Nothing
R/AllClass.R
In MOSim: Multi-Omics Simulation (MOSim)
#' @import methods stringi matrixStats
#' @importFrom stats rnorm runif rbeta rbinom rpois quantile
#' @importFrom utils data
NULL
#'
#' This class manages the global simulation process, like associating genes with
#' gene classes, regulatory programs and other settings. Finally it will
#' initialize the simulators with their options that will use the previously
#' generated settings to simulate the data.
#'
#' @slot simulators Vector containing either S4 initialized classes of
#' simulators or a list with the class name as keys, and its options as value,
#' see example.
#' @slot totalGenes A number with the total number of genes including not
#' expressed. Overwrited if a genome reference is provided. Currently not used
#' as we force to provide real data.
#' @slot diffGenes A number with the total number of differential genes (if
#' value > 1) or \% or total genes (if value < 1).
#' @slot numberReps Number of replicates of the experiment.
#' @slot numberGroups Number of samples considered on the experiment.
#' @slot times Numeric vector containing the measured times. If numberGroups <
#' 2, the number of times must be at least 2.
#' @slot geneNames Read only. List containing the IDs of the genes. Overwrited
#' by the genome reference if provided. Currently not used as we force to
#' provide real data.
#' @slot simSettings List of settings that overrides initializing the
#' configuration of the simulation by passing a previously generated list.
#' This could be used to tweak by hand the assigned profiles, genes,
#' regulatory programs, etc.
#' @slot noiseFunction Noise function to apply when simulating counts. Must
#' accept the parameter 'n' and return a vector of the same length. Defaults
#' to `rnorm`
#' @slot profiles Named list containing the patterns with their coefficients.
#' @slot profileProbs Numeric vector with the probabilities to assign each of
#' the patterns. Defaults to 0.2 for each.
#' @slot noiseParams Default noise parameters to be used with noise function.
#' @slot depth Default depth to simulate.
#' @slot TFtoGene Boolean (for default data) or 3 column data frame containing
#' Symbol-TFGene-LinkedGene
#' @slot minMaxQuantile Numeric vector of length 2 indicating the quantiles to
#' use in order to retrieve the absolute minimum and maximum value that a
#' differentially expressed feature can have.
#' @slot minMaxFC Numeric vector of length 2 indicating the minimum and maximum
#' fold-change that a differentially expressed feature can have.
#'
#' @export
#' @keywords internal
#'
#'
setClass(
"MOSimulation",
slots = c(
simulators = "list",
totalGenes = "numeric",
diffGenes = "numeric",
numberReps = "numeric",
numberGroups = "numeric",
times = "vector",
geneNames = "character",
simSettings = "list",
profiles = "list",
profileProbs = "list",
noiseFunction = "function",
noiseParams = "list",
depth = "numeric",
minMaxQuantile = "numeric",
minMaxFC = "numeric",
TFtoGene = "ANY"
),
prototype = list(
diffGenes = .15,
numberReps = 3,
numberGroups = 2,
times = c(0, 2, 4, 12, 24),
depth = 74,
noiseFunction = stats::rnorm,
noiseParams = list("sd" = 0.3),
profiles = list(
continuous.induction = c("a1", "b1", 0),
continuous.repression = c("a1", "b1.neg", 0),
transitory.induction = c("a2", "b2", "c2"),
transitory.repression = c("a2.neg", "b2.neg", "c2.neg"),
flat = c(0, 0, 0)
),
profileProbs = list(
continuous.induction = .235,
continuous.repression = .235,
transitory.induction = .235,
transitory.repression = .235,
flat = .06
),
minMaxQuantile = c(0.3, 0.95),
minMaxFC = c(3, 8),
TFtoGene = NULL
)
)
#'
#' Virtual class containing common methods and slots for child classes.
#'
#' @slot name Name of the simulator to be used in messages.
#' @slot data Data frame containing the initial sample to be used, with the
#' features IDs as rownames and only one column named "Counts".
#' @slot regulator Boolean flag to indicate if the omic is a regulator or not.
#' @slot regulatorEffect Possible regulation effects of the omic (enhancer,
#' repressor or both).
#' @slot idToGene Data frame with the association table between genes and other
#' features. The structure must be 2 columns, one named "ID" and the other
#' "Gene".
#' @slot min Minimum value allowed in the omic.
