dartR (version 2.0.4)

gl.filter.rdepth: Filters loci based on counts of sequence tags scored at a locus (read depth)

Description

SNP datasets generated by DArT report AvgCountRef and AvgCountSnp as counts of sequence tags for the reference and alternate alleles respectively. These can be used to back calculate Read Depth. Fragment presence/absence datasets as provided by DArT (SilicoDArT) provide Average Read Depth and Standard Deviation of Read Depth as standard columns in their report.

Filtering on Read Depth using the companion script gl.filter.rdepth can be on the basis of loci with exceptionally low counts, or loci with exceptionally high counts.

Usage

gl.filter.rdepth(
  x,
  lower = 5,
  upper = 50,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

x

Name of the genlight object containing the SNP or tag presence/absence data [required].

lower

Lower threshold value below which loci will be removed [default 5].

upper

Upper threshold value above which loci will be removed [default 50].

plot.out

Specify if plot is to be produced [default TRUE].

plot_theme

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

plot_colors

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

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

Returns a genlight object retaining loci with a Read Depth in the range specified by the lower and upper threshold.

Details

For examples of themes, see:

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

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

See Also

gl.filter.rdepth

Other filters and filter reports: gl.filter.allna(), gl.filter.monomorphs(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.report.callrate(), gl.report.hamming(), gl.report.locmetric(), gl.report.monomorphs(), gl.report.rdepth(), gl.report.reproducibility(), gl.report.taglength()

Examples

# NOT RUN {
# SNP data
  gl.report.rdepth(testset.gl)
  result <- gl.filter.rdepth(testset.gl, lower=8, upper=50, verbose=3)
# Tag P/A data
  result <- gl.filter.rdepth(testset.gs, lower=8, upper=50, verbose=3)

# }