Skip to contents

ConsortiumMetabolism Class

ConsortiumMetabolism.Rmd

Class

MF - ConsortiumMetabolism

ConsortiumMetabolism (MF) objects build the backbone of the ramen functionality by providing a novel approach to the representation of microbial consortia from a functional perspective. This is one of the three main object types of ramen. The ConsortiumMetabolism class serves as the standard way to save and work with data on the metabolism, the function, of a microbial community.

Content

Upon the creation of a ConsortiumMetabolism class based on a three column input, additional information is calculated:

  • graph representation of the network in MiCo object with data from the different matrices as weights
  • If the graph is purely directional, disable the calculations for other than bin_mat
  • how can we define optimised / generalist / robust consortia?
  • Which metrics to have in the object?
  • Tanimoto?
  • MES score (relationship between consumption and production of metabolites)
  • SCS, MUS, MPS, MIP/MRO, SMETANA
  • species
  • metabolites
  • fluxes
  • edges
  • binary matrix
  • flux matrix
  • name
  • flux_consumption
  • flux_production
  • effective_flux_consumption
  • effective_flux_production

Each of these slots is described in the ConsortiumMetabolism-class() class documentation.

Validation

  • unkown metabolites in the database for ChemmineR
  • 0 fluxes in the input data
  • correct column names
  • Need a function that calcs the phylo tree for the metabolites
  • Return output on the different issues with the input data

Logic

  1. Create unnamed matrix and save metabolite names to col/row data

Available Methods

  • getCo(): Returns the community in tibble format.
  • getSpecies(): Retrieves the species from a community.
  • getMet(): Retrieves metabolites from a community.
  • getFlux(): Retrieves the fluxes from a community.
  • getEdges(): Retrieves the edges from a community.

Examples

To create an object of type MiCo, the user must input the data listed below. This data can either be contained in a single tibble or data frame, saved in a CSV file, or input as separate vectors.

  • species: A character vector specifying the species present in the community.
  • met: A character vector specifying the metabolites present in the community.
  • flux: A numeric vector specifying the fluxes of each metabolite in the community.
  • name: Character string giving the name of the community.
data <- tibble::tibble(
  species = c(
    "RQR9693L",
    "RQR9693L",
    "RQR9693L",
    "RQR9693L",
    "MEV1152G",
    "MEV1152G",
    "MEV1152G",
    "XIQ2234Q",
    "XIQ2234Q",
    "XIQ2234Q"
  ),
  met = c(
    "met3",
    "met1",
    "met4",
    "met4",
    "met3",
    "met2",
    "met2",
    "met3",
    "met4",
    "met1"
  ),
  flux = c(
    -0.3023640,
    0.5344641,
    -2.2337716,
    3.3063823,
    2.5520278,
    0.3388498,
    -1.8460633,
    -1.3043675,
    -0.4788769,
    0.6027292
  )
)

# ConsortiumMetabolism(data, name = "example_MiCo")

ramen provides the function syn_community() to generate random MiCo objects based on user parameters.

# makeSynMiCo(n_species = 8, max_met = 10, name = "example_syn_MiCo")

Create a MiCo Object from a tibble

ramen contains a set of example data that can be used for the analysis of microbial communities. For this example, we will use data created by MiSoS(oup). Two example communities are provided in the package ac_A1R12_1 and cit_A1R12_1.

The MiCo function can take either a path to a .csv file or a data frame/tibble as input.