Filters loci based on counts of sequence tags scored at a locus (read depth)

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 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.pa(), gl.filter.parent.offspring(), 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
  gl.report.rdepth(testset.gl)
#> Starting gl.report.rdepth 
#>   Processing genlight object with SNP data
#>   Reporting Read Depth by Locus
#>   No. of loci = 255 
#>   No. of individuals = 250 
#>     Minimum      :  3.1 
#>     1st quartile :  6.3 
#>     Median       :  9.5 
#>     Mean         :  16.60392 
#>     3r quartile  :  22.75 
#>     Maximum      :  66.4 
#>     Missing Rate Overall:  0.12 
#> 

#>    Quantile Threshold Retained Percent Filtered Percent
#> 1      100%      66.4        1     0.4      254    99.6
#> 2       95%      50.7       13     5.1      242    94.9
#> 3       90%      41.0       26    10.2      229    89.8
#> 4       85%      34.2       39    15.3      216    84.7
#> 5       80%      28.9       52    20.4      203    79.6
#> 6       75%      23.0       64    25.1      191    74.9
#> 7       70%      20.0       77    30.2      178    69.8
#> 8       65%      16.2       90    35.3      165    64.7
#> 9       60%      12.1      102    40.0      153    60.0
#> 10      55%      10.8      116    45.5      139    54.5
#> 11      50%       9.5      129    50.6      126    49.4
#> 12      45%       8.1      143    56.1      112    43.9
#> 13      40%       7.5      157    61.6       98    38.4
#> 14      35%       7.0      167    65.5       88    34.5
#> 15      30%       6.5      182    71.4       73    28.6
#> 16      25%       6.3      192    75.3       63    24.7
#> 17      20%       5.6      205    80.4       50    19.6
#> 18      15%       4.9      217    85.1       38    14.9
#> 19      10%       4.4      232    91.0       23     9.0
#> 20       5%       4.2      244    95.7       11     4.3
#> 21       0%       3.1      255   100.0        0     0.0
#> Completed: gl.report.rdepth 
#> 
  result <- gl.filter.rdepth(testset.gl, lower=8, upper=50, verbose=3)
#> Starting gl.filter.rdepth 
#>   Processing genlight object with SNP data
#>   Removing loci with rdepth <= 8 and >= 50 
#> 

#>   Summary of filtered dataset
#>     Initial no. of loci = 255 
#>     No. of loci deleted = 122 
#>     No. of loci retained: 133 
#>     No. of individuals: 250 
#>     No. of populations:  30 
#> Completed: gl.filter.rdepth 
#> 
# Tag P/A data
  result <- gl.filter.rdepth(testset.gs, lower=8, upper=50, verbose=3)
#> Starting gl.filter.rdepth 
#>   Processing genlight object with Presence/Absence (SilicoDArT) data
#>   Removing loci with rdepth <= 8 and >= 50 
#> 

#>   Summary of filtered dataset
#>     Initial no. of loci = 255 
#>     No. of loci deleted = 35 
#>     No. of loci retained: 220 
#>     No. of individuals: 218 
#>     No. of populations:  29 
#> Completed: gl.filter.rdepth 
#> 
  # }
  res <- gl.filter.rdepth(platypus.gl)
#> Starting gl.filter.rdepth 
#>   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)
#>   Removing loci with rdepth <= 5 and >= 50 
#> 

#> Completed: gl.filter.rdepth 
#>