<img src="inst/www/GeneTonic.png" align="right" alt="" width="120" /> <!-- README.md is generated from README.Rmd. Please edit that file --> # GeneTonic <!-- badges: start --> [![R build status](https://github.com/federicomarini/GeneTonic/workflows/R-CMD-check/badge.svg)](https://github.com/federicomarini/GeneTonic/actions) [![](https://bioconductor.org/shields/build/devel/bioc/GeneTonic.svg)](https://bioconductor.org/checkResults/devel/bioc-LATEST/GeneTonic/) [![](https://img.shields.io/github/last-commit/federicomarini/GeneTonic.svg)](https://github.com/federicomarini/GeneTonic/commits/master) [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable) [![Codecov.io coverage status](https://codecov.io/github/federicomarini/GeneTonic/coverage.svg?branch=master)](https://codecov.io/github/federicomarini/GeneTonic) <!-- badges: end --> The goal of GeneTonic is to analyze and integrate the results from Differential Expression analysis and functional enrichment analysis. This package provides a Shiny application that aims to combine at different levels the existing pieces of the transcriptome data and results, in a way that makes it easier to generate insightful observations and hypothesis - 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. GeneTonic can be found on Bioconductor (<https://www.bioconductor.org/packages/GeneTonic>). A preprint :page\_facing\_up: on GeneTonic is now available on bioRxiv at <https://www.biorxiv.org/content/10.1101/2021.05.19.444862v1>. ## Installation You can install the development version of GeneTonic from GitHub with: ``` r library("remotes") remotes::install_github("federicomarini/GeneTonic", dependencies = TRUE, build_vignettes = TRUE) ``` ## Example This is a basic example which shows you how to use `GeneTonic` on a demo dataset (the one included in the `macrophage` package). ``` r library("GeneTonic") example("GeneTonic") # which will in the end run library("macrophage") library("DESeq2") library("org.Hs.eg.db") library("AnnotationDbi") # dds object data("gse", package = "macrophage") dds_macrophage <- DESeqDataSet(gse, design = ~line + condition) rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15) dds_macrophage <- estimateSizeFactors(dds_macrophage) # annotation object anno_df <- data.frame( gene_id = rownames(dds_macrophage), gene_name = mapIds(org.Hs.eg.db, keys = rownames(dds_macrophage), column = "SYMBOL", keytype = "ENSEMBL"), stringsAsFactors = FALSE, row.names = rownames(dds_macrophage) ) # res object data(res_de_macrophage, package = "GeneTonic") res_de <- res_macrophage_IFNg_vs_naive # res_enrich object data(res_enrich_macrophage, package = "GeneTonic") res_enrich <- shake_topGOtableResult(topgoDE_macrophage_IFNg_vs_naive) GeneTonic(dds = dds_macrophage, res_de = res_de, res_enrich = res_enrich, annotation_obj = anno_df, project_id = "my_first_genetonic") ``` ## Usage overview You can find the rendered version of the documentation of `GeneTonic` at the project website <https://federicomarini.github.io/GeneTonic>, created with `pkgdown`. ## Sneak peek? Please visit <http://shiny.imbei.uni-mainz.de:3838/GeneTonic/> to see a small demo instance running, on the `macrophage` dataset. ## Development If you encounter a bug, have usage questions, or want to share ideas and functionality to make this package better, feel free to file an [issue](https://github.com/federicomarini/GeneTonic/issues). ## Code of Conduct Please note that the `GeneTonic` project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms. ## License MIT © Federico Marini