Nothing
R/args_classes.R
In sismonr: Simulation of in Silico Multi-Omic Networks
Defines functions
insilicosystemargs
insilicoindividualargs
Documented in
insilicoindividualargs
insilicosystemargs
## insilicosystemsargs class ----
#' Constructor function for the \code{insilicosystemsargs} class.
#'
#' Constructor function for the \code{insilicosystemsargs} class, with default values for the parameters if not provided by the user.
#'
#' For the protein-coding (and non-coding) biological function ratios (i.e. PC.TC.p, PC.TL.p, etc): if none of the ratios are provided,
#' then they are set to their default values. Otherwise, if at least one value among the 6 (5 for noncoding genes) is set by the user:
#' \itemize{
#' \item if the sum of the provided values is 1 or more: the non-specified values are set to 0, and the specified values are normalised
#' such that their sum is 1.
#' \item if the sum of the provided values is less than 1: the non-specified values are set such that the sum of all ratios equals 1.
#' }
#' Example: if the user sets PC.TC.p to 1 and PC.TL.p to 0.6, but does not provide any values for the other ratios,
#' then PC.TC.p is set to 1/(1+0.6)=0.625, PC.TL.p to 0.6/(1+0.6)=0.375, and PC.RD.p, PC.PD.p, PC.PTM.p and PC.MR.p are all set to 0.
#' Accordingly, if the user only sets NC.TC.p to 0.6, then NC.TL.p, NC.RD.p, NC.PD.p and NC.PTM.p are all set to 0.1.
#'
#' @param G Integer. Number of genes in the system. Default value is 10.
#' @param ploidy Numeric. The ploidy of the system, i.e. how many copies of each gene are present in the system. Default value is 2.
#' @param PC.p Numeric. Probability of each gene to be a protein-coding gene. Default value is 0.7.
#' @param PC.TC.p Numeric. Probability of a protein-coding gene to be a regulator of transcription. Default value is 0.4 (see details).
#' @param PC.TL.p Numeric. Probability of a protein-coding gene to be a regulator of translation. Default value is 0.3 (see details).
#' @param PC.RD.p Numeric. Probability of a protein-coding gene to be a regulator of RNA decay. Default value is 0.1 (see details).
#' @param PC.PD.p Numeric. Probability of a protein-coding gene to be a regulator of protein decay. Default value is 0.1 (see details).
#' @param PC.PTM.p Numeric. Probability of a protein-coding gene to be a regulator of protein post-translational modification. Default value is 0.05 (see details).
#' @param PC.MR.p Numeric. Probability of a protein-coding gene to be a metabolic enzyme. Default value is 0.05 (see details).
#' @param NC.TC.p Numeric. Probability of a noncoding gene to be a regulator of transcription. Default value is 0.3 (see details).
#' @param NC.TL.p Numeric. Probability of a noncoding gene to be a regulator of translation. Default value is 0.3 (see details).
#' @param NC.RD.p Numeric. Probability of a noncoding gene to be a regulator of RNA decay. Default value is 0.3 (see details).
#' @param NC.PD.p Numeric. Probability of a noncoding gene to be a regulator of protein decay. Default value is 0.05 (see details).
#' @param NC.PTM.p Numeric. Probability of a noncoding gene to be a regulator of protein post-translational modification. Default value is 0.05 (see details).
#' @param TC.pos.p Numeric. Probability of a regulation targeting gene transcription to be positive. Default value is 0.5.
#' @param TL.pos.p Numeric. Probability of a regulation targeting gene translation to be positive. Default value is 0.5.
#' @param PTM.pos.p Numeric. Probability of a regulation targeting protein post-translational modification to be positive (i.e the targeted protein
#' is transformed into its modified form, as opposed to the modified protein being transformed back into its original form). Default value is 0.5.
#' @param basal_transcription_rate_samplingfct Function from which the transcription rates of genes are sampled (input x is the required sample size). Default value is
#' a function returning \code{(10^v)/3600}, with \code{v} a vector of size x sampled from a normal distribution with mean of 3 and sd of 0.5.
#' @param basal_translation_rate_samplingfct Function from which the translation rates of genes are sampled (input x is the required sample size). Default value is
#' a function returning \code{(10^v)/3600}, with \code{v} a vector of size x sampled from a normal distribution with mean of 2.146 and sd of 0.7.
#' @param basal_RNAlifetime_samplingfct Function from which the transcript lifetimes are sampled (input x is the required sample size). Default value is
#' a function returning \code{(10^v)*3600}, with \code{v} a vector of size x sampled from a normal distribution with mean of 0.95 and sd of 0.2.
#' @param basal_protlifetime_samplingfct Function from which the protein lifetime are sampled (input x is the required sample size). Default value is
#' a function returning \code{(10^v)*3600}, with \code{v} a vector of size x sampled from a normal distribution with mean of 1.3 and sd of 0.4.
#' @param TC.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the transcription regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TC.NC.outdeg.distr Form of the distribution of the number of targets (out-degree) of noncoding regulators in the transcription regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TC.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the transcription regulation graph. Default value is 3.
#' @param TC.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the transcription regulation graph. Default value is 5.
#' @param TC.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the transcription regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TC.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the transcription regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TC.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation in the transcription regulation graph. Default value is 0.2.
#' @param TC.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation in the transcription regulation graph. Default value is 0.
#' @param TC.PC.twonodesloop Logical. Are 2-nodes loops authorised in the transcription regulation graph with protein regulators? Default value is FALSE.
#' @param TC.NC.twonodesloop Logical. Are 2-nodes loops authorised in the transcription regulation graph with noncoding regulators? Default value is FALSE.
#' @param TCbindingrate_samplingfct Function from which the binding rates of transcription regulators on their targets are sampled (input \code{means} is a vector of length equal to the required sample size, giving for each
#' edge (regulatory interaction) for which a binding rate is being sampled the value of the sampled unbinding rate divided by the steady-state abundance of the regulator
#' in absence of any regulation in the system). Default value is a function returning \code{10^v}, where \code{v} is a vector with the same length as \code{means} whose elements are sampled
#' from a truncated normal distribution with mean equal to the log10 of the corresponding element in \code{means}, and sd = 0.1, the minimum authorised value being the log10 of the corresponding element in \code{means}.
#' @param TCunbindingrate_samplingfct Function from which the unbinding rates of transcription regulators from their target are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -3 and sd of 0.2.
#' @param TCfoldchange_samplingfct Function from which the transcription fold change induced by a bound regulator is sampled (input x is the required sample size). Default value is
#' a truncated normal distribution with a mean of 3, sd of 10 and minimum authorised value of 1.5.
#' @param TL.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the translation regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TL.NC.outdeg.distr Form of the the distribution of the number of targets (out-degree) of noncoding regulators in the translation regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TL.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the translation regulation graph. Default value is 4.
#' @param TL.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the translation regulation graph. Default value is 6.
#' @param TL.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the translation regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TL.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the translation regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TL.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation in the translation regulation graph. Default value is 0.2.
#' @param TL.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation in the translation regulation graph. Default value is 0.
#' @param TL.PC.twonodesloop Logical. Are 2-nodes loops authorised in the translation regulation graph with protein regulators? Default value is FALSE.
#' @param TL.NC.twonodesloop Logical. Are 2-nodes loops authorised in the translation regulation graph with noncoding regulators? Default value is FALSE.
#' @param TLbindingrate_samplingfct Function from which the binding rate of translation regulators on target are sampled (input \code{means} is a vector of length equal to the required sample size, giving for each
#' edge (regulatory interaction) for which a binding rate is being sampled the value of the sampled unbinding rate divided by the steady-state abundance of the regulator
#' in absence of any regulation in the system). Default value is a function returning \code{10^v}, where \code{v} is a vector with the same length as \code{means} whose elements are sampled
#' from a truncated normal distribution with mean equal to the log10 of the corresponding element in \code{means}, and sd = 0.1, the minimum authorised value being the log10 of the corresponding element in \code{means}.
