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bioc

ReactomeGSA:Client for the Reactome Analysis Service for comparative multi-omics gene set analysis

The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited.

Maintained by Johannes Griss. Last updated 4 months ago.

genesetenrichmentproteomicstranscriptomicssystemsbiologygeneexpressionreactome

22 stars 8.50 score 67 scripts 1 dependents

bioc

DEP:Differential Enrichment analysis of Proteomics data

This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package.

Maintained by Arne Smits. Last updated 5 months ago.

immunooncologyproteomicsmassspectrometrydifferentialexpressiondatarepresentation

7.10 score 628 scripts

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.

immunooncologygeneexpressiondifferentialexpressionrnaseqsequencingvisualizationqualitycontrolguigenesetenrichmentreportwritingshinyappsbioconductordifferential-expressionreproducible-researchrna-seqrna-seq-analysisshinyuser-friendly

29 stars 6.78 score 5 scripts

fredhutch

gimap:Calculate Genetic Interactions for Paired CRISPR Targets

Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the 'gimap' package is genetic interaction scores which are the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) <doi:10.1016/j.celrep.2021.109597>.

Maintained by Candace Savonen. Last updated 17 days ago.

6.43 score 7 scripts

bioc

CRISPRball:Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering

A Shiny application for visualization, exploration, comparison, and filtering of CRISPR screens analyzed with MAGeCK RRA or MLE. Features include interactive plots with on-click labeling, full customization of plot aesthetics, data upload and/or download, and much more. Quickly and easily explore your CRISPR screen results and generate publication-quality figures in seconds.

Maintained by Jared Andrews. Last updated 3 months ago.

softwareshinyappscrisprqualitycontrolvisualizationguicrispr-screendata-visualizationinteractive-visualizationsmageckplotlyscreeningshiny

9 stars 6.03 score 24 scripts

bioc

autonomics:Unified Statistical Modeling of Omics Data

This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.

Maintained by Aditya Bhagwat. Last updated 2 months ago.

softwaredataimportpreprocessingdimensionreductionprincipalcomponentregressiondifferentialexpressiongenesetenrichmenttranscriptomicstranscriptiongeneexpressionrnaseqmicroarrayproteomicsmetabolomicsmassspectrometry

5.95 score 5 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

mspms:Tools for the analysis of MSP-MS data

This package provides functions for the analysis of data generated by the multiplex substrate profiling by mass spectrometry for proteases (MSP-MS) method. Data exported from upstream proteomics software is accepted as input and subsequently processed for analysis. Tools for statistical analysis, visualization, and interpretation of the data are provided.

Maintained by Charlie Bayne. Last updated 4 months ago.

proteomicsmassspectrometrypreprocessingproteaseproteomics-data-analysis

4.95 score 4 scripts

connor-reid-tiffany

omu:A Metabolomics Analysis Tool for Intuitive Figures and Convenient Metadata Collection

Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data from the KEGG-REST API.

Maintained by Connor Tiffany. Last updated 1 years ago.

3 stars 4.89 score 52 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.

geneexpressionrnaseqmicroarrayvisualizationcopynumbervariationgenetargetquarto

3 stars 4.78 score 3 scripts

ftwkoopmans

goat:Gene Set Analysis Using the Gene Set Ordinal Association Test

Perform gene set enrichment analyses using the Gene set Ordinal Association Test (GOAT) algorithm and visualize your results. Koopmans, F. (2024) <doi:10.1038/s42003-024-06454-5>.

Maintained by Frank Koopmans. Last updated 1 months ago.

bioinformaticsgeneset-enrichmentgeneset-enrichment-analysiscppopenmp

10 stars 4.40 score 8 scripts

philipberg

baldur:Bayesian Hierarchical Modeling for Label-Free Proteomics

Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (<doi:10.1101/2023.05.11.540411>).

Maintained by Philip Berg. Last updated 8 months ago.

cpp

1 stars 4.00 score 8 scripts

bioc

ProteoMM:Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009).

Maintained by Yuliya V Karpievitch. Last updated 5 months ago.

immunooncologymassspectrometryproteomicsnormalizationdifferentialexpression

3.38 score 12 scripts

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