#' @slot max Maximum value allowed in the omic.
#' @slot depth Sequencing depth to simulate.
#' @slot depthRound Number of decimal places to round when adjusting depth.
#' @slot depthAdjust Boolean indicating whether to adjust by sequencing depth or
#' not.
#' @slot totalFeatures Number of features to simulate. This will replace the
#' data with a subset.
#' @slot noiseFunction Noise function to apply when simulating counts. Must
#' accept the parameter 'n' and return a vector of the same length. Defaults
#' to `rnorm`
#' @slot increment Read-only. Minimum value to increase when simulating counts.
#' @slot simData Contains the final simulated data.
#' @slot pregenerated Indicates if the child class will generate the simulated
#' data instead of the general process.
#' @slot randData Auxiliary vector containing the original count data in random
#' order with other adjustments.
#' @slot noiseParams Noise parameters to be used with noise function.
#' @slot roundDigits Number of digits to round the simulated count values.
#' @slot minMaxQuantile Numeric vector of length 2 indicating the quantiles to
#' use in order to retrieve the absolute minimum and maximum value that a
#' differentially expressed feature can have.
#' @slot minMaxFC Numeric vector of length 2 indicating the minimum and maximum
#' fold-change that a differentially expressed feature can have.
#' @slot minMaxDist Named list containing different minimum and maximum
#' constraints values calculated at the beginning of the simulation process.
#' @slot replicateParams Named list containing the parameters a and b to be used
#' in the replicates generation process, see the vignette for more info.
#'
#' @export
#' @keywords internal
#'
setClass(
"MOSimulator",
slots = c(
name = "character",
data = "ANY",
simData = "ANY",
randData = "ANY",
regulator = "logical",
regulatorEffect = "vector",
idToGene = "ANY",
min = "numeric",
max = "numeric",
increment = "numeric",
depth = "numeric",
depthRound = "numeric",
depthAdjust = "logical",
noiseFunction = "function",
noiseParams = "list",
roundDigits = "numeric",
pregenerated = "logical",
totalFeatures = "numeric",
minMaxQuantile = "numeric",
minMaxFC = "numeric",
minMaxDist = "list",
replicateParams = "list"
),
prototype = list(
regulator = TRUE,
min = 0,
increment = 0,
depthRound = 0,
depthAdjust = TRUE,
regulatorEffect = NA,
roundDigits = 0,
pregenerated = FALSE,
totalFeatures = Inf,
replicateParams = list(
"a" = 0.01,
"b" = 1.5
)
),
contains = c("VIRTUAL")
)
#'
#' Virtual class containing general methods for simulators based on regions of
#' the chromosomes, like DNase-seq, ChIP-seq or Methyl-seq
#'
#' @slot locs Vector containing the list of locations of the sites.
#' @slot locsName Type of the site to simulate, only for debug.
#' @slot splitChar Character symbol used to split identifiers in chr/start/end
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"MOSimulatorRegion",
slots = c(
locs = "ANY",
splitChar = "character"
),
prototype = list(
splitChar = "_"
),
contains = c("MOSimulator", "VIRTUAL")
)
#'
#' Class to simulate RNA-seq data
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimRNAseq",
prototype = list(
name = "RNA-seq",
# idToGene = matrix(NA),
regulator = FALSE,
replicateParams = list(
"a" = 0.5, #0.01,
"b" = 1.5 #1.5
)
),
contains = "MOSimulator"
)
#'
#' Class to simulate transcription factor data
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimTF",
prototype = list(
name = "TF",
# idToGene = matrix(NA),
regulator = TRUE
),
contains = "SimRNAseq"
)
#'
#' Class to simulate miRNA-seq
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimmiRNAseq",
prototype = list(
name = "miRNA-seq",
regulator = TRUE,
idToGene = matrix(),
regulatorEffect = list(
'repressor' = 0.05,
'NE' = 0.95
),
replicateParams = list(
"a" = -0.065,
"b" = 1.566
)
),
contains = "MOSimulator"
)
#'
#' Class to simulate ChIP-seq data
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimChIPseq",
prototype = list(
name = "ChIP-seq",
regulator = TRUE,
idToGene = matrix(),
regulatorEffect = list(
'enhancer' = 0.48,
'repressor' = 0.48,
'NE' = 0.02
),
replicateParams = list(
# "a" = -0.042,
# "b" = 1.265,
"a" = 0.5,
"b" = 1.5
)
),
contains = "MOSimulatorRegion"
)
#'
#' Class to simulate DNase-seq data
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimDNaseseq",
prototype = list(
name = "DNase-seq",
regulator = TRUE,
idToGene = matrix(),
regulatorEffect = list(
'enhancer' = 0.48,
'repressor' = 0.48,
'NE' = 0.02
),
replicateParams = list(
# "a" = -0.042,
# "b" = 1.265
"a" = 0.5,
"b" = 1.5
)
),
contains = "MOSimulatorRegion"
)
#'
#' Class to simulate Methyl-seq data.
