compositionGenomesMetaT: Define the species composition of all samples in a...
In vllorens/metaester: Simulate metatranscriptomics datasets

Description Usage Arguments Details Value References Examples

View source: R/read_distribution_genomes.R

compositionGenomesMetaT returns a composition matrix with rows as species/ genomes and columns as samples (cases or controls)

compositionGenomesMetaT(
  composition = c("custom", "empirical", "even"),
  empiricalSeed = NULL,
  genomes,
  compositionMatrix = NULL,
  nReads = 1e+06,
  nReplicates = 10,
  seed = 42
)

`composition`	A character string indicating which method to use to determine the composition of the matrix. Any of "custom", "empirical" or "even" can be used. See "details"
`empiricalSeed`	A single number or a numeric vector of length equal to nReplicates. Indicates the random seed to assign the reads to different species in each sample. If NULL or a single number, the same seed will be applied to all samples so that they will have the same composition, with differences only due to random sampling
`genomes`	Character vector of genome names or genome IDs for the genomes to include in the simulation
`compositionMatrix`	A composition matrix predefined by the user, in which rows represent species and columns represent samples. Id user provides a composition matrix, this function only performs the random sampling to scale to the number of reads desired. Defaults to NULL
`nReads`	An integer, indicating the number of reads to simulate per sample.
`nReplicates`	An integer, indicating the total samples (cases and controls).
`seed`	An integer, sets the random seed for the read distribution.

There are three options to distribute the total reads among the different species to simulate: - custom: requires that the user provides a custom composition matrix and scales it via random sampling (with replacement) to the specified number of reads in nReads - even: reads are distributed evenly among all the species listed in the genomes argument. - empirical: fits a negative binomial distribution (Vandeputte D. et al, 2017) to a real dataset (Martinez X. et al, 2016) of metatranscriptomics data. Uses this distribution to generate a composition matrix from the species listed in the genomes argument.

A microbial composition matrix of nReplicates columns and nrow(genomes) rows.

Martinez X. et al (2016): MetaTrans: an open source pipeline for metatranscriptomics Scientific Reports 6, Article number: 26447 Vandeputte D. et al (2017): Quantitative microbiome profiling links gut community variation to microbial load. Nature 551:507–511

# define a list of genomes to simulate
genomesToSimulate <- c("F. prausnitzii", "R. intestialis", "L. lactis", "E. faecalis",
                       "R. obeum")
# obtain the empirical composition matrix for this 5 species
compositionGenomesMetaT(composition="empirical", empiricalSeed=1,
                        genomes=genomesToSimulate, nReads=500000,nReplicates=10)