gl.filter.callrate {dartR.base}

Filters loci or specimens in a genlight {adegenet} object based on call rate

Description

SNP datasets generated by DArT have missing values primarily arising from failure to call a SNP because of a mutation at one or both of the restriction enzyme recognition sites. The script gl.filter.callrate() will filter out the loci with call rates below a specified threshold. Tag Presence/Absence datasets (SilicoDArT) have missing values where it is not possible to determine reliably if there the sequence tag can be called at a particular locus.

Usage

gl.filter.callrate(
  x,
  method = "loc",
  threshold = 0.95,
  mono.rm = FALSE,
  recalc = FALSE,
  recursive = FALSE,
  plot.display = TRUE,
  plot.theme = theme_dartR(),
  plot.colors = NULL,
  plot.file = NULL,
  plot.dir = NULL,
  bins = 25,
  verbose = NULL
)

Arguments

x

Name of the genlight object containing the SNP data, or the genind object containing the SilocoDArT data [required].

method

Use method='loc' to specify that loci are to be filtered, 'ind' to specify that specimens are to be filtered, 'pop' to remove loci that fail to meet the specified threshold in any one population [default 'loc'].

threshold

Threshold value below which loci will be removed [default 0.95].

mono.rm

Remove monomorphic loci after analysis is complete [default FALSE].

recalc

Recalculate the locus metadata statistics if any individuals are deleted in the filtering [default FALSE].

recursive

Repeatedly filter individuals on call rate, each time removing monomorphic loci. Only applies if method='ind' and mono.rm=TRUE [default FALSE].

plot.display

If TRUE, histograms are displayed in the plot window [default TRUE].

plot.theme

Theme for the plot. See Details for options [default theme_dartR()].

plot.colors

Vector with two color names for the borders and fill [default c("#2171B5", "#6BAED6")].

plot.file

Name for the RDS binary file to save (base name only, exclude extension) [default NULL]

plot.dir

Directory in which to save files [default = working directory]

bins

Number of bins to display in histograms [default 25].

verbose

Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log ; 3, progress and results summary; 5, full report [default 2, unless specified using gl.set.verbosity].

Details

Because this filter operates on call rate, this function recalculates Call Rate, if necessary, before filtering. If individuals are removed using method='ind', then the call rate stored in the genlight object is, optionally, recalculated after filtering. Note that when filtering individuals on call rate, the initial call rate is calculated and compared against the threshold. After filtering, if mono.rm=TRUE, the removal of monomorphic loci will alter the call rates. Some individuals with a call rate initially greater than the nominated threshold, and so retained, may come to have a call rate lower than the threshold. If this is a problem, repeated iterations of this function will resolve the issue. This is done by setting mono.rm=TRUE and recursive=TRUE, or it can be done manually. Callrate is summarized by locus or by individual to allow sensible decisions on thresholds for filtering taking into consideration consequential loss of data. The summary is in the form of a tabulation and plots. Plot themes can be obtained from

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Resultant ggplot(s) and the tabulation(s) are saved to the session's temporary directory.

Value

The reduced genlight or genind object, plus a summary

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.report.callrate

Other matched filter: gl.filter.hamming(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.secondaries()

Examples

 
# SNP data
  result <- gl.filter.callrate(testset.gl[1:10], method='loc', threshold=0.8,
   verbose=3)
  result <- gl.filter.callrate(testset.gl[1:10], method='ind', threshold=0.8,
   verbose=3)
  result <- gl.filter.callrate(testset.gl[1:10], method='pop', threshold=0.8,
   verbose=3)
# Tag P/A data
  result <- gl.filter.callrate(testset.gs[1:10], method='loc', 
  threshold=0.95, verbose=3)
  result <- gl.filter.callrate(testset.gs[1:10], method='ind', 
  threshold=0.8, verbose=3)
  result <- gl.filter.callrate(testset.gs[1:10], method='pop', 
  threshold=0.8, verbose=3)
  
  res <- gl.filter.callrate(platypus.gl)