#' @param TLunbindingrate_samplingfct Function from which the unbinding rate of translation regulators from target are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -3 and sd of 0.2.
#' @param TLfoldchange_samplingfct Function from which the translation fold change induced by a bound regulator are sampled (input x is the required sample size). Default value is
#' a truncated normal distribution with a mean of 3, sd of 10 and minimum authorised value of 1.5.
#' @param RD.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the RNA decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param RD.NC.outdeg.distr Form of the the distribution of the number of targets (out-degree) of noncoding regulators in the RNA decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param RD.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the RNA decay regulation graph. Default value is 4.
#' @param RD.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the RNA decay regulation graph. Default value is 6.
#' @param RD.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the RNA decay graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param RD.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the RNA decay graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param RD.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation in the RNA decay regulation graph. Default value is 0.2.
#' @param RD.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation in the RNA decay regulation graph. Default value is 0.
#' @param RD.PC.twonodesloop Logical. Are 2-nodes loops authorised in the RNA decay regulation graph with protein regulators? Default value is FALSE.
#' @param RD.NC.twonodesloop Logical. Are 2-nodes loops authorised in the RNA decay regulation graph with noncoding regulators? Default value is FALSE.
#' @param RDregrate_samplingfct Function from which the RNA decay rates of targets of RNA decay regulators are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -5 and sd of 1.5.
#' @param PD.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the protein decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PD.NC.outdeg.distr Form of the the distribution of the number of targets (out-degree) of noncoding regulators in the protein decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PD.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the protein decay regulation graph. Default value is 4.
#' @param PD.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the protein decay regulation graph. Default value is 6.
#' @param PD.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the protein decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PD.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the protein decay graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PD.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation in the protein decay regulation graph. Default value is 0.2.
#' @param PD.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation in the protein decay regulation graph. Default value is 0.
#' @param PD.PC.twonodesloop Logical. Are 2-nodes loops authorised in the protein decay graph with protein regulators in the protein decay regulation graph? Default value is FALSE.
#' @param PD.NC.twonodesloop Logical. Are 2-nodes loops authorised in the protein decay graph with noncoding regulators in the protein decay regulation graph? Default value is FALSE.
#' @param PDregrate_samplingfct Function from which the protein decay rates of targets of protein decay regulators are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -5 and sd of 1.5.
#' @param PTM.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the post-translational modification regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PTM.NC.outdeg.distr Form of the the distribution of the number of targets (out-degree) of noncoding regulators in the post-translational modification regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PTM.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the protein post-translational modification graph. Default value is 4.
#' @param PTM.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the protein post-translational modification graph. Default value is 6.
#' @param PTM.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the protein post-translational modification graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PTM.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the protein post-translational modification graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PTM.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation. Default value is 0.2.
#' @param PTM.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation. Default value is 0.
#' @param PTM.PC.twonodesloop Logical. Are 2-nodes loops authorised in the protein post-translational modification graph with protein regulators? Default value is FALSE.
#' @param PTM.NC.twonodesloop Logical. Are 2-nodes loops authorised in the protein post-translational modification graph with noncoding regulators? Default value is FALSE.
#' @param PTMregrate_samplingfct Function from which the protein transformation rates of targets of post-translational modification regulators are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -5 and sd of 1.5.
#' @param regcomplexes Can the regulators controlling a common target form regulatory complexes in the different regulatory graphs? Can be 'none', 'prot' (only protein can form regulatory complexes) or 'both'
#' (both regulatory RNAs and proteins can form regulatory complexes). Default value is "prot".
#' @param regcomplexes.p Numeric. Probability that regulators controlling a common target form regulatory complexes; ignore if \code{regcomplexes} = 'none'. Default value is 0.3.
#' @param regcomplexes.size Integer. Number of components of a regulatory complex; ignore if \code{regcomplexes} = 'none'. Default value is 2.
#' @param complexesformationrate_samplingfct Function from which the formation rate of regulatory complexes are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -3 and sd of 0.7.
#' @param complexesdissociationrate_samplingfct Function from which the dissociation rate of regulatory complexes are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of 3 and sd of 0.9.
#' @return An object of the class \code{insilicosystemargs}, that is a named list of the different parameters.
#' @examples
#' sysargs = insilicosystemargs(G = 15, PC.p = 0.2,
#' basal_transcription_rate_samplingfct = function(x){runif(x, 0.1, 0.2)})
#' @export
insilicosystemargs <- function(
G = 10,
ploidy = 2,
PC.p = 0.7,
PC.TC.p = NULL,
PC.TL.p = NULL,
PC.RD.p = NULL,
PC.PD.p = NULL,
PC.PTM.p = NULL,
PC.MR.p = NULL,
NC.TC.p = NULL,
