ts.test: Monte Carlo test of correlation between tAI and Nc adjusted

In mariodosreis/tai: The tRNA adaptation index

Monte Carlo test of correlation between tAI and Nc adjusted

Description

Calculates the p-value (using a Monte Carlo or randomisation test) that the correlation (the S value) between tAI and the adjusted Nc for a set of genes is different from zero.

Usage

ts.test(m, ws, nc, gc3s, ts.obs, samp.size, n = 1000)

Arguments

m	a k by 60 matrix of codon frequencies for k genes
ws	vector of length 60 of relative adaptiveness values of codons
nc	vector of length k of Nc values for genes
gc3s	vector of length k of GC content at third codon position for genes
ts.obs	vector of length 1 with observed correlation between tAI and Nc adjusted for the k genes
samp.size	a vector of length 1 with the number of genes to be sampled from m (see details)
n	the number of permutations of ws in the randomisation test

Details

The Monte Carlo test is described in dos Reis et al. (2004). When working with complete genomes, matrix m can have a very large number of rows (large k). In this case it may be advisable to choose samp.size < k to speed up the computation.

Value

A list with elements p.value, the p-value for the test, and ts.simulated, a vector of length n with the simulated correlations between tAI and adjusted Nc.

Author(s)

Mario dos Reis

References

dos Reis M., Savva R., and Wernisch L. (2004) Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res., 32: 5036–44.

Examples

eco.ws <- get.ws(tRNA=ecolik12$trna, sking=1)
eco.tai <- get.tai(ecolik12$m[,-33], eco.ws)
ts.obs <- get.s(eco.tai, ecolik12$w$Nc, ecolik12$w$GC3s)

# The S-value (dos Reis et al. 2004):
ts.obs # [1] 0.9065442

# There seems to be a high correlation between tAI and Nc adjusted for
# the 49 genes in ecolik12$m. Is the correlation statistically significant?
ts.mc <- ts.test(ecolik12$m[,-33], eco.ws, ecolik12$w$Nc, ecolik12$w$GC3s, 
                 ts.obs, samp.size=dim(ecolik12$m)[1])
# The p-value is zero:
ts.mc$p.value # [1] 0

# Histogram of simulated S-values:
hist(ts.mc$ts.simulated, n=50, xlab = "Simulated S values", 
     xlim=c(min(ts.mc$ts.simulated), ts.obs))
# Add the observed S-value as a red vertical line:
abline(v=ts.obs, col="red")

mariodosreis/tai documentation built on June 9, 2022, 7:37 p.m.