createIndividual: Creates an in silico individual.

In oliviaAB/sismonr: Simulation of in Silico Multi-Omic Networks

Creates an in silico individual.

Description

Creates a in silico individual to be simulated (object of class insilicoindividual).

Usage

createIndividual(
  insilicosystem,
  variantsList,
  variantsFreq,
  indargs,
  InitVar = NULL,
  initialNoise = TRUE
)

Arguments

insilicosystem	An insilicosystem object. The in silico system based on which individuals are created. See createInSilicoSystem.
variantsList	A named list giving the variants segregating in the population for each gene (e.g. created by createVariants). Each element corresponds to one gene in the system (name of the element = gene ID). Each element is a matrix, in which each column represents a variant of the gene segregating in the population. The rows represent the QTL effect coefficients of each variant (i.e. the impact of each mutation the variant carries).
variantsFreq	A named list giving for each gene the allelic frequency of each segregating variant. Each element corresponds to one gene in the system (name of the element = gene ID). Each element is a vector, of length equal to the number of variants of the gene segregating in the population, giving the allele frequency of each of the variants.
indargs	An object of class insilicoindividualargs (i.e. a list with parameters for in silico individuals generation).
InitVar	A list of the multiplicative coefficients to be applied to the initial abundance of the different molecules: elements "R" and "P" of the list giving the coefficients for the RNA and protein form of the genes, respectively (coefficient for gene i at the i-th position in the vectors). If NULL, all coefficients set to 1.
initialNoise	Logical. Is stochastic noise applied to the initial abundance of the different molecules? Default value is TRUE (see Details).

Details

initialNoise: by default, the initial abundance of a molecule is equal to its steady state abundance in the absence of any regulation (e.g. for the RNA abundance of a gene, it is transcription rate / decay rate). If initialNoise = TRUE, instead the initial abundance of the molecule will be sampled from a truncated Normal distribution of mean SSabund and SD sqrt(SSabund), where SSabund is its steady state abundance in the absence of any regulation, as specified above. The Normal distribution is truncated to only return positive values.

Value

An object of class insilicoindividual, that is a list composed of:

• QTLeffects: a list of the variants carried by the individual. 1st level of the list: the different "GCN" (Gene Copy Number), that is the different alleles of the genes (as defined by the ploidy of the individual: a diploid will have GCN1 and GCN2); 2nd level: the different QTL effect coefficients. The elements in this 2nd-level list are vectors of QTL effect coefficients for the different genes (coefficient for gene i at the i-th position in the vector).

• haplotype: data-frame (rows = genes, columns = Gene copy number) giving the ID of the gene variant carried by the individual for each gene copy number (allele).

• InitAbundance: A list of the initial abundance of the different molecules. 1st level of the list: the different "GCN" (Gene Copy Number), that is the different alleles of the genes (as defined by the ploidy of the individual: a diploid will have GCN1 and GCN2); 2nd level of the list: initial abundance of the protein ("P") and RNA ("R") form of the genes (coefficient for gene i at the i-th position in the vectors).

Examples

## Not run: 
mysystem = createInSilicoSystem(G = 3, ploidy = 4)
indargs = insilicoindividualargs()
## We will create only 1 variant of gene 1, 3 variants of gene 2 and
## 2 variants of gene 3
nbvariants = c(1, 3, 2)

qtlnames = c("qtlTCrate", "qtlRDrate",
             "qtlTCregbind", "qtlRDregrate",
             "qtlactivity", "qtlTLrate",
             "qtlPDrate", "qtlTLregbind",
             "qtlPDregrate", "qtlPTMregrate")

genvariants = lapply(nbvariants, function(x){
  matrix(1, nrow = length(qtlnames), ncol = x,
         dimnames = list(qtlnames, 1:x))
})
names(genvariants) = 1:length(nbvariants)

## the 2nd variant of gene 2 has a mutation reducing its transcription rate by 3
genvariants$`2`["qtlTCrate", 2] = 0.33
## and the 3rd variant has an increased translation rate
genvariants$`2`["qtlTLrate", 2] = 1.5

## The 2nd variant of gene 3 has a mutation decreasing the activity of
## its active product
genvariants$`3`["qtlactivity", 2] = 0.7

## Allelic frequency of each variant
genvariants.freq = list('1' = c(1),
                        '2' = c(0.6, 0.3, 0.1),
                        '3' = c(0.9, 0.1))

## The third gene is not expressed at the beginning of the simulation
## (its initial abundance is 0)
InitVar = list("R" = c(1, 1, 0), "P" = c(1, 1, 0))

myind = createIndividual(mysystem, genvariants, genvariants.freq, indargs, InitVar = InitVar)

## End(Not run)

oliviaAB/sismonr documentation built on June 25, 2022, 11:59 p.m.