gl.report.locmetric {dartR.base} | R Documentation |
Reports summary of the slot $other$loc.metrics
Description
This function reports summary statistics (mean, minimum, average, quantiles), histograms
and boxplots for any loc.metric with numeric values (stored in
$other$loc.metrics) to assist the decision of choosing thresholds for the filter
function gl.filter.locmetric
.
Usage
gl.report.locmetric(
x,
metric,
plot.display = TRUE,
plot.theme = theme_dartR(),
plot.colors = NULL,
plot.dir = NULL,
plot.file = NULL,
verbose = NULL
)
Arguments
x |
Name of the genlight object containing the SNP or presence/absence (SilicoDArT) data [required]. |
metric |
Name of the metric to be used for filtering [required]. |
plot.display |
Specify if plot is to be produced [default TRUE]. |
plot.theme |
User specified theme [default theme_dartR()]. |
plot.colors |
Vector with two color names for the borders and fill [default c("#2171B5", "#6BAED6")]. |
plot.dir |
Directory to save the plot RDS files [default as specified by the global working directory or tempdir()] |
plot.file |
Filename (minus extension) for the RDS plot file [Required for plot save] |
verbose |
Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log; 3, progress and results summary; 5, full report [default NULL, unless specified using gl.set.verbosity]. |
Details
The function gl.filter.locmetric
will filter out the
loci with a locmetric value below a specified threshold.
The fields that are included in dartR, and a short description, are found
below. Optionally, the user can also set his/her own field by adding a vector
into $other$loc.metrics as shown in the example. You can check the names of
all available loc.metrics via: names(gl$other$loc.metrics).
SnpPosition - position (zero is position 1) in the sequence tag of the defined SNP variant base.
CallRate - proportion of samples for which the genotype call is non-missing (that is, not '-' ).
OneRatioRef - proportion of samples for which the genotype score is 0.
OneRatioSnp - proportion of samples for which the genotype score is 2.
FreqHomRef - proportion of samples homozygous for the Reference allele.
FreqHomSnp - proportion of samples homozygous for the Alternate (SNP) allele.
FreqHets - proportion of samples which score as heterozygous, that is, scored as 1.
PICRef - polymorphism information content (PIC) for the Reference allele.
PICSnp - polymorphism information content (PIC) for the SNP.
AvgPIC - average of the polymorphism information content (PIC) of the reference and SNP alleles.
AvgCountRef - sum of the tag read counts for all samples, divided by the number of samples with non-zero tag read counts, for the Reference allele row.
AvgCountSnp - sum of the tag read counts for all samples, divided by the number of samples with non-zero tag read counts, for the Alternate (SNP) allele row.
RepAvg - proportion of technical replicate assay pairs for which the marker score is consistent.
rdepth - read depth.
Function's output The minimum, maximum, mean and a tabulation of quantiles of the locmetric values against thresholds rate are provided. Output also includes a boxplot and a histogram. Quantiles are partitions of a finite set of values into q subsets of (nearly) equal sizes. In this function q = 20. Quantiles are useful measures because they are less susceptible to long-tailed distributions and outliers. Plot colours can be set with gl.select.colors(). If plot.file is specified, plots are saved to the directory specified by the user, or the global default working directory set by gl.set.wd() or to the tempdir(). Examples of other themes that can be used can be consulted in:
Value
An unaltered genlight object.
Author(s)
Luis Mijangos (Post to https://groups.google.com/d/forum/dartr)
See Also
Other matched report:
gl.report.callrate()
,
gl.report.hamming()
,
gl.report.maf()
,
gl.report.overshoot()
,
gl.report.pa()
,
gl.report.rdepth()
,
gl.report.reproducibility()
,
gl.report.secondaries()
,
gl.report.taglength()
Examples
# SNP data
out <- gl.report.locmetric(testset.gl,metric='SnpPosition')
# Tag P/A data
out <- gl.report.locmetric(testset.gs,metric='AvgReadDepth')