Description Usage Arguments Details Value Author(s) References See Also Examples
Computes various genelevel scores for the analysis of somatic point mutations in cancer.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  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(1.0e6,25))),
allow.separate.rates = TRUE,
filter.above=0,
filter.below=0,
filter.threshold=0,
filter.mutations=0,
aa=1e10,
bb=1e10,
priorH0=1300/13020,
prior.a0=100,
prior.a1=5,
prior.fold=10)

cma.alter 
Data frame with somatic mutation information, broken down by
gene, sample, screen, and mutation type.
See 
cma.cov 
Data frame with the total number of nucleotides "at
risk" ("coverage"), broken down by gene, screen, and mutation type.
See 
cma.samp 
Data frame with the number of samples analyzed,
broken down by gene and screen.
See 
scores 
Vector with the scores which are to be computed.
It can include: 
cma.data 
Provided for backcompatibility and internal
operations. 
coverage 
Provided for backcompatibility and internal
operations. 
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 
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 
filter.below 
Minimum number of mutations per
Mb, applied to genes of size lower than 
filter.mutations 
Only consider genes
whose total number of mutations is greater than or equal to

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 nonpassenger rates used in compute.poisson.BF 
prior.fold 
Hyperparameter of gamma prior on nonpassenger
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

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 nonrandom 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.
A data frame giving genebygene values for each score. The columns in this data frame are:
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 PoissonGamma model. 
PoissonPosterior 
The posterior probability that a given gene is a driver, using a PoissonGamma model. 
Poissonlmlik0 
Marginal likelihood under the null hypothesis in the PoissonGamma model 
Poissonlmlik1 
Marginal likelihood under the alternative hypothesis in the PoissonGamma model 
Giovanni Parmigiani, Simina M. Boca
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
GeneCov
, GeneSamp
, GeneAlter
,
BackRates
,
cma.set.stat
1 2 3 4  data(ParsonsGBM08)
ScoresGBM < cma.scores(cma.alter = GeneAlterGBM,
cma.cov = GeneCovGBM,
cma.samp = GeneSampGBM)

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