qpgraph:Estimation of genetic and molecular regulatory networks from high-throughput genomics data

Estimate gene and eQTL networks from high-throughput expression and genotyping assays.

Maintained by Robert Castelo. Last updated 4 days ago.

microarray geneexpression transcription pathways networkinference graphandnetwork generegulation genetics geneticvariability snp software openblas

10.0 match 5.75 score 20 scripts 3 dependents

bioc

EmpiricalBrownsMethod:Uses Brown's method to combine p-values from dependent tests

Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments.

Maintained by David Gibbs. Last updated 5 months ago.

statisticalmethod geneexpression pathways

10.0 match 25 stars 5.65 score 7 scripts 3 dependents

bioc

mnem:Mixture Nested Effects Models

Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.

Maintained by Martin Pirkl. Last updated 4 months ago.

pathways systemsbiology networkinference network rnaseq pooledscreens singlecell crispr atacseq dnaseq geneexpression cpp

10.0 match 4 stars 5.64 score 15 scripts 4 dependents

bioc

TMSig:Tools for Molecular Signatures

The TMSig package contains tools to prepare, analyze, and visualize named lists of sets, with an emphasis on molecular signatures (such as gene or kinase sets). It includes fast, memory efficient functions to construct sparse incidence and similarity matrices and filter, cluster, invert, and decompose sets. Additionally, bubble heatmaps can be created to visualize the results of any differential or molecular signatures analysis.

Maintained by Tyler Sagendorf. Last updated 5 months ago.

clustering genesetenrichment graphandnetwork pathways visualization gene-sets molecular-signatures

10.0 match 4 stars 5.60 score 4 scripts

bioc

safe:Significance Analysis of Function and Expression

SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions.

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

differentialexpression pathways genesetenrichment statisticalmethod software

10.0 match 5.60 score 32 scripts 5 dependents

sachaepskamp

qgraph:Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation

Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.

Maintained by Sacha Epskamp. Last updated 1 years ago.

cpp

4.9 match 69 stars 11.43 score 1.2k scripts 63 dependents

bioc

GeDi:Defining and visualizing the distances between different genesets

The package provides different distances measurements to calculate the difference between genesets. Based on these scores the genesets are clustered and visualized as graph. This is all presented in an interactive Shiny application for easy usage.

Maintained by Annekathrin Nedwed. Last updated 5 months ago.

gui genesetenrichment software transcription rnaseq visualization clustering pathways reportwriting go kegg reactome shinyapps

10.0 match 1 stars 5.52 score 22 scripts

bioc

MetaboDynamics:Bayesian analysis of longitudinal metabolomics data

MetaboDynamics is an R-package that provides a framework of probabilistic models to analyze longitudinal metabolomics data. It enables robust estimation of mean concentrations despite varying spread between timepoints and reports differences between timepoints as well as metabolite specific dynamics profiles that can be used for identifying "dynamics clusters" of metabolites of similar dynamics. Provides probabilistic over-representation analysis of KEGG functional modules and pathways as well as comparison between clusters of different experimental conditions.

Maintained by Katja Danielzik. Last updated 11 hours ago.

software metabolomics bayesian functionalprediction multiplecomparison kegg pathways dynamics functional-analysis longitudinal-analysis metabolomics-data metabolomics-pipeline cpp

10.5 match 5 stars 5.24 score 3 scripts

bioc

goProfiles:goProfiles: an R package for the statistical analysis of functional profiles

The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'.

Maintained by Alex Sanchez. Last updated 5 months ago.

annotation go geneexpression genesetenrichment graphandnetwork microarray multiplecomparison pathways software

10.0 match 5.48 score 6 scripts 1 dependents

hanjunwei-lab

ssMutPA:Single-Sample Mutation-Based Pathway Analysis

A systematic bioinformatics tool to perform single-sample mutation-based pathway analysis by integrating somatic mutation data with the Protein-Protein Interaction (PPI) network. In this method, we use local and global weighted strategies to evaluate the effects of network genes from mutations according to the network topology and then calculate the mutation-based pathway enrichment score (ssMutPES) to reflect the accumulated effect of mutations of each pathway. Subsequently, the ssMutPES profiles are used for unsupervised spectral clustering to identify cancer subtypes.

Maintained by Junwei Han. Last updated 5 months ago.

13.7 match 4.00 score 9 scripts

bioc

gsean:Gene Set Enrichment Analysis with Networks

Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis.

