consensus.traj: Consensus Allele Frequency Trajectory

In ThomasTaus/poolSeq: Simulate and Analyze Pool-seq Data

Description

Combine multiple allele frequency trajectories to get a consensus trajectory.

Usage

consensus.traj(traj, t, cov, Ne, N.sim = 10000, bias = NA)

Arguments

traj	numeric matrix of allele frequency trajectories, where rows and columns correspond to individual replicates and time points, respectively.
t	numeric vector indicating the number of generations for each column in traj.
cov	numeric matrix of sequence coverages, where columns and rows correspond to measurement time points and replicates, respectively
Ne	numeric specifying the effective population size
N.sim	numeric determining the number of simulations that should be performed to correct for bias (by default sim = 10000).
bias	numeric specifying the bias correction, which is necessesary to account for conditioning on fixation. By default (bias = NA) fixationBias is called to compute the bias.

Value

A list with class "ctraj" containing the following components:

af	consensus allele frequencies.
t	time in generations.
cov	combined sequence coverages (if available).

Author(s)

Thomas Taus

Examples

# simulate allele frequency trajectories of individual loci using the Wright-Fisher model (diploid individuals)
p0 <- 0.01
s <- 0.1
h <- 0.5
Ne <- 1000
tp <- seq(0, 100, by=10)
alleleFreqs <- wf.traj(p0=rep(p0, times=100), Ne=Ne, t=tp, s=s, h=h)

# compute consensus trajectory
cTraj <- consensus.traj(alleleFreqs, t=tp, cov=NA, Ne=Ne)

# plot individual and consensus allele frequency trajectories, as well as the trajectory assuming an infinitely large population (no random drift)
plot(1, type="n", xlim=c(0, max(tp)), ylim=c(0, 1), main="Allele frequency trajectories with selection", xlab="Generation", ylab="Allele frequency")
for(r in 1:nrow(alleleFreqs)) {
  lines(tp, alleleFreqs[r,])
}
lines(tp, cTraj$af, col="red", lwd=2)
lines(tp, wf.traj(p0=p0, Ne=NA, t=tp, s=s, h=h), col="green", lwd=2)
legend("topleft", legend=c("replicate", "consensus", "deterministic"), col=c("black", "red", "green"), lwd=c(1, 2, 2), bty="n")

ThomasTaus/poolSeq documentation built on Feb. 17, 2020, 1:52 p.m.