NC.TL.p = NULL,
NC.RD.p = NULL,
NC.PD.p = NULL,
NC.PTM.p = NULL,
TC.pos.p = 0.5,
TL.pos.p = 0.5,
PTM.pos.p = 0.5,
# TC.PC.pos.p = 0.5,
# TC.NC.pos.p = 0.5,
# TL.PC.pos.p = 0.5,
# TL.NC.pos.p = 0.5,
# PTM.PC.pos.p = 0.5,
# PTM.NC.pos.p = 0.5,
basal_transcription_rate_samplingfct = NULL,
basal_translation_rate_samplingfct = NULL,
basal_RNAlifetime_samplingfct = NULL,
basal_protlifetime_samplingfct = NULL,
TC.PC.outdeg.distr = "powerlaw",
TC.NC.outdeg.distr = "powerlaw",
TC.PC.outdeg.exp = 3,
TC.NC.outdeg.exp = 5,
TC.PC.indeg.distr = "powerlaw",
TC.NC.indeg.distr = "powerlaw",
TC.PC.autoregproba = 0.2,
TC.NC.autoregproba = 0,
TC.PC.twonodesloop = FALSE,
TC.NC.twonodesloop = FALSE,
TCbindingrate_samplingfct = NULL,
TCunbindingrate_samplingfct = NULL,
TCfoldchange_samplingfct = NULL,
TL.PC.outdeg.distr = "powerlaw",
TL.NC.outdeg.distr = "powerlaw",
TL.PC.outdeg.exp = 4,
TL.NC.outdeg.exp = 6,
TL.PC.indeg.distr = "powerlaw",
TL.NC.indeg.distr = "powerlaw",
TL.PC.autoregproba = 0.2,
TL.NC.autoregproba = 0,
TL.PC.twonodesloop = FALSE,
TL.NC.twonodesloop = FALSE,
TLbindingrate_samplingfct = NULL,
TLunbindingrate_samplingfct = NULL,
TLfoldchange_samplingfct = NULL,
RD.PC.outdeg.distr = "powerlaw",
RD.NC.outdeg.distr = "powerlaw",
RD.PC.outdeg.exp = 4,
RD.NC.outdeg.exp = 6,
RD.PC.indeg.distr = "powerlaw",
RD.NC.indeg.distr = "powerlaw",
RD.PC.autoregproba = 0.2,
RD.NC.autoregproba = 0,
RD.PC.twonodesloop = FALSE,
RD.NC.twonodesloop = FALSE,
RDregrate_samplingfct = NULL,
PD.PC.outdeg.distr = "powerlaw",
PD.NC.outdeg.distr = "powerlaw",
PD.PC.outdeg.exp = 4,
PD.NC.outdeg.exp = 6,
PD.PC.indeg.distr = "powerlaw",
PD.NC.indeg.distr = "powerlaw",
PD.PC.autoregproba = 0.2,
PD.NC.autoregproba = 0,
PD.PC.twonodesloop = FALSE,
PD.NC.twonodesloop = FALSE,
PDregrate_samplingfct = NULL,
PTM.PC.outdeg.distr = "powerlaw",
PTM.NC.outdeg.distr = "powerlaw",
PTM.PC.outdeg.exp = 4,
PTM.NC.outdeg.exp = 6,
PTM.PC.indeg.distr = "powerlaw",
PTM.NC.indeg.distr = "powerlaw",
PTM.PC.autoregproba = 0.2,
PTM.NC.autoregproba = 0,
PTM.PC.twonodesloop = FALSE,
PTM.NC.twonodesloop = FALSE,
PTMregrate_samplingfct = NULL,
regcomplexes = "prot",
regcomplexes.p = 0.3,
regcomplexes.size = 2,
complexesformationrate_samplingfct = NULL,
complexesdissociationrate_samplingfct = NULL
){
if(is.null(basal_transcription_rate_samplingfct)) basal_transcription_rate_samplingfct = function(x){ logval = rnorm(x, mean = -0.92, sd = 0.35); val = 10^logval; return(val/60) }
if(is.null(basal_translation_rate_samplingfct)) basal_translation_rate_samplingfct = function(x){ logval = rnorm(x, mean = 2.146, sd = 0.7); val = 10^logval; return(val/3600) }
if(is.null(basal_RNAlifetime_samplingfct)) basal_RNAlifetime_samplingfct = function(x){ logval = rnorm(x, mean = 1.36, sd = 0.2); val = 10^logval; return(val*60) }
if(is.null(basal_protlifetime_samplingfct)) basal_protlifetime_samplingfct = function(x){ logval = rnorm(x, mean = 5.43, sd = 1); val = 2^logval; return(val*60) }
if(is.null(TCbindingrate_samplingfct)) TCbindingrate_samplingfct = function(means){ logval = truncnorm::rtruncnorm(length(means),a = log10(means), mean = log10(means), sd = 0.1); return(10^logval) }
if(is.null(TCunbindingrate_samplingfct)) TCunbindingrate_samplingfct = function(x){ logval = rnorm(x, mean = -3, sd = 0.2); return(10^(logval)) }
if(is.null(TCfoldchange_samplingfct)) TCfoldchange_samplingfct = function(x){ truncnorm::rtruncnorm(x, a = 1.5, mean = 3, sd = 10) }
if(is.null(TLbindingrate_samplingfct)) TLbindingrate_samplingfct = function(means){ logval = truncnorm::rtruncnorm(length(means),a = log10(means), mean = log10(means), sd = 0.1); return(10^logval) }
if(is.null(TLunbindingrate_samplingfct)) TLunbindingrate_samplingfct = function(x){ logval = rnorm(x, mean = -3, sd = 0.2); return(10^(logval)) }
if(is.null(TLfoldchange_samplingfct)) TLfoldchange_samplingfct = function(x){ truncnorm::rtruncnorm(x, a = 1.5, mean = 3, sd = 10) }
if(is.null(RDregrate_samplingfct)) RDregrate_samplingfct = function(x){ logval = rnorm(x, mean = -5, sd = 1.5); return(10^logval) }
if(is.null(PDregrate_samplingfct)) PDregrate_samplingfct = function(x){ logval = rnorm(x, mean = -5, sd = 1.5); return(10^logval) }
if(is.null(PTMregrate_samplingfct)) PTMregrate_samplingfct = function(x){ logval = rnorm(x, mean = -8, sd = 1.5); return(10^logval) }
if(is.null(complexesformationrate_samplingfct)) complexesformationrate_samplingfct = function(x){ logval = rnorm(x, mean = -3, sd = 0.7); return(10^(logval)) }
if(is.null(complexesdissociationrate_samplingfct)) complexesdissociationrate_samplingfct = function(x){ logval = rnorm(x, mean = 2, sd = 1.2); return(10^(logval)) }
NC.p = 1 - PC.p
## Probability of each protein-coding gene biological function
temp = c(PC.TC.p, PC.TL.p, PC.RD.p, PC.PD.p, PC.PTM.p, PC.MR.p)
if(is.null(temp)){ ## if no values are provided, give default values
PC.TC.p = 0.4
PC.TL.p = 0.3
PC.RD.p = 0.1
PC.PD.p = 0.1
PC.PTM.p = 0.05
PC.MR.p = 0.05
} else if(sum(temp) >= 1 | length(temp) == 6){ ## if at least one value is provided, and their sum is >=1, normalise the given values and set to 0 the others
for(v in c("PC.TC.p", "PC.TL.p", "PC.RD.p", "PC.PD.p", "PC.PTM.p", "PC.MR.p")){
assign(v, ifelse(is.null(get(v)), 0, get(v)/sum(temp)))
}
} else if(sum(temp) < 1){ ## if at least one value is provided but don't sum up to 1, assign the remaining proba such that the sum of all proba is 1
residual = (1-sum(temp))/(6-length(temp))
for(v in c("PC.TC.p", "PC.TL.p", "PC.RD.p", "PC.PD.p", "PC.PTM.p", "PC.MR.p")){
assign(v, ifelse(is.null(get(v)), residual, get(v)))
}
}
## Probability of each noncoding gene biological function
temp = c(NC.TC.p, NC.TL.p, NC.RD.p, NC.PD.p, NC.PTM.p)
if(is.null(temp)){ ## if no values are provided, give default values
NC.TC.p = 0.3
NC.TL.p = 0.3
NC.RD.p = 0.3
NC.PD.p = 0.05
NC.PTM.p = 0.05
} else if(sum(temp) >= 1 | length(temp) == 5){ ## if at least one value is provided, and their sum is >=1, normalise the given values and set to 0 the others
for(v in c("NC.TC.p", "NC.TL.p", "NC.RD.p", "NC.PD.p", "NC.PTM.p")){
assign(v, ifelse(is.null(get(v)), 0, get(v)/sum(temp)))
}
} else if(sum(temp) < 1){ ## if at least one value is provided but don't sum up to 1, assign the remaining proba such that the sum of all proba is 1
residual = (1-sum(temp))/(6-length(temp))
for(v in c("NC.TC.p", "NC.TL.p", "NC.RD.p", "NC.PD.p", "NC.PTM.p")){
assign(v, ifelse(is.null(get(v)), residual, get(v)))
}
}
## There cannot be negative regulation for transcript and protein decay
RD.pos.p = 1 ## RD.PC.pos.p: probability that the RNA decay is positively regulated by protein regulators (faster decay)
PD.pos.p = 1 ## PD.PC.pos.p: probability that the protein decay is positively regulated by protein regulators (faster decay)