Maintained by Dongmin Jung. Last updated 5 months ago.

software statisticalmethod network graphandnetwork genesetenrichment geneexpression networkenrichment pathways differentialexpression

13.6 match 4.00 score 1 scripts

bioc

SurfR:Surface Protein Prediction and Identification

Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots.

Maintained by Aurora Maurizio. Last updated 16 hours ago.

software sequencing rnaseq geneexpression transcription differentialexpression principalcomponent genesetenrichment pathways batcheffect functionalgenomics visualization dataimport functionalprediction geneprediction go dge enrichment-analysis metaanalysis plots proteins public-data surface surfaceome

10.0 match 3 stars 5.43 score 3 scripts

cran

BioM2:Biologically Explainable Machine Learning Framework

Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.

Maintained by Shunjie Zhang. Last updated 24 days ago.

20.4 match 2.65 score 9 scripts

samaneh-bioinformatics

BNrich:Pathway Enrichment Analysis Based on Bayesian Network

Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters.

Maintained by Samaneh Maleknia. Last updated 5 years ago.

networkenrichment geneexpression pathways bayesian kegg

18.1 match 3.00 score

hanjunwei-lab

pathwayTMB:Pathway Based Tumor Mutational Burden

A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy.

Maintained by Junwei Han. Last updated 3 years ago.

21.8 match 2.48 score 2 scripts 1 dependents

bioc

DeepTarget:Deep characterization of cancer drugs

This package predicts a drug’s primary target(s) or secondary target(s) by integrating large-scale genetic and drug screens from the Cancer Dependency Map project run by the Broad Institute. It further investigates whether the drug specifically targets the wild-type or mutated target forms. To show how to use this package in practice, we provided sample data along with step-by-step example.

Maintained by Trinh Nguyen. Last updated 5 months ago.

genetarget geneprediction pathways geneexpression rnaseq immunooncology differentialexpression genesetenrichment reportwriting crispr

11.7 match 4.54 score 1 scripts

bioc

MultiRNAflow:An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions

Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time.

Maintained by Rodolphe Loubaton. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription timecourse preprocessing visualization normalization principalcomponent clustering differentialexpression genesetenrichment pathways

10.0 match 6 stars 5.26 score 4 scripts

ingorohlfing

MMRcaseselection:Case Classification and Selection Based on Regression Results

Researchers doing a mixed-methods analysis (nested analysis as developed by Lieberman (2005) <doi:10.1017/S0003055405051762>) can use the package for the classification of cases and case selection using results of a linear regression. One can designate cases as typical, deviant, extreme and pathway case and use different case selection strategies for the choice of a case belonging to one of these types.

Maintained by Ingo Rohlfing. Last updated 3 years ago.

11.7 match 1 stars 4.38 score 12 scripts

bioc

cTRAP:Identification of candidate causal perturbations from differential gene expression data

Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations.

Maintained by Nuno Saraiva-Agostinho. Last updated 5 months ago.

differentialexpression geneexpression rnaseq transcriptomics pathways immunooncology genesetenrichment bioconductor bioinformatics cmap gene-expression l1000

10.0 match 5 stars 5.08 score 16 scripts

bioc

fobitools:Tools for Manipulating the FOBI Ontology

A set of tools for interacting with the Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data.

Maintained by Pol Castellano-Escuder. Last updated 4 months ago.

massspectrometry metabolomics software visualization biomedicalinformatics graphandnetwork annotation cheminformatics pathways genesetenrichment biological-intrerpretation biological-knowledge biological-significance-analysis enrichment-analysis food-biomarker-ontology knowledge-graph nutrition obofoundry ontology text-mining

10.0 match 1 stars 5.08 score 5 scripts

bioc

clipper:Gene Set Analysis Exploiting Pathway Topology

Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.

Maintained by Paolo Martini. Last updated 5 months ago.

10.8 match 4.66 score 19 scripts

bioc

DEsubs:DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments

DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases.

Maintained by Aristidis G. Vrahatis. Last updated 5 months ago.

systemsbiology graphandnetwork pathways kegg geneexpression networkenrichment network rnaseq differentialexpression normalization immunooncology

13.3 match 3.78 score 1 scripts

bioc

DART:Denoising Algorithm based on Relevance network Topology

Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples.

Maintained by Charles Shijie Zheng. Last updated 5 months ago.

geneexpression differentialexpression graphandnetwork pathways

11.6 match 4.30 score 1 scripts

hanjunwei-lab

PMAPscore:Identify Prognosis-Related Pathways Altered by Somatic Mutation

We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).

