RITHMS

Our framework, R Implementation of a Transgenerational Hologenomic Model-based Simulator (RITHMS) is an open-source package, builds upon the MoBPS package and incorporates the distinctive architecture of the microbiota, notably vertical and horizontal transfer as well as modulation due to the environment and host genetics. In addition, RITHMS can account for a variety of selection strategies, is adaptable to different genetic architectures.

Full documentation website on: https://SolenePety.github.io/RITHMS
Last code version on: https://github.com/SolenePety/RITHMS

Here is a little summary of how RITHMS work, but you can read the preprint for more details.

Installation

You can install the development version of RITHMS from GitHub using devtools as shown below.

Note: Before installing the RITHMS package, please make sure that version 1.10.49 of the MoBPS package is manually installed from the following link:

if (!requireNamespace("MoBPS", quietly = TRUE) || packageVersion("MoBPS") != "1.10.49") {
    message("Installing MoBPS 1.10.49...")
    devtools::install_url("https://github.com/tpook92/MoBPS/raw/master/Previous%20versions/MoBPS_1.10.49.tar.gz")
}

# install.packages("devtools")
devtools::install_github("SolenePety/RITHMS")

This requirement will be handled more automatically in a future version of RITHMS.

Toy dataset

You already have a toy dataset, a subset from Déru et al. 2020, there are 1845 species and 780 individuals, that show you the expected structure of input data :

library(RITHMS)
datafile <- system.file("DeruPop.rds", package = "RITHMS")
ToyData <- readRDS(datafile)

To import your own dataset, you can refer to the following vignette.

Quick Start from the toy dataset

taxa_assign_g <- assign_taxa(ToyData)
generations_simu <- holo_simu(h2 = 0.25, b2 = 0.25, founder_object = ToyData, n_clust = taxa_assign_g)

Quick Start from Ped/Map format

founder_object <- read_input_data(path_to_microbiome = "/path/to/microbiome.txt",
                                  path_to_pedmap = "/path/to/pedmap/'prefix'",
                                  biome_id_column = "ind_id")

taxa_assign_g <- assign_taxa(founder_object)
generations_simu <- holo_simu(h2 = 0.25, b2 = 0.25, founder_object = founder_object, n_clust = taxa_assign_g, n_ind = 500)
# Choose n_ind such that it is consistent with the initial dimensions of your data set

To go further

If you’re interested into reproducing the figures of the article you can take a look at this vignette to generate the figures coming from the article