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DRomics: Dose Response for Omics

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DRomics is a freely available tool for dose-response (or concentration-response) characterization from omics data. It is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates to use DRomics).

After a first step which consists in importing, checking and if needed normalizing/transforming the data (step 1), the aim of the proposed workflow is to select monotonic and/or biphasic significantly responsive items (e.g. probes, contigs, metabolites) (step 2), to choose the best-fit model among a predefined family of monotonic and biphasic models to describe the response of each selected item (step 3), and to derive a benchmark dose or concentration from each fitted curve (step 4). Those steps can be performed in R using DRomics functions, or using the shiny application named DRomics-shiny.

In the available version, DRomics supports single-channel microarray data (in log2 scale), RNAseq data (in raw counts) and other continuous omics data such as metabolomics or proteomics (in log scale). In order to link responses across biological levels based on a common method, DRomics also handles continuous apical data as long as they meet the use conditions of least squares regression (homoscedastic Gaussian regression).

As built in the environmental risk assessment context where omics data are more often collected on non-sequenced species or species communities, DRomics does not provide an annotation pipeline. The annotation of items selected by DRomics may be complex in this context, and must be done outside DRomics using databases such as KEGG or Gene Ontology. DRomics functions can then be used to help the interpretation of the workflow results in view of the biological annotation. It enables a multi-omics approach, with the comparison of the responses at the different levels of organization (in view of a common biological annotation). It can also be used to compare the responses at one organization level, but measured under different experimental conditions (e.g. different time points). This interpretation can be performed in R using DRomics functions, or using a second shiny application DRomicsInterpreter-shiny.

All informations about DRomics can also be found at https://lbbe.univ-lyon1.fr/fr/dromics.

Keywords : dose response modelling / benchmark dose (BMD) / environmental risk assessment / transcriptomics / proteomics / metabolomics / toxicogenomics / multi-omics

The package

The limma and DESeq2 packages from Bioconductor must be installed for the use of DRomics:

if (!requireNamespace("BiocManager", quietly = TRUE))
   install.packages("BiocManager")

BiocManager::install(c("limma", "DESeq2"))

The stable version of DRomics can be installed from CRAN using:

install.packages("DRomics")

The development version of DRomics can be installed from GitHub (remotes needed):

if (!requireNamespace("remotes", quietly = TRUE))
   install.packages("remotes")
   
remotes::install_github("aursiber/DRomics")

Finally load the package in your current R session with the following R command:

Vignette and cheat sheet

A vignette is attached to the DRomics package. This vignette is intended to help users to start using the DRomics package. It is complementary to the reference manual where you can find more details on each function of the package. The first part of this vignette (Main workflow, steps 1 to 4) could also help users of the first shiny application DRomics-shiny. The second part (Help for biological interpretation of DRomics outputs) could also help users of the second shiny application DRomicsInterpreter-shiny.

This vignette can be reached by:

vignette("DRomics_vignette")

Note that, by default, the vignette is not installed when the package is installed through GitHub. The following command (rather long to execute because of the large size of the vignette) will allow you to access the vignette of the development version of the package you installed from GitHub:

remotes::install_github("aursiber/DRomics", build_vignettes = TRUE)

A cheat sheet that sum up the DRomics workflow is also available here.

Two shiny apps

The two shiny apps (DRomics-shiny and DRomicsInterpreter-shiny) that work with DRomics are available :

These shiny apps are runing with the development version of DRomics.

Authors & Contacts

If you have any need that is not yet covered, any feedback on the package / Shiny app, or any training needs, feel free to email us at .

Issues can be reported on https://github.com/aursiber/DRomics/issues .

Citation

If you use Dromics, you should cite:

Delignette-Muller ML, Siberchicot A, Larras F, Billoir E (2023). DRomics, a workflow to exploit dose-response omics data in ecotoxicology. Peer Community Journal. https://peercommunityjournal.org/articles/10.24072/pcjournal.325/

Larras F, Billoir E, Baillard V, Siberchicot A, Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M and Delignette-Muller ML (2018). DRomics : a turnkey tool to support the use of the dose-response framework for omics data in ecological risk assessment. Environmental Science & Technology. https://pubs.acs.org/doi/10.1021/acs.est.8b04752 You can find this article at: https://hal.science/hal-02309919

You can also look at the following citation for a complete example of use:
Larras F, Billoir E, Scholz S, Tarkka M, Wubet T, Delignette-Muller ML, Schmitt-Jansen M (2020). A multi-omics concentration-response framework uncovers novel understanding of triclosan effects in the chlorophyte Scenedesmus vacuolatus. Journal of Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2020.122727.