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 📄 on GeneTonic is now available on bioRxiv at https://www.biorxiv.org/content/10.1101/2021.05.19.444862v1.
You can install the development version of GeneTonic from GitHub with:
library("remotes")
remotes::install_github("federicomarini/GeneTonic",
dependencies = TRUE, build_vignettes = TRUE)
This is a basic example which shows you how to use GeneTonic
on a demo
dataset (the one included in the macrophage
package).
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")
You can find the rendered version of the documentation of GeneTonic
at
the project website https://federicomarini.github.io/GeneTonic,
created with pkgdown
.
Please visit http://shiny.imbei.uni-mainz.de:3838/GeneTonic/ to see a
small demo instance running, on the macrophage
dataset.
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.
Please note that the GeneTonic project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
MIT © Federico Marini