View source: R/gl.report.rdepth.r
gl.report.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. This function reports the read depth by locus for each of several quantiles.
gl.report.rdepth(
x,
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 presence/absence (SilicoDArT) data [required]. |
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]. |
The function displays a table of minimum, maximum, mean and quantiles for
read depth against possible thresholds that might subsequently be specified
in gl.filter.rdepth
. If plot.out=TRUE, display also includes a
boxplot and a histogram to guide in the selection of a threshold for
filtering on read depth.
If save2tmp=TRUE, ggplots and relevant tabulations are saved to the session's temp directory (tempdir).
For examples of themes, see
An unaltered genlight object
Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr
gl.filter.rdepth
Other report functions:
gl.report.bases()
,
gl.report.callrate()
,
gl.report.diversity()
,
gl.report.hamming()
,
gl.report.heterozygosity()
,
gl.report.hwe()
,
gl.report.ld.map()
,
gl.report.locmetric()
,
gl.report.maf()
,
gl.report.monomorphs()
,
gl.report.overshoot()
,
gl.report.parent.offspring()
,
gl.report.pa()
,
gl.report.reproducibility()
,
gl.report.secondaries()
,
gl.report.sexlinked()
,
gl.report.taglength()
# SNP data
df <- gl.report.rdepth(testset.gl)
df <- gl.report.rdepth(testset.gs)
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