cma.scores: Gene-level scores for the analysis of somatic point mutations in cancer

Description

Computes various gene-level scores for the analysis of somatic point mutations in cancer.

Usage

cma.scores(cma.alter = NULL, cma.cov, cma.samp, scores = c("CaMP", "logLRT"), cma.data = NULL, coverage = NULL,
	   passenger.rates = t(data.frame(0.55*rep(1.0e-6,25))), allow.separate.rates = TRUE, filter.above=0,  filter.below=0,  filter.threshold=0,
	   filter.mutations=0, aa=1e-10,  bb=1e-10, priorH0=1-300/13020,  prior.a0=100, prior.a1=5, prior.fold=10)

Arguments

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

cma.data

Provided for back-compatibility and internal operations. cma.data and coverage objects were used in prior versons of this package, and may be specified instead of cma.alter, cma.cov, and cma.samp.

coverage

passenger.rates

Data frame of "passenger" (or "background") 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.

allow.separate.rates

If TRUE, allows for use separate rates for Discovery and Prevalence 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.

Hyperparameter of beta prior used in compute.binomial.posterior.

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.

Value

CaMP: The CaMP score of Sjoeblom and colleagues.
neglogPg: The negative log10 of Pg, where Pg represents the probability that a gene has its exact observed mutation profile under the null, i.e. assuming the given passenger rates.
logLRT: The log10 of the likelihood ratio test (LRT).
logitBinomialPosteriorDriver: logit of the posterior probability that a gene's mutation rates above the specified passenger rates using a binomial model
PoissonlogBF: The log10 of the Bayes Factor (BF) using a Poisson-Gamma model.
PoissonPosterior: The posterior probability that a given gene is a driver, using a Poisson-Gamma model.
Poissonlmlik0: Marginal likelihood under the null hypothesis in the Poisson-Gamma model
Poissonlmlik1: Marginal likelihood under the alternative hypothesis in the Poisson-Gamma model

Details

The scores computed by this function are relevant for two stage experiments like the one in the Sjoeblom et al. article. In this design genes are sequenced in a first "Discovery" sample. A non-random set of genes is then also sequenced in a subsequent "Prevalence" (or "Validation") screen. For instance, in Sjoeblom et al. and Wood et al., genes "pass" the Discovery screen if they are mutated at least once in it. The goal of this tool is to facilitate reanalysis of the Sjoeblom et al. 2006, Wood et al. 2007, Jones et al. 2008, Parsons et al. 2008, and Parsons et al. 2011 datasets. Application to other projects requires a detailed understanding of these projects.

References

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/

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

Examples

Run this code

data(ParsonsGBM08)
ScoresGBM <- cma.scores(cma.alter = GeneAlterGBM,
                        cma.cov = GeneCovGBM,
                        cma.samp = GeneSampGBM)

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