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STOAT Plot overview

Jean Monlong

2026-07-15

intro.Rmd
library(StoatPlot)
library(ggplot2)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(knitr)

Import association results 

## path to association file (~stoat.assoc.pvalues.tsv.gz)
test_data_dir <- system.file('extdata', package='StoatPlot')
assoc_file = file.path(test_data_dir, 'stoat.quantitative.assoc.pvalues.tsv.gz')

## import association results
assoc = import_assoc(assoc_file)

Summary 

sum.l = summary_stoat(assoc)
Metric Value
Total variants 680
Variant PV<0.01 31
Variant adjusted PV<0.01 2
Genomic inflation factor 1.15
Type Total Pv_BH_below_0.01
SNP 519 2
SV 161 0

QQ-plot 

qq_plot(assoc)

Manhattan plot 

manhattan_plot(assoc, show_top_points=1000)

Top-associated variants 

assoc |> arrange(P) |> head(10) |> kable()
CHR START_OFFSET START_NODE END_NODE ALLELE_LENGTHS P
ref 32325 >629 <632 1,1 0.0000007
ref 16257 >310 <313 1,1 0.0000041
ref 68351 >1271 <1274 1,1 0.0000447
ref 99994 >1815 <1818 1,1 0.0000884
ref 53887 >1010 <1012 0,248 0.0000957
ref 105752 >1896 <1899 1,1 0.0001014
ref 39235 >765 <768 1,1 0.0001537
ref 13062 >255 <266 0,602 0.0004176
ref 105360 >1888 <1893 0,571 0.0004948
ref 19088 >356 <359 1,1 0.0005337

Make a plot for the top association 

## prepare file names for the genotypes and phenotype
gt_fn = file.path(test_data_dir, 'snarl_genotypes.sorted.tsv.gz')
pheno_fn = file.path(test_data_dir, 'phenotype.quantitative.tsv')

## get the most associated variant
top.assoc = assoc |> arrange(P) |> head(1)

## boxplot with samples grouped by genotype
genotype_boxplots(gt_fn, pheno_fn, top.assoc)


## boxplot with samples grouped by allele
genotype_boxplots(gt_fn, pheno_fn, top.assoc, by_allele=TRUE)

Other graphs 

# Histogram of p-values
plot_pvalue_hist(
  file.path(test_data_dir, "gwas", "binary.assoc.chi2.tsv"),
  p_column="P_CHI2",
  min = 0.00001,
  max = 0.5
)


# Dot plot of Path Length Frequencies
path_length_distribution(file.path(test_data_dir, "snarl_paths", "binary.snarl.tsv"))
#> Warning: Use of `freq_df$Path_Length` is discouraged.
#> ℹ Use `Path_Length` instead.
#> Warning: Use of `freq_df$Frequency` is discouraged.
#> ℹ Use `Frequency` instead.
#> Warning: Use of `ecdf_df$Cumulative_scaled` is discouraged.
#> ℹ Use `Cumulative_scaled` instead.


# Snarl type histogram
snarl_type_histogram(file.path(test_data_dir, "snarl_paths", "binary.snarl.tsv"))
#>   Variant_Type Count
#> 1        INDEL     3
#> 2          SNP  1495
#> 3           SV     1