R-universe search: needs:graph

bioc

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

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

Maintained by Robert Castelo. Last updated 11 days ago.

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

212 stars 14.74 score 1.6k scripts 19 dependents

bioc

RCy3:Functions to Access and Control Cytoscape

Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function.

Maintained by Alex Pico. Last updated 6 days ago.

visualization graphandnetwork thirdpartyclient network

52 stars 13.47 score 628 scripts 17 dependents

bioc

ReactomePA:Reactome Pathway Analysis

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

Maintained by Guangchuang Yu. Last updated 5 months ago.

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

40 stars 12.25 score 1.5k scripts 7 dependents

bioc

ggbio:Visualization tools for genomic data

The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries.

Maintained by Michael Lawrence. Last updated 5 months ago.

infrastructure visualization

111 stars 12.23 score 734 scripts 16 dependents

bioc

Rgraphviz:Provides plotting capabilities for R graph objects

Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package.

Maintained by Kasper Daniel Hansen. Last updated 6 days ago.

graphandnetwork visualization zlib

11.51 score 1.2k scripts 107 dependents

bioc

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 4 days ago.

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

40 stars 11.37 score 1.6k scripts 10 dependents

bioc

ggcyto:Visualize Cytometry data with ggplot

With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot.

Maintained by Mike Jiang. Last updated 5 months ago.

immunooncology flowcytometry cellbasedassays infrastructure visualization

58 stars 11.25 score 362 scripts 5 dependents

bioc

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.

geneexpression genesetenrichment graphandnetwork go kegg

10.27 score 1.5k scripts 77 dependents

bioc

CAMERA:Collection of annotation related methods for mass spectrometry data

Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments

Maintained by Steffen Neumann. Last updated 5 months ago.

immunooncology massspectrometry metabolomics

11 stars 10.27 score 175 scripts 6 dependents

bioc

graphite:GRAPH Interaction from pathway Topological Environment

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

Maintained by Gabriele Sales. Last updated 5 months ago.

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

8 stars 10.24 score 122 scripts 21 dependents

bioc

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

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

Maintained by Joshua David Campbell. Last updated 1 months ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

182 stars 10.17 score 252 scripts

bioc

BiocCheck:Bioconductor-specific package checks

BiocCheck guides maintainers through Bioconductor best practicies. It runs Bioconductor-specific package checks by searching through package code, examples, and vignettes. Maintainers are required to address all errors, warnings, and most notes produced.

Maintained by Marcel Ramos. Last updated 1 months ago.

infrastructure bioconductor-package core-services

8 stars 10.03 score 114 scripts 6 dependents

bioc

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.

software geneexpression genesetenrichment bioinformatics

41 stars 10.03 score 124 scripts 4 dependents

bioc

biocViews:Categorized views of R package repositories

Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages.

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

infrastructure bioconductor-package core-package

4 stars 9.71 score 30 scripts 14 dependents

bioc

pcaExplorer:Interactive Visualization of RNA-seq Data Using a Principal Components Approach

This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology visualization rnaseq dimensionreduction principalcomponent qualitycontrol gui reportwriting shinyapps bioconductor principal-components reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

56 stars 9.63 score 180 scripts

bioc

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.

generegulation motifannotation transcriptomics transcription genesetenrichment genetarget

37 stars 9.18 score 191 scripts

bioc

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

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

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

pathways graphandnetwork visualization kegg

9.09 score 114 scripts 23 dependents

bioc

topGO:Enrichment Analysis for Gene Ontology

topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied.

Maintained by Adrian Alexa. Last updated 5 months ago.

microarray visualization

8.96 score 2.0k scripts 20 dependents

bioc

ChIPpeakAnno:Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments, or any experiments that result in large number of genomic interval data

The package encompasses a range of functions for identifying the closest gene, exon, miRNA, or custom features—such as highly conserved elements and user-supplied transcription factor binding sites. Additionally, users can retrieve sequences around the peaks and obtain enriched Gene Ontology (GO) or Pathway terms. In version 2.0.5 and beyond, new functionalities have been introduced. These include features for identifying peaks associated with bi-directional promoters along with summary statistics (peaksNearBDP), summarizing motif occurrences in peaks (summarizePatternInPeaks), and associating additional identifiers with annotated peaks or enrichedGO (addGeneIDs). The package integrates with various other packages such as biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and stat to enhance its analytical capabilities.

Maintained by Jianhong Ou. Last updated 3 months ago.

annotation chipseq chipchip

8.75 score 584 scripts 6 dependents

bioc

qpgraph:Estimation of Genetic and Molecular Regulatory Networks from High-Throughput Genomics Data

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

Maintained by Robert Castelo. Last updated 4 days ago.

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

3 stars 8.72 score 20 scripts 3 dependents

bioc

monocle:Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.

Maintained by Cole Trapnell. Last updated 5 months ago.

immunooncology sequencing rnaseq geneexpression differentialexpression infrastructure dataimport datarepresentation visualization clustering multiplecomparison qualitycontrol cpp

8.71 score 1.6k scripts 2 dependents

bioc

gage:Generally Applicable Gene-set Enrichment for Pathway Analysis

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

Maintained by Weijun Luo. Last updated 5 months ago.

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

5 stars 8.68 score 784 scripts 1 dependents

bioc

RBGL:An interface to the BOOST graph library

A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library.

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

graphandnetwork network cpp

8.59 score 320 scripts 132 dependents

bioc

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.

singlecell genesetenrichment transcriptomics transcription geneexpression workflowstep normalization

8.59 score 860 scripts 4 dependents

bioc

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.

software dataimport sequencing geneexpression microarray go genesetenrichment bioinformatics enrichment-analysis rna-seq scrna-seq single-cell

15 stars 8.46 score 19 scripts 1 dependents

bioc

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

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

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

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

20 stars 8.37 score 164 scripts 3 dependents

bioc

TRONCO:TRONCO, an R package for TRanslational ONCOlogy

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

Maintained by Luca De Sano. Last updated 6 days ago.

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

30 stars 8.35 score 38 scripts

bioc

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

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

Maintained by Federico Marini. Last updated 3 months ago.

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

77 stars 8.28 score 37 scripts 1 dependents

bioc

flowStats:Statistical methods for the analysis of flow cytometry data

Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package.

Maintained by Greg Finak. Last updated 5 months ago.

immunooncology flowcytometry cellbasedassays

14 stars 8.27 score 195 scripts 1 dependents

bioc

IPO:Automated Optimization of XCMS Data Processing parameters

The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models.

Maintained by Thomas Lieb. Last updated 5 months ago.

immunooncology metabolomics massspectrometry

34 stars 8.14 score 41 scripts

bioc

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.

rnaseq geneexpression differentialexpression batcheffect qualitycontrol regression genesetenrichment generegulation epigenetics functionalgenomics transcriptomics normalization singlecell preprocessing sequencing immunooncology software cpp

12 stars 8.14 score 128 scripts

bioc

FLAMES:FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data

Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution.

