4 Data Visualization
ApplyPolygenicScore provides four plotting functions designed to
operate on the output of apply.polygenic.score:
create.pgs.density.plotcreate.pgs.boxplotcreate.pgs.with.continuous.phenotype.plotcreate.pgs.rank.plot
These visualizations are intended to provide quick quality assessments of PGS in a cohort. They really shine when combined with phenotype data to produce at-a-glance summaries of the relationship between PGS and phenotypes.
4.1 Common plotting arguments
Plotting functions are built on the BoutrosLab.plotting.general
package and produce lattice multipanel plot objects. Each function can
be provided with an output.directory, a
filename.prefix, and a file.extension to write
the plots to a file. A filename will be generated using the current date
and the provided prefix, and the plot will be written in the format
specified by the extension.
Note: in the examples below, all plots are written to a temporary directory from which they are read and displayed inline in the vignette.
Plotting functions automatically detect and plot data from the
standardized PGS output columns produced by
apply.polygenic.score: PGS,
PGS.with.normalized.missing, and
PGS.with.replaced.missing. If you are modifying and
renaming your PGS outputs, or wish to plot data generated elsewhere, you
can also specify a custom PGS column to plot using the
pgs.columns argument. Plots are automatically titled with
the name of the PGS column being plotted, and the titles can be “tidied
up” to remove period characters and replace them with spaces using the
tidy.titles argument.
Multipanel plot dimensions can be controlled with width
and height arguments, which are interpreted as inches. If
your plots are looking squished, try increasing these values. Axis and
title label font sizes can be controlled with xaxis.cex,
yaxis.cex, and titles.cex arguments. Border
padding can be controlled with border.padding and is
applied to all four sides of the plot at once.
4.2 PGS Density
create.pgs.density.plot plots the PGS distribution in
the cohort.
4.2.1 Basic plot
create.pgs.density.plot(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
output.dir = temp.dir,
filename.prefix = 'vignette-example-basic',
file.extension = 'png',
width = 5,
height = 5
)NULL
4.2.2 Add phenotypes
If provided with phenotype column names, this function will plot individual PGS density curves for each category in a categorical phenotype.
- Non-categorical phenotypes are ignored.
- Colors are automatically assigned up to 12 categories.
- If more than 12 categories are present, all categories are plotted in black.
- When multiple PGS columns are present and/or multiple phenotype columns are specified, each combination is plotted in a separate panel.
- Panels are arranged in a grid with unique PGS inputs on the x-axis and unique phenotype inputs on the y-axis.
create.pgs.density.plot(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
output.dir = temp.dir,
filename.prefix = 'vignette-example-phenotype',
file.extension = 'png',
tidy.titles = TRUE,
phenotype.columns = c('binary.factor.phenotype', 'categorical.phenotype', 'continuous.phenotype'),
width = 5,
height = 7,
yaxes.cex = 1.2
)NULL
4.3 PGS Boxplot
create.pgs.boxplot plots a boxplot of the PGS
distribution in the cohort, optionally grouped by phenotype categories.
By default, individual scores are plotted as points over the boxplot in
a stripplot style, and this option can be disabled with the
add.stripplot = FALSE argument.
4.3.1 Basic plot
create.pgs.boxplot(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
output.dir = temp