View source: R/gl.filter.rdepth.r
gl.filter.rdepth | R Documentation |
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.
gl.filter.rdepth(
x,
lower = 5,
upper = 50,
plot.out = TRUE,
plot_theme = theme_dartR(),
plot_colors = two_colors,
save2tmp = FALSE,
verbose = NULL
)
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]. |
For examples of themes, see:
Returns a genlight object retaining loci with a Read Depth in the range specified by the lower and upper threshold.
Custodian: Arthur Georges (Post to https://groups.google.com/d/forum/dartr)
gl.filter.rdepth
Other filter functions:
gl.filter.allna()
,
gl.filter.callrate()
,
gl.filter.heterozygosity()
,
gl.filter.hwe()
,
gl.filter.ld()
,
gl.filter.locmetric()
,
gl.filter.maf()
,
gl.filter.monomorphs()
,
gl.filter.overshoot()
,
gl.filter.parent.offspring()
,
gl.filter.pa()
,
gl.filter.reproducibility()
,
gl.filter.secondaries()
,
gl.filter.sexlinked()
,
gl.filter.taglength()
# 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)
res <- gl.filter.rdepth(platypus.gl)
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