Maintained by Junwei Han. Last updated 3 years ago.

13.4 match 3.70 score 2 scripts

bioc

easier:Estimate Systems Immune Response from RNA-seq data

This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy.

Maintained by Oscar Lapuente-Santana. Last updated 5 months ago.

geneexpression software transcription systemsbiology pathways genesetenrichment immunooncology epigenetics classification biomedicalinformatics regression experimenthubsoftware

11.7 match 4.20 score 16 scripts

bioc

Path2PPI:Prediction of pathway-related protein-protein interaction networks

Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins.

Maintained by Oliver Philipp. Last updated 5 months ago.

networkinference systemsbiology network proteomics pathways

14.9 match 3.30 score 1 scripts

bioc

clustifyr:Classifier for Single-cell RNA-seq Using Cell Clusters

Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment.

Maintained by Rui Fu. Last updated 5 months ago.

singlecell annotation sequencing microarray geneexpression assign-identities clusters marker-genes rna-seq single-cell-rna-seq

5.0 match 119 stars 9.63 score 296 scripts

bioc

phantasus:Visual and interactive gene expression analysis

Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported.

Maintained by Alexey Sergushichev. Last updated 5 months ago.

geneexpression gui visualization datarepresentation transcriptomics rnaseq microarray normalization clustering differentialexpression principalcomponent immunooncology

6.2 match 43 stars 7.68 score 15 scripts

bioc

SBMLR:SBML-R Interface and Analysis Tools

This package contains a systems biology markup language (SBML) interface to R.

Maintained by Tomas Radivoyevitch. Last updated 5 months ago.

graphandnetwork pathways network

10.0 match 4.73 score 45 scripts

bioc

MethylMix:MethylMix: Identifying methylation driven cancer genes

MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8.

Maintained by Olivier Gevaert. Last updated 5 months ago.

dnamethylation statisticalmethod differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network

10.0 match 4.72 score 26 scripts

bioc

rsbml:R support for SBML, using libsbml

Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models.

Maintained by Michael Lawrence. Last updated 17 days ago.

graphandnetwork pathways network libsbml cpp

10.0 match 4.71 score 19 scripts 1 dependents

xinghuq

DA:Discriminant Analysis for Evolutionary Inference

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.

Maintained by Xinghu Qin. Last updated 4 years ago.

biomedicalinformatics chipseq clustering coverage dnamethylation differentialexpression differentialmethylation software differentialsplicing epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways qualitycontrol rnaseq regression sage sequencing systemsbiology timecourse transcription transcriptomics dapc discriminant-analysis ecological kernel kernel-local kernel-principle-components population-structure-inference principal-components

10.0 match 1 stars 4.70 score 1 scripts

bioc

tidysbml:Extract SBML's data into dataframes

Starting from one SBML file, it extracts information from each listOfCompartments, listOfSpecies and listOfReactions element by saving them into data frames. Each table provides one row for each entity (i.e. either compartment, species, reaction or speciesReference) and one set of columns for the attributes, one column for the content of the 'notes' subelement and one set of columns for the content of the 'annotation' subelement.

Maintained by Veronica Paparozzi. Last updated 5 months ago.

graphandnetwork network pathways software

10.0 match 1 stars 4.65 score 2 scripts

bioc

FELLA:Interpretation and enrichment for metabolomics data

Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported.

Maintained by Sergio Picart-Armada. Last updated 5 months ago.

software metabolomics graphandnetwork kegg go pathways network networkenrichment

10.5 match 4.41 score 32 scripts

bioc

FGNet:Functional Gene Networks derived from biological enrichment analyses

Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO.

Maintained by Sara Aibar. Last updated 5 months ago.

annotation go pathways genesetenrichment network visualization functionalgenomics networkenrichment clustering

10.0 match 4.62 score 5 scripts 1 dependents

bioc

nempi:Inferring unobserved perturbations from gene expression data

Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.

Maintained by Martin Pirkl. Last updated 5 months ago.

software geneexpression differentialexpression differentialmethylation genesignaling pathways network classification neuralnetwork networkinference atacseq dnaseq rnaseq pooledscreens crispr singlecell systemsbiology

10.0 match 2 stars 4.60 score 2 scripts

bioc

SPIA:Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations

This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study.

Maintained by Adi Laurentiu Tarca. Last updated 2 months ago.

microarray graphandnetwork

6.9 match 6.62 score 113 scripts 4 dependents

bioc

NetActivity:Compute gene set scores from a deep learning framework

#' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models.

