R-universe search: enrichr

Showing 14 of total 14 results (show query)

wjawaid

enrichR:Provides an R Interface to 'Enrichr'

Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See <https://maayanlab.cloud/Enrichr/> for further details.

Maintained by Wajid Jawaid. Last updated 1 months ago.

79.2 match 90 stars 9.96 score 7 dependents

moosa-r

rbioapi:User-Friendly R Interface to Biologic Web Services' API

Currently fully supports Enrichr, JASPAR, miEAA, PANTHER, Reactome, STRING, and UniProt! The goal of rbioapi is to provide a user-friendly and consistent interface to biological databases and services. In a way that insulates the user from the technicalities of using web services API and creates a unified and easy-to-use interface to biological and medical web services. This is an ongoing project; New databases and services will be added periodically. Feel free to suggest any databases or services you often use.

Maintained by Moosa Rezwani. Last updated 1 months ago.

api-client bioinformatics biology enrichment enrichment-analysis enrichr jaspar mieaa over-representation-analysis panther reactome string uniprot

30.3 match 20 stars 7.60 score 55 scripts

bioc

SurfR:Surface Protein Prediction and Identification

Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots.

Maintained by Aurora Maurizio. Last updated 1 days ago.

software sequencing rnaseq geneexpression transcription differentialexpression principalcomponent genesetenrichment pathways batcheffect functionalgenomics visualization dataimport functionalprediction geneprediction go dge enrichment-analysis metaanalysis plots proteins public-data surface surfaceome

12.7 match 3 stars 5.43 score 3 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 2 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

6.0 match 77 stars 8.28 score 37 scripts 1 dependents

bioc

hypeR:An R Package For Geneset Enrichment Workflows

An R Package for Geneset Enrichment Workflows.

Maintained by Anthony Federico. Last updated 5 months ago.

genesetenrichment annotation pathways bioinformatics computational-biology geneset-enrichment-analysis

5.4 match 76 stars 8.22 score 145 scripts

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 23 days ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

3.8 match 181 stars 10.16 score 252 scripts

bioc

normr:Normalization and difference calling in ChIP-seq data

Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions.

Maintained by Johannes Helmuth. Last updated 5 months ago.

bayesian differentialpeakcalling classification dataimport chipseq ripseq functionalgenomics genetics multiplecomparison normalization peakdetection preprocessing alignment cpp openmp

6.2 match 11 stars 5.93 score 13 scripts

satijalab

Seurat:Tools for Single Cell Genomics

A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.

Maintained by Paul Hoffman. Last updated 1 years ago.

human-cell-atlas single-cell-genomics single-cell-rna-seq cpp

1.8 match 2.4k stars 16.86 score 50k scripts 73 dependents

cogdisreslab

KRSA:KRSA: Kinome Random Sampling Analyzer

The goal of this package is to analyze the PamChip data and identify the changes in the active kinome. The package can preprocess the PamChip data output from BioNavigator and use Random Sampling and Permutation Analysis to identify upstream kinases. Additionally, this package provides a set of useful visualizations for the PamChip data.

Maintained by Ali Sajid Imami. Last updated 10 days ago.

kinase phosphatases pamchip kinome random sampling permutation analysis

5.4 match 4 stars 4.42 score 49 scripts

eltebioinformatics

mulea:Enrichment Analysis Using Multiple Ontologies and False Discovery Rate

Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Maintained by Tamas Stirling. Last updated 3 months ago.

annotation differentialexpression geneexpression genesetenrichment go graphandnetwork multiplecomparison pathways reactome software transcription visualization enrichment enrichment-analysis functional-enrichment-analysis gene-set-enrichment ontologies transcriptomics cpp

1.8 match 28 stars 7.36 score 34 scripts

bioc

phantasus:Visual and interactive gene expression analysis

Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported.

Maintained by Alexey Sergushichev. Last updated 5 months ago.

geneexpression gui visualization datarepresentation transcriptomics rnaseq microarray normalization clustering differentialexpression principalcomponent immunooncology

1.3 match 43 stars 7.68 score 15 scripts

bioc

TDbasedUFEadv:Advanced package of tensor decomposition based unsupervised feature extraction

This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.

Maintained by Y-h. Taguchi. Last updated 5 months ago.

geneexpression featureextraction methylationarray singlecell software bioconductor-package bioinformatics tensor-decomposition

2.2 match 4.48 score 4 scripts

cogdisreslab

BioPathNet:BioPathNet: Three Pod Analysis System

This package aims to provide a simple interface to perform the Three Pod Analysis of RNASeq dataaset. In addition, this also provides utility functions to perform the individual components.

Maintained by Ali Sajid Imami. Last updated 2 years ago.

bioinformatics bioinformatics-pipeline ilincs transcriptomics

3.8 match 2 stars 2.00 score 5 scripts

dzhang777

SlideCNA:Calls Copy Number Alterations from Slide-Seq Data

This takes spatial single-cell-type RNA-seq data (specifically designed for Slide-seq v2) that calls copy number alterations (CNAs) using pseudo-spatial binning, clusters cellular units (e.g. beads) based on CNA profile, and visualizes spatial CNA patterns. Documentation about 'SlideCNA' is included in the the pre-print by Zhang et al. (2022, <doi:10.1101/2022.11.25.517982>). The package 'enrichR' (>= 3.0), conditionally used to annotate SlideCNA-determined clusters with gene ontology terms, can be installed at <https://github.com/wjawaid/enrichR> or with install_github("wjawaid/enrichR").

Maintained by Diane Zhang. Last updated 2 months ago.

0.9 match 1.70 score 3 scripts