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

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.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  bins = 25,
  save2tmp = FALSE,
  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.out

Specify if histograms of call rate, before and after, are to be produced [default TRUE].

plot_theme

User specified theme for the plot [default theme_dartR()].

plot_colors

List of two color names for the borders and fill of the plots [default two_colors].

bins

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

save2tmp

If TRUE, saves any ggplots and listings to the session temporary directory (tempdir) [default FALSE].

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].

Value

The reduced genlight or genind object, plus a summary

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.

See also

gl.report.callrate

Other filter functions: gl.filter.allna(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Author

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

Examples

 # \donttest{
# SNP data
  result <- gl.filter.callrate(testset.gl[1:10], method='loc', threshold=0.8,
   verbose=3)
#> Starting gl.filter.callrate 
#>   Processing genlight object with SNP data
#>   Warning: data include loci that are scored NA across all individuals.
#>   Consider filtering using gl <- gl.filter.allna(gl)
#>   Warning: Data may include monomorphic loci in call rate 
#>                     calculations for filtering
#>   Recalculating Call Rate
#>   Removing loci based on Call Rate, threshold = 0.8 
#>   Summary of filtered dataset
#>     Call Rate for loci > 0.8 
#>     Original No. of loci : 255 
#>     Original No. of individuals: 10 
#>     No. of loci retained: 209 
#>     No. of individuals retained: 10 
#>     No. of populations:  7 

#> Completed: gl.filter.callrate 
#> 
  result <- gl.filter.callrate(testset.gl[1:10], method='ind', threshold=0.8,
   verbose=3)
#> Starting gl.filter.callrate 
#>   Processing genlight object with SNP data
#>   Warning: data include loci that are scored NA across all individuals.
#>   Consider filtering using gl <- gl.filter.allna(gl)
#>   Warning: Data may include monomorphic loci in call rate 
#>                     calculations for filtering
#>   Recalculating Call Rate
#>   Removing individuals based on Call Rate, threshold = 0.8 
#>   Individuals deleted (CallRate <=  0.8 ):
#> AA032760[EmmacMDBMaci],
#>   Summary of filtered dataset
#>     Call Rate for individuals > 0.8 
#>     Original No. of loci : 255 
#>     Original No. of individuals: 10 
#>     No. of loci retained: 255 
#>     No. of individuals retained: 9 
#>     No. of populations:  6 

#>   Note: Locus metrics not recalculated
#>   Note: Resultant monomorphic loci not deleted
#> Completed: gl.filter.callrate 
#> 
  result <- gl.filter.callrate(testset.gl[1:10], method='pop', threshold=0.8,
   verbose=3)
#> Starting gl.filter.callrate 
#>   Processing genlight object with SNP data
#>   Warning: data include loci that are scored NA across all individuals.
#>   Consider filtering using gl <- gl.filter.allna(gl)
#>   Warning: Data may include monomorphic loci in call rate 
#>                     calculations for filtering
#>   Recalculating Call Rate
#>   Removing loci based on Call Rate by population
#>   
#>                     Call Rate must be equal to or exceed threshold = 0.8 in all populations
#>   Summary of filtered dataset
#>     Call Rate in any one population > 0.8 
#>     Original No. of loci : 255 
#>     Original No. of individuals: 10 
#>     No. of loci retained: 164 
#>     No. of individuals retained: 10 
#>     No. of populations:  7 

#> Completed: gl.filter.callrate 
#> 
# Tag P/A data
  result <- gl.filter.callrate(testset.gs[1:10], method='loc', 
  threshold=0.95, verbose=3)
#> Starting gl.filter.callrate 
#>   Processing genlight object with Presence/Absence (SilicoDArT) data
#>   Recalculating Call Rate
#>   Removing loci based on Call Rate, threshold = 0.95 
#>   Summary of filtered dataset
#>     Call Rate for loci > 0.95 
#>     Original No. of loci : 255 
#>     Original No. of individuals: 10 
#>     No. of loci retained: 186 
#>     No. of individuals retained: 10 
#>     No. of populations:  7 

#> Completed: gl.filter.callrate 
#> 
  result <- gl.filter.callrate(testset.gs[1:10], method='ind', 
  threshold=0.8, verbose=3)
#> Starting gl.filter.callrate 
#>   Processing genlight object with Presence/Absence (SilicoDArT) data
#>   Recalculating Call Rate
#>   Removing individuals based on Call Rate, threshold = 0.8 
#>   Summary of filtered dataset
#>     Call Rate for individuals > 0.8 
#>     Original No. of loci : 255 
#>     Original No. of individuals: 10 
#>     No. of loci retained: 255 
#>     No. of individuals retained: 10 
#>     No. of populations:  7 

#>   Note: Locus metrics not recalculated
#>   Note: Resultant monomorphic loci not deleted
#> Completed: gl.filter.callrate 
#> 
  result <- gl.filter.callrate(testset.gs[1:10], method='pop', 
  threshold=0.8, verbose=3)
#> Starting gl.filter.callrate 
#>   Processing genlight object with Presence/Absence (SilicoDArT) data
#>   Recalculating Call Rate
#>   Removing loci based on Call Rate by population
#>   
#>                     Call Rate must be equal to or exceed threshold = 0.8 in all populations
#>   Summary of filtered dataset
#>     Call Rate in any one population > 0.8 
#>     Original No. of loci : 255 
#>     Original No. of individuals: 10 
#>     No. of loci retained: 186 
#>     No. of individuals retained: 10 
#>     No. of populations:  7 

#> Completed: gl.filter.callrate 
#> 
  # }
  res <- gl.filter.callrate(platypus.gl)
#> Starting gl.filter.callrate 
#>   Processing genlight object with SNP data
#>   Warning: data include loci that are scored NA across all individuals.
#>   Consider filtering using gl <- gl.filter.allna(gl)
#>   Warning: Data may include monomorphic loci in call rate 
#>                     calculations for filtering
#>   Recalculating Call Rate
#>   Removing loci based on Call Rate, threshold = 0.95 
#> 

#> Completed: gl.filter.callrate 
#>