t5_scu: Function for Selection on Codon Usage (SCU)

In cubfits: Codon Usage Bias Fits

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.

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().

Value

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

Author(s)

Wei-Chen Chen [e-mail].

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)

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