Maintained by Changqing Wang. Last updated 2 days ago.

rnaseq singlecell transcriptomics dataimport differentialsplicing alternativesplicing geneexpression longread zlib curl bzip2 xz-utils cpp

33 stars 8.04 score 12 scripts

bioc

openCyto:Hierarchical Gating Pipeline for flow cytometry data

This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy.

Maintained by Mike Jiang. Last updated 6 days ago.

immunooncology flowcytometry dataimport preprocessing datarepresentation cpp

8.02 score 404 scripts 1 dependents

ax3man

phylopath:Perform Phylogenetic Path Analysis

A comprehensive and easy to use R implementation of confirmatory phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.

Maintained by Wouter van der Bijl. Last updated 6 months ago.

analysis comparative-methods path phylogenetics

13 stars 8.00 score 81 scripts 1 dependents

bioc

netZooR:Unified methods for the inference and analysis of gene regulatory networks

netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.

Maintained by Tara Eicher. Last updated 14 days ago.

networkinference network generegulation geneexpression transcription microarray graphandnetwork gene-regulatory-network transcription-factors

105 stars 7.98 score

ropensci

osmplotr:Bespoke Images of 'OpenStreetMap' Data

Bespoke images of 'OpenStreetMap' ('OSM') data and data visualisation using 'OSM' objects.

Maintained by Mark Padgham. Last updated 1 months ago.

data-visualisation highlighting-clusters openstreetmap osm overpass overpass-api peer-reviewed

139 stars 7.97 score 80 scripts

bioc

Category:Category Analysis

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

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

annotation go pathways genesetenrichment

7.93 score 183 scripts 16 dependents

bioc

flowWorkspace:Infrastructure for representing and interacting with gated and ungated cytometry data sets.

This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis.

Maintained by Greg Finak. Last updated 26 days ago.

immunooncology flowcytometry dataimport preprocessing datarepresentation zlib openblas cpp

7.89 score 576 scripts 10 dependents

bioc

SRAdb:A compilation of metadata from NCBI SRA and tools

The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata.

Maintained by Jack Zhu. Last updated 4 months ago.

infrastructure sequencing dataimport

2 stars 7.81 score 200 scripts

bioc

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.

sequencing chipseq rnaseq differentialexpression geneexpression clustering immunooncology

61 stars 7.80 score 65 scripts

bioc

BiocPkgTools:Collection of simple tools for learning about Bioconductor Packages

Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages.

Maintained by Sean Davis. Last updated 28 days ago.

software infrastructure bioconductor metadata

21 stars 7.67 score 68 scripts

bioc

CytoML:A GatingML Interface for Cross Platform Cytometry Data Sharing

Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms.

Maintained by Mike Jiang. Last updated 26 days ago.

immunooncology flowcytometry dataimport datarepresentation zlib openblas libxml2 cpp

30 stars 7.60 score 132 scripts

bioc

metaMS:MS-based metabolomics annotation pipeline

MS-based metabolomics data processing and compound annotation pipeline.

Maintained by Yann Guitton. Last updated 5 months ago.

immunooncology massspectrometry metabolomics

15 stars 7.50 score 15 scripts

bioc

PeacoQC:Peak-based selection of high quality cytometry data

This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data.

Maintained by Annelies Emmaneel. Last updated 5 months ago.

flowcytometry qualitycontrol preprocessing peakdetection

16 stars 7.38 score 28 scripts 3 dependents

bioc

flowClust:Clustering for Flow Cytometry

Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'.

Maintained by Greg Finak. Last updated 5 months ago.

immunooncology clustering visualization flowcytometry

7.31 score 83 scripts 6 dependents

r-forge

pcalg:Methods for Graphical Models and Causal Inference

Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

Maintained by Markus Kalisch. Last updated 7 months ago.

openblas cpp

7.30 score 700 scripts 19 dependents

bioc

OrganismDbi:Software to enable the smooth interfacing of different database packages

The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods.

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

annotation infrastructure

7.26 score 34 scripts 34 dependents

bioc

TBSignatureProfiler:Profile RNA-Seq Data Using TB Pathway Signatures

Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility.

Maintained by Aubrey R. Odom. Last updated 3 months ago.

geneexpression differentialexpression bioconductor-package biomarkers gene-signatures tuberculosis

12 stars 7.25 score 23 scripts

bioc

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

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

Maintained by Attila Gabor. Last updated 5 months ago.

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

59 stars 7.22 score 35 scripts

bioc

signatureSearch:Environment for Gene Expression Searching Combined with Functional Enrichment Analysis

This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods.

Maintained by Brendan Gongol. Last updated 5 months ago.

software geneexpression go kegg networkenrichment sequencing coverage differentialexpression cpp

18 stars 7.20 score 74 scripts 1 dependents

bioc

ATACseqQC:ATAC-seq Quality Control

ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints.

Maintained by Jianhong Ou. Last updated 3 months ago.

sequencing dnaseq atacseq generegulation qualitycontrol coverage nucleosomepositioning immunooncology

7.12 score 146 scripts 1 dependents

stathin

ggm:Graphical Markov Models with Mixed Graphs

Provides functions for defining mixed graphs containing three types of edges, directed, undirected and bi-directed, with possibly multiple edges. These graphs are useful because they capture fundamental independence structures in multivariate distributions and in the induced distributions after marginalization and conditioning. The package is especially concerned with Gaussian graphical models for (i) ML estimation for directed acyclic graphs, undirected and bi-directed graphs and ancestral graph models (ii) testing several conditional independencies (iii) checking global identification of DAG Gaussian models with one latent variable (iv) testing Markov equivalences and generating Markov equivalent graphs of specific types.

Maintained by Giovanni M. Marchetti. Last updated 1 years ago.

7.11 score 295 scripts 29 dependents

bioc

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

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

Maintained by Sebastian Canzler. Last updated 3 months ago.

genesetenrichment pathways reactome biocarta

18 stars 7.06 score 32 scripts

bioc

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

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

Maintained by Attila Gabor. Last updated 8 days ago.

cellbasedassays cellbiology proteomics pathways network timecourse immunooncology

6.95 score 98 scripts 6 dependents

bioc

scClassify:scClassify: single-cell Hierarchical Classification

scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references.

Maintained by Yingxin Lin. Last updated 5 months ago.

singlecell geneexpression classification

23 stars 6.92 score 30 scripts

james-thorson-noaa

dsem:Fit Dynamic Structural Equation Models

Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms."

Maintained by James Thorson. Last updated 21 days ago.

cpp openmp

11 stars 6.90 score 24 scripts

bioc

GOstats:Tools for manipulating GO and microarrays

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

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

annotation go multiplecomparison geneexpression microarray pathways genesetenrichment graphandnetwork

6.89 score 528 scripts 11 dependents

furrer-lab

abn:Modelling Multivariate Data with Additive Bayesian Networks

The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website.

Maintained by Matteo Delucchi. Last updated 21 days ago.

bayesian-network binomial categorical-data gaussian grouped-datasets mixed-effects multinomial multivariate poisson structure-learning gsl openblas cpp openmp jags

6 stars 6.88 score 90 scripts

bioc

SomaticSignatures:Somatic Signatures

The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms.

