aboot: Calculate a dendrogram with bootstrap support using any...

In grunwaldlab/poppr: Genetic Analysis of Populations with Mixed Reproduction

Calculate a dendrogram with bootstrap support using any distance applicable to genind or genclone objects.

Description

Calculate a dendrogram with bootstrap support using any distance applicable to genind or genclone objects.

Usage

aboot(
  x,
  strata = NULL,
  tree = "upgma",
  distance = "nei.dist",
  sample = 100,
  cutoff = 0,
  showtree = TRUE,
  missing = "mean",
  mcutoff = 0,
  quiet = FALSE,
  root = NULL,
  ...
)

Arguments

x	a genind-class, genpop-class, genclone-class, genlight, snpclone or matrix object.
strata	a formula specifying the strata to be used to convert x to a genclone object if x is a genind object. Defaults to NULL. See details.
tree	a text string or function that can calculate a tree from a distance matrix. Defaults to "upgma". Note that you must load the package with the function for it to work.
distance	a character or function defining the distance to be applied to x. Defaults to nei.dist().
sample	An integer representing the number of bootstrap replicates Default is 100.
cutoff	An integer from 0 to 100 setting the cutoff value to return the bootstrap values on the nodes. Default is 0.
showtree	If TRUE (Default), a dendrogram will be plotted. If FALSE, nothing will be plotted.
missing	any method to be used by missingno(): "mean" (default), "zero", "loci", "genotype", or "ignore".
mcutoff	a value between 0 (default) and 1 defining the percentage of tolerable missing data if the missing parameter is set to "loci" or "genotype". This should only be set if the distance metric can handle missing data.
quiet	if FALSE (default), a progress bar will be printed to screen.
root	is the tree rooted? This is a parameter passed off to ape::boot.phylo(). If the tree parameter returns a rooted tree (like UPGMA), this should be TRUE, otherwise (like neighbor-joining), it should be false. When set to NULL (default), the tree is considered rooted if ape::is.ultrametric() is true.
...	any parameters to be passed off to the distance method.

Details

This function automates the process of bootstrapping genetic data to create a dendrogram with bootstrap support on the nodes. It will randomly sample with replacement the loci of a gen (genind/genpop) object or the columns of a numeric matrix, assuming that all loci/columns are independent. The process of randomly sampling gen objects with replacement is carried out through the use of an internal class called bootgen. This is necessary due to the fact that columns in the genind matrix are defined as alleles and are thus interrelated. This function will specifically bootstrap loci so that results are biologically relevant. With this function, the user can also define a custom distance to be performed on the genind or genclone object. If you have a data frame-like object where all of the columns are independent or pairs of columns are independent, then it may be simpler to use ape::boot.phylo() to calculate your bootstrap support values.

the strata argument

There is an argument called strata. This argument is useful for when you want to bootstrap by populations from a adegenet::genind() object. When you specify strata, the genind object will be converted to adegenet::genpop() with the specified strata.

Value

an object of class ape::phylo().

Note

prevosti.dist() and diss.dist() are exactly the same, but diss.dist() scales better for large numbers of individuals (n > 125) at the cost of required memory.

missing data

Missing data is not allowed by many of the distances. Thus, one of the first steps of this function is to treat missing data by setting it to the average allele frequency in the data set. If you are using a distance that can handle missing data (Prevosti's distance), you can set missing = "ignore" to allow the distance function to handle any missing data. See missingno() for details on missing data.

Bruvo's Distance

While calculation of Bruvo's distance is possible with this function, it is optimized in the function bruvo.boot().

References

Kamvar ZN, Brooks JC and Grünwald NJ (2015) Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Frontiers in Genetics 6:208. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3389/fgene.2015.00208")}

See Also

nei.dist() edwards.dist() rogers.dist() reynolds.dist() prevosti.dist() diss.dist() bruvo.boot() ape::boot.phylo() adegenet::dist.genpop() dist() bootgen2genind() bootgen

Examples

data(nancycats)
nan9 <- popsub(nancycats, 9)

set.seed(9999)
# Generate a tree using nei's distance
neinan <- aboot(nan9, dist = nei.dist)

set.seed(9999)
# Generate a tree using custom distance
bindist <- function(x) dist(tab(x), method = "binary")
binnan <- aboot(nan9, dist = bindist)

## Not run: 
# Distances from other packages.
#
# Sometimes, distance functions from other packages will have the constraint
# that the incoming data MUST be genind. Internally, aboot uses the 
# bootgen class ( class?bootgen ) to shuffle loci, and will throw an error
# The function bootgen2genind helps fix that. Here's an example of a function
# that expects a genind class from above
bindist <- function(x){
  stopifnot(is.genind(x))
  dist(tab(x), method = "binary")
}
#
# Fails:
# aboot(nan9, dist = bindist)
## Error: is.genind(x) is not TRUE
#
# Add bootgen2genind to get it working!
# Works:
aboot(nan9, dist = function(x) bootgen2genind(x) %>% bindist)

# AFLP data
data(Aeut)

# Nei's distance
anei <- aboot(Aeut, dist = nei.dist, sample = 1000, cutoff = 50)

# Rogers' distance
arog <- aboot(Aeut, dist = rogers.dist, sample = 1000, cutoff = 50)

# This can also be run on genpop objects
strata(Aeut) <- other(Aeut)$population_hierarchy[-1]
Aeut.gc <- as.genclone(Aeut)
setPop(Aeut.gc) <- ~Pop/Subpop
Aeut.pop <- genind2genpop(Aeut.gc)
set.seed(5000)
aboot(Aeut.pop, sample = 1000) # compare to Grunwald et al. 2006

# You can also use the strata argument to convert to genpop inside the function.
set.seed(5000)
aboot(Aeut.gc, strata = ~Pop/Subpop, sample = 1000)

# And genlight objects 
# From glSim:
## 1,000 non structured SNPs, 100 structured SNPs
x <- glSim(100, 1e3, n.snp.struc=100, ploid=2)
aboot(x, distance = bitwise.dist)

# Utilizing other tree methods

library("ape")

aboot(Aeut.pop, tree = fastme.bal, sample = 1000)

# Utilizing options in other tree methods

myFastME <- function(x) fastme.bal(x, nni = TRUE, spr = FALSE, tbr = TRUE)
aboot(Aeut.pop, tree = myFastME, sample = 1000)


## End(Not run)

grunwaldlab/poppr documentation built on March 18, 2024, 11:24 p.m.