R-universe search: geneontology

Showing 6 of total 6 results (show query)

bioc

GOexpress:Visualise microarray and RNAseq data using gene ontology annotations

The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

software geneexpression transcription differentialexpression genesetenrichment datarepresentation clustering timecourse microarray sequencing rnaseq annotation multiplecomparison pathways go visualization immunooncology bioconductor bioconductor-package bioconductor-stats geneontology geneset-enrichment

10.0 match 9 stars 6.75 score 31 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

4.5 match 2 stars 5.12 score 44 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

2.0 match 77 stars 8.28 score 37 scripts 1 dependents

jpquast

protti:Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools

Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions.

Maintained by Jan-Philipp Quast. Last updated 5 months ago.

data-analysis lip-ms mass-spectrometry omics protein proteomics systems-biology

1.8 match 61 stars 8.58 score 83 scripts

bioc

fenr:Fast functional enrichment for interactive applications

Perform fast functional enrichment on feature lists (like genes or proteins) using the hypergeometric distribution. Tailored for speed, this package is ideal for interactive platforms such as Shiny. It supports the retrieval of functional data from sources like GO, KEGG, Reactome, Bioplanet and WikiPathways. By downloading and preparing data first, it allows for rapid successive tests on various feature selections without the need for repetitive, time-consuming preparatory steps typical of other packages.

Maintained by Marek Gierlinski. Last updated 24 days ago.

functionalprediction differentialexpression genesetenrichment go kegg reactome proteomics

1.8 match 4.60 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

1.7 match 4.30 score 6 scripts