Maintained by Julian Gehring. Last updated 5 months ago.

sequencing somaticmutation visualization clustering genomicvariation statisticalmethod

22 stars 6.85 score 54 scripts 1 dependents

bioc

mnem:Mixture Nested Effects Models

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

Maintained by Martin Pirkl. Last updated 6 days ago.

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

4 stars 6.81 score 15 scripts 4 dependents

bioc

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.

immunooncology geneexpression differentialexpression rnaseq sequencing visualization qualitycontrol gui genesetenrichment reportwriting shinyapps bioconductor differential-expression reproducible-research rna-seq rna-seq-analysis shiny user-friendly

29 stars 6.78 score 5 scripts

bioc

CytoPipeline:Automation and visualization of flow cytometry data analysis pipelines

This package provides support for automation and visualization of flow cytometry data analysis pipelines. In the current state, the package focuses on the preprocessing and quality control part. The framework is based on two main S4 classes, i.e. CytoPipeline and CytoProcessingStep. The pipeline steps are linked to corresponding R functions - that are either provided in the CytoPipeline package itself, or exported from a third party package, or coded by the user her/himself. The processing steps need to be specified centrally and explicitly using either a json input file or through step by step creation of a CytoPipeline object with dedicated methods. After having run the pipeline, obtained results at all steps can be retrieved and visualized thanks to file caching (the running facility uses a BiocFileCache implementation). The package provides also specific visualization tools like pipeline workflow summary display, and 1D/2D comparison plots of obtained flowFrames at various steps of the pipeline.

Maintained by Philippe Hauchamps. Last updated 5 months ago.

flowcytometry preprocessing qualitycontrol workflowstep immunooncology software visualization

4 stars 6.71 score 18 scripts 2 dependents

bioc

chimeraviz:Visualization tools for gene fusions

chimeraviz manages data from fusion gene finders and provides useful visualization tools.

Maintained by Stian Lågstad. Last updated 5 months ago.

infrastructure alignment

37 stars 6.71 score 14 scripts

bioc

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

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

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

annotation go multiplecomparison pathways geneexpression bioconductor

6 stars 6.68 score

bioc

epiregulon:Gene regulatory network inference from single cell epigenomic data

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Maintained by Xiaosai Yao. Last updated 23 days ago.

singlecell generegulation networkinference network geneexpression transcription genetarget cpp

14 stars 6.67 score 17 scripts

bioc

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.

software annotation go genesetenrichment multiplecomparison clustering visualization

6.64 score 22 scripts

bioc

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

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

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

microarray graphandnetwork

6.61 score 113 scripts 4 dependents

bioc

LOBSTAHS:Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences

LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment.

Maintained by Henry Holm. Last updated 5 months ago.

immunooncology massspectrometry metabolomics lipidomics dataimport adduct algae bioconductor hplc-esi-ms lipid mass-spectrometry oxidative-stress-biomarkers oxidized-lipids oxylipins plankton

8 stars 6.56 score 9 scripts

bioc

GSEABenchmarkeR:Reproducible GSEA Benchmarking

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

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

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

13 stars 6.55 score 23 scripts

jokergoo

CePa:Centrality-Based Pathway Enrichment

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

Maintained by Zuguang Gu. Last updated 4 years ago.

3 stars 6.53 score 75 scripts

bioc

BioCor:Functional similarities

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

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

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

14 stars 6.47 score

bioc

YAPSA:Yet Another Package for Signature Analysis

This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata.

Maintained by Zuguang Gu. Last updated 5 months ago.

sequencing dnaseq somaticmutation visualization clustering genomicvariation statisticalmethod biologicalquestion

6.41 score 57 scripts

bioc

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.

rnaseq geneexpression genesetenrichment differentialexpression batcheffect qualitycontrol regression epigenetics functionalgenomics transcriptomics normalization preprocessing microarray immunooncology software

6.39 score 91 scripts 1 dependents

bioc

ontoProc:processing of ontologies of anatomy, cell lines, and so on

Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies.

Maintained by Vincent Carey. Last updated 19 days ago.

infrastructure go bioinformatics genomics ontology

3 stars 6.37 score 75 scripts 2 dependents

bioc

APL:Association Plots

APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest.

Maintained by Clemens Kohl. Last updated 5 months ago.

statisticalmethod dimensionreduction singlecell sequencing rnaseq geneexpression

15 stars 6.31 score 15 scripts

bioc

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

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

Maintained by Noriaki Sato. Last updated 5 months ago.

visualization bayesian geneexpression networkinference pathways reactome network networkenrichment genesetenrichment

64 stars 6.28 score 9 scripts

bioc

signifinder:Collection and implementation of public transcriptional cancer signatures

signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes.

Maintained by Stefania Pirrotta. Last updated 3 months ago.

geneexpression genetarget immunooncology biomedicalinformatics rnaseq microarray reportwriting visualization singlecell spatial genesignaling

7 stars 6.28 score 15 scripts

rickhelmus

patRoon:Workflows for Mass-Spectrometry Based Non-Target Analysis

Provides an easy-to-use interface to a mass spectrometry based non-target analysis workflow. Various (open-source) tools are combined which provide algorithms for extraction and grouping of features, extraction of MS and MS/MS data, automatic formula and compound annotation and grouping related features to components. In addition, various tools are provided for e.g. data preparation and cleanup, plotting results and automatic reporting.

Maintained by Rick Helmus. Last updated 11 days ago.

mass-spectrometry non-target cpp openjdk

65 stars 6.24 score 43 scripts

bioc

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 13 days ago.

geneexpression transcriptomics microarray rnaseq singlecell differentialexpression immunooncology genesetenrichment

7 stars 6.24 score 15 scripts

bioc

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

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

Maintained by Weijun Luo. Last updated 5 months ago.

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

26 stars 6.23 score 22 scripts

bioc

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.

immunooncology software visualization microarray rnaseq go datarepresentation genesetenrichment

6.23 score 93 scripts 1 dependents

bioc

OmaDB:R wrapper for the OMA REST API

A package for the orthology prediction data download from OMA database.

Maintained by Klara Kaleb. Last updated 5 months ago.

software comparativegenomics functionalgenomics genetics annotation go functionalprediction

2 stars 6.23 score 5 scripts

bioc

VariantFiltering:Filtering of coding and non-coding genetic variants

Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc.

Maintained by Robert Castelo. Last updated 2 months ago.

genetics homo_sapiens annotation snp sequencing highthroughputsequencing

4 stars 6.23 score 21 scripts

bioc

iNETgrate:Integrates DNA methylation data with gene expression in a single gene network

The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes.

Maintained by Habil Zare. Last updated 5 months ago.

geneexpression rnaseq dnamethylation networkinference network graphandnetwork biomedicalinformatics systemsbiology transcriptomics classification clustering dimensionreduction principalcomponent mrnamicroarray normalization geneprediction kegg survival core-services

74 stars 6.21 score 1 scripts

bioc

scruff:Single Cell RNA-Seq UMI Filtering Facilitator (scruff)

A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics.

