cma.set.sim: Simulates data and performs gene-set analysis methods on the...
In CancerMutationAnalysis: Cancer mutation analysis

Description Usage Arguments Value Author(s) References See Also Examples

This function simulates data under the passenger or permutation null, either under the null or including spiked-in gene-sets. It then calculates the p-values and q-values for all the selected gene-set analysis methods.

cma.set.sim(cma.alter,
                  cma.cov,
                  cma.samp,
		  GeneSets, 
		  passenger.rates = t(data.frame(0.55*rep(1.0e-6,25))),
		  ID2name=NULL,
		  BH = TRUE, 
		  nr.iter, 
		  pass.null = FALSE,
		  perc.samples = NULL, 
		  spiked.set.sizes = NULL,
		  gene.method = FALSE, 
		  perm.null.method = TRUE,
		  perm.null.het.method = FALSE, 
		  pass.null.method = FALSE,
		  pass.null.het.method = FALSE, 
		  show.iter,
		  KnownMountains = c("EGFR","SMAD4","KRAS",
		  "TP53","CDKN2A","MYC","MYCN","PTEN","RB1"),
		  exclude.mountains=TRUE,
                  verbose=TRUE)

`cma.alter`	Data frame with somatic mutation information, broken down by gene, sample, screen, and mutation type. See `GeneAlterBreast` for an example.
`cma.cov`	Data frame with the total number of nucleotides "at risk" ("coverage"), broken down by gene, screen, and mutation type. See `GeneCovBreast` for an example.
`cma.samp`	Data frame with the number of samples analyzed, broken down by gene and screen. See `GeneSampBreast` for an example.
`GeneSets`	An object which annotates genes to gene-sets; it can either be a list with each component representing a set, or an object of the class AnnDbBimap.
`passenger.rates`	Data frame with 1 row and 25 columns, of passenger mutation rates per nucleotide, by type, or "context". Columns denote types and must be in the same order as the first 25 columns in the `MutationsBrain` objects.
`ID2name`	Vector mapping the gene identifiers used in the GeneSets object to the gene names used in the other objects; if they are the same, this parameter is not needed. See `EntrezID2Name` for an example.
`BH`	If set to `TRUE`, uses the Benjamini-Hochberg method to get q-values; if set to `FALSE`, uses the Storey method from the `qvalue` package.
`nr.iter`	The number of iterations to be simulated.
`pass.null`	If set to true `TRUE`, implements the passenger null hypothesis, using the rates from `passenger.rates`; otherwise, implements the permutation null, permuting mutational events.
`perc.samples`	Vector representing the probabilities of the spiked-in gene-sets being altered in any given sample, as percentages; for example `perc.samples = c(75, 90)` means that these probabilities are `0.75` and `0.90`.
`spiked.set.sizes`	Vector representing the sizes, in genes, of the spiked-in gene-sets; for example, if `perc.samples = c(75, 90)` and `spiked.set.sizes = c(50, 100)`, there would be 4 spiked-in sets, one with 50 genes and probability of being altered of 0.75 in each sample, one with 50 genes and probability of being altered of 0.90 in each sample, one with 100 genes and probability of being altered of 0.75 in each sample, and one with 100 genes and probability of being altered of 0.90 in each sample.
`gene.method`	If set to `TRUE`, implements gene-oriented method.
`perm.null.method`	If set to `TRUE`, implements patient-oriented method with permutation null and no heterogeneity.
`perm.null.het.method`	If set to `TRUE`, implements patient-oriented method with permutation null and heterogeneity.
`pass.null.method`	If set to `TRUE`, implements patient-oriented method with passenger null and no heterogeneity.
`pass.null.het.method`	If set to `TRUE`, implements patient-oriented method with passenger null and heterogeneity.
`show.iter`	If set to `TRUE` and `verbose` is also set to `TRUE`, shows what simulation is currently running.
`KnownMountains`	Vector of genes to be excluded from the permutation null simulations if `exclude.mountains = TRUE`.
`exclude.mountains`	If set to `TRUE`, excludes the genes in `KnownMountains.`
`verbose`	If `TRUE`, prints intermediate messages.

An object of the class SetMethodsSims. See SetMethodsSims for more details.

Simina M. Boca, Giovanni Parmigiani.

Boca SM, Kinzler KW, Velculescu VE, Vogelstein B, Parmigiani G. Patient-oriented gene-set analysis for cancer mutation data. Genome Biology. DOI: 10.1186/gb-2010-11-11-r112

Parmigiani G, Lin J, Boca S, Sjoeblom T, Kinzler KW, Velculescu VE, Vogelstein B. Statistical methods for the analysis of cancer genome sequencing data. http://www.bepress.com/jhubiostat/paper126/

Benjamini Y and Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, DOI: 10.2307/2346101

Storey JD and Tibshirani R. Statistical significance for genome-wide experimens. Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.1530509100

Parsons DW, Jones S, Zhang X, Lin JCH, Leary RJ, Angenendt P, Mankoo P, Carter H, Siu I, et al. An Integrated Genomic Analysis of Human Glioblastoma Multiforme. Science. DOI: 10.1126/science.1164382

Wood LD, Parsons DW, Jones S, Lin J, Sjoeblom, Leary RJ, Shen D, Boca SM, Barber T, Ptak J, et al. The Genomic Landscapes of Human Breast and Colorectal Cancer. Science. DOI: 10.1126/science.1145720

SetMethodsSims-class, CoverageBrain, EventsBySampleBrain, GeneSizes08, MutationsBrain, ID2name, cma.set.stat, extract.sims.method, combine.sims

##Note that this takes a few minutes to run:
library(KEGG.db)
data(ParsonsGBM08)
data(EntrezID2Name)

setIDs <- c("hsa00250", "hsa05213")
set.seed(831984)
ResultsSim <- 
    cma.set.sim(cma.alter = GeneAlterGBM,
                      cma.cov = GeneCovGBM,
                      cma.samp = GeneSampGBM,
                      GeneSets =  KEGGPATHID2EXTID[setIDs],
                      ID2name = EntrezID2Name,
                      nr.iter = 2,
                      pass.null = TRUE,
                      perc.samples = c(75, 95),
                      spiked.set.sizes = 50,
                      perm.null.method = TRUE,
                      pass.null.method = TRUE)

ResultsSim