#'
#' @slot nCpG numeric. Number of CpG sites to simulate.
#' @slot pSuccessMethReg numeric. Probability of success in methylated region.
#' @slot pSuccessDemethReg numeric. Probability of success in non methylated
#' region
#' @slot errorMethReg numeric. Error rate in methylated region
#' @slot errorDemethReg numeric. Error rate in methylated region
#' @slot nReadsMethReg numeric. Mean number of reads in methylated region.
#' @slot nReadsDemethReg numeric. Mean number of reads in non methylated
#' regions.
#' @slot phaseDiff numeric. Phase difference in the differentially methylated
#' regions between two samples
#' @slot balanceHypoHyper numeric. Balance of hypo/hyper methylation
#' @slot ratesHMMMatrix numeric. Matrix of values that describes the exponential
#' decay functions that define the distances between CpG values.
#' @slot distType character. Distribution used to generate replicates:
#' @slot transitionSize numeric.
#' @slot PhiMeth matrix. Transition matrix for CpG locations.
#' @slot PhiDemeth matrix. <Not used>
#' @slot typesLocation numeric. <Not used>
#' @slot returnValue character. Selected column:
#' @slot betaThreshold numeric. Beta threshold value used to calculate M values.
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimMethylseq",
slots = c(
nCpG = "numeric",
betaThreshold = "numeric",
WGBSparams = "list",
Mvalues = "logical"
),
prototype = list(
name = "Methyl-seq",
regulator = TRUE,
Mvalues = TRUE,
nCpG = 5000,
betaThreshold = 0.01,
# maxValue = 1,
# nRepeats = 1,
idToGene = matrix(),
pregenerated = TRUE,
roundDigits = 5,
WGBSparams = list(
# Phase difference in the differentially methylated regions between two samples
phase_diff = c(0, 0.1),
#error_rate_in_differentially_methylated_region = 0.1
# File to write results
outfile = '/tmp/',
#output_path
probs = c(1, 1, 0.9, 0.8, 0.7, 0.6),
# Transition size
transition_size = 0,
# Number of repeats (NOT REPLICATES!)
m = 1,
# Transition matrix for CpG locations
Pi_m = matrix(c(0.65, 0.35, 0.2, 0.8), byrow =
TRUE, nrow = 2),
# Transition matrix for probability distributions
Pi_d = matrix(c(0.9, 0.1, 0.1, 0.9), byrow =
TRUE, nrow = 2),
# Type of locations
type_of_locations = 2,
# Mean number of reads in methylated region
mean_m = 29,
# Mean number of reads in non methylated regions
mean_d = 29,
# Probability of success in methylated region
prob_m = 0.9203,
# Probability of success in non methylated region
prob_d = 0.076,
# Error rate in dif. methylated region
error_m = 0.1,
# Error rate in non methylated region
error_d = 0.1,
# Balance: this is the balance of hypo/hyper methylation
balance = 0.5,
# Rates for HMM for CpG locations: this is the matrix of values that
# describes the exponential decay functions that define the distances
# between CpG values.
rates_for_HMM_for_CpG_locations = c(0.019, 0.002),
# Type: this can be either binomial or truncated and defines the
# distribution of methylated reads at each CpG.
type = "binomial",
# methReads = 1, demethReads = 2, totalReads = 3, proportionReads = 4
returnColumn = 4,
distType = "nbin"#binomial"
),
regulatorEffect = list(
'enhancer' = 0.48,
'repressor' = 0.48,
'NE' = 0.01
),
depthAdjust = FALSE
),
contains = "MOSimulatorRegion"
)
Try the MOSim package in your browser
Any scripts or data that you put into this service are public.