Maintained by Zhe Wang. Last updated 5 months ago.

software technology sequencing alignment rnaseq singlecell workflowstep preprocessing qualitycontrol visualization immunooncology bioinformatics scrna-seq single-cell umi

8 stars 6.20 score 22 scripts

bioc

BioNet:Routines for the functional analysis of biological networks

This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork.

Maintained by Marcus Dittrich. Last updated 5 months ago.

microarray dataimport graphandnetwork network networkenrichment geneexpression differentialexpression

6.14 score 114 scripts 2 dependents

bioc

gDRstyle:A package with style requirements for the gDR suite

Package fills a helper package role for whole gDR suite. It helps to support good development practices by keeping style requirements and style tests for other packages. It also contains build helpers to make all package requirements met.

Maintained by Arkadiusz Gladki. Last updated 2 months ago.

software infrastructure

2 stars 6.10 score 2 scripts

bioc

affycoretools:Functions useful for those doing repetitive analyses with Affymetrix GeneChips

Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see.

Maintained by James W. MacDonald. Last updated 5 months ago.

reportwriting microarray onechannel geneexpression

6.07 score 117 scripts

bioc

OncoSimulR:Forward Genetic Simulation of Cancer Progression with Epistasis

Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Fitness (including just birth, just death, or both birth and death) can also be a function of the relative and absolute frequencies of other genotypes (i.e., frequency-dependent fitness). Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulating multi-species scenarios and therapeutic interventions, including adaptive therapy, is also possible. Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them.

Maintained by Ramon Diaz-Uriarte. Last updated 28 days ago.

biologicalquestion somaticmutation cpp

7 stars 6.06 score 68 scripts

ropensci

dendroNetwork:Create Networks of Dendrochronological Series using Pairwise Similarity

Creating dendrochronological networks based on the similarity between tree-ring series or chronologies. The package includes various functions to compare tree-ring curves building upon the 'dplR' package. The networks can be used to visualise and understand the relations between tree-ring curves. These networks are also very useful to estimate the provenance of wood as described in Visser (2021) <DOI:10.5334/jcaa.79> or wood-use within a structure/context/site as described in Visser and Vorst (2022) <DOI:10.1163/27723194-bja10014>.

Maintained by Ronald Visser. Last updated 2 months ago.

visualization graphandnetwork thirdpartyclient network archaeology dendrochronology dendroprovenance network-analysis tree-rings

7 stars 6.05 score 9 scripts

bioc

MOGAMUN:MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks

MOGAMUN is a multi-objective genetic algorithm that identifies active modules in a multiplex biological network. This allows analyzing different biological networks at the same time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting Genetic Algorithm, version II), which we adapted to work on networks.

Maintained by Elva-María Novoa-del-Toro. Last updated 5 months ago.

systemsbiology graphandnetwork differentialexpression biomedicalinformatics transcriptomics clustering network

12 stars 6.03 score 5 scripts

bioc

EventPointer:An effective identification of alternative splicing events using junction arrays and RNA-Seq data

EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation.

Maintained by Juan A. Ferrer-Bonsoms. Last updated 5 months ago.

alternativesplicing differentialsplicing mrnamicroarray rnaseq transcription sequencing timecourse immunooncology

4 stars 6.00 score 6 scripts

bioc

biscuiteer:Convenience Functions for Biscuit

A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc.

Maintained by Jacob Morrison. Last updated 5 months ago.

dataimport methylseq dnamethylation

6 stars 5.98 score 16 scripts

bioc

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.

geneexpression software transcription transcriptomics differentialexpression visualization reportwriting genesetenrichment go

5.98 score 4 dependents

bioc

consensusOV:Gene expression-based subtype classification for high-grade serous ovarian cancer

This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype.

Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.

classification clustering differentialexpression geneexpression microarray transcriptomics cancer-data cancer-genomics cancer-research expression-database ovarian-cancer

3 stars 5.98 score 15 scripts 1 dependents

bioc

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.

software geneexpression genesetenrichment networkenrichment network bioinformatics

15 stars 5.93 score 19 scripts

bioc

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.

genesetenrichment geneexpression sequencing

5.93 score 57 scripts

bioc

miRspongeR:Identification and analysis of miRNA sponge regulation

This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network.

Maintained by Junpeng Zhang. Last updated 5 months ago.

geneexpression biomedicalinformatics networkenrichment survival microarray software singlecell spatial rnaseq cerna mirna sponge

5 stars 5.88 score 8 scripts

duncantl

CodeDepends:Analysis of R Code for Reproducible Research and Code Comprehension

Tools for analyzing R expressions or blocks of code and determining the dependencies between them. It focuses on R scripts, but can be used on the bodies of functions. There are many facilities including the ability to summarize or get a high-level view of code, determining dependencies between variables, code improvement suggestions.

Maintained by Gabriel Becker. Last updated 1 years ago.

89 stars 5.87 score 70 scripts 1 dependents

bioc

escape:Easy single cell analysis platform for enrichment

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

Maintained by Nick Borcherding. Last updated 11 days ago.

software singlecell classification annotation genesetenrichment sequencing genesignaling pathways

5.84 score 138 scripts

bioc

BioNAR:Biological Network Analysis in R

the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms.

Maintained by Anatoly Sorokin. Last updated 1 months ago.

software graphandnetwork network

3 stars 5.83 score 35 scripts

bioc

epiNEM:epiNEM

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

Maintained by Martin Pirkl. Last updated 5 months ago.

pathways systemsbiology networkinference network

1 stars 5.83 score 1 scripts 3 dependents

bioc

EGSEA:Ensemble of Gene Set Enrichment Analyses

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

Maintained by Monther Alhamdoosh. Last updated 5 months ago.

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

5.81 score 64 scripts

bioc

cicero:Predict cis-co-accessibility from single-cell chromatin accessibility data

Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data.

Maintained by Hannah Pliner. Last updated 5 months ago.

sequencing clustering cellbasedassays immunooncology generegulation genetarget epigenetics atacseq singlecell

5.80 score 312 scripts

bioc

BiocFHIR:Illustration of FHIR ingestion and transformation using R

FHIR R4 bundles in JSON format are derived from https://synthea.mitre.org/downloads. Transformation inspired by a kaggle notebook published by Dr Alexander Scarlat, https://www.kaggle.com/code/drscarlat/fhir-starter-parse-healthcare-bundles-into-tables. This is a very limited illustration of some basic parsing and reorganization processes. Additional tooling will be required to move beyond the Synthea data illustrations.

Maintained by Vincent Carey. Last updated 5 months ago.

infrastructure dataimport datarepresentation fhir

4 stars 5.78 score 15 scripts

bioc

bioCancer:Interactive Multi-Omics Cancers Data Visualization and Analysis

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

Maintained by Karim Mezhoud. Last updated 5 months ago.

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

20 stars 5.78 score 7 scripts

cbhurley

PairViz:Visualization using Graph Traversal

Improving graphics by ameliorating order effects, using Eulerian tours and Hamiltonian decompositions of graphs. References for the methods presented here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.