# RD.PC.pos.p = 1 ## RD.PC.pos.p: probability that the RNA decay is positively regulated by protein regulators (faster decay)
# RD.NC.pos.p = 1 ## RD.NC.pos.p: probability that the RNA decay is positively regulated by noncoding regulators (faster decay)
# PD.PC.pos.p = 1 ## PD.PC.pos.p: probability that the protein decay is positively regulated by protein regulators (faster decay)
# PD.NC.pos.p = 1 ## PD.NC.pos.p: probability that the protein decay is positively regulated by noncoding regulators (faster decay)
gcnList = sapply(1:ploidy, function(x){paste0("GCN", x)})
value = list( "G" = G,
"ploidy" = ploidy,
"PC.p" = PC.p,
"PC.TC.p" = PC.TC.p,
"PC.TL.p" = PC.TL.p,
"PC.RD.p" = PC.RD.p,
"PC.PD.p" = PC.PD.p,
"PC.PTM.p" = PC.PTM.p,
"PC.MR.p" = PC.MR.p,
"NC.p" = NC.p,
"NC.TC.p" = NC.TC.p,
"NC.TL.p" = NC.TL.p,
"NC.RD.p" = NC.RD.p,
"NC.PD.p" = NC.PD.p,
"NC.PTM.p" = NC.PTM.p,
"TC.pos.p" = TC.pos.p,
"TL.pos.p" = TL.pos.p,
"RD.pos.p" = RD.pos.p,
"PD.pos.p" = PD.pos.p,
"PTM.pos.p" = PTM.pos.p,
# "TC.PC.pos.p" = TC.PC.pos.p,
# "TC.NC.pos.p" = TC.NC.pos.p,
# "TL.PC.pos.p" = TL.PC.pos.p,
# "TL.NC.pos.p" = TL.NC.pos.p,
# "RD.PC.pos.p" = RD.PC.pos.p,
# "RD.NC.pos.p" = RD.NC.pos.p,
# "PD.PC.pos.p" = PD.PC.pos.p,
# "PD.NC.pos.p" = PD.NC.pos.p,
# "PTM.PC.pos.p" = PTM.PC.pos.p,
# "PTM.NC.pos.p" = PTM.NC.pos.p,
"basal_transcription_rate_samplingfct" = basal_transcription_rate_samplingfct,
"basal_translation_rate_samplingfct" = basal_translation_rate_samplingfct,
"basal_RNAlifetime_samplingfct" = basal_RNAlifetime_samplingfct,
"basal_protlifetime_samplingfct" = basal_protlifetime_samplingfct,
"TC.PC.outdeg.distr" = TC.PC.outdeg.distr,
"TC.NC.outdeg.distr" = TC.NC.outdeg.distr,
"TC.PC.outdeg.exp" = TC.PC.outdeg.exp,
"TC.NC.outdeg.exp" = TC.NC.outdeg.exp,
"TC.PC.indeg.distr" = TC.PC.indeg.distr,
"TC.NC.indeg.distr" = TC.NC.indeg.distr,
"TC.PC.autoregproba" = TC.PC.autoregproba,
"TC.NC.autoregproba" = TC.NC.autoregproba,
"TC.PC.twonodesloop" = TC.PC.twonodesloop,
"TC.NC.twonodesloop" = TC.NC.twonodesloop,
"TCbindingrate_samplingfct" = TCbindingrate_samplingfct,
"TCunbindingrate_samplingfct" = TCunbindingrate_samplingfct,
"TCfoldchange_samplingfct" = TCfoldchange_samplingfct,
"TL.PC.outdeg.distr" = TL.PC.outdeg.distr,
"TL.NC.outdeg.distr" = TL.NC.outdeg.distr,
"TL.PC.outdeg.exp" = TL.PC.outdeg.exp,
"TL.NC.outdeg.exp" = TL.NC.outdeg.exp,
"TL.PC.indeg.distr" = TL.PC.indeg.distr,
"TL.NC.indeg.distr" = TL.NC.indeg.distr,
"TL.PC.autoregproba" = TL.PC.autoregproba,
"TL.NC.autoregproba" = TL.NC.autoregproba,
"TL.PC.twonodesloop" = TL.PC.twonodesloop,
"TL.NC.twonodesloop" = TL.NC.twonodesloop,
"TLbindingrate_samplingfct" = TLbindingrate_samplingfct,
"TLunbindingrate_samplingfct" = TLunbindingrate_samplingfct,
"TLfoldchange_samplingfct" = TLfoldchange_samplingfct,
"RD.PC.outdeg.distr" = RD.PC.outdeg.distr,
"RD.NC.outdeg.distr" = RD.NC.outdeg.distr,
"RD.PC.outdeg.exp" = RD.PC.outdeg.exp,
"RD.NC.outdeg.exp" = RD.NC.outdeg.exp,
"RD.PC.indeg.distr" = RD.PC.indeg.distr,
"RD.NC.indeg.distr" = RD.NC.indeg.distr,
"RD.PC.autoregproba" = RD.PC.autoregproba,
"RD.NC.autoregproba" = RD.NC.autoregproba,
"RD.PC.twonodesloop" = RD.PC.twonodesloop,
"RD.NC.twonodesloop" = RD.NC.twonodesloop,
"RDregrate_samplingfct" = RDregrate_samplingfct,
"PD.PC.outdeg.distr" = PD.PC.outdeg.distr,
"PD.NC.outdeg.distr" = PD.NC.outdeg.distr,
"PD.PC.outdeg.exp" = PD.PC.outdeg.exp,
"PD.NC.outdeg.exp" = PD.NC.outdeg.exp,
"PD.PC.indeg.distr" = PD.PC.indeg.distr,
"PD.NC.indeg.distr" = PD.NC.indeg.distr,
"PD.PC.autoregproba" = PD.PC.autoregproba,
"PD.NC.autoregproba" = PD.NC.autoregproba,
"PD.PC.twonodesloop" = PD.PC.twonodesloop,
"PD.NC.twonodesloop" = PD.NC.twonodesloop,
"PDregrate_samplingfct" = PDregrate_samplingfct,
"PTM.PC.outdeg.distr" = PTM.PC.outdeg.distr,
"PTM.NC.outdeg.distr" = PTM.NC.outdeg.distr,
"PTM.PC.outdeg.exp" = PTM.PC.outdeg.exp,
"PTM.NC.outdeg.exp" = PTM.NC.outdeg.exp,
"PTM.PC.indeg.distr" = PTM.PC.indeg.distr,
"PTM.NC.indeg.distr" = PTM.NC.indeg.distr,
"PTM.PC.autoregproba" = PTM.PC.autoregproba,
"PTM.NC.autoregproba" = PTM.NC.autoregproba,
"PTM.PC.twonodesloop" = PTM.PC.twonodesloop,
"PTM.NC.twonodesloop" = PTM.NC.twonodesloop,
"PTMregrate_samplingfct" = PTMregrate_samplingfct,
"regcomplexes" = regcomplexes,
"regcomplexes.p" = regcomplexes.p,
"regcomplexes.size" = regcomplexes.size ,
"complexesformationrate_samplingfct" = complexesformationrate_samplingfct,
"complexesdissociationrate_samplingfct" = complexesdissociationrate_samplingfct,
"gcnList" = gcnList)
attr(value, "class") = "insilicosystemargs"
return(value)
}
## insilicoindividualargs class ----
#' Constructor function for the \code{insilicoindividualargs} class.
#'
#' Constructor function for the \code{insilicoindividualargs} class, with default values for the parameters if not provided by the user.
#'
#' @param ngenevariants Integer. Number of alleles existing for each gene and segregating in the in silico population. Default value is 5.
#' @param qtleffect_samplingfct Function from which is sampled the value of a QTL effect coefficient (input x is the required sample size). Default value is a truncated normal distribution with mean 1 and sd 0.1 (only gives positive values).
#' @param initvar_samplingfct Function from which is sampled the variation of the initial abundance of a species (input x is the required sample size). Default value is a truncated normal distribution with mean 1 and sd 0.1 (only gives positive values).
#' @return An object of the class \code{insilicoindividualargs}, that is a named list of the different parameters.
#' @examples
#' indargs = insilicoindividualargs(ngenevariants = 3)
#' @export
insilicoindividualargs <- function(
ngenevariants = 5,
qtleffect_samplingfct = function(x){truncnorm::rtruncnorm(x, a = 0, b = Inf, mean = 1, sd = 0.1)},
initvar_samplingfct = function(x){truncnorm::rtruncnorm(x, a = 0, b = Inf, mean = 1, sd = 0.1)}
){
## qtlnames: names of the qtl effect coefficients
## The first 5 are the qtl affecting all genes, the last 5 only affect protein coding genes
qtlnames = c("qtlTCrate", "qtlRDrate", "qtlTCregbind", "qtlRDregrate", "qtlactivity", "qtlTLrate", "qtlPDrate", "qtlTLregbind", "qtlPDregrate", "qtlPTMregrate")
value = list("ngenevariants" = ngenevariants,
"qtleffect_samplingfct" = qtleffect_samplingfct,
"initvar_samplingfct" = initvar_samplingfct,
"qtlnames" = qtlnames
)
attr(value, "class") = "insilicoindividualargs"
return(value)
}
Try the sismonr package in your browser
Any scripts or data that you put into this service are public.
sismonr documentation built on Feb. 11, 2020, 9:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions insilicosystemargs insilicoindividualargs
Documented in insilicoindividualargs insilicosystemargs
## insilicosystemsargs class ----
#' Constructor function for the \code{insilicosystemsargs} class.
