tidypopgen (version 0.4.3)

autoplot.qc_report_loci: Autoplots for qc_report_loci objects

Description

For qc_report_loci, the following types of plots are available:

• overview: an UpSet plot, giving counts of snps over the threshold for missingness, minor allele frequency, and Hardy-Weinberg equilibrium P-value, and visualising the interaction between these

• all: a four panel plot, containing missing high maf, missing low maf, hwe, and significant hwe plots

• missing: a histogram of proportion of missing data

• missing low maf: a histogram of the proportion of missing data for snps with low minor allele frequency

• missing high maf:a histogram of the proportion of missing data for snps with high minor allele frequency

• maf: a histogram of minor allele frequency

• hwe: a histogram of HWE exact test p-values

• significant hwe: a histogram of significant HWE exact test p-values

Usage

# S3 method for qc_report_loci
autoplot(
  object,
  type = c("overview", "all", "missing", "missing low maf", "missing high maf", "maf",
    "hwe", "significant hwe"),
  maf_threshold = 0.05,
  miss_threshold = 0.01,
  hwe_p = 0.01,
  ...
)

Value

a ggplot2 object

Arguments

object

an object of class qc_report_loci

type

the type of plot (one of overview, all, missing, missing low maf, missing high maf, maf, hwe, and significant hwe)

maf_threshold

default 0.05, a threshold for the accepted rate of minor allele frequency of loci

miss_threshold

default 0.01, a threshold for the accepted rate of missingness per loci

hwe_p

default 0.01, a threshold of significance for Hardy-Weinberg exact p-values

...

not currently used.

Details

autoplot produces simple plots to quickly inspect an object. They are not customisable; we recommend that you use ggplot2 to produce publication ready plots.

Examples

# Create a gen_tibble
bed_file <-
  system.file("extdata", "related", "families.bed", package = "tidypopgen")
example_gt <- gen_tibble(bed_file,
  backingfile = tempfile("families"),
  quiet = TRUE,
  valid_alleles = c("1", "2")
)

loci_report <- example_gt %>% qc_report_loci()

# Plot the QC report overview
autoplot(loci_report, type = "overview")

# Plot the QC report all
autoplot(loci_report, type = "all")

# Plot missing data
autoplot(loci_report, type = "missing")

# Plot missing with low maf
autoplot(loci_report, type = "missing low maf", maf_threshold = 0.05)

# Plot missing with high maf
autoplot(loci_report, type = "missing high maf", maf_threshold = 0.05)

# Plot maf
autoplot(loci_report, type = "maf", maf_threshold = 0.05)

# Plot hwe
autoplot(loci_report, type = "hwe", hwe_p = 0.01)

# Plot significant hwe
autoplot(loci_report, type = "significant hwe", hwe_p = 0.01)