Maintained by Catherine Hurley. Last updated 3 years ago.

1 stars 5.75 score 42 scripts 3 dependents

bioc

CNORode:ODE add-on to CellNOptR

Logic based ordinary differential equation (ODE) add-on to CellNOptR.

Maintained by Attila Gabor. Last updated 5 months ago.

immunooncology cellbasedassays cellbiology proteomics bioinformatics timecourse

5.74 score 37 scripts 1 dependents

bioc

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

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

Maintained by Steve Lianoglou. Last updated 15 days ago.

genesetenrichment pathways bioinformatics gsea

21 stars 5.74 score 13 scripts

bioc

scFeatures:scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor.

Maintained by Yue Cao. Last updated 5 months ago.

cellbasedassays singlecell spatial software transcriptomics

11 stars 5.69 score 15 scripts

thermostats

RVA:RNAseq Visualization Automation

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

Maintained by Xingpeng Li. Last updated 3 years ago.

9 stars 5.65 score 6 scripts

bioc

netresponse:Functional Network Analysis

Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling.

Maintained by Leo Lahti. Last updated 5 months ago.

cellbiology clustering geneexpression genetics network graphandnetwork differentialexpression microarray networkinference transcription

3 stars 5.64 score 21 scripts

bioc

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.

immunooncology geneexpression differentialexpression generegulation genetarget networkinference survival visualization genesetenrichment networkenrichment network rnaseq go kegg

5.64 score 44 scripts

cfwp

rags2ridges:Ridge Estimation of Precision Matrices from High-Dimensional Data

Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) <doi:10.18637/jss.v102.i04> and associated publications.

Maintained by Carel F.W. Peeters. Last updated 1 years ago.

c-plus-plus graphical-models machine-learning networkscience statistics openblas cpp

8 stars 5.60 score 46 scripts

alesmascaro

BCDAG:Bayesian Structure and Causal Learning of Gaussian Directed Graphs

A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables. References: F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>.

Maintained by Alessandro Mascaro. Last updated 1 months ago.

3 stars 5.58 score 17 scripts

bioc

TMSig:Tools for Molecular Signatures

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

Maintained by Tyler Sagendorf. Last updated 5 months ago.

clustering genesetenrichment graphandnetwork pathways visualization gene-sets molecular-signatures

4 stars 5.58 score 4 scripts

bioc

methylclock:Methylclock - DNA methylation-based clocks

This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock.

Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.

dnamethylation biologicalquestion preprocessing statisticalmethod normalization cpp

39 stars 5.52 score 28 scripts

bioc

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.

geneexpression biomedicalinformatics clustering genesetenrichment microarray software generegulation genetarget cerna mirna mirna-sponge mirna-targets modules openjdk

4 stars 5.51 score 5 scripts

tinnlab

RCPA:Consensus Pathway Analysis

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

Maintained by Ha Nguyen. Last updated 5 months ago.

biobase deseq2 geoquery edger limma rcyjs fgsea browserviz summarizedexperiment annotationdbi rontotools

1 stars 5.50 score 70 scripts

bioc

enrichViewNet:From functional enrichment results to biological networks

This package enables the visualization of functional enrichment results as network graphs. First the package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network. In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). The package also provides the option to create enrichment maps from functional enrichment results. Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes.

Maintained by Astrid Deschênes. Last updated 5 months ago.

biologicalquestion software network networkenrichment go cystocape functional-enrichment

5 stars 5.48 score 6 scripts

great-northern-diver

zenplots:Zigzag Expanded Navigation Plots

Graphical tools for visualizing high-dimensional data along a path of alternating one- and two-dimensional plots. Note that this includes interactive graphics plots based on 'loon' in turn based on 'tcltk' (included as part of the standard R distribution). It also requires 'graph' from Bioconductor. For more detail on use and algorithms, see <doi:10.18637/jss.v095.i04>.

Maintained by Wayne Oldford. Last updated 1 years ago.

dimensional-data dimensional-plots graphical-systems pairs zigzag

4 stars 5.46 score 12 scripts 1 dependents

bioc

R3CPET:3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process

The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results.

Maintained by Mohamed Nadhir Djekidel. Last updated 5 months ago.

networkinference geneprediction bayesian graphandnetwork network geneexpression hic chia-pet chromatin-interaction dirichlet-process-mixtures transcription-facto cpp

4 stars 5.45 score 5 scripts

bioc

PROMISE:PRojection Onto the Most Interesting Statistical Evidence

A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019

Maintained by Stan Pounds. Last updated 5 months ago.

microarray onechannel multiplecomparison geneexpression

5.44 score 46 scripts 1 dependents

bioc

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.

software geneexpression generegulation networkinference genesetenrichment biomedicalinformatics genetics transcriptomics atacseq rnaseq graphandnetwork regression transcription chipseq

5.40 score 24 scripts

bioc

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.

qualitycontrol network networkenrichment networkinference genesetenrichment functionalgenomics graphandnetwork

5.40 score 9 scripts

igordot

clustermole:Unbiased Single-Cell Transcriptomic Data Cell Type Identification

Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.

Maintained by Igor Dolgalev. Last updated 1 years ago.

cell-type cell-type-annotation cell-type-classification cell-type-identification cell-type-matching gene-expression-signatures scrna-seq single-cell

13 stars 5.37 score 36 scripts

bioc

GeDi:Defining and visualizing the distances between different genesets

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

Maintained by Annekathrin Nedwed. Last updated 5 months ago.

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

1 stars 5.36 score 22 scripts

bioc

MOSClip:Multi Omics Survival Clip

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

Maintained by Paolo Martini. Last updated 5 months ago.

software statisticalmethod graphandnetwork survival regression dimensionreduction pathways reactome

5.34 score 5 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.

genesetenrichment pathways networkenrichment network bioconductor enrichment

7 stars 5.32 score 10 scripts

bioc

GlobalAncova:Global test for groups of variables via model comparisons

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

Maintained by Manuela Hummel. Last updated 5 months ago.

microarray onechannel differentialexpression pathways regression

5.31 score 9 scripts 1 dependents

bioc

SplicingGraphs:Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them

This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways.

Maintained by H. Pagès. Last updated 5 months ago.

genetics annotation datarepresentation visualization sequencing rnaseq geneexpression alternativesplicing transcription immunooncology bioconductor-package

2 stars 5.26 score 8 scripts

bioc

gatom:Finding an Active Metabolic Module in Atom Transition Network

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

Maintained by Alexey Sergushichev. Last updated 5 months ago.

geneexpression differentialexpression pathways network

6 stars 5.26 score 8 scripts

bioc

epivizr:R Interface to epiviz web app

This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well.

