| Type: | Package |
| Title: | Simulation of Inter-Related Genomic Datasets |
| Version: | 2.3.0 |
| Date: | 2025-01-10 |
| Author: | Prabhakar Chalise [aut, cre], Rama Raghavan [aut], Brooke Fridley [aut] |
| Maintainer: | Prabhakar Chalise <pchalise@kumc.edu> |
| Description: | Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Depends: | R (≥ 3.5.0), MASS, NMF, tools |
| NeedsCompilation: | no |
| Packaged: | 2025-01-10 17:38:30 UTC; |
| Repository: | CRAN |
| Date/Publication: | 2025-01-10 23:00:06 UTC |
| RoxygenNote: | 6.0.1 |
Simulation of inter-related genomic datasets
Description
Generates three inter-related data set with realistic inter- and intra- relationships based on the DNA methylation, mRNA expression and protein expression from the TCGA ovarian cancer study.
Details
| Package: | InterSIM |
| Type: | Package |
| Version: | 2.3.0 |
| Date: | 2025-01-10 |
| License: | GPL>=2 |
Author(s)
Prabhakar Chalise, Rama Raghavan, Brooke Fridley;
Maintainer: Prabhakar Chalise
InterSIM
Description
This function simulates three inter-related genomic datasets : DNA methylation, gene expression and protein expression.
Usage
InterSIM(n.sample=500, cluster.sample.prop=c(0.30,0.30,0.40), delta.methyl=2.0,
delta.expr=2.0, delta.protein=2.0, p.DMP=0.2,
p.DEG=NULL, p.DEP=NULL, sigma.methyl=NULL, sigma.expr=NULL, sigma.protein=NULL,
cor.methyl.expr=NULL, cor.expr.protein=NULL, do.plot=FALSE, sample.cluster=TRUE,
feature.cluster=TRUE)
Arguments
n.sample |
Number of subjects to simulate |
cluster.sample.prop |
Proportion of samples in the clusters. The number of proportions entered is used to determine the number of clusters in the simulated data. e.g. if (0.3,0.4,0.3) is entered then the number of clusters will be 3. |
delta.methyl |
Cluster mean shift for methylation data |
delta.expr |
Cluster mean shift for expression data |
delta.protein |
Cluster mean shift for protein data |
p.DMP |
proportion of DE CpGs (DE = Differentially Expressed) |
p.DEG |
proportion of DE mRNA, if NULL (default) mRNAs mapped by DE CpGs will be selected |
p.DEP |
proportion of DE protein, if NULL (default) proteins mapped by DE mRNAs will be selected |
sigma.methyl |
Covariance structure methylation data, if NULL (default) precomputed values will be used. "indep" gives covariance structure with diagonal elements only (Independent features) |
sigma.expr |
Covariance structure mRNA data, if NULL (default) precomputed values will be used. "indep" gives covariance structure with diagonal elements only (Independent features) |
sigma.protein |
Covariance structure Protein data, if NULL (default) precomputed values will be used. "indep" gives covariance structure with diagonal elements only (Independent features) |
do.plot |
TRUE to generate heatmap, default is FALSE |
sample.cluster |
TRUE (default), if clustering should be done on samples for heatmap. This option will be applicable only if do.plot=TRUE. |
feature.cluster |
TRUE (default), if clustering should be done on genomic features for heatmap.This option will be applicable only if do.plot=TRUE. |
cor.methyl.expr |
Correlation between methylation and mRNA, if NULL (default) precomputed values will be used |
cor.expr.protein |
Correlation between mRNA and protein, if NULL (default) precomputed values will be used |
Value
This function returns three interrelated datasets having user specified cluster patterns as matrices - DNA methylation, gene expression and protein expression. It also returns a vector that has true cluster assignment for each subject in the generated data.
Author(s)
Prabhakar Chalise <pchalise@kumc.edu>, Rama Raghavan <rraghavan@kumc.edu>, Brooke Fridley <bfridley@kumc.edu>
References
Chalise P, Raghavan R and Fridley B (2016). InterSIM: Simulation tool for multiple integrative 'omic datasets. Computer Methods and Programs in Biomedicine, 128:69-74, https://doi.org/10.1016/j.cmpb.2016.02.011
Examples
#
prop <- c(0.20,0.30,0.27,0.23)
effect <- 5
sim.data <- InterSIM(n.sample=500, cluster.sample.prop = prop,
delta.methyl=effect, delta.expr=effect, delta.protein=effect,
p.DMP=0.2, p.DEG=NULL, p.DEP=NULL,
sigma.methyl=NULL, sigma.expr=NULL, sigma.protein=NULL,
cor.methyl.expr=NULL, cor.expr.protein=NULL,
do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE)
sim.methyl <- sim.data$dat.methyl
sim.expr <- sim.data$dat.expr
sim.protein <- sim.data$dat.protein