goatea

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.

Highly interactive & modular shiny app to explore three facets of RNA-Seq analysis: differential expression (DE), functional enrichment and pattern analysis. Several visualizations are implemented to provide a wide-ranging view of data sets. For DE analysis, we provide PCA plot, MA plot, Upset plot & heatmaps, in addition to a highly customizable gene plot. Seven different visualizations are available for functional enrichment analysis, and we also support gene pattern analysis. Genes of interest can be tracked across all modules using the gene scratchpad. In addition, carnation provides an integrated platform to manage multiple projects and user access that can be run on a central server to share with collaborators.

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

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.

To facilitate and streamline phosphoproteomics data analysis, we developed SmartPhos, an R package for the pre-processing, quality control, and exploratory analysis of phosphoproteomics data generated by MaxQuant and Spectronaut. The package can be used either through the R command line or through an interactive ShinyApp called SmartPhos Explorer. The package contains methods such as normalization and normalization correction, transformation, imputation, batch effect correction, PCA, heatmap, differential expression, time-series clustering, gene set enrichment analysis, and kinase activity inference.

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.