MOSim documentation built on Nov. 8, 2020, 5:50 p.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.
#' @import methods stringi matrixStats
#' @importFrom stats rnorm runif rbeta rbinom rpois quantile
#' @importFrom utils data
NULL
#'
#' This class manages the global simulation process, like associating genes with
#' gene classes, regulatory programs and other settings. Finally it will
#' initialize the simulators with their options that will use the previously
#' generated settings to simulate the data.
#'
#' @slot simulators Vector containing either S4 initialized classes of
#' simulators or a list with the class name as keys, and its options as value,
#' see example.
#' @slot totalGenes A number with the total number of genes including not
#' expressed. Overwrited if a genome reference is provided. Currently not used
#' as we force to provide real data.
#' @slot diffGenes A number with the total number of differential genes (if
#' value > 1) or \% or total genes (if value < 1).
#' @slot numberReps Number of replicates of the experiment.
#' @slot numberGroups Number of samples considered on the experiment.
#' @slot times Numeric vector containing the measured times. If numberGroups <
#' 2, the number of times must be at least 2.
#' @slot geneNames Read only. List containing the IDs of the genes. Overwrited
#' by the genome reference if provided. Currently not used as we force to
#' provide real data.
#' @slot simSettings List of settings that overrides initializing the
#' configuration of the simulation by passing a previously generated list.
#' This could be used to tweak by hand the assigned profiles, genes,
#' regulatory programs, etc.
#' @slot noiseFunction Noise function to apply when simulating counts. Must
#' accept the parameter 'n' and return a vector of the same length. Defaults
#' to `rnorm`
#' @slot profiles Named list containing the patterns with their coefficients.
#' @slot profileProbs Numeric vector with the probabilities to assign each of
#' the patterns. Defaults to 0.2 for each.
#' @slot noiseParams Default noise parameters to be used with noise function.
#' @slot depth Default depth to simulate.
#' @slot TFtoGene Boolean (for default data) or 3 column data frame containing
#' Symbol-TFGene-LinkedGene
#' @slot minMaxQuantile Numeric vector of length 2 indicating the quantiles to
#' use in order to retrieve the absolute minimum and maximum value that a
#' differentially expressed feature can have.
#' @slot minMaxFC Numeric vector of length 2 indicating the minimum and maximum
#' fold-change that a differentially expressed feature can have.
#'
#' @export
#' @keywords internal
#'
#'
setClass(
"MOSimulation",
slots = c(
simulators = "list",
totalGenes = "numeric",
diffGenes = "numeric",
numberReps = "numeric",
numberGroups = "numeric",
times = "vector",
geneNames = "character",
simSettings = "list",
profiles = "list",
profileProbs = "list",
noiseFunction = "function",
noiseParams = "list",
depth = "numeric",
minMaxQuantile = "numeric",
minMaxFC = "numeric",
TFtoGene = "ANY"
),
prototype = list(
diffGenes = .15,
numberReps = 3,
numberGroups = 2,
times = c(0, 2, 4, 12, 24),
depth = 74,
noiseFunction = stats::rnorm,
noiseParams = list("sd" = 0.3),
profiles = list(
continuous.induction = c("a1", "b1", 0),
continuous.repression = c("a1", "b1.neg", 0),
transitory.induction = c("a2", "b2", "c2"),
transitory.repression = c("a2.neg", "b2.neg", "c2.neg"),
flat = c(0, 0, 0)
),
profileProbs = list(
continuous.induction = .235,
continuous.repression = .235,
transitory.induction = .235,
transitory.repression = .235,
flat = .06
),
minMaxQuantile = c(0.3, 0.95),
minMaxFC = c(3, 8),
TFtoGene = NULL
)
)
#'
#' Virtual class containing common methods and slots for child classes.
#'
#' @slot name Name of the simulator to be used in messages.
#' @slot data Data frame containing the initial sample to be used, with the
#' features IDs as rownames and only one column named "Counts".
#' @slot regulator Boolean flag to indicate if the omic is a regulator or not.
#' @slot regulatorEffect Possible regulation effects of the omic (enhancer,
#' repressor or both).
#' @slot idToGene Data frame with the association table between genes and other
#' features. The structure must be 2 columns, one named "ID" and the other
#' "Gene".
#' @slot min Minimum value allowed in the omic.
#' @slot max Maximum value allowed in the omic.
