Showing 156 of total 156 results (show query)
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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.
annotationclusteringgenesetenrichmentgokeggmultiplecomparisonpathwaysreactomevisualizationenrichment-analysisgsea
1.1k stars 17.03 score 11k scripts 48 dependentsbioc
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 10 days ago.
geneexpressiondifferentialexpressiongenesetenrichmentpathwayscpp
392 stars 16.31 score 3.9k scripts 101 dependentsbioc
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 3 months ago.
annotationgenesetenrichmentgokeggpathwayssoftwarevisualizationenrichment-analysispathway-analysis
239 stars 15.71 score 3.1k scripts 58 dependentsbioc
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.
annotationvisualizationmultiplecomparisongenesetenrichmentpathwayssoftwaredisease-ontologyenrichment-analysissemantic-similarity
119 stars 14.97 score 2.0k scripts 61 dependentsbioc
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 9 days ago.
functionalgenomicsmicroarrayrnaseqpathwaysgenesetenrichmentgene-set-enrichmentgenomicspathway-enrichment-analysis
212 stars 14.74 score 1.6k scripts 19 dependentsbioc
limma:Linear Models for Microarray and Omics Data
Data analysis, linear models and differential expression for omics data.
Maintained by Gordon Smyth. Last updated 11 days ago.
exonarraygeneexpressiontranscriptionalternativesplicingdifferentialexpressiondifferentialsplicinggenesetenrichmentdataimportbayesianclusteringregressiontimecoursemicroarraymicrornaarraymrnamicroarrayonechannelproprietaryplatformstwochannelsequencingrnaseqbatcheffectmultiplecomparisonnormalizationpreprocessingqualitycontrolbiomedicalinformaticscellbiologycheminformaticsepigeneticsfunctionalgenomicsgeneticsimmunooncologymetabolomicsproteomicssystemsbiologytranscriptomics
13.81 score 16k scripts 586 dependentsbioc
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 19 days ago.
alternativesplicingbatcheffectbayesianbiomedicalinformaticscellbiologychipseqclusteringcoveragedifferentialexpressiondifferentialmethylationdifferentialsplicingdnamethylationepigeneticsfunctionalgenomicsgeneexpressiongenesetenrichmentgeneticsimmunooncologymultiplecomparisonnormalizationpathwaysproteomicsqualitycontrolregressionrnaseqsagesequencingsinglecellsystemsbiologytimecoursetranscriptiontranscriptomicsopenblas
13.40 score 17k scripts 255 dependentsbioc
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.
pathwaysvisualizationannotationmultiplecomparisongenesetenrichmentreactomeenrichment-analysisreactome-pathway-analysisreactomepa
40 stars 12.25 score 1.5k scripts 7 dependentsbioc
variancePartition:Quantify and interpret drivers of variation in multilevel gene expression experiments
Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures.
Maintained by Gabriel E. Hoffman. Last updated 3 months ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
7 stars 11.69 score 1.1k scripts 3 dependentsbioc
systemPipeR:systemPipeR: Workflow Environment for Data Analysis and Report Generation
systemPipeR is a multipurpose data analysis workflow environment that unifies R with command-line tools. It enables scientists to analyze many types of large- or small-scale data on local or distributed computer systems with a high level of reproducibility, scalability and portability. At its core is a command-line interface (CLI) that adopts the Common Workflow Language (CWL). This design allows users to choose for each analysis step the optimal R or command-line software. It supports both end-to-end and partial execution of workflows with built-in restart functionalities. Efficient management of complex analysis tasks is accomplished by a flexible workflow control container class. Handling of large numbers of input samples and experimental designs is facilitated by consistent sample annotation mechanisms. As a multi-purpose workflow toolkit, systemPipeR enables users to run existing workflows, customize them or design entirely new ones while taking advantage of widely adopted data structures within the Bioconductor ecosystem. Another important core functionality is the generation of reproducible scientific analysis and technical reports. For result interpretation, systemPipeR offers a wide range of plotting functionality, while an associated Shiny App offers many useful functionalities for interactive result exploration. The vignettes linked from this page include (1) a general introduction, (2) a description of technical details, and (3) a collection of workflow templates.
Maintained by Thomas Girke. Last updated 5 months ago.
geneticsinfrastructuredataimportsequencingrnaseqriboseqchipseqmethylseqsnpgeneexpressioncoveragegenesetenrichmentalignmentqualitycontrolimmunooncologyreportwritingworkflowstepworkflowmanagement
53 stars 11.52 score 344 scripts 3 dependentsbioc
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 1 days ago.
pathwaysgraphandnetworkvisualizationgenesetenrichmentdifferentialexpressiongeneexpressionmicroarrayrnaseqgeneticsmetabolomicsproteomicssystemsbiologysequencing
40 stars 11.37 score 1.6k scripts 10 dependentsbioc
MAST:Model-based Analysis of Single Cell Transcriptomics
Methods and models for handling zero-inflated single cell assay data.
Maintained by Andrew McDavid. Last updated 5 months ago.
geneexpressiondifferentialexpressiongenesetenrichmentrnaseqtranscriptomicssinglecell
232 stars 11.28 score 1.8k scripts 5 dependentsbioc
UCell:Rank-based signature enrichment analysis for single-cell data
UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects.
Maintained by Massimo Andreatta. Last updated 5 months ago.
singlecellgenesetenrichmenttranscriptomicsgeneexpressioncellbasedassays
143 stars 10.43 score 454 scripts 2 dependentsbioc
GSEABase:Gene set enrichment data structures and methods
This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).
Maintained by Bioconductor Package Maintainer. Last updated 2 months ago.
geneexpressiongenesetenrichmentgraphandnetworkgokegg
10.27 score 1.5k scripts 77 dependentsbioc
singscore:Rank-based single-sample gene set scoring method
A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level.