#'
#' Constructor function for the \code{insilicosystemsargs} class, with default values for the parameters if not provided by the user.
#'
#' For the protein-coding (and non-coding) biological function ratios (i.e. PC.TC.p, PC.TL.p, etc): if none of the ratios are provided,
#' then they are set to their default values. Otherwise, if at least one value among the 6 (5 for noncoding genes) is set by the user:
#' \itemize{
#' \item if the sum of the provided values is 1 or more: the non-specified values are set to 0, and the specified values are normalised
#' such that their sum is 1.
#' \item if the sum of the provided values is less than 1: the non-specified values are set such that the sum of all ratios equals 1.
#' }
#' Example: if the user sets PC.TC.p to 1 and PC.TL.p to 0.6, but does not provide any values for the other ratios,
#' then PC.TC.p is set to 1/(1+0.6)=0.625, PC.TL.p to 0.6/(1+0.6)=0.375, and PC.RD.p, PC.PD.p, PC.PTM.p and PC.MR.p are all set to 0.
#' Accordingly, if the user only sets NC.TC.p to 0.6, then NC.TL.p, NC.RD.p, NC.PD.p and NC.PTM.p are all set to 0.1.
#'
#' @param G Integer. Number of genes in the system. Default value is 10.
#' @param ploidy Numeric. The ploidy of the system, i.e. how many copies of each gene are present in the system. Default value is 2.
#' @param PC.p Numeric. Probability of each gene to be a protein-coding gene. Default value is 0.7.
#' @param PC.TC.p Numeric. Probability of a protein-coding gene to be a regulator of transcription. Default value is 0.4 (see details).
#' @param PC.TL.p Numeric. Probability of a protein-coding gene to be a regulator of translation. Default value is 0.3 (see details).
#' @param PC.RD.p Numeric. Probability of a protein-coding gene to be a regulator of RNA decay. Default value is 0.1 (see details).
#' @param PC.PD.p Numeric. Probability of a protein-coding gene to be a regulator of protein decay. Default value is 0.1 (see details).
#' @param PC.PTM.p Numeric. Probability of a protein-coding gene to be a regulator of protein post-translational modification. Default value is 0.05 (see details).
#' @param PC.MR.p Numeric. Probability of a protein-coding gene to be a metabolic enzyme. Default value is 0.05 (see details).
#' @param NC.TC.p Numeric. Probability of a noncoding gene to be a regulator of transcription. Default value is 0.3 (see details).
#' @param NC.TL.p Numeric. Probability of a noncoding gene to be a regulator of translation. Default value is 0.3 (see details).
#' @param NC.RD.p Numeric. Probability of a noncoding gene to be a regulator of RNA decay. Default value is 0.3 (see details).
#' @param NC.PD.p Numeric. Probability of a noncoding gene to be a regulator of protein decay. Default value is 0.05 (see details).
#' @param NC.PTM.p Numeric. Probability of a noncoding gene to be a regulator of protein post-translational modification. Default value is 0.05 (see details).
#' @param TC.pos.p Numeric. Probability of a regulation targeting gene transcription to be positive. Default value is 0.5.
#' @param TL.pos.p Numeric. Probability of a regulation targeting gene translation to be positive. Default value is 0.5.
#' @param PTM.pos.p Numeric. Probability of a regulation targeting protein post-translational modification to be positive (i.e the targeted protein
#' is transformed into its modified form, as opposed to the modified protein being transformed back into its original form). Default value is 0.5.
#' @param basal_transcription_rate_samplingfct Function from which the transcription rates of genes are sampled (input x is the required sample size). Default value is
#' a function returning \code{(10^v)/3600}, with \code{v} a vector of size x sampled from a normal distribution with mean of 3 and sd of 0.5.
#' @param basal_translation_rate_samplingfct Function from which the translation rates of genes are sampled (input x is the required sample size). Default value is
#' a function returning \code{(10^v)/3600}, with \code{v} a vector of size x sampled from a normal distribution with mean of 2.146 and sd of 0.7.
#' @param basal_RNAlifetime_samplingfct Function from which the transcript lifetimes are sampled (input x is the required sample size). Default value is
#' a function returning \code{(10^v)*3600}, with \code{v} a vector of size x sampled from a normal distribution with mean of 0.95 and sd of 0.2.
#' @param basal_protlifetime_samplingfct Function from which the protein lifetime are sampled (input x is the required sample size). Default value is
#' a function returning \code{(10^v)*3600}, with \code{v} a vector of size x sampled from a normal distribution with mean of 1.3 and sd of 0.4.
#' @param TC.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the transcription regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TC.NC.outdeg.distr Form of the distribution of the number of targets (out-degree) of noncoding regulators in the transcription regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TC.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the transcription regulation graph. Default value is 3.
#' @param TC.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the transcription regulation graph. Default value is 5.
#' @param TC.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the transcription regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TC.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the transcription regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TC.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation in the transcription regulation graph. Default value is 0.2.
#' @param TC.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation in the transcription regulation graph. Default value is 0.
#' @param TC.PC.twonodesloop Logical. Are 2-nodes loops authorised in the transcription regulation graph with protein regulators? Default value is FALSE.
#' @param TC.NC.twonodesloop Logical. Are 2-nodes loops authorised in the transcription regulation graph with noncoding regulators? Default value is FALSE.
#' @param TCbindingrate_samplingfct Function from which the binding rates of transcription regulators on their targets are sampled (input \code{means} is a vector of length equal to the required sample size, giving for each
#' edge (regulatory interaction) for which a binding rate is being sampled the value of the sampled unbinding rate divided by the steady-state abundance of the regulator
#' in absence of any regulation in the system). Default value is a function returning \code{10^v}, where \code{v} is a vector with the same length as \code{means} whose elements are sampled
#' from a truncated normal distribution with mean equal to the log10 of the corresponding element in \code{means}, and sd = 0.1, the minimum authorised value being the log10 of the corresponding element in \code{means}.
#' @param TCunbindingrate_samplingfct Function from which the unbinding rates of transcription regulators from their target are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -3 and sd of 0.2.
#' @param TCfoldchange_samplingfct Function from which the transcription fold change induced by a bound regulator is sampled (input x is the required sample size). Default value is
#' a truncated normal distribution with a mean of 3, sd of 10 and minimum authorised value of 1.5.
#' @param TL.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the translation regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TL.NC.outdeg.distr Form of the the distribution of the number of targets (out-degree) of noncoding regulators in the translation regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TL.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the translation regulation graph. Default value is 4.
#' @param TL.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the translation regulation graph. Default value is 6.
#' @param TL.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the translation regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TL.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the translation regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param TL.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation in the translation regulation graph. Default value is 0.2.
#' @param TL.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation in the translation regulation graph. Default value is 0.
#' @param TL.PC.twonodesloop Logical. Are 2-nodes loops authorised in the translation regulation graph with protein regulators? Default value is FALSE.
#' @param TL.NC.twonodesloop Logical. Are 2-nodes loops authorised in the translation regulation graph with noncoding regulators? Default value is FALSE.
#' @param TLbindingrate_samplingfct Function from which the binding rate of translation regulators on target are sampled (input \code{means} is a vector of length equal to the required sample size, giving for each
#' edge (regulatory interaction) for which a binding rate is being sampled the value of the sampled unbinding rate divided by the steady-state abundance of the regulator
#' in absence of any regulation in the system). Default value is a function returning \code{10^v}, where \code{v} is a vector with the same length as \code{means} whose elements are sampled
#' from a truncated normal distribution with mean equal to the log10 of the corresponding element in \code{means}, and sd = 0.1, the minimum authorised value being the log10 of the corresponding element in \code{means}.
#' @param TLunbindingrate_samplingfct Function from which the unbinding rate of translation regulators from target are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -3 and sd of 0.2.
#' @param TLfoldchange_samplingfct Function from which the translation fold change induced by a bound regulator are sampled (input x is the required sample size). Default value is
#' a truncated normal distribution with a mean of 3, sd of 10 and minimum authorised value of 1.5.
