Showing 7 of total 7 results (show query)
bioc
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
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
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 dependents
guido-s
netmeta:Network Meta-Analysis using Frequentist Methods
A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - subgroup network meta-analysis.
Maintained by Guido Schwarzer. Last updated 8 days ago.
meta-analysisnetwork-meta-analysisrstudio
33 stars 11.84 score 199 scripts 10 dependentsbioc
meshes:MeSH Enrichment and Semantic analyses
MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species.
Maintained by Guangchuang Yu. Last updated 5 months ago.
annotationclusteringmultiplecomparisonsoftwareenrichment-analysismedical-subject-headingssemantic-similarity
12 stars 7.19 score 43 scriptsbioc
made4:Multivariate analysis of microarray data using ADE4
Multivariate data analysis and graphical display of microarray data. Functions include for supervised dimension reduction (between group analysis) and joint dimension reduction of 2 datasets (coinertia analysis). It contains functions that require R package ade4.
Maintained by Aedin Culhane. Last updated 5 months ago.
clusteringclassificationdimensionreductionprincipalcomponenttranscriptomicsmultiplecomparisongeneexpressionsequencingmicroarray
6.11 score 107 scripts 2 dependents
cran
metainc:Assessment of Inconsistency in Meta-Analysis using Decision Thresholds
Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index. These indices quantify inconsistency taking into account outcome-level decision thresholds.
Maintained by Bernardo Sousa-Pinto. Last updated 18 days ago.
1.00 score