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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
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 7 days ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
12 stars 8.14 score 128 scriptsbioc
debrowser:Interactive Differential Expresion Analysis Browser
Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.
Maintained by Alper Kucukural. Last updated 5 months ago.
sequencingchipseqrnaseqdifferentialexpressiongeneexpressionclusteringimmunooncology
61 stars 7.80 score 65 scriptsbioc
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
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 7 days ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
6.39 score 91 scripts 1 dependentsbioc
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
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
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 scripts