#' @param RD.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the RNA decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param RD.NC.outdeg.distr Form of the the distribution of the number of targets (out-degree) of noncoding regulators in the RNA decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param RD.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the RNA decay regulation graph. Default value is 4.
#' @param RD.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the RNA decay regulation graph. Default value is 6.
#' @param RD.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the RNA decay graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param RD.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the RNA decay graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param RD.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation in the RNA decay regulation graph. Default value is 0.2.
#' @param RD.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation in the RNA decay regulation graph. Default value is 0.
#' @param RD.PC.twonodesloop Logical. Are 2-nodes loops authorised in the RNA decay regulation graph with protein regulators? Default value is FALSE.
#' @param RD.NC.twonodesloop Logical. Are 2-nodes loops authorised in the RNA decay regulation graph with noncoding regulators? Default value is FALSE.
#' @param RDregrate_samplingfct Function from which the RNA decay rates of targets of RNA decay regulators are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -5 and sd of 1.5.
#' @param PD.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the protein decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PD.NC.outdeg.distr Form of the the distribution of the number of targets (out-degree) of noncoding regulators in the protein decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PD.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the protein decay regulation graph. Default value is 4.
#' @param PD.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the protein decay regulation graph. Default value is 6.
#' @param PD.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the protein decay regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PD.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the protein decay graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PD.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation in the protein decay regulation graph. Default value is 0.2.
#' @param PD.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation in the protein decay regulation graph. Default value is 0.
#' @param PD.PC.twonodesloop Logical. Are 2-nodes loops authorised in the protein decay graph with protein regulators in the protein decay regulation graph? Default value is FALSE.
#' @param PD.NC.twonodesloop Logical. Are 2-nodes loops authorised in the protein decay graph with noncoding regulators in the protein decay regulation graph? Default value is FALSE.
#' @param PDregrate_samplingfct Function from which the protein decay rates of targets of protein decay regulators are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -5 and sd of 1.5.
#' @param PTM.PC.outdeg.distr Form of the distribution of the number of targets (out-degree) of protein regulators in the post-translational modification regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PTM.NC.outdeg.distr Form of the the distribution of the number of targets (out-degree) of noncoding regulators in the post-translational modification regulation graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PTM.PC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the protein regulators in the protein post-translational modification graph. Default value is 4.
#' @param PTM.NC.outdeg.exp Numeric. Exponent of the distribution for the out-degree of the noncoding regulators in the protein post-translational modification graph. Default value is 6.
#' @param PTM.PC.indeg.distr Type of preferential attachment for the targets of protein regulators in the protein post-translational modification graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PTM.NC.indeg.distr Type of preferential attachment for the targets of noncoding regulators in the protein post-translational modification graph; can be either "powerlaw" or "exponential". Default value is "powerlaw".
#' @param PTM.PC.autoregproba Numeric. Probability of protein regulators to perform autoregulation. Default value is 0.2.
#' @param PTM.NC.autoregproba Numeric. Probability of noncoding regulators to perform autoregulation. Default value is 0.
#' @param PTM.PC.twonodesloop Logical. Are 2-nodes loops authorised in the protein post-translational modification graph with protein regulators? Default value is FALSE.
#' @param PTM.NC.twonodesloop Logical. Are 2-nodes loops authorised in the protein post-translational modification graph with noncoding regulators? Default value is FALSE.
#' @param PTMregrate_samplingfct Function from which the protein transformation rates of targets of post-translational modification regulators are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -5 and sd of 1.5.
#' @param regcomplexes Can the regulators controlling a common target form regulatory complexes in the different regulatory graphs? Can be 'none', 'prot' (only protein can form regulatory complexes) or 'both'
#' (both regulatory RNAs and proteins can form regulatory complexes). Default value is "prot".
#' @param regcomplexes.p Numeric. Probability that regulators controlling a common target form regulatory complexes; ignore if \code{regcomplexes} = 'none'. Default value is 0.3.
#' @param regcomplexes.size Integer. Number of components of a regulatory complex; ignore if \code{regcomplexes} = 'none'. Default value is 2.
#' @param complexesformationrate_samplingfct Function from which the formation rate of regulatory complexes are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of -3 and sd of 0.7.
#' @param complexesdissociationrate_samplingfct Function from which the dissociation rate of regulatory complexes are sampled (input x is the required sample size). Default value is
#' a function returning \code{10^v}, with \code{v} a vector of size x sampled from a normal distribution with mean of 3 and sd of 0.9.
#' @return An object of the class \code{insilicosystemargs}, that is a named list of the different parameters.
#' @examples
#' sysargs = insilicosystemargs(G = 15, PC.p = 0.2,
#' basal_transcription_rate_samplingfct = function(x){runif(x, 0.1, 0.2)})
#' @export
insilicosystemargs <- function(
G = 10,
ploidy = 2,
PC.p = 0.7,
PC.TC.p = NULL,
PC.TL.p = NULL,
