cma.fdr: Gene-level Empirical Bayes (EB) false discovery rate (FDR)...
In CancerMutationAnalysis: Cancer mutation analysis

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

Empirical Bayes estimates of the False Discovery Rate (FDR) and passenger probabilities in the analysis of somatic mutations in cancer.

cma.fdr(cma.alter,
        cma.cov,
        cma.samp,
        scores = c("CaMP", "logLRT"),
        passenger.rates = t(data.frame(.55*rep(1.0e-6,25))),
        allgenes=TRUE,
        estimate.p0=FALSE,
        p0.step=1,
        p0=1,
        eliminate.noval=FALSE,
        filter.threshold=0, 
        filter.above=0,
        filter.below=0,
        filter.mutations=0, 
        aa=1e-10, 
        bb=1e-10,
        priorH0=1-500/13020, 
        prior.a0=100,
        prior.a1=5,
        prior.fold=10,
        M=2,
        DiscOnly=FALSE,
        PrevSamp="Sjoeblom06",
        KnownCANGenes=NULL,
        showFigure=FALSE,
        cutoffFdr=0.1)

`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.
`scores`	Vector with the scores which are to be computed. It can include: `CaMP` (Cancer Mutation Prevalence score), `logLRT` (log Likelihood Ratio Test score), `neglogPg`, `logLRT`, `logitBinomialPosteriorDriver`, `PoissonlogBF`, `PoissonPosterior`, `Poissonlmlik0`, `Poissonlmlik1`
`passenger.rates`	Data frame of passenger mutation rates per nucleotide, by type, or "context". If two rows are present, the first refers to the Discovery screen and the second to the Prevalence screen.
`allgenes`	If TRUE, genes where no mutations were found are considered in the analysis.
`estimate.p0`	If TRUE, estimates the percent of genes with only passenger mutations. Requires `allgenes=TRUE`
`p0.step`	Size of bins of histograms in the distribution of scores, to use in estimating p0 if estimate.p0 = TRUE. All scores are in the log 10 scale.
`p0`	Proportion of genes with only passenger mutations. Only used if `estimate.p0=FALSE`
`eliminate.noval`	If `TRUE`, the genes which are not validated are eliminated from the analysis. Validated genes are those where at least one mutation was found in both the Discovery and Prevalence (or Validation) screens.
`filter.threshold`	This and the following three input control filtering of genes, allowing to exclude genes from analysis, by size and number of mutations. Different criteria can be set above and below this threshold. The threshold is a gene size in base pairs.
`filter.above`	Minimum number of mutations per Mb, applied to genes of size greater than `threshold.size`.
`filter.below`	Minimum number of mutations per Mb, applied to genes of size lower than `threshold.size`.
`filter.mutations`	Only consider genes whose total number of mutations is greater than or equal to `filter.mutations`.
`aa`	Hyperparameter of beta prior used in compute.binomial.posterior.
`bb`	Hyperparameter of beta prior used in compute.binomial.posterior.
`priorH0`	Prior probability of the null hypothesis, used to convert the BF in compute.poisson.BF to a posterior probability
`prior.a0`	Shape hyperparameter of gamma prior on passenger rates used in compute.poisson.BF
`prior.a1`	Shape hyperparameter of gamma prior on non-passenger rates used in compute.poisson.BF
`prior.fold`	Hyperparameter of gamma prior on non-passenger rates used compute.poisson.BF. The mean of the gamma is set so that the ratio of the mean to the passenger rate is the specified `prior.fold` in each type.
`M`	The number of null datasets generated to get the false discovery rates. Numbers on the order of 100 are recommended, but this will cause the function to run very slowly.
`DiscOnly`	If TRUE, only considers data from Discovery screen.
`PrevSamp`	If "Sjoeblom06", then the experimental design from Sjoeblom et al. or Wood et al. is used, namely, genes "pass" from the Discovery into the Prevalence (or Validation) screens if they are mutated at least once in the Discovery samples. If "Parsons11", the experimental design from Parsons et al. 2011 is approximated, namely, in the null datasets, a gene passes into the Prevalence screen if it is mutated at least once, and is found on a specified list of known cancer candidate (CAN) genes, or if it is mutated at least twice.
`KnownCANGenes`	Vector of known CAN genes, to be used if PrevSamp is not set to "Sjoeblom07".
`showFigure`	If TRUE, displays a figure for each score in `scores`, showing the right tail of the density of scores under the null, the right tail of the density of real scores as a rug (1-d) plot and the number of real genes and average number of null genes to the right of the cutoff chosen based on `cutoffFdr.`
`cutoffFdr`	If `showFigure` is set to TRUE, it gives the value at which we are interested in controlling the false discovery rate (Fdr). The corresponding score threshold is plotted on the figure, with the number of real genes greater than it and the average number of null genes greater than it specified. The estimated Fdr at that threshold is the ratio of the average number of null genes and the number of real genes, multiplied by `p0`, which is often taken to be 1.

A list of data frames. Each gives a gene gene-by-gene significance for one of the score requested. The columns in each data frame are:

`score`	The score requested (e.g. the LRT).
`F`	Number of genes experimentally observed to give a larger score than the gene in question.
`F0`	Number of genes giving a larger score than the gene in question in datasets simulated from passenger mutation rates.
`Fdr`	The Empirical Bayes False Discovery Rate, as defined in Efron and Tibshirani 2002.
`fdr`	The Empirical Bayes Local False Discovery Rate, as defined in Efron and Tibshirani 2002.
`p0`	Scalar, Proportion of genes with only passenger mutations. Estimated or passed on from input (depending on whether estimate.p0 is `TRUE`

Giovanni Parmigiani, Simina M. Boca

Efron B, Tibshirani R. Empirical Bayes methods and false discovery rates for microarrays. Genetic Epidemiology. DOI: 10.1002/gepi.1124

Parmigiani G, Lin J, Boca S, Sjoeblom T, Kinzler KW, Velculescu VE, Vogelstein B. Statistical methods for the analysis of cancer genome sequencing data, 2007. http://www.bepress.com/jhubiostat/paper126/

Sjoeblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber T, Mandelker D, Leary R, Ptak J, Silliman N, et al. The consensus coding sequences of breast and colorectal cancers. Science. DOI: 10.1126/science.1133427

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

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

Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Kamiyama H, Jimeno A, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. DOI: 10.1126/science.1164368

Parsons DW, Li M, Zhang X, Jones S, Leary RJ, Lin J, Boca SM, Carter H, Samayoa J, Bettegowda C, et al. The genetic landscape of the childhood cancer medulloblastoma. Science. DOI: 10.1126/science.1198056

GeneCov, GeneSamp, GeneAlter, BackRates,cma.scores

data(ParsonsMB11)
set.seed(188310)
cma.fdr.out <- cma.fdr(cma.alter = GeneAlterMB,
                       cma.cov = GeneCovMB,
                       cma.samp = GeneSampMB,
                       allgenes = TRUE,
                       estimate.p0=FALSE,
                       eliminate.noval=FALSE,
                       filter.mutations=0,
                       M = 2)
names(cma.fdr.out)