#' @slot depth Sequencing depth to simulate.
#' @slot depthRound Number of decimal places to round when adjusting depth.
#' @slot depthAdjust Boolean indicating whether to adjust by sequencing depth or
#' not.
#' @slot totalFeatures Number of features to simulate. This will replace the
#' data with a subset.
#' @slot noiseFunction Noise function to apply when simulating counts. Must
#' accept the parameter 'n' and return a vector of the same length. Defaults
#' to `rnorm`
#' @slot increment Read-only. Minimum value to increase when simulating counts.
#' @slot simData Contains the final simulated data.
#' @slot pregenerated Indicates if the child class will generate the simulated
#' data instead of the general process.
#' @slot randData Auxiliary vector containing the original count data in random
#' order with other adjustments.
#' @slot noiseParams Noise parameters to be used with noise function.
#' @slot roundDigits Number of digits to round the simulated count values.
#' @slot minMaxQuantile Numeric vector of length 2 indicating the quantiles to
#' use in order to retrieve the absolute minimum and maximum value that a
#' differentially expressed feature can have.
#' @slot minMaxFC Numeric vector of length 2 indicating the minimum and maximum
#' fold-change that a differentially expressed feature can have.
#' @slot minMaxDist Named list containing different minimum and maximum
#' constraints values calculated at the beginning of the simulation process.
#' @slot replicateParams Named list containing the parameters a and b to be used
#' in the replicates generation process, see the vignette for more info.
#'
#' @export
#' @keywords internal
#'
setClass(
"MOSimulator",
slots = c(
name = "character",
data = "ANY",
simData = "ANY",
randData = "ANY",
regulator = "logical",
regulatorEffect = "vector",
idToGene = "ANY",
min = "numeric",
max = "numeric",
increment = "numeric",
depth = "numeric",
depthRound = "numeric",
depthAdjust = "logical",
noiseFunction = "function",
noiseParams = "list",
roundDigits = "numeric",
pregenerated = "logical",
totalFeatures = "numeric",
minMaxQuantile = "numeric",
minMaxFC = "numeric",
minMaxDist = "list",
replicateParams = "list"
),
prototype = list(
regulator = TRUE,
min = 0,
increment = 0,
depthRound = 0,
depthAdjust = TRUE,
regulatorEffect = NA,
roundDigits = 0,
pregenerated = FALSE,
totalFeatures = Inf,
replicateParams = list(
"a" = 0.01,
"b" = 1.5
)
),
contains = c("VIRTUAL")
)
#'
#' Virtual class containing general methods for simulators based on regions of
#' the chromosomes, like DNase-seq, ChIP-seq or Methyl-seq
#'
#' @slot locs Vector containing the list of locations of the sites.
#' @slot locsName Type of the site to simulate, only for debug.
#' @slot splitChar Character symbol used to split identifiers in chr/start/end
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"MOSimulatorRegion",
slots = c(
locs = "ANY",
splitChar = "character"
),
prototype = list(
splitChar = "_"
),
contains = c("MOSimulator", "VIRTUAL")
)
#'
#' Class to simulate RNA-seq data
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimRNAseq",
prototype = list(
name = "RNA-seq",
# idToGene = matrix(NA),
regulator = FALSE,
replicateParams = list(
"a" = 0.5, #0.01,
"b" = 1.5 #1.5
)
),
contains = "MOSimulator"
)
#'
#' Class to simulate transcription factor data
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimTF",
prototype = list(
name = "TF",
# idToGene = matrix(NA),
regulator = TRUE
),
contains = "SimRNAseq"
)
#'
#' Class to simulate miRNA-seq
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimmiRNAseq",
prototype = list(
name = "miRNA-seq",
regulator = TRUE,
idToGene = matrix(),
regulatorEffect = list(
'repressor' = 0.05,
'NE' = 0.95
),
replicateParams = list(
"a" = -0.065,
"b" = 1.566
)
),
contains = "MOSimulator"
)
#'
#' Class to simulate ChIP-seq data
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimChIPseq",
prototype = list(
name = "ChIP-seq",
regulator = TRUE,
idToGene = matrix(),
regulatorEffect = list(
'enhancer' = 0.48,
'repressor' = 0.48,
'NE' = 0.02
),
replicateParams = list(
# "a" = -0.042,
# "b" = 1.265,
"a" = 0.5,
"b" = 1.5
)
),
contains = "MOSimulatorRegion"
)
#'
#' Class to simulate DNase-seq data
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimDNaseseq",
prototype = list(
name = "DNase-seq",
regulator = TRUE,
idToGene = matrix(),
regulatorEffect = list(
'enhancer' = 0.48,
'repressor' = 0.48,
'NE' = 0.02
),
replicateParams = list(
# "a" = -0.042,
# "b" = 1.265
"a" = 0.5,
"b" = 1.5
)
),
contains = "MOSimulatorRegion"
)
#'
#' Class to simulate Methyl-seq data.