Maintained by Hector Corrada Bravo. Last updated 5 months ago.

visualization infrastructure gui

5.24 score 29 scripts 2 dependents

bioc

CytoMDS:Low Dimensions projection of cytometry samples

This package implements a low dimensional visualization of a set of cytometry samples, in order to visually assess the 'distances' between them. This, in turn, can greatly help the user to identify quality issues like batch effects or outlier samples, and/or check the presence of potential sample clusters that might align with the exeprimental design. The CytoMDS algorithm combines, on the one hand, the concept of Earth Mover's Distance (EMD), a.k.a. Wasserstein metric and, on the other hand, the Multi Dimensional Scaling (MDS) algorithm for the low dimensional projection. Also, the package provides some diagnostic tools for both checking the quality of the MDS projection, as well as tools to help with the interpretation of the axes of the projection.

Maintained by Philippe Hauchamps. Last updated 2 months ago.

flowcytometry qualitycontrol dimensionreduction multidimensionalscaling software visualization

1 stars 5.23 score 2 scripts

bioc

regutools:regutools: an R package for data extraction from RegulonDB

RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks.

Maintained by Joselyn Chavez. Last updated 4 months ago.

generegulation geneexpression systemsbiology network networkinference visualization transcription bioconductor cdsb regulondb

4 stars 5.20 score 6 scripts

bioc

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.

differentialmethylation peakdetection geneprediction genesetenrichment

5.20 score 20 scripts

manueleleonelli

bnRep:A Repository of Bayesian Networks from the Academic Literature

A collection of Bayesian networks (discrete, Gaussian, and conditional linear Gaussian) collated from recent academic literature. The 'bnRep_summary' object provides an overview of the Bayesian networks in the repository and the package documentation includes details about the variables in each network. A Shiny app to explore the repository can be launched with 'bnRep_app()' and is available online at <https://manueleleonelli.shinyapps.io/bnRep>. For details see <https://github.com/manueleleonelli/bnRep>.

Maintained by Manuele Leonelli. Last updated 6 months ago.

6 stars 5.18 score 7 scripts

bioc

mastR:Markers Automated Screening Tool in R

mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of group markers in various biological and medical applications, including cancer research and developmental biology.

Maintained by Jinjin Chen. Last updated 5 months ago.

software geneexpression transcriptomics differentialexpression visualization

5 stars 5.18 score 3 scripts

cbg-ethz

clustNet:Network-Based Clustering

Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.

Maintained by Fritz Bayer. Last updated 1 years ago.

bayesian-network bayesian-networks clustering dag genomics mixture-model network-clustering

7 stars 5.16 score 41 scripts

bioc

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.

geneexpression genesetenrichment networkenrichment systemsbiology classification clustering dimensionreduction graphandnetwork neuralnetwork network mrnamicroarray rnaseq visualization bioinformatics genecoexpressionnetwork graphs networkclustering networks self-training semi-supervised-learning unsupervised-learning

2 stars 5.12 score 44 scripts

bioc

AnnotationHubData:Transform public data resources into Bioconductor Data Structures

These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures.

Maintained by Bioconductor Package Maintainer. Last updated 12 days ago.

dataimport

5.12 score 22 scripts 4 dependents

bioc

ROntoTools:R Onto-Tools suite

Suite of tools for functional analysis.

Maintained by Sorin Draghici. Last updated 5 months ago.

networkanalysis microarray graphsandnetworks

5.10 score 15 scripts 2 dependents

bioc

epivizrData:Data Management API for epiviz interactive visualization app

Serve data from Bioconductor Objects through a WebSocket connection.

Maintained by Hector Corrada Bravo. Last updated 5 months ago.

infrastructure visualization

1 stars 5.08 score 4 scripts 4 dependents

bioc

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.

gui geneexpression clustering go genesetenrichment kegg multiplecomparison cancer cancer-data gene gene-expression gene-methylation gene-mutation gene-sets methylation mskcc mutations tcltk

7 stars 5.08 score 17 scripts

bioc

derfinderPlot:Plotting functions for derfinder

This package provides plotting functions for results from the derfinder package. This helps separate the graphical dependencies required for making these plots from the core functionality of derfinder.

Maintained by Leonardo Collado-Torres. Last updated 4 months ago.

differentialexpression sequencing rnaseq software visualization immunooncology bioconductor derfinder

2 stars 5.00 score 5 scripts

bioc

altcdfenvs:alternative CDF environments (aka probeset mappings)

Convenience data structures and functions to handle cdfenvs

Maintained by Laurent Gautier. Last updated 5 months ago.

microarray onechannel qualitycontrol preprocessing annotation proprietaryplatforms transcription

4.95 score 5 scripts 1 dependents

james-thorson-noaa

phylosem:Phylogenetic Structural Equation Model

Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using structural equation models (SEM), extending methods from Thorson et al. (2023) <doi:10.1111/2041-210X.14076>. This implementation includes a minimal set of features, to allow users to easily read all of the documentation and source code. PCM using SEM includes phylogenetic linear models and structural equation models as nested submodels, but also allows imputation of missing values. Features and comparison with other packages are described in Thorson and van der Bijl (2023) <doi:10.1111/jeb.14234>.

Maintained by James Thorson. Last updated 21 days ago.

cpp

1 stars 4.92 score 14 scripts

bioc

sigFeature:sigFeature: Significant feature selection using SVM-RFE & t-statistic

This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy.

Maintained by Pijush Das Developer. Last updated 5 months ago.

featureextraction geneexpression microarray transcription mrnamicroarray geneprediction normalization classification supportvectormachine

4.92 score 21 scripts

bioc

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

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

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

graphandnetwork genesignaling genetarget network pathways kegg reactome software

4.90 score 8 scripts

bioc

PanomiR:Detection of miRNAs that regulate interacting groups of pathways

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

Maintained by Pourya Naderi. Last updated 5 months ago.

geneexpression genesetenrichment genetarget mirna pathways

3 stars 4.89 score 13 scripts

andrewdhawan

sigQC:Quality Control Metrics for Gene Signatures

Provides gene signature quality control metrics in publication ready plots. Namely, enables the visualization of properties such as expression, variability, correlation, and comparison of methods of standardisation and scoring metrics.

Maintained by Andrew Dhawan. Last updated 8 months ago.

4 stars 4.89 score 13 scripts

bioc

MSstatsBioNet:Network Analysis for MS-based Proteomics Experiments

A set of tools for network analysis using mass spectrometry-based proteomics data and network databases. The package takes as input the output of MSstats differential abundance analysis and provides functions to perform enrichment analysis and visualization in the context of prior knowledge from past literature. Notably, this package integrates with INDRA, which is a database of biological networks extracted from the literature using text mining techniques.

Maintained by Anthony Wu. Last updated 2 months ago.

immunooncology massspectrometry proteomics software qualitycontrol networkenrichment network

1 stars 4.85 score 3 scripts

bioc

sSNAPPY:Single Sample directioNAl Pathway Perturbation analYsis

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

Maintained by Wenjun Liu. Last updated 5 months ago.

software geneexpression genesetenrichment genesignaling

1 stars 4.83 score 15 scripts

bioc

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.

software network visualization systemsbiology geneexpression genesetenrichment graphandnetwork clustering differentialexpression microarray rnaseq transcription immunooncology

4 stars 4.81 score 9 scripts

bioc

TFutils:TFutils

This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided.

