a3_cubpred: Codon Usage Bias Prediction for Observed ORFs

In cubfits: Codon Usage Bias Fits

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.

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.

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.

Note

Note that phi.Init and phi.pred.Init need to be normalized to mean 1.

p.DrawScale may cause scaling prior if adaptive MCMC is used, and it can result in non-exits of equilibrium distribution.

Author(s)

Wei-Chen Chen [e-mail].

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

## Not run: 
suppressMessages(library(cubfits, quietly = TRUE))

demo(roc.pred, 'cubfits', ask = F, echo = F)

## End(Not run)

cubfits documentation built on Nov. 8, 2021, 1:07 a.m.