#'
#' @slot nCpG numeric. Number of CpG sites to simulate.
#' @slot pSuccessMethReg numeric. Probability of success in methylated region.
#' @slot pSuccessDemethReg numeric. Probability of success in non methylated
#' region
#' @slot errorMethReg numeric. Error rate in methylated region
#' @slot errorDemethReg numeric. Error rate in methylated region
#' @slot nReadsMethReg numeric. Mean number of reads in methylated region.
#' @slot nReadsDemethReg numeric. Mean number of reads in non methylated
#' regions.
#' @slot phaseDiff numeric. Phase difference in the differentially methylated
#' regions between two samples
#' @slot balanceHypoHyper numeric. Balance of hypo/hyper methylation
#' @slot ratesHMMMatrix numeric. Matrix of values that describes the exponential
#' decay functions that define the distances between CpG values.
#' @slot distType character. Distribution used to generate replicates:
#' @slot transitionSize numeric.
#' @slot PhiMeth matrix. Transition matrix for CpG locations.
#' @slot PhiDemeth matrix. <Not used>
#' @slot typesLocation numeric. <Not used>
#' @slot returnValue character. Selected column:
#' @slot betaThreshold numeric. Beta threshold value used to calculate M values.
#'
#' @export
#' @keywords internal
#' @rdname Simulator-class
#'
setClass(
"SimMethylseq",
slots = c(
nCpG = "numeric",
betaThreshold = "numeric",
WGBSparams = "list",
Mvalues = "logical"
),
prototype = list(
name = "Methyl-seq",
regulator = TRUE,
Mvalues = TRUE,
nCpG = 5000,
betaThreshold = 0.01,
# maxValue = 1,
# nRepeats = 1,
idToGene = matrix(),
pregenerated = TRUE,
roundDigits = 5,
WGBSparams = list(
# Phase difference in the differentially methylated regions between two samples
phase_diff = c(0, 0.1),
#error_rate_in_differentially_methylated_region = 0.1
# File to write results
outfile = '/tmp/',
#output_path
probs = c(1, 1, 0.9, 0.8, 0.7, 0.6),
# Transition size
transition_size = 0,
# Number of repeats (NOT REPLICATES!)
m = 1,
# Transition matrix for CpG locations
Pi_m = matrix(c(0.65, 0.35, 0.2, 0.8), byrow =
TRUE, nrow = 2),
# Transition matrix for probability distributions
Pi_d = matrix(c(0.9, 0.1, 0.1, 0.9), byrow =
TRUE, nrow = 2),
# Type of locations
type_of_locations = 2,
# Mean number of reads in methylated region
mean_m = 29,
# Mean number of reads in non methylated regions
mean_d = 29,
# Probability of success in methylated region
prob_m = 0.9203,
# Probability of success in non methylated region
prob_d = 0.076,
# Error rate in dif. methylated region
error_m = 0.1,
# Error rate in non methylated region
error_d = 0.1,
# Balance: this is the balance of hypo/hyper methylation
balance = 0.5,
# Rates for HMM for CpG locations: this is the matrix of values that
# describes the exponential decay functions that define the distances
# between CpG values.
rates_for_HMM_for_CpG_locations = c(0.019, 0.002),
# Type: this can be either binomial or truncated and defines the
# distribution of methylated reads at each CpG.
type = "binomial",
# methReads = 1, demethReads = 2, totalReads = 3, proportionReads = 4
returnColumn = 4,
distType = "nbin"#binomial"
),
regulatorEffect = list(
'enhancer' = 0.48,
'repressor' = 0.48,
'NE' = 0.01
),
depthAdjust = FALSE
),
contains = "MOSimulatorRegion"
)
Try the MOSim 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.