Maintained by Vincent Carey. Last updated 5 months ago.

transcriptomics

4.80 score 21 scripts

netcoupler

NetCoupler:Inference of Causal Links Between a Network and an External Variable

The 'NetCoupler' algorithm identifies potential direct effects of correlated, high-dimensional variables formed as a network with an external variable. The external variable may act as the dependent/response variable or as an independent/predictor variable to the network.

Maintained by Luke Johnston. Last updated 1 years ago.

6 stars 4.78 score 7 scripts

bioc

gINTomics:Multi-Omics data integration

gINTomics is an R package for Multi-Omics data integration and visualization. gINTomics is designed to detect the association between the expression of a target and of its regulators, taking into account also their genomics modifications such as Copy Number Variations (CNV) and methylation. What is more, gINTomics allows integration results visualization via a Shiny-based interactive app.

Maintained by Angelo Velle. Last updated 5 months ago.

geneexpression rnaseq microarray visualization copynumbervariation genetarget quarto

3 stars 4.78 score 3 scripts

annennenne

causalDisco:Tools for Causal Discovery on Observational Data

Various tools for inferring causal models from observational data. The package includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools for evaluating differences in adjacency matrices, which can be used for evaluating performance of causal discovery procedures.

Maintained by Anne Helby Petersen. Last updated 29 days ago.

19 stars 4.76 score 10 scripts

bioc

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.

annotation visualization genesetenrichment software

4.74 score 9 scripts

bioc

miRLAB:Dry lab for exploring miRNA-mRNA relationships

Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses.

Maintained by Thuc Duy Le. Last updated 5 months ago.

mirna geneexpression networkinference network

4.72 score 11 scripts

bioc

rsbml:R support for SBML, using libsbml

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

Maintained by Michael Lawrence. Last updated 1 months ago.

graphandnetwork pathways network libsbml cpp

4.71 score 19 scripts 1 dependents

bioc

CytoPipelineGUI:GUI's for visualization of flow cytometry data analysis pipelines

This package is the companion of the `CytoPipeline` package. It provides GUI's (shiny apps) for the visualization of flow cytometry data analysis pipelines that are run with `CytoPipeline`. Two shiny applications are provided, i.e. an interactive flow frame assessment and comparison tool and an interactive scale transformations visualization and adjustment tool.

Maintained by Philippe Hauchamps. Last updated 5 months ago.

flowcytometry preprocessing qualitycontrol workflowstep immunooncology software visualization gui shinyapps

4.70 score 2 scripts

sap01

TGS:Rapid Reconstruction of Time-Varying Gene Regulatory Networks

Rapid advancements in high-throughput gene sequencing technologies have resulted in genome-scale time-series datasets. Uncovering the underlying temporal sequence of gene regulatory events in the form of time-varying gene regulatory networks demands accurate and computationally efficient algorithms. Such an algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan Kumar, and Ashish Anand; Rapid reconstruction of time-varying gene regulatory networks; IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS algorithm is shown to consume only 29 minutes for a microarray dataset with 4028 genes. This package provides an implementation of the TGS algorithm and its variants.

Maintained by Saptarshi Pyne. Last updated 5 years ago.

networkinference graphandnetwork network geneexpression microarray systemsbiology software

4.70 score 3 scripts

bioc

GenomicInteractionNodes:A R/Bioconductor package to detect the interaction nodes from HiC/HiChIP/HiCAR data

The GenomicInteractionNodes package can import interactions from bedpe file and define the interaction nodes, the genomic interaction sites with multiple interaction loops. The interaction nodes is a binding platform regulates one or multiple genes. The detected interaction nodes will be annotated for downstream validation.

Maintained by Jianhong Ou. Last updated 2 months ago.

hic sequencing software

4.70 score 1 scripts

bioc

RCyjs:Display and manipulate graphs in cytoscape.js

Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets.

Maintained by Paul Shannon. Last updated 5 months ago.

visualization graphandnetwork thirdpartyclient

4.68 score 48 scripts

bioc

epivizrChart:R interface to epiviz web components

This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer.

Maintained by Hector Corrada Bravo. Last updated 5 months ago.

visualization gui

4.68 score 12 scripts

audreyqyfu

MRPC:PC Algorithm with the Principle of Mendelian Randomization

A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>.

Maintained by Audrey Fu. Last updated 3 years ago.

8 stars 4.68 score 20 scripts

bioc

BiGGR:Constraint based modeling in R using metabolic reconstruction databases

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

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

systems biology pathway network graphandnetwork visualization metabolomics

4.67 score 58 scripts

bioc

methodical:Discovering genomic regions where methylation is strongly associated with transcriptional activity

DNA methylation is generally considered to be associated with transcriptional silencing. However, comprehensive, genome-wide investigation of this relationship requires the evaluation of potentially millions of correlation values between the methylation of individual genomic loci and expression of associated transcripts in a relatively large numbers of samples. Methodical makes this process quick and easy while keeping a low memory footprint. It also provides a novel method for identifying regions where a number of methylation sites are consistently strongly associated with transcriptional expression. In addition, Methodical enables housing DNA methylation data from diverse sources (e.g. WGBS, RRBS and methylation arrays) with a common framework, lifting over DNA methylation data between different genome builds and creating base-resolution plots of the association between DNA methylation and transcriptional activity at transcriptional start sites.

Maintained by Richard Heery. Last updated 2 months ago.

dnamethylation methylationarray transcription genomewideassociation software openjdk

4.65 score 14 scripts

bioc

Cepo:Cepo for the identification of differentially stable genes

Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations. We present Cepo, a new method to explore cell identities from single-cell RNA-sequencing data using differential stability as a new metric to define cell identity genes. Cepo computes cell-type specific gene statistics pertaining to differential stable gene expression.

Maintained by Hani Jieun Kim. Last updated 5 months ago.

classification geneexpression singlecell software sequencing differentialexpression

4.62 score 14 scripts 1 dependents

bioc

MAIT:Statistical Analysis of Metabolomic Data

The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions.

Maintained by Pol Sola-Santos. Last updated 5 months ago.

immunooncology massspectrometry metabolomics software

4.60 score 20 scripts

papatheodorou-group

scOntoMatch:Aligning Ontology Annotation Across Single Cell Datasets with 'scOntoMatch'

Unequal granularity of cell type annotation makes it difficult to compare scRNA-seq datasets at scale. Leveraging the ontology system for defining cell type hierarchy, 'scOntoMatch' aims to align cell type annotations to make them comparable across studies. The alignment involves two core steps: first is to trim the cell type tree within each dataset so each cell type does not have descendants, and then map cell type labels cross-studies by direct matching and mapping descendants to ancestors. Various functions for plotting cell type trees and manipulating ontology terms are also provided. In the Single Cell Expression Atlas hosted at EBI, a compendium of datasets with curated ontology labels are great inputs to this package.

Maintained by Yuyao Song. Last updated 1 years ago.