Maintained by Malvika Kharbanda. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichmentbioinformatics
41 stars 10.03 score 124 scripts 4 dependentsbioc
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.
immunooncologysequencinggogeneexpressiontranscriptionrnaseqdifferentialexpressionannotationgenesetenrichmentkeggpathwayssoftware
2 stars 9.97 score 636 scripts 9 dependentsbioc
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 17 days ago.
genesetenrichmentgopathwayssoftwaresequencingwholegenomegenomeannotationcoveragecpp
86 stars 9.96 score 320 scripts 1 dependentsbioc
RcisTarget:RcisTarget Identify transcription factor binding motifs enriched on a list of genes or genomic regions
RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge).
Maintained by Gert Hulselmans. Last updated 5 months ago.
generegulationmotifannotationtranscriptomicstranscriptiongenesetenrichmentgenetarget
37 stars 9.47 score 191 scriptsbioc
LOLA:Locus overlap analysis for enrichment of genomic ranges
Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data.
Maintained by Nathan Sheffield. Last updated 5 months ago.
genesetenrichmentgeneregulationgenomeannotationsystemsbiologyfunctionalgenomicschipseqmethylseqsequencing
76 stars 9.34 score 160 scriptsbioc
EWCE:Expression Weighted Celltype Enrichment
Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.
Maintained by Alan Murphy. Last updated 1 months ago.
geneexpressiontranscriptiondifferentialexpressiongenesetenrichmentgeneticsmicroarraymrnamicroarrayonechannelrnaseqbiomedicalinformaticsproteomicsvisualizationfunctionalgenomicssinglecelldeconvolutionsingle-cellsingle-cell-rna-seqtranscriptomics
56 stars 9.29 score 99 scriptsbioc
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.
pathwaysgodifferentialexpressionmicroarrayonechanneltwochannelrnaseqgeneticsmultiplecomparisongenesetenrichmentgeneexpressionsystemsbiologysequencing
5 stars 8.68 score 784 scripts 1 dependentsbioc
AUCell:AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures)
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed.
Maintained by Gert Hulselmans. Last updated 5 months ago.
singlecellgenesetenrichmenttranscriptomicstranscriptiongeneexpressionworkflowstepnormalization
8.59 score 860 scripts 4 dependentsbioc
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.
genesetenrichmentproteomicstranscriptomicssystemsbiologygeneexpressionreactome
22 stars 8.50 score 67 scripts 1 dependentsbioc
BgeeDB:Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology
A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.
Maintained by Julien Wollbrett. Last updated 5 months ago.
softwaredataimportsequencinggeneexpressionmicroarraygogenesetenrichmentbioinformaticsenrichment-analysisrna-seqscrna-seqsingle-cell
15 stars 8.46 score 19 scripts 1 dependentsbioc
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.
immunooncologymicroarrayrnaseqgeneexpressiondifferentialexpressionpathwaysgraphandnetworknetworkgenesetenrichmentnetworkenrichmentvisualizationreportwriting
20 stars 8.37 score 164 scripts 3 dependentsbioc
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.
microarraypreprocessingqualitycontroldifferentialexpressionvisualizationgeneexpressiongenesetenrichmentpathwaysbioconductorbioconductor-packagebioinformaticsgene-set-enrichmenttranscriptomics
13 stars 8.30 score 183 scripts 7 dependentsbioc
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 3 months ago.
guigeneexpressionsoftwaretranscriptiontranscriptomicsvisualizationdifferentialexpressionpathwaysreportwritinggenesetenrichmentannotationgoshinyappsbioconductorbioconductor-packagedata-explorationdata-visualizationfunctional-enrichment-analysisgene-expressionpathway-analysisreproducible-researchrna-seq-analysisrna-seq-datashinytranscriptomeuser-friendly
77 stars 8.28 score 37 scripts 1 dependentsbioc
hypeR:An R Package For Geneset Enrichment Workflows
An R Package for Geneset Enrichment Workflows.
Maintained by Anthony Federico. Last updated 5 months ago.
genesetenrichmentannotationpathwaysbioinformaticscomputational-biologygeneset-enrichment-analysis
76 stars 8.22 score 145 scriptsbioc
nullranges:Generation of null ranges via bootstrapping or covariate matching
Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package.
Maintained by Michael Love. Last updated 5 months ago.
visualizationgenesetenrichmentfunctionalgenomicsepigeneticsgeneregulationgenetargetgenomeannotationannotationgenomewideassociationhistonemodificationchipseqatacseqdnaseseqrnaseqhiddenmarkovmodelbioconductorbootstrapgenomicsmatchingstatistics
27 stars 8.16 score 50 scripts 1 dependentsbioc
BioQC:Detect tissue heterogeneity in expression profiles with gene sets
BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance.
Maintained by Jitao David Zhang. Last updated 5 months ago.
geneexpressionqualitycontrolstatisticalmethodgenesetenrichmentcpp
5 stars 8.16 score 86 scriptsbioc
dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.
Maintained by Gabriel Hoffman. Last updated 4 days ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
12 stars 8.14 score 128 scriptsbioc
simplifyEnrichment:Simplify Functional Enrichment Results
A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings.