Maintained by Carlos Ruiz-Arenas. Last updated 5 months ago.

rnaseq microarray transcription functionalgenomics go geneexpression pathways software

10.0 match 4.59 score 26 scripts

bioc

goSorensen:Statistical inference based on the Sorensen-Dice dissimilarity and the Gene Ontology (GO)

This package implements inferential methods to compare gene lists in terms of their biological meaning as expressed in the GO. The compared gene lists are characterized by cross-tabulation frequency tables of enriched GO items. Dissimilarity between gene lists is evaluated using the Sorensen-Dice index. The fundamental guiding principle is that two gene lists are taken as similar if they share a great proportion of common enriched GO items.

Maintained by Pablo Flores. Last updated 5 months ago.

annotation go genesetenrichment software microarray pathways geneexpression multiplecomparison graphandnetwork reactome clustering kegg

10.0 match 4.56 score 12 scripts

jranke

mkin:Kinetic Evaluation of Chemical Degradation Data

Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using automatically generated C functions. Non-constant errors can be taken into account using variance by variable or two-component error models <doi:10.3390/environments6120124>. Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end <doi:10.3390/environments8080071>. Please note that no warranty is implied for correctness of results or fitness for a particular purpose.

Maintained by Johannes Ranke. Last updated 29 days ago.

degradation focus-kinetics kinetic-models kinetics ode ode-model

5.6 match 11 stars 8.06 score 78 scripts 1 dependents

bioc

KnowSeq:KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline

KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study.

Maintained by Daniel Castillo-Secilla. Last updated 5 months ago.

geneexpression differentialexpression genesetenrichment dataimport classification featureextraction sequencing rnaseq batcheffect normalization preprocessing qualitycontrol genetics transcriptomics microarray alignment pathways systemsbiology go immunooncology

13.7 match 3.30 score 5 scripts

bioc

PPInfer:Inferring functionally related proteins using protein interaction networks

Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions.

Maintained by Dongmin Jung. Last updated 5 months ago.

software statisticalmethod network graphandnetwork genesetenrichment networkenrichment pathways

10.0 match 4.48 score 4 scripts 1 dependents

bioc

ADAMgui:Activity and Diversity Analysis Module Graphical User Interface

ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function.

Maintained by Jose Luiz Rybarczyk Filho. Last updated 5 months ago.

genesetenrichment pathways kegg

13.5 match 3.30 score 1 scripts

mikehellstern

netgsa:Network-Based Gene Set Analysis

Carry out Network-based Gene Set Analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>

Maintained by Michael Hellstern. Last updated 3 years ago.

cpp

11.8 match 4 stars 3.75 score 28 scripts

bioc

transomics2cytoscape:A tool set for 3D Trans-Omic network visualization with Cytoscape

transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4).

Maintained by Kozo Nishida. Last updated 5 months ago.

network software pathways dataimport kegg

11.0 match 4.00 score 2 scripts

openbiox

UCSCXenaShiny:Interactive Analysis of UCSC Xena Data

Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.

Maintained by Shixiang Wang. Last updated 4 months ago.

cancer-dataset shiny-apps ucsc-xena

5.1 match 96 stars 8.54 score 35 scripts

bioc

wppi:Weighting protein-protein interactions

Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm.

Maintained by Ana Galhoz. Last updated 5 months ago.

graphandnetwork network pathways software genesignaling genetarget systemsbiology transcriptomics annotation gene-ontology gene-prioritization human-phenotype-ontology omnipath ppi-networks random-walk-with-restart quarto

10.0 match 1 stars 4.30 score 4 scripts

xiaozhangryy

CAESAR.Suite:CAESAR: a Cross-Technology and Cross-Resolution Framework for Spatial Omics Annotation

Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types.

Maintained by Xiao Zhang. Last updated 6 months ago.

openblas cpp openmp

9.8 match 4.30 score 2 scripts

bioc

qusage:qusage: Quantitative Set Analysis for Gene Expression

This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu)

Maintained by Christopher Bolen. Last updated 5 months ago.

genesetenrichment microarray rnaseq software immunooncology

7.5 match 5.65 score 185 scripts 1 dependents

juananvg

GSEMA:Gene Set Enrichment Meta-Analysis

Performing the different steps of gene set enrichment meta-analysis. It provides different functions that allow the application of meta-analysis based on the combination of effect sizes from different pathways in different studies to obtain significant pathways that are common to all of them.