PC.RD.p = NULL,
PC.PD.p = NULL,
PC.PTM.p = NULL,
PC.MR.p = NULL,
NC.TC.p = NULL,
NC.TL.p = NULL,
NC.RD.p = NULL,
NC.PD.p = NULL,
NC.PTM.p = NULL,
TC.pos.p = 0.5,
TL.pos.p = 0.5,
PTM.pos.p = 0.5,
# TC.PC.pos.p = 0.5,
# TC.NC.pos.p = 0.5,
# TL.PC.pos.p = 0.5,
# TL.NC.pos.p = 0.5,
# PTM.PC.pos.p = 0.5,
# PTM.NC.pos.p = 0.5,
basal_transcription_rate_samplingfct = NULL,
basal_translation_rate_samplingfct = NULL,
basal_RNAlifetime_samplingfct = NULL,
basal_protlifetime_samplingfct = NULL,
TC.PC.outdeg.distr = "powerlaw",
TC.NC.outdeg.distr = "powerlaw",
TC.PC.outdeg.exp = 3,
TC.NC.outdeg.exp = 5,
TC.PC.indeg.distr = "powerlaw",
TC.NC.indeg.distr = "powerlaw",
TC.PC.autoregproba = 0.2,
TC.NC.autoregproba = 0,
TC.PC.twonodesloop = FALSE,
TC.NC.twonodesloop = FALSE,
TCbindingrate_samplingfct = NULL,
TCunbindingrate_samplingfct = NULL,
TCfoldchange_samplingfct = NULL,
TL.PC.outdeg.distr = "powerlaw",
TL.NC.outdeg.distr = "powerlaw",
TL.PC.outdeg.exp = 4,
TL.NC.outdeg.exp = 6,
TL.PC.indeg.distr = "powerlaw",
TL.NC.indeg.distr = "powerlaw",
TL.PC.autoregproba = 0.2,
TL.NC.autoregproba = 0,
TL.PC.twonodesloop = FALSE,
TL.NC.twonodesloop = FALSE,
TLbindingrate_samplingfct = NULL,
TLunbindingrate_samplingfct = NULL,
TLfoldchange_samplingfct = NULL,
RD.PC.outdeg.distr = "powerlaw",
RD.NC.outdeg.distr = "powerlaw",
RD.PC.outdeg.exp = 4,
RD.NC.outdeg.exp = 6,
RD.PC.indeg.distr = "powerlaw",
RD.NC.indeg.distr = "powerlaw",
RD.PC.autoregproba = 0.2,
RD.NC.autoregproba = 0,
RD.PC.twonodesloop = FALSE,
RD.NC.twonodesloop = FALSE,
RDregrate_samplingfct = NULL,
PD.PC.outdeg.distr = "powerlaw",
PD.NC.outdeg.distr = "powerlaw",
PD.PC.outdeg.exp = 4,
PD.NC.outdeg.exp = 6,
PD.PC.indeg.distr = "powerlaw",
PD.NC.indeg.distr = "powerlaw",
PD.PC.autoregproba = 0.2,
PD.NC.autoregproba = 0,
PD.PC.twonodesloop = FALSE,
PD.NC.twonodesloop = FALSE,
PDregrate_samplingfct = NULL,
PTM.PC.outdeg.distr = "powerlaw",
PTM.NC.outdeg.distr = "powerlaw",
PTM.PC.outdeg.exp = 4,
PTM.NC.outdeg.exp = 6,
PTM.PC.indeg.distr = "powerlaw",
PTM.NC.indeg.distr = "powerlaw",
PTM.PC.autoregproba = 0.2,
PTM.NC.autoregproba = 0,
PTM.PC.twonodesloop = FALSE,
PTM.NC.twonodesloop = FALSE,
PTMregrate_samplingfct = NULL,
regcomplexes = "prot",
regcomplexes.p = 0.3,
regcomplexes.size = 2,
complexesformationrate_samplingfct = NULL,
complexesdissociationrate_samplingfct = NULL
){
if(is.null(basal_transcription_rate_samplingfct)) basal_transcription_rate_samplingfct = function(x){ logval = rnorm(x, mean = -0.92, sd = 0.35); val = 10^logval; return(val/60) }
if(is.null(basal_translation_rate_samplingfct)) basal_translation_rate_samplingfct = function(x){ logval = rnorm(x, mean = 2.146, sd = 0.7); val = 10^logval; return(val/3600) }
if(is.null(basal_RNAlifetime_samplingfct)) basal_RNAlifetime_samplingfct = function(x){ logval = rnorm(x, mean = 1.36, sd = 0.2); val = 10^logval; return(val*60) }
if(is.null(basal_protlifetime_samplingfct)) basal_protlifetime_samplingfct = function(x){ logval = rnorm(x, mean = 5.43, sd = 1); val = 2^logval; return(val*60) }
if(is.null(TCbindingrate_samplingfct)) TCbindingrate_samplingfct = function(means){ logval = truncnorm::rtruncnorm(length(means),a = log10(means), mean = log10(means), sd = 0.1); return(10^logval) }
if(is.null(TCunbindingrate_samplingfct)) TCunbindingrate_samplingfct = function(x){ logval = rnorm(x, mean = -3, sd = 0.2); return(10^(logval)) }
if(is.null(TCfoldchange_samplingfct)) TCfoldchange_samplingfct = function(x){ truncnorm::rtruncnorm(x, a = 1.5, mean = 3, sd = 10) }
if(is.null(TLbindingrate_samplingfct)) TLbindingrate_samplingfct = function(means){ logval = truncnorm::rtruncnorm(length(means),a = log10(means), mean = log10(means), sd = 0.1); return(10^logval) }
if(is.null(TLunbindingrate_samplingfct)) TLunbindingrate_samplingfct = function(x){ logval = rnorm(x, mean = -3, sd = 0.2); return(10^(logval)) }
if(is.null(TLfoldchange_samplingfct)) TLfoldchange_samplingfct = function(x){ truncnorm::rtruncnorm(x, a = 1.5, mean = 3, sd = 10) }
if(is.null(RDregrate_samplingfct)) RDregrate_samplingfct = function(x){ logval = rnorm(x, mean = -5, sd = 1.5); return(10^logval) }
if(is.null(PDregrate_samplingfct)) PDregrate_samplingfct = function(x){ logval = rnorm(x, mean = -5, sd = 1.5); return(10^logval) }
if(is.null(PTMregrate_samplingfct)) PTMregrate_samplingfct = function(x){ logval = rnorm(x, mean = -8, sd = 1.5); return(10^logval) }
if(is.null(complexesformationrate_samplingfct)) complexesformationrate_samplingfct = function(x){ logval = rnorm(x, mean = -3, sd = 0.7); return(10^(logval)) }
if(is.null(complexesdissociationrate_samplingfct)) complexesdissociationrate_samplingfct = function(x){ logval = rnorm(x, mean = 2, sd = 1.2); return(10^(logval)) }
NC.p = 1 - PC.p
## Probability of each protein-coding gene biological function
temp = c(PC.TC.p, PC.TL.p, PC.RD.p, PC.PD.p, PC.PTM.p, PC.MR.p)
if(is.null(temp)){ ## if no values are provided, give default values
PC.TC.p = 0.4
PC.TL.p = 0.3
PC.RD.p = 0.1
PC.PD.p = 0.1
PC.PTM.p = 0.05
PC.MR.p = 0.05
} else if(sum(temp) >= 1 | length(temp) == 6){ ## if at least one value is provided, and their sum is >=1, normalise the given values and set to 0 the others
for(v in c("PC.TC.p", "PC.TL.p", "PC.RD.p", "PC.PD.p", "PC.PTM.p", "PC.MR.p")){
assign(v, ifelse(is.null(get(v)), 0, get(v)/sum(temp)))
}
} else if(sum(temp) < 1){ ## if at least one value is provided but don't sum up to 1, assign the remaining proba such that the sum of all proba is 1
residual = (1-sum(temp))/(6-length(temp))
for(v in c("PC.TC.p", "PC.TL.p", "PC.RD.p", "PC.PD.p", "PC.PTM.p", "PC.MR.p")){
assign(v, ifelse(is.null(get(v)), residual, get(v)))
}
}
## Probability of each noncoding gene biological function
temp = c(NC.TC.p, NC.TL.p, NC.RD.p, NC.PD.p, NC.PTM.p)
if(is.null(temp)){ ## if no values are provided, give default values
NC.TC.p = 0.3
NC.TL.p = 0.3
NC.RD.p = 0.3
NC.PD.p = 0.05
NC.PTM.p = 0.05
} else if(sum(temp) >= 1 | length(temp) == 5){ ## if at least one value is provided, and their sum is >=1, normalise the given values and set to 0 the others
for(v in c("NC.TC.p", "NC.TL.p", "NC.RD.p", "NC.PD.p", "NC.PTM.p")){
assign(v, ifelse(is.null(get(v)), 0, get(v)/sum(temp)))
}
} else if(sum(temp) < 1){ ## if at least one value is provided but don't sum up to 1, assign the remaining proba such that the sum of all proba is 1
residual = (1-sum(temp))/(6-length(temp))
for(v in c("NC.TC.p", "NC.TL.p", "NC.RD.p", "NC.PD.p", "NC.PTM.p")){
assign(v, ifelse(is.null(get(v)), residual, get(v)))
}
}
## There cannot be negative regulation for transcript and protein decay
RD.pos.p = 1 ## RD.PC.pos.p: probability that the RNA decay is positively regulated by protein regulators (faster decay)