Maintained by Zuguang Gu. Last updated 5 months ago.
softwarevisualizationgoclusteringgenesetenrichment
113 stars 8.02 score 196 scriptsbioc
Category:Category Analysis
A collection of tools for performing category (gene set enrichment) analysis.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
annotationgopathwaysgenesetenrichment
7.93 score 183 scripts 16 dependentsbioc
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.
copynumbervariationdnamethylationgeneexpressionsnptranscriptiongenepredictiongenesetenrichmentgenesignalinggenetargetgenomewideassociationgenomicvariationcellbiologyepigeneticsfunctionalgenomicsgeneticslipidomicsmetabolomicsproteomicssystemsbiologytranscriptomicsclassificationdimensionreductionfeatureextractionprincipalcomponentregressionsurvivalmultiplecomparisonpathways
11 stars 7.74 score 42 scriptsbioc
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.
softwaremetagenomicsgeneexpressiongenesetenrichmentgenepredictionvisualizationkeggpathwaysgenetics cellbiologybiomedicalinformaticsimmunooncology
20 stars 7.71 score 48 scripts 1 dependentsbioc
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.
clusteringgenesetenrichmentgeneexpressionvisualizationpathwayskegggomicroarraysequencingsystemsbiologydatarepresentationdataimportbioconductorbioinformatics
12 stars 7.36 score 32 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 4 months ago.
annotationdifferentialexpressiongeneexpressiongenesetenrichmentgographandnetworkmultiplecomparisonpathwaysreactomesoftwaretranscriptionvisualizationenrichmentenrichment-analysisfunctional-enrichment-analysisgene-set-enrichmentontologiestranscriptomicscpp
28 stars 7.36 score 34 scriptsbioc
missMethyl:Analysing Illumina HumanMethylation BeadChip Data
Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.
Maintained by Belinda Phipson. Last updated 27 days ago.
normalizationdnamethylationmethylationarraygenomicvariationgeneticvariabilitydifferentialmethylationgenesetenrichment
7.24 score 300 scripts 1 dependentsbioc
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.
geneexpressiongenesetenrichmentsinglecelltranscriptomicsepigeneticsproteomicsdifferentialexpressionreactomednamethylationmethylationarraygene-regulationgene-seq-analysispathway-analysis
16 stars 7.06 score 15 scriptsbioc
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 3 months ago.
genesetenrichmentpathwaysreactomebiocarta
18 stars 7.06 score 32 scriptsbioc
CoGAPS:Coordinated Gene Activity in Pattern Sets
Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.
Maintained by Elana J. Fertig. Last updated 18 days ago.
geneexpressiontranscriptiongenesetenrichmentdifferentialexpressionbayesianclusteringtimecoursernaseqmicroarraymultiplecomparisondimensionreductionimmunooncologycpp
6.97 score 104 scriptsbioc
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.
annotationgomultiplecomparisongeneexpressionmicroarraypathwaysgenesetenrichmentgraphandnetwork
6.93 score 528 scripts 12 dependentsbioc
ideal:Interactive Differential Expression AnaLysis
This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared.
Maintained by Federico Marini. Last updated 3 months ago.
immunooncologygeneexpressiondifferentialexpressionrnaseqsequencingvisualizationqualitycontrolguigenesetenrichmentreportwritingshinyappsbioconductordifferential-expressionreproducible-researchrna-seqrna-seq-analysisshinyuser-friendly
29 stars 6.78 score 5 scriptsbioc
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.
softwaregeneexpressiontranscriptiondifferentialexpressiongenesetenrichmentdatarepresentationclusteringtimecoursemicroarraysequencingrnaseqannotationmultiplecomparisonpathwaysgovisualizationimmunooncologybioconductorbioconductor-packagebioconductor-statsgeneontologygeneset-enrichment
9 stars 6.75 score 31 scriptsbioc
ViSEAGO:ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity
The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.
Maintained by Aurelien Brionne. Last updated 3 months ago.
softwareannotationgogenesetenrichmentmultiplecomparisonclusteringvisualization
6.64 score 22 scriptsbioc
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.
genesetenrichmentnetworkpathwaysreactomernaseqnetworkenrichmentbioinformaticsnetworkspathway-enrichment-analysisvisualization
28 stars 6.59 score 2 scriptsbioc
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.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
17 stars 6.57 scorebioc
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.
immunooncologymicroarrayrnaseqgeneexpressiondifferentialexpressionpathwaysgraphandnetworknetworkgenesetenrichmentnetworkenrichmentvisualizationreportwritingbioconductor-packageu24ca289073
13 stars 6.55 score 23 scriptsbioc
bugsigdbr:R-side access to published microbial signatures from BugSigDB
The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT.
Maintained by Ludwig Geistlinger. Last updated 22 days ago.
dataimportgenesetenrichmentmetagenomicsmicrobiomebioconductor-package
3 stars 6.46 score 48 scriptsbioc
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.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
5 stars 6.41 score 43 scriptsbioc
artMS:Analytical R tools for Mass Spectrometry
artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details.
Maintained by David Jimenez-Morales. Last updated 5 months ago.
proteomicsdifferentialexpressionbiomedicalinformaticssystemsbiologymassspectrometryannotationqualitycontrolgenesetenrichmentclusteringnormalizationimmunooncologymultiplecomparisonanalysisanalyticalap-msbioconductorbioinformaticsmass-spectrometryphosphoproteomicspost-translational-modificationquantitative-analysis
14 stars 6.41 score 13 scriptsbioc
zenith:Gene set analysis following differential expression using linear (mixed) modeling with dream
Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream().
Maintained by Gabriel Hoffman. Last updated 5 days ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
6.39 score 91 scripts 1 dependentsbioc
RCAS:RNA Centric Annotation System
RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery.
Maintained by Bora Uyar. Last updated 5 months ago.
softwaregenetargetmotifannotationmotifdiscoverygotranscriptomicsgenomeannotationgenesetenrichmentcoverage
6.32 score 29 scripts 1 dependentsbioc
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.
visualizationbayesiangeneexpressionnetworkinferencepathwaysreactomenetworknetworkenrichmentgenesetenrichment
64 stars 6.28 score 9 scriptsbioc
xCell2:A Tool for Generic Cell Type Enrichment Analysis
xCell2 provides methods for cell type enrichment analysis using cell type signatures. It includes three main functions - 1. xCell2Train for training custom references objects from bulk or single-cell RNA-seq datasets. 2. xCell2Analysis for conducting the cell type enrichment analysis using the custom reference. 3. xCell2GetLineage for identifying dependencies between different cell types using ontology.
Maintained by Almog Angel. Last updated 11 days ago.
geneexpressiontranscriptomicsmicroarrayrnaseqsinglecelldifferentialexpressionimmunooncologygenesetenrichment
7 stars 6.24 score 15 scriptsbioc
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.
genetargetpathwaysgraphandnetworkvisualizationgenesetenrichmentdifferentialexpressiongeneexpressionmicroarrayrnaseqgeneticsmetabolomicsproteomicssystemsbiologysequencing
26 stars 6.23 score 22 scriptsbioc
ReportingTools:Tools for making reports in various formats
The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools.
Maintained by Jason A. Hackney. Last updated 5 months ago.
immunooncologysoftwarevisualizationmicroarrayrnaseqgodatarepresentationgenesetenrichment
6.23 score 93 scripts 1 dependentsbioc
dearseq:Differential Expression Analysis for RNA-seq data through a robust variance component test
Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093.
Maintained by Boris P. Hejblum. Last updated 5 months ago.
biomedicalinformaticscellbiologydifferentialexpressiondnaseqgeneexpressiongeneticsgenesetenrichmentimmunooncologykeggregressionrnaseqsequencingsystemsbiologytimecoursetranscriptiontranscriptomics
8 stars 6.20 score 11 scripts 1 dependentsbioc
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.
pathwaysgogenesetenrichmentopenmp
5 stars 6.08 score 12 scriptsbioc
metaseqR2:An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms
Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.
Maintained by Panagiotis Moulos. Last updated 18 days ago.
softwaregeneexpressiondifferentialexpressionworkflowsteppreprocessingqualitycontrolnormalizationreportwritingrnaseqtranscriptionsequencingtranscriptomicsbayesianclusteringcellbiologybiomedicalinformaticsfunctionalgenomicssystemsbiologyimmunooncologyalternativesplicingdifferentialsplicingmultiplecomparisontimecoursedataimportatacseqepigeneticsregressionproprietaryplatformsgenesetenrichmentbatcheffectchipseq
7 stars 6.05 score 3 scriptsbioc
omicsViewer:Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer
omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript.
Maintained by Chen Meng. Last updated 2 months ago.
softwarevisualizationgenesetenrichmentdifferentialexpressionmotifdiscoverynetworknetworkenrichment
4 stars 6.02 score 22 scriptsbioc
mosdef:MOSt frequently used and useful Differential Expression Functions
This package provides functionality to run a number of tasks in the differential expression analysis workflow. This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports.
Maintained by Federico Marini. Last updated 3 months ago.
geneexpressionsoftwaretranscriptiontranscriptomicsdifferentialexpressionvisualizationreportwritinggenesetenrichmentgo
5.98 score 4 dependentsbioc
vissE:Visualising Set Enrichment Analysis Results
This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.
Maintained by Dharmesh D. Bhuva. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichmentnetworkenrichmentnetworkbioinformatics
15 stars 5.93 score 19 scriptsbioc
TissueEnrich:Tissue-specific gene enrichment analysis
The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments.
Maintained by Ashish Jain. Last updated 5 months ago.
genesetenrichmentgeneexpressionsequencing
5.93 score 57 scriptsbioc
autonomics:Unified Statistical Modeling of Omics Data
This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.
Maintained by Aditya Bhagwat. Last updated 2 months ago.
softwaredataimportpreprocessingdimensionreductionprincipalcomponentregressiondifferentialexpressiongenesetenrichmenttranscriptomicstranscriptiongeneexpressionrnaseqmicroarrayproteomicsmetabolomicsmassspectrometry
5.89 score 5 scriptsbioc
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 9 days ago.
softwaresinglecellclassificationannotationgenesetenrichmentsequencinggenesignalingpathways
5.84 score 138 scriptsbioc
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.
shinyappssoftwaresinglecellchipseqatacseqmethylseqclassificationclusteringepigeneticsprincipalcomponentannotationbatcheffectmultiplecomparisonnormalizationpathwayspreprocessingqualitycontrolreportwritingvisualizationgenesetenrichmentdifferentialpeakcallingepigenomicsshinysingle-cellcpp
14 stars 5.83 score 16 scriptsbioc
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.
immunooncologydifferentialexpressiongogeneexpressiongenesetenrichmentgeneticsmicroarraymultiplecomparisononechannelpathwaysrnaseqsequencingsoftwaresystemsbiologytwochannelmetabolomicsproteomicskegggraphandnetworkgenesignalinggenetargetnetworkenrichmentnetworkclassification
5.81 score 64 scriptsbioc
RTN:RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons
A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons.
Maintained by Mauro Castro. Last updated 5 months ago.
transcriptionnetworknetworkinferencenetworkenrichmentgeneregulationgeneexpressiongraphandnetworkgenesetenrichmentgeneticvariability
5.80 score 53 scripts 2 dependentsbioc
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.
softwaregeneexpressionnetworkclusteringgraphandnetworkgenesetenrichmentpathwaysvisualizationrnaseqtranscriptomicsmrnamicroarraymicroarraynetworkenrichmentsequencinggoco-expressionenrichment-analysisgenenetwork-analysispipeline
24 stars 5.76 score 12 scriptsbioc
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 12 days ago.
genesetenrichmentpathwaysbioinformaticsgsea
21 stars 5.74 score 13 scriptsbioc
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.
5 stars 5.70 score 7 scriptsbioc
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.
genesetenrichmentmicroarrayrnaseqsoftwareimmunooncology
5.65 score 185 scripts 1 dependentsbioc
GDCRNATools:GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC
This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.
Maintained by Ruidong Li. Last updated 5 months ago.
immunooncologygeneexpressiondifferentialexpressiongeneregulationgenetargetnetworkinferencesurvivalvisualizationgenesetenrichmentnetworkenrichmentnetworkrnaseqgokegg
5.64 score 44 scriptsbioc
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.
differentialexpressionpathwaysgenesetenrichmentstatisticalmethodsoftware
5.60 score 32 scripts 5 dependentsbioc
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.
clusteringgenesetenrichmentgraphandnetworkpathwaysvisualizationgene-setsmolecular-signatures
4 stars 5.58 score 4 scriptsbioc
miRSM:Inferring miRNA sponge modules in heterogeneous data
The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules.