Maintained by Juan Antonio Villatoro-García. Last updated 5 months ago.

statisticalmethod genesetenrichment pathways

10.8 match 3.90 score 3 scripts

sysbiolab

PathwaySpace:Spatial Projection of Network Signals along Geodesic Paths

For a given graph containing vertices, edges, and a signal associated with the vertices, the 'PathwaySpace' package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package then uses a decay function to project these signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' could have various applications, such as visualizing and analyzing network data in a graphical format that highlights the relationships and signal strengths between vertices. It can be particularly useful for understanding the influence of signals through complex networks. By combining graph theory, signal processing, and visualization, the 'PathwaySpace' package provides a novel way of representing and analyzing graph data.

Maintained by Mauro Castro. Last updated 2 months ago.

bioinformatics biological-networks graph

8.6 match 2 stars 4.85 score 5 scripts

bioc

singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.

Maintained by Joshua David Campbell. Last updated 22 days ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

4.1 match 181 stars 10.16 score 252 scripts

bioc

attract:Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape

This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression.

Maintained by Samuel Zimmerman. Last updated 5 months ago.

immunooncology kegg reactome geneexpression pathways genesetenrichment microarray rnaseq

12.4 match 3.30 score 4 scripts

egeulgen

driveR:Prioritizing Cancer Driver Genes Using Genomics Data

Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations. 'driveR' is a tool for personalized or batch analysis of genomic data for driver gene prioritization by combining genomic information and prior biological knowledge. As features, 'driveR' uses coding impact metaprediction scores, non-coding impact scores, somatic copy number alteration scores, hotspot gene/double-hit gene condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG pathways. It uses these features to estimate cancer-type-specific probability for each gene of being a cancer driver using the related task of a multi-task learning classification model. The method is described in detail in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics <doi:10.1186/s12859-021-04203-7>.

Maintained by Ege Ulgen. Last updated 2 years ago.

cancer-driverness driver driver-gene-prioritization identify-driver-genes ranking-genes scoring

6.5 match 15 stars 6.29 score 260 scripts

cran

topologyGSA:Gene Set Analysis Exploiting Pathway Topology

Using Gaussian graphical models we propose a novel approach to perform pathway analysis using gene expression. Given the structure of a graph (a pathway) we introduce two statistical tests to compare the mean and the concentration matrices between two groups. Specifically, these tests can be performed on the graph and on its connected components (cliques). The package is based on the method described in Massa M.S., Chiogna M., Romualdi C. (2010) <doi:10.1186/1752-0509-4-121>.

Maintained by Gabriele Sales. Last updated 1 years ago.

25.5 match 1.60 score

bioc

rsemmed:An interface to the Semantic MEDLINE database

A programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths.

Maintained by Leslie Myint. Last updated 5 months ago.

software annotation pathways systemsbiology

10.0 match 4.00 score 8 scripts

bioc

PhenoGeneRanker:PhenoGeneRanker: A gene and phenotype prioritization tool

This package is a gene/phenotype prioritization tool that utilizes multiplex heterogeneous gene phenotype network. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p-values of gene/phenotype ranking using random stratified sampling of genes/phenotypes based on their connectivity degree in the network. https://dl.acm.org/doi/10.1145/3307339.3342155.

Maintained by Cagatay Dursun. Last updated 5 months ago.

biomedicalinformatics geneprediction graphandnetwork network networkinference pathways software systemsbiology

10.0 match 4.00 score 1 scripts

bioc

GSReg:Gene Set Regulation (GS-Reg)

A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA).

Maintained by Bahman Afsari. Last updated 5 months ago.

generegulation pathways geneexpression geneticvariability genesetenrichment alternativesplicing

10.0 match 3.98 score 16 scripts

nilsmechtel

MetAlyzer:Read and Analyze 'MetIDQ™' Software Output Files

The 'MetAlyzer' S4 object provides methods to read and reformat metabolomics data for convenient data handling, statistics and downstream analysis. The resulting format corresponds to input data of the Shiny app 'MetaboExtract' (<https://www.metaboextract.shiny.dkfz.de/MetaboExtract/>).

Maintained by Nils Mechtel. Last updated 3 months ago.

7.2 match 3 stars 5.35 score 4 scripts

bioc

LiquidAssociation:LiquidAssociation

The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data.

Maintained by Yen-Yi Ho. Last updated 5 months ago.

pathways geneexpression cellbiology genetics network timecourse

10.0 match 3.78 score 3 scripts 1 dependents