PD.pos.p = 1 ## PD.PC.pos.p: probability that the protein decay is positively regulated by protein regulators (faster decay)
# RD.PC.pos.p = 1 ## RD.PC.pos.p: probability that the RNA decay is positively regulated by protein regulators (faster decay)
# RD.NC.pos.p = 1 ## RD.NC.pos.p: probability that the RNA decay is positively regulated by noncoding regulators (faster decay)
# PD.PC.pos.p = 1 ## PD.PC.pos.p: probability that the protein decay is positively regulated by protein regulators (faster decay)
# PD.NC.pos.p = 1 ## PD.NC.pos.p: probability that the protein decay is positively regulated by noncoding regulators (faster decay)
gcnList = sapply(1:ploidy, function(x){paste0("GCN", x)})
value = list( "G" = G,
"ploidy" = ploidy,
"PC.p" = PC.p,
"PC.TC.p" = PC.TC.p,
"PC.TL.p" = PC.TL.p,
"PC.RD.p" = PC.RD.p,
"PC.PD.p" = PC.PD.p,
"PC.PTM.p" = PC.PTM.p,
"PC.MR.p" = PC.MR.p,
"NC.p" = NC.p,
"NC.TC.p" = NC.TC.p,
"NC.TL.p" = NC.TL.p,
"NC.RD.p" = NC.RD.p,
"NC.PD.p" = NC.PD.p,
"NC.PTM.p" = NC.PTM.p,
"TC.pos.p" = TC.pos.p,
"TL.pos.p" = TL.pos.p,
"RD.pos.p" = RD.pos.p,
"PD.pos.p" = PD.pos.p,
"PTM.pos.p" = PTM.pos.p,
# "TC.PC.pos.p" = TC.PC.pos.p,
# "TC.NC.pos.p" = TC.NC.pos.p,
# "TL.PC.pos.p" = TL.PC.pos.p,
# "TL.NC.pos.p" = TL.NC.pos.p,
# "RD.PC.pos.p" = RD.PC.pos.p,
# "RD.NC.pos.p" = RD.NC.pos.p,
# "PD.PC.pos.p" = PD.PC.pos.p,
# "PD.NC.pos.p" = PD.NC.pos.p,
# "PTM.PC.pos.p" = PTM.PC.pos.p,
# "PTM.NC.pos.p" = PTM.NC.pos.p,
"basal_transcription_rate_samplingfct" = basal_transcription_rate_samplingfct,
"basal_translation_rate_samplingfct" = basal_translation_rate_samplingfct,
"basal_RNAlifetime_samplingfct" = basal_RNAlifetime_samplingfct,
"basal_protlifetime_samplingfct" = basal_protlifetime_samplingfct,
"TC.PC.outdeg.distr" = TC.PC.outdeg.distr,
"TC.NC.outdeg.distr" = TC.NC.outdeg.distr,
"TC.PC.outdeg.exp" = TC.PC.outdeg.exp,
"TC.NC.outdeg.exp" = TC.NC.outdeg.exp,
"TC.PC.indeg.distr" = TC.PC.indeg.distr,
"TC.NC.indeg.distr" = TC.NC.indeg.distr,
"TC.PC.autoregproba" = TC.PC.autoregproba,
"TC.NC.autoregproba" = TC.NC.autoregproba,
"TC.PC.twonodesloop" = TC.PC.twonodesloop,
"TC.NC.twonodesloop" = TC.NC.twonodesloop,
"TCbindingrate_samplingfct" = TCbindingrate_samplingfct,
"TCunbindingrate_samplingfct" = TCunbindingrate_samplingfct,
"TCfoldchange_samplingfct" = TCfoldchange_samplingfct,
"TL.PC.outdeg.distr" = TL.PC.outdeg.distr,
"TL.NC.outdeg.distr" = TL.NC.outdeg.distr,
"TL.PC.outdeg.exp" = TL.PC.outdeg.exp,
"TL.NC.outdeg.exp" = TL.NC.outdeg.exp,
"TL.PC.indeg.distr" = TL.PC.indeg.distr,
"TL.NC.indeg.distr" = TL.NC.indeg.distr,
"TL.PC.autoregproba" = TL.PC.autoregproba,
"TL.NC.autoregproba" = TL.NC.autoregproba,
"TL.PC.twonodesloop" = TL.PC.twonodesloop,
"TL.NC.twonodesloop" = TL.NC.twonodesloop,
"TLbindingrate_samplingfct" = TLbindingrate_samplingfct,
"TLunbindingrate_samplingfct" = TLunbindingrate_samplingfct,
"TLfoldchange_samplingfct" = TLfoldchange_samplingfct,
"RD.PC.outdeg.distr" = RD.PC.outdeg.distr,
"RD.NC.outdeg.distr" = RD.NC.outdeg.distr,
"RD.PC.outdeg.exp" = RD.PC.outdeg.exp,
"RD.NC.outdeg.exp" = RD.NC.outdeg.exp,
"RD.PC.indeg.distr" = RD.PC.indeg.distr,
"RD.NC.indeg.distr" = RD.NC.indeg.distr,
"RD.PC.autoregproba" = RD.PC.autoregproba,
"RD.NC.autoregproba" = RD.NC.autoregproba,
"RD.PC.twonodesloop" = RD.PC.twonodesloop,
"RD.NC.twonodesloop" = RD.NC.twonodesloop,
"RDregrate_samplingfct" = RDregrate_samplingfct,
"PD.PC.outdeg.distr" = PD.PC.outdeg.distr,
"PD.NC.outdeg.distr" = PD.NC.outdeg.distr,
"PD.PC.outdeg.exp" = PD.PC.outdeg.exp,
"PD.NC.outdeg.exp" = PD.NC.outdeg.exp,
"PD.PC.indeg.distr" = PD.PC.indeg.distr,
"PD.NC.indeg.distr" = PD.NC.indeg.distr,
"PD.PC.autoregproba" = PD.PC.autoregproba,
"PD.NC.autoregproba" = PD.NC.autoregproba,
"PD.PC.twonodesloop" = PD.PC.twonodesloop,
"PD.NC.twonodesloop" = PD.NC.twonodesloop,
"PDregrate_samplingfct" = PDregrate_samplingfct,
"PTM.PC.outdeg.distr" = PTM.PC.outdeg.distr,
"PTM.NC.outdeg.distr" = PTM.NC.outdeg.distr,
"PTM.PC.outdeg.exp" = PTM.PC.outdeg.exp,
"PTM.NC.outdeg.exp" = PTM.NC.outdeg.exp,
"PTM.PC.indeg.distr" = PTM.PC.indeg.distr,
"PTM.NC.indeg.distr" = PTM.NC.indeg.distr,
"PTM.PC.autoregproba" = PTM.PC.autoregproba,
"PTM.NC.autoregproba" = PTM.NC.autoregproba,
"PTM.PC.twonodesloop" = PTM.PC.twonodesloop,
"PTM.NC.twonodesloop" = PTM.NC.twonodesloop,
"PTMregrate_samplingfct" = PTMregrate_samplingfct,
"regcomplexes" = regcomplexes,
"regcomplexes.p" = regcomplexes.p,
"regcomplexes.size" = regcomplexes.size ,
"complexesformationrate_samplingfct" = complexesformationrate_samplingfct,
"complexesdissociationrate_samplingfct" = complexesdissociationrate_samplingfct,
"gcnList" = gcnList)
attr(value, "class") = "insilicosystemargs"
return(value)
}
## insilicoindividualargs class ----
#' Constructor function for the \code{insilicoindividualargs} class.
#'
#' Constructor function for the \code{insilicoindividualargs} class, with default values for the parameters if not provided by the user.
#'
#' @param ngenevariants Integer. Number of alleles existing for each gene and segregating in the in silico population. Default value is 5.
#' @param qtleffect_samplingfct Function from which is sampled the value of a QTL effect coefficient (input x is the required sample size). Default value is a truncated normal distribution with mean 1 and sd 0.1 (only gives positive values).
#' @param initvar_samplingfct Function from which is sampled the variation of the initial abundance of a species (input x is the required sample size). Default value is a truncated normal distribution with mean 1 and sd 0.1 (only gives positive values).
#' @return An object of the class \code{insilicoindividualargs}, that is a named list of the different parameters.
#' @examples
#' indargs = insilicoindividualargs(ngenevariants = 3)
#' @export
insilicoindividualargs <- function(
ngenevariants = 5,
qtleffect_samplingfct = function(x){truncnorm::rtruncnorm(x, a = 0, b = Inf, mean = 1, sd = 0.1)},
initvar_samplingfct = function(x){truncnorm::rtruncnorm(x, a = 0, b = Inf, mean = 1, sd = 0.1)}
){
## qtlnames: names of the qtl effect coefficients
## The first 5 are the qtl affecting all genes, the last 5 only affect protein coding genes
qtlnames = c("qtlTCrate", "qtlRDrate", "qtlTCregbind", "qtlRDregrate", "qtlactivity", "qtlTLrate", "qtlPDrate", "qtlTLregbind", "qtlPDregrate", "qtlPTMregrate")
value = list("ngenevariants" = ngenevariants,
"qtleffect_samplingfct" = qtleffect_samplingfct,
"initvar_samplingfct" = initvar_samplingfct,
"qtlnames" = qtlnames
)
attr(value, "class") = "insilicoindividualargs"
return(value)
}
Try the sismonr package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.