cubfits (version 0.1-2)

Selection on Codon Usage: Function for Selection on Codon Usage (SCU)

Description

Calculate the average translational selection per transcript include mSCU and SCU (if gene expression is provided) for each gene.

Usage

calc_scu_values(b, y.list, phi.Obs = NULL)

Arguments

b

an object of format b.

y.list

an object of format y.list.

phi.Obs

an object of format phi.Obs, for SCU only.

Value

A list with two named elements SCU and mSCU are returned.

Details

This function computes SCU and mSCU for each gene. Typically, this method is completely based on estimated parameters of mutation and selection such as outputs of MCMC or fitMultinom().

References

Wallace E.W.J., Airoldi E.M., and Drummond D.A. ``Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data'' Mol Biol Evol (2013) 30(6):1438--1453.

See Also

calc_scuo_values(), calc_cai_values().

Examples

## Not run: 
# library(cubfits, quietly = TRUE)
# 
# b <- b.Init$roc
# phi.Obs <- ex.train$phi.Obs
# y <- ex.train$y
# y.list <- convert.y.to.list(y)
# mSCU <- calc_scu_values(b, y.list, phi.Obs)$mSCU
# plot(mSCU, log10(phi.Obs), main = "Expression vs mSCU",
#      xlab = "mSCU", ylab = "Expression (log10)")
# 
# ### Compare with CAI with weights seqinr::cubtab$sc.
# library(seqinr, quietly = TRUE)
# w <- caitab$sc
# names(w) <- codon.low2up(rownames(caitab))
# CAI <- calc_cai_values(y, y.list, w = w)$CAI
# 
# plot(mSCU, CAI, main = "CAI vs mSCU",
#      xlab = "mSCU", ylab = "CAI")
# ## End(Not run)