cma.set.stat: Implements gene-set analysis methods.
In CancerMutationAnalysis: Cancer mutation analysis

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

This function implements the gene-set analysis methods. It returns a data-frame with p-values and q-values for all the methods selected.

cma.set.stat(cma.alter,
                     cma.cov,
                     cma.samp,
                     GeneSets,
                     ID2name=NULL, 
		     Scores,
		     passenger.rates = t(data.frame(0.55*rep(1.0e-6,25))),
		     BH = TRUE,
		     gene.method = FALSE, 
		     perm.null.method = TRUE, 
		     perm.null.het.method = FALSE,
		     pass.null.method = FALSE, 
		     pass.null.het.method = FALSE,
                     score = "logLRT",
                     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.
`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.
`Scores`	Data frame of gene scores. The logLRT scores are used for the gene.method option. It can be the output of `cma.scores`. If the gene.method option is set to FALSE, this parameter is not needed.
`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.
`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.
`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.
`score`	Can be any of the scores which result from `cma.scores`. Specifies the gene-scoring mechanism used in the gene-oriented method.
`verbose`	If `TRUE`, prints intermediate messages.

A data frame, with the rows representing set names and the columns representing the p-values and q-values corresponding to the different methods.

Simina M. Boca, Giovanni Parmigiani, Luigi Marchionni, Michael A. Newton.

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 experiments. Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.1530509100

Schaeffer EM, Marchionni L, Huang Z, Simons B, Blackman A, Yu W, Parmigiani G, Berman DM. Androgen-induced programs for prostate epithelial growth and invasion arise in embryogenesis and are reactivated in cancer. Oncogene. DOI: 10.1038/onc.2008.327

Thomas MA, Taub AE. Calculating binomial probabilities when the trial probabilities are unequal. Journal of Statistical Computation and Simulation. DOI: 10.1080/00949658208810534

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

GeneCov, GeneSamp, GeneAlter, BackRates, cma.scores, cma.set.sim

library(KEGG.db)
data(ParsonsGBM08)
data(EntrezID2Name)

setIDs <- c("hsa00250", "hsa05213")
SetResults <- cma.set.stat(cma.alter = GeneAlterGBM,
                                   cma.cov = GeneCovGBM,
                                   cma.samp = GeneSampGBM,
                                   GeneSets =  KEGGPATHID2EXTID[setIDs],
                                   ID2name = EntrezID2Name,
                                   perm.null.method = TRUE,
                                   pass.null.method = TRUE)

SetResults