8 stars 4.60 score 6 scripts

bioc

nempi:Inferring unobserved perturbations from gene expression data

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

Maintained by Martin Pirkl. Last updated 5 months ago.

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

2 stars 4.60 score 2 scripts

bioc

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

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

Maintained by Martin Pirkl. Last updated 5 months ago.

pathways systemsbiology networkinference network geneexpression generegulation preprocessing

2 stars 4.60 score 5 scripts

bioc

dce:Pathway Enrichment Based on Differential Causal Effects

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

Maintained by Kim Philipp Jablonski. Last updated 4 months ago.

software statisticalmethod graphandnetwork regression geneexpression differentialexpression networkenrichment network kegg bioconductor causality

13 stars 4.59 score 4 scripts

bioc

meshr:Tools for conducting enrichment analysis of MeSH

A set of annotation maps describing the entire MeSH assembled using data from MeSH.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

annotationdata functionalannotation bioinformatics statistics annotation multiplecomparisons meshdb

4.56 score 9 scripts 1 dependents

bioc

flowMerge:Cluster Merging for Flow Cytometry Data

Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed.

Maintained by Greg Finak. Last updated 5 months ago.

immunooncology clustering flowcytometry

4.56 score 6 scripts 1 dependents

bioc

Pigengene:Infers biological signatures from gene expression data

Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes.

Maintained by Habil Zare. Last updated 5 months ago.

geneexpression rnaseq networkinference network graphandnetwork biomedicalinformatics systemsbiology transcriptomics classification clustering decisiontree dimensionreduction principalcomponent microarray normalization immunooncology

4.56 score 10 scripts 1 dependents

bioc

phenoTest:Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation.

Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided.

Maintained by Evarist Planet. Last updated 5 months ago.

microarray differentialexpression multiplecomparison clustering classification

4.56 score 9 scripts 1 dependents

hanjunwei-lab

ICDS:Identification of Cancer Dysfunctional Subpathway with Omics Data

Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.

Maintained by Junwei Han. Last updated 8 months ago.

4.54 score 3 scripts

bioc

BioGA:Bioinformatics Genetic Algorithm (BioGA)

Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package allows users to analyze and optimize high throughput genomic data using genetic algorithms. The functions provided are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R.

Maintained by Dany Mukesha. Last updated 5 months ago.

experimentaldesign technology genetic-algorithm optimization cpp

4.54 score 5 scripts

bioc

clipper:Gene Set Analysis Exploiting Pathway Topology

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

Maintained by Paolo Martini. Last updated 5 months ago.

4.48 score 19 scripts

bioc

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

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

Maintained by Francesco Napolitano. Last updated 5 months ago.

geneexpression differentialexpression genesetenrichment dimensionreduction pathways go

4.48 score 4 scripts

bioc

uncoverappLib:Interactive graphical application for clinical assessment of sequence coverage at the base-pair level

a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name.

Maintained by Emanuela Iovino. Last updated 5 months ago.

software visualization annotation coverage

3 stars 4.48 score 4 scripts

daniel-jg

ontologyPlot:Visualising Sets of Ontological Terms

Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.

Maintained by Daniel Greene. Last updated 1 years ago.

4.48 score 50 scripts 5 dependents

bioc

intansv:Integrative analysis of structural variations

This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package.

Maintained by Wen Yao. Last updated 5 months ago.

genetics annotation sequencing software

4.48 score 2 scripts

bioc

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.

geneexpression differentialexpression genesetenrichment datarepresentation visualization cpp

4.48 score 7 scripts

bioc

fgga:Hierarchical ensemble method based on factor graph

Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models.

Maintained by Flavio Spetale. Last updated 5 months ago.

software statisticalmethod classification network networkinference supportvectormachine graphandnetwork go

3 stars 4.48 score 6 scripts

bioc

PPInfer:Inferring functionally related proteins using protein interaction networks

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

Maintained by Dongmin Jung. Last updated 5 months ago.

software statisticalmethod network graphandnetwork genesetenrichment networkenrichment pathways

4.48 score 4 scripts 1 dependents

bioc

msgbsR:msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions

Pipeline for the anaysis of a MS-GBS experiment.

Maintained by Benjamin Mayne. Last updated 5 months ago.

immunooncology differentialmethylation dataimport epigenetics methylseq

4.48 score 1 scripts

bioc

PhenStat:Statistical analysis of phenotypic data

Package contains methods for statistical analysis of phenotypic data.

Maintained by Hamed Haselimashhadi. Last updated 5 months ago.

statisticalmethod

4.45 score 14 scripts

bioc

flowTrans:Parameter Optimization for Flow Cytometry Data Transformation

Profile maximum likelihood estimation of parameters for flow cytometry data transformations.

Maintained by Greg Finak. Last updated 5 months ago.

immunooncology flowcytometry

4.40 score 21 scripts

bioc

DEGraph:Two-sample tests on a graph

DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results.

Maintained by Laurent Jacob. Last updated 5 months ago.

microarray differentialexpression graphandnetwork network networkenrichment decisiontree

4.34 score 11 scripts

bioc

cyanoFilter:Phytoplankton Population Identification using Cell Pigmentation and/or Complexity

An approach to filter out and/or identify phytoplankton cells from all particles measured via flow cytometry pigment and cell complexity information. It does this using a sequence of one-dimensional gates on pre-defined channels measuring certain pigmentation and complexity. The package is especially tuned for cyanobacteria, but will work fine for phytoplankton communities where there is at least one cell characteristic that differentiates every phytoplankton in the community.

Maintained by Oluwafemi Olusoji. Last updated 5 months ago.

flowcytometry clustering onechannel

4.30 score 4 scripts

bioc

compEpiTools:Tools for computational epigenomics

Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples.

Maintained by Mattia Furlan. Last updated 5 months ago.

geneexpression sequencing visualization genomeannotation coverage

4.30 score 6 scripts

bioc

CeTF:Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis

This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data).

Maintained by Carlos Alberto Oliveira de Biagi Junior. Last updated 5 months ago.

sequencing rnaseq microarray geneexpression transcription normalization differentialexpression singlecell network regression chipseq immunooncology coverage cpp

4.30 score 9 scripts

bioc

flagme:Analysis of Metabolomics GC/MS Data

Fragment-level analysis of gas chromatography-massspectrometry metabolomics data.

Maintained by Mark Robinson. Last updated 5 months ago.

differentialexpression massspectrometry

4.30 score 2 scripts

bioc

pogos:PharmacOGenomics Ontology Support

Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies.

Maintained by VJ Carey. Last updated 3 months ago.

pharmacogenomics pooledscreens immunooncology

4.30 score 10 scripts

bioc

nethet:A bioconductor package for high-dimensional exploration of biological network heterogeneity

Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).

Maintained by Nicolas Staedler. Last updated 5 months ago.

clustering graphandnetwork

4.30 score 7 scripts

bioc

consICA:consensus Independent Component Analysis

consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems.

Maintained by Petr V. Nazarov. Last updated 5 months ago.

technology statisticalmethod sequencing rnaseq transcriptomics classification featureextraction

4.30 score 2 scripts