Maintained by Junpeng Zhang. Last updated 5 months ago.
geneexpressionbiomedicalinformaticsclusteringgenesetenrichmentmicroarraysoftwaregeneregulationgenetargetcernamirnamirna-spongemirna-targetsmodulesopenjdk
4 stars 5.51 score 5 scriptsbioc
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.
annotationgogeneexpressiongenesetenrichmentgraphandnetworkmicroarraymultiplecomparisonpathwayssoftware
5.48 score 6 scripts 1 dependentsbioc
INTACT:Integrate TWAS and Colocalization Analysis for Gene Set Enrichment Analysis
This package integrates colocalization probabilities from colocalization analysis with transcriptome-wide association study (TWAS) scan summary statistics to implicate genes that may be biologically relevant to a complex trait. The probabilistic framework implemented in this package constrains the TWAS scan z-score-based likelihood using a gene-level colocalization probability. Given gene set annotations, this package can estimate gene set enrichment using posterior probabilities from the TWAS-colocalization integration step.
Maintained by Jeffrey Okamoto. Last updated 5 months ago.
15 stars 5.47 score 13 scriptsbioc
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 days ago.
softwaresequencingrnaseqgeneexpressiontranscriptiondifferentialexpressionprincipalcomponentgenesetenrichmentpathwaysbatcheffectfunctionalgenomicsvisualizationdataimportfunctionalpredictiongenepredictiongodgeenrichment-analysismetaanalysisplotsproteinspublic-datasurfacesurfaceome
3 stars 5.43 score 3 scriptsbioc
GRaNIE:GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data
Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.
Maintained by Christian Arnold. Last updated 5 months ago.
softwaregeneexpressiongeneregulationnetworkinferencegenesetenrichmentbiomedicalinformaticsgeneticstranscriptomicsatacseqrnaseqgraphandnetworkregressiontranscriptionchipseq
5.40 score 24 scriptsbioc
RITAN:Rapid Integration of Term Annotation and Network resources
Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research.
Maintained by Michael Zimmermann. Last updated 5 months ago.
qualitycontrolnetworknetworkenrichmentnetworkinferencegenesetenrichmentfunctionalgenomicsgraphandnetwork
5.40 score 9 scriptsbioc
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.
guigenesetenrichmentsoftwaretranscriptionrnaseqvisualizationclusteringpathwaysreportwritinggokeggreactomeshinyapps
1 stars 5.36 score 22 scriptsbioc
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.
genesetenrichmentpathwaysnetworkenrichmentnetworkbioconductorenrichment
7 stars 5.32 score 10 scriptsbioc
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.
sequencingrnaseqgeneexpressiontranscriptiontimecoursepreprocessingvisualizationnormalizationprincipalcomponentclusteringdifferentialexpressiongenesetenrichmentpathways
6 stars 5.26 score 4 scriptsbioc
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.
softwaredifferentialexpressiongenesetenrichmentbiomedicalinformaticscellbiologytranscriptomicsrnaseqsinglecelltranscriptionsequencingimmunooncologydataimport
7 stars 5.24 score 3 scriptsbioc
Damsel:Damsel: an end to end analysis of DamID
Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R.
Maintained by Caitlin Page. Last updated 5 months ago.
differentialmethylationpeakdetectiongenepredictiongenesetenrichment
5.20 score 20 scriptsbioc
SGCP:SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks
SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.
Maintained by Niloofar AghaieAbiane. Last updated 5 months ago.
geneexpressiongenesetenrichmentnetworkenrichmentsystemsbiologyclassificationclusteringdimensionreductiongraphandnetworkneuralnetworknetworkmrnamicroarrayrnaseqvisualizationbioinformaticsgenecoexpressionnetworkgraphsnetworkclusteringnetworksself-trainingsemi-supervised-learningunsupervised-learning
2 stars 5.12 score 44 scriptsbioc
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.
massspectrometrymetabolomicssoftwarevisualizationbiomedicalinformaticsgraphandnetworkannotationcheminformaticspathwaysgenesetenrichmentbiological-intrerpretationbiological-knowledgebiological-significance-analysisenrichment-analysisfood-biomarker-ontologyknowledge-graphnutritionobofoundryontologytext-mining
1 stars 5.08 score 5 scriptsbioc
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.
differentialexpressiongeneexpressionrnaseqtranscriptomicspathwaysimmunooncologygenesetenrichmentbioconductorbioinformaticscmapgene-expressionl1000
5 stars 5.08 score 16 scriptsbioc
canceR:A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC
The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).
Maintained by Karim Mezhoud. Last updated 5 months ago.
guigeneexpressionclusteringgogenesetenrichmentkeggmultiplecomparisoncancercancer-datagenegene-expressiongene-methylationgene-mutationgene-setsmethylationmskccmutationstcltk
7 stars 5.08 score 17 scriptsbioc
broadSeq:broadSeq : for streamlined exploration of RNA-seq data
This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods.
Maintained by Rishi Das Roy. Last updated 5 months ago.
geneexpressiondifferentialexpressionrnaseqtranscriptomicssequencingcoveragegenesetenrichmentgo
4 stars 5.00 score 7 scriptsbioc
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.
softwareinfrastructuredifferentialexpressiongeneexpressionguivisualizationpathwaysgenesetenrichmentgoshinyappsbioconductorhacktoberfestiseeiseeu
1 stars 4.95 score 10 scriptsbioc
ttgsea:Tokenizing Text of Gene Set Enrichment Analysis
Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens.
Maintained by Dongmin Jung. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichment
4.95 score 3 scripts 3 dependentsbioc
chipenrich:Gene Set Enrichment For ChIP-seq Peak Data
ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes.
Maintained by Kai Wang. Last updated 18 days ago.
immunooncologychipseqepigeneticsfunctionalgenomicsgenesetenrichmenthistonemodificationregression
4.94 score 29 scriptsbioc
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.
geneexpressiongenesetenrichmentgenetargetmirnapathways
3 stars 4.89 score 13 scriptsbioc
AMARETTO:Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression
Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.
Maintained by Olivier Gevaert. Last updated 5 months ago.
statisticalmethoddifferentialmethylationgeneregulationgeneexpressionmethylationarraytranscriptionpreprocessingbatcheffectdataimportmrnamicroarraymicrornaarrayregressionclusteringrnaseqcopynumbervariationsequencingmicroarraynormalizationnetworkbayesianexonarrayonechanneltwochannelproprietaryplatformsalternativesplicingdifferentialexpressiondifferentialsplicinggenesetenrichmentmultiplecomparisonqualitycontroltimecourse
4.88 score 15 scriptsbioc
CelliD:Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis
CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.
Maintained by Akira Cortal. Last updated 5 months ago.
rnaseqsinglecelldimensionreductionclusteringgenesetenrichmentgeneexpressionatacseqopenblascppopenmp
4.85 score 70 scriptsbioc
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.
softwaregeneexpressiongenesetenrichmentgenesignaling
1 stars 4.83 score 15 scriptsbioc
transcriptogramer:Transcriptional analysis based on transcriptograms
R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered.
Maintained by Diego Morais. Last updated 5 months ago.
softwarenetworkvisualizationsystemsbiologygeneexpressiongenesetenrichmentgraphandnetworkclusteringdifferentialexpressionmicroarrayrnaseqtranscriptionimmunooncology
4 stars 4.81 score 9 scriptsbioc
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.
genesetenrichmentpathwayskegggeneexpressionmicroarraycpp
4.78 score 8 scripts 1 dependentsbioc
EnrichDO:a Global Weighted Model for Disease Ontology Enrichment Analysis
To implement disease ontology (DO) enrichment analysis, this package is designed and presents a double weighted model based on the latest annotations of the human genome with DO terms, by integrating the DO graph topology on a global scale. This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data.
Maintained by Hongyu Fu. Last updated 4 months ago.
annotationvisualizationgenesetenrichmentsoftware
4.74 score 9 scripts
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.
biomedicalinformaticschipseqclusteringcoveragednamethylationdifferentialexpressiondifferentialmethylationsoftwaredifferentialsplicingepigeneticsfunctionalgenomicsgeneexpressiongenesetenrichmentgeneticsimmunooncologymultiplecomparisonnormalizationpathwaysqualitycontrolrnaseqregressionsagesequencingsystemsbiologytimecoursetranscriptiontranscriptomicsdapcdiscriminant-analysisecologicalkernelkernel-localkernel-principle-componentspopulation-structure-inferenceprincipal-components
1 stars 4.70 score 1 scriptsbioc
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.
annotationgopathwaysgenesetenrichmentnetworkvisualizationfunctionalgenomicsnetworkenrichmentclustering
4.62 score 5 scripts 1 dependentsbioc
fenr:Fast functional enrichment for interactive applications
Perform fast functional enrichment on feature lists (like genes or proteins) using the hypergeometric distribution. Tailored for speed, this package is ideal for interactive platforms such as Shiny. It supports the retrieval of functional data from sources like GO, KEGG, Reactome, Bioplanet and WikiPathways. By downloading and preparing data first, it allows for rapid successive tests on various feature selections without the need for repetitive, time-consuming preparatory steps typical of other packages.
Maintained by Marek Gierlinski. Last updated 1 months ago.
functionalpredictiondifferentialexpressiongenesetenrichmentgokeggreactomeproteomics
4.60 score 4 scriptsbioc
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.
biologicalquestiondifferentialexpressiongenomeannotationgenesetenrichmentgenetargetgenomeassemblygo
1 stars 4.60 score 6 scriptsbioc
SVMDO:Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology
It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets.
Maintained by Mustafa Erhan Ozer. Last updated 5 months ago.
genesetenrichmentdifferentialexpressionguiclassificationrnaseqtranscriptomicssurvivalmachine-learningrna-seqshiny
4.60 score 2 scriptsbioc
iBBiG:Iterative Binary Biclustering of Genesets
iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes
Maintained by Aedin Culhane. Last updated 5 months ago.
clusteringannotationgenesetenrichment
4.56 score 3 scripts 2 dependentsbioc
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.
genetargetgenepredictionpathwaysgeneexpressionrnaseqimmunooncologydifferentialexpressiongenesetenrichmentreportwritingcrispr
4.54 score 1 scriptsbioc
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.
softwarestatisticalmethodnetworkgraphandnetworkgenesetenrichmentnetworkenrichmentpathways
4.48 score 4 scripts 1 dependentsbioc
oposSOM:Comprehensive analysis of transcriptome data
This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data.
Maintained by Henry Loeffler-Wirth. Last updated 5 months ago.
geneexpressiondifferentialexpressiongenesetenrichmentdatarepresentationvisualizationcpp
4.48 score 7 scriptsbioc
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.
geneexpressiondifferentialexpressiongenesetenrichmentdimensionreductionpathwaysgo
4.48 score 4 scriptsbioc
LRcell:Differential cell type change analysis using Logistic/linear Regression
The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus).
Maintained by Wenjing Ma. Last updated 5 months ago.
singlecellgenesetenrichmentsequencingregressiongeneexpressiondifferentialexpressionenrichmentmarker-genes
3 stars 4.48 score 5 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.
genesetenrichmentgosoftwarepathwayskegg
4.43 score 18 scripts 1 dependentsbioc
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 11 days ago.
annotationgogenesetenrichmentsoftwaremicroarraypathwaysgeneexpressionmultiplecomparisongraphandnetworkreactomeclusteringkegg
4.38 score 12 scriptsbioc
SeqGSEA:Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing
The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively.
Maintained by Xi Wang. Last updated 5 months ago.
sequencingrnaseqgenesetenrichmentgeneexpressiondifferentialexpressiondifferentialsplicingimmunooncology
4.34 score 11 scriptsbioc
loci2path:Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs
loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB.
Maintained by Tianlei Xu. Last updated 5 months ago.
functionalgenomicsgeneticsgenesetenrichmentsoftwaregeneexpressionsequencingcoveragebiocarta
1 stars 4.30 score 2 scriptsbioc
transite:RNA-binding protein motif analysis
transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins.
Maintained by Konstantin Krismer. Last updated 5 months ago.
geneexpressiontranscriptiondifferentialexpressionmicroarraymrnamicroarraygeneticsgenesetenrichmentcpp
4.30 score 20 scriptsbioc
goSTAG:A tool to use GO Subtrees to Tag and Annotate Genes within a set
Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster.
Maintained by Brian D. Bennett. Last updated 5 months ago.
geneexpressiondifferentialexpressiongenesetenrichmentclusteringmicroarraymrnamicroarrayrnaseqvisualizationgoimmunooncology
4.30 score 1 scriptsbioc
GIGSEA:Genotype Imputed Gene Set Enrichment Analysis
We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal.
Maintained by Shijia Zhu. Last updated 5 months ago.
genesetenrichmentsnpvariantannotationgeneexpressiongeneregulationregressiondifferentialexpression
4.30 score 2 scriptsbioc
CAFE:Chromosmal Aberrations Finder in Expression data
Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input
Maintained by Sander Bollen. Last updated 5 months ago.
geneexpressionmicroarrayonechannelgenesetenrichment
4.30 score 2 scriptsbioc
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.
geneexpressionsoftwaretranscriptionsystemsbiologypathwaysgenesetenrichmentimmunooncologyepigeneticsclassificationbiomedicalinformaticsregressionexperimenthubsoftware
4.20 score 16 scriptsbioc
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.
genesetenrichmentpathwaysdifferentialexpressionmicroarray
4.18 score 51 scriptsbioc
splineTimeR:Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction
This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks.
Maintained by Herbert Braselmann. Last updated 5 months ago.
geneexpressiondifferentialexpressiontimecourseregressiongenesetenrichmentnetworkenrichmentnetworkinferencegraphandnetwork
4.01 score 17 scriptsbioc
EasyCellType:Annotate cell types for scRNA-seq data
We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster.
Maintained by Ruoxing Li. Last updated 5 months ago.
singlecellsoftwaregeneexpressiongenesetenrichment
4.00 score 6 scriptsbioc
sincell:R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data
Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies.
Maintained by Miguel Julia. Last updated 5 months ago.
immunooncologysequencingrnaseqclusteringgraphandnetworkvisualizationgeneexpressiongenesetenrichmentbiomedicalinformaticscellbiologyfunctionalgenomicssystemsbiologycpp
4.00 score 6 scriptsbioc
ctsGE:Clustering of Time Series Gene Expression data
Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles.
Maintained by Michal Sharabi-Schwager. Last updated 5 months ago.
immunooncologygeneexpressiontranscriptiondifferentialexpressiongenesetenrichmentgeneticsbayesianclusteringtimecoursesequencingrnaseq
1 stars 4.00 score 3 scriptsbioc
GSEAmining:Make Biological Sense of Gene Set Enrichment Analysis Outputs
Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study.
Maintained by Oriol Arqués. Last updated 5 months ago.
genesetenrichmentclusteringvisualization
4.00 score 7 scriptsbioc
octad:Open Cancer TherApeutic Discovery (OCTAD)
OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds.
Maintained by E. Chekalin. Last updated 5 months ago.
classificationgeneexpressionpharmacogeneticspharmacogenomicssoftwaregenesetenrichment
4.00 score 4 scriptsbioc
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.
softwarestatisticalmethodnetworkgraphandnetworkgenesetenrichmentgeneexpressionnetworkenrichmentpathwaysdifferentialexpression
4.00 score 1 scriptsbioc
GSALightning:Fast Permutation-based Gene Set Analysis
GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation.
Maintained by Billy Heung Wing Chang. Last updated 5 months ago.
softwarebiologicalquestiongenesetenrichmentdifferentialexpressiongeneexpressiontranscription
5 stars 4.00 score 4 scriptsbioc
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.
generegulationpathwaysgeneexpressiongeneticvariabilitygenesetenrichmentalternativesplicing
3.98 score 16 scriptsbioc
terapadog:Translational Efficiency Regulation Analysis using the PADOG Method
This package performs a Gene Set Analysis with the approach adopted by PADOG on the genes that are reported as translationally regulated (ie. exhibit a significant change in TE) by the DeltaTE package. It can be used on its own to see the impact of translation regulation on gene sets, but it is also integrated as an additional analysis method within ReactomeGSA, where results are further contextualised in terms of pathways and directionality of the change.
Maintained by Gionmattia Carancini. Last updated 25 days ago.
riboseqtranscriptomicsgenesetenrichmentgeneregulationreactomesoftware
3.90 score
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 6 months ago.
statisticalmethodgenesetenrichmentpathways
3.90 score 3 scriptsbioc
RegEnrich:Gene regulator enrichment analysis
This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking.
Maintained by Weiyang Tao. Last updated 5 months ago.
geneexpressiontranscriptomicsrnaseqtwochanneltranscriptiongenetargetnetworkenrichmentdifferentialexpressionnetworknetworkinferencegenesetenrichmentfunctionalprediction
3.82 score 22 scriptsbioc
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.
functionalpredictiongenesetenrichmentpathwaysgoreactomekeggcompoundgene-ontologygenesshiny
1 stars 3.78 score 2 scriptsbioc
pairedGSEA:Paired DGE and DGS analysis for gene set enrichment analysis
pairedGSEA makes it simple to run a paired Differential Gene Expression (DGE) and Differencital Gene Splicing (DGS) analysis. The package allows you to store intermediate results for further investiation, if desired. pairedGSEA comes with a wrapper function for running an Over-Representation Analysis (ORA) and functionalities for plotting the results.
Maintained by Søren Helweg Dam. Last updated 4 days ago.
differentialexpressionalternativesplicingdifferentialsplicinggeneexpressionimmunooncologygenesetenrichmentpathwaysrnaseqsoftwaretranscription
2 stars 3.60 scorebioc
GOpro:Find the most characteristic gene ontology terms for groups of human genes
Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng).
Maintained by Lidia Chrabaszcz. Last updated 5 months ago.
annotationclusteringgogeneexpressiongenesetenrichmentmultiplecomparisoncpp
2 stars 3.60 score 4 scriptsbioc
TFEA.ChIP:Analyze Transcription Factor Enrichment
Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments.
Maintained by Laura Puente Santamaría. Last updated 5 months ago.
transcriptiongeneregulationgenesetenrichmenttranscriptomicssequencingchipseqrnaseqimmunooncology
3.45 score 14 scriptsbioc
rgsepd:Gene Set Enrichment / Projection Displays
R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group.
Maintained by Karl Stamm. Last updated 5 months ago.
immunooncologysoftwaredifferentialexpressiongenesetenrichmentrnaseq
3.30 score 10 scriptsbioc
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.
3.30 score 1 scriptsbioc
cpvSNP:Gene set analysis methods for SNP association p-values that lie in genes in given gene sets
Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results.
Maintained by Caitlin McHugh. Last updated 5 months ago.
geneticsstatisticalmethodpathwaysgenesetenrichmentgenomicvariation
3.30 score 3 scriptsbioc
gCrisprTools:Suite of Functions for Pooled Crispr Screen QC and Analysis
Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity.
Maintained by Russell Bainer. Last updated 5 months ago.
immunooncologycrisprpooledscreensexperimentaldesignbiomedicalinformaticscellbiologyfunctionalgenomicspharmacogenomicspharmacogeneticssystemsbiologydifferentialexpressiongenesetenrichmentgeneticsmultiplecomparisonnormalizationpreprocessingqualitycontrolrnaseqregressionsoftwarevisualization
3.30 score 8 scriptsbioc
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.
immunooncologykeggreactomegeneexpressionpathwaysgenesetenrichmentmicroarrayrnaseq
3.30 score 4 scriptsbioc
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.
geneexpressiondifferentialexpressiongenesetenrichmentdataimportclassificationfeatureextractionsequencingrnaseqbatcheffectnormalizationpreprocessingqualitycontrolgeneticstranscriptomicsmicroarrayalignmentpathwayssystemsbiologygoimmunooncology
3.30 score 5 scriptsbioc
npGSEA:Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA)
Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results.
Maintained by Jessica Larson. Last updated 5 months ago.
genesetenrichmentmicroarraystatisticalmethodpathways
3.30 score 4 scriptsbioc
GSRI:Gene Set Regulation Index
The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI).
Maintained by Julian Gehring. Last updated 5 months ago.
microarraytranscriptiondifferentialexpressiongenesetenrichmentgeneregulation
3.30 score 2 scriptsbioc
RGSEA:Random Gene Set Enrichment Analysis
Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements.
Maintained by Chengcheng Ma. Last updated 5 months ago.
genesetenrichmentstatisticalmethodclassification
3.30 score 1 scriptsbioc
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.
pathwaysgenesetenrichmentsystemsbiologygeneexpressionsoftware
3.30 score 3 scriptsbioc
RTNsurvival:Survival analysis using transcriptional networks inferred by the RTN package
RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications.
Maintained by Clarice Groeneveld. Last updated 5 months ago.
networkenrichmentsurvivalgeneregulationgenesetenrichmentnetworkinferencegraphandnetwork
3.30 score 9 scriptsbioc
SigCheck:Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata
While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata.
Maintained by Rory Stark. Last updated 1 months ago.
geneexpressionclassificationgenesetenrichment
3.00 score 1 scriptsbioc
roastgsa:Rotation based gene set analysis
This package implements a variety of functions useful for gene set analysis using rotations to approximate the null distribution. It contributes with the implementation of seven test statistic scores that can be used with different goals and interpretations. Several functions are available to complement the statistical results with graphical representations.
Maintained by Adria Caballe. Last updated 5 months ago.
microarraypreprocessingnormalizationgeneexpressionsurvivaltranscriptionsequencingtranscriptomicsbayesianclusteringregressionrnaseqmicrornaarraymrnamicroarrayfunctionalgenomicssystemsbiologyimmunooncologydifferentialexpressiongenesetenrichmentbatcheffectmultiplecomparisonqualitycontroltimecoursemetabolomicsproteomicsepigeneticscheminformaticsexonarrayonechanneltwochannelproprietaryplatformscellbiologybiomedicalinformaticsalternativesplicingdifferentialsplicingdataimportpathways
2.30 scorebioc
dcGSA:Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles
Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes.
Maintained by Jiehuan sun. Last updated 5 months ago.
immunooncologygenesetenrichmentmicroarraystatisticalmethodsequencingrnaseqgeneexpression
2.30 score 1 scriptsbioc
CTDquerier:Package for CTDbase data query, visualization and downstream analysis
Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses.
Maintained by Xavier Escribà-Montagut. Last updated 5 months ago.
softwarebiomedicalinformaticsinfrastructuredataimportdatarepresentationgenesetenrichmentnetworkenrichmentpathwaysnetworkgokegg
2.30 score 2 scriptsbioc
ISoLDE:Integrative Statistics of alleLe Dependent Expression
This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below.
Maintained by Christelle Reynès. Last updated 5 months ago.
immunooncologygeneexpressiontranscriptiongenesetenrichmentgeneticssequencingrnaseqmultiplecomparisonsnpgeneticvariabilityepigeneticsmathematicalbiologygeneregulationopenmp
2.30 score 2 scripts