Showing 6 of total 6 results (show query)
egeulgen
pathfindR:Enrichment Analysis Utilizing Active Subnetworks
Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
Maintained by Ege Ulgen. Last updated 1 months ago.
active-subnetworksenrichmentpathwaypathway-enrichment-analysissubnetwork
187 stars 10.38 score 138 scripts
moosa-r
rbioapi:User-Friendly R Interface to Biologic Web Services' API
Currently fully supports Enrichr, JASPAR, miEAA, PANTHER, Reactome, STRING, and UniProt! The goal of rbioapi is to provide a user-friendly and consistent interface to biological databases and services. In a way that insulates the user from the technicalities of using web services API and creates a unified and easy-to-use interface to biological and medical web services. This is an ongoing project; New databases and services will be added periodically. Feel free to suggest any databases or services you often use.
Maintained by Moosa Rezwani. Last updated 2 months ago.
api-clientbioinformaticsbiologyenrichmentenrichment-analysisenrichrjasparmieaaover-representation-analysispantherreactomestringuniprot
20 stars 7.60 score 55 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 scripts
bioc
fedup:Fisher's Test for Enrichment and Depletion of User-Defined Pathways
An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results.
Maintained by Catherine Ross. Last updated 5 months ago.
genesetenrichmentpathwaysnetworkenrichmentnetworkbioconductorenrichment
7 stars 5.32 score 10 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
eonurk
cinaRgenesets:Ready-to-Use Curated Gene Sets for 'cinaR'
Immune related gene sets provided along with the 'cinaR' package.
Maintained by Onur Karakaslar. Last updated 4 years ago.
diceenrichmentenrichment-analysisgene-setsgenesetspbmcwikipathways
3 stars 3.35 score 15 scripts