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cubfits (version 0.1-2)

CUB Model Prediction: Codon Usage Bias Prediction for Observed ORFs

Description

This function provides codon usage bias fits of training set which has observed ORFs and expressions possibly containing measurement errors, and provides predictions of testing set which has other observed ORFs but without expression.

Usage

cubpred(reu13.df.obs, phi.Obs, y, n, reu13.df.pred, y.pred, n.pred, nIter = 1000, b.Init = NULL, init.b.Scale = .CF.CONF$init.b.Scale, b.DrawScale = .CF.CONF$b.DrawScale, b.RInit = NULL, p.Init = NULL, p.nclass = .CF.CONF$p.nclass, p.DrawScale = .CF.CONF$p.DrawScale, phi.Init = NULL, init.phi.Scale = .CF.CONF$init.phi.Scale, phi.DrawScale = .CF.CONF$phi.DrawScale, phi.pred.Init = NULL, phi.pred.DrawScale = .CF.CONF$phi.pred.DrawScale, model = .CF.CT$model[1], model.Phi = .CF.CT$model.Phi[1], adaptive = .CF.CT$adaptive[1], verbose = .CF.DP$verbose, iterThin = .CF.DP$iterThin, report = .CF.DP$report)

Arguments

reu13.df.obs
a reu13.df to be trained.
phi.Obs
a phi.Obs to be trained.
y
a y to be trained.
n
a n to be trained.
reu13.df.pred
a reu13.df to be predicted.
y.pred
a y to be predicted.
n.pred
a n to be predicted.
nIter
number of iterations after burn-in iterations.
b.Init
initial values for parameters b.
init.b.Scale
for initial b if b.Init = NULL.
b.DrawScale
scaling factor for adaptive MCMC with random walks when drawing new b.
b.RInit
initial values (in a list) for R matrices of parameters b yielding from QR decomposition of vglm() for the variance-covariance matrix of b.
p.Init
initial values for hyper-parameters.
p.nclass
number of components for model.Phi = "logmixture".
p.DrawScale
scaling factor for adaptive MCMC with random walks when drawing new sigma.Phi.
phi.Init
initial values for Phi.
init.phi.Scale
for initial phi if phi.Init = NULL.
phi.DrawScale
scaling factor for adaptive MCMC with random walks when drawing new Phi.
phi.pred.Init
initial values for Phi of predicted set.
phi.pred.DrawScale
as phi.DrawScale but for predicted set.
model
model to be fitted, currently "roc" only.
model.Phi
prior model for Phi, currently "lognormal".
adaptive
adaptive method of MCMC for proposing new b and Phi.
verbose
print iteration messages.
iterThin
thinning iterations.
report
number of iterations to report more information.

Value

A list contains four big lists of MCMC traces including: b.Mat for mutation and selection coefficients of b, p.Mat for hyper-parameters, phi.Mat for expected expression values Phi, and phi.pred.Mat for predictive expression values Phi. All lists have nIter / iterThin + 1 elements, and each element contains the output of each iteration.All lists also can be binded as trace matrices, such as via do.call("rbind", b.Mat) yielding a matrix of dimension number of iterations by number of parameters. Then, those traces can be analyzed further via other MCMC packages such as coda.

Details

This function correctly and carefully implements an extension of Shah and Gilchrist (2011) and Wallace et al. (2013).

Total number of MCMC iterations is nIter + 1, but the outputs may be thinned to nIter / iterThin + 1 iterations.

Temporary result dumping may be controlled by .CF.DP.

References

https://github.com/snoweye/cubfits/

Shah P. and Gilchrist M.A. ``Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift'' Proc Natl Acad Sci USA (2011) 108:10231--10236.

See Also

DataIO, DataConverting, cubfits() and cubappr().

Examples

Run this code
## Not run: 
# suppressMessages(library(cubfits, quietly = TRUE))
# 
# demo(roc.pred, 'cubfits', ask = F, echo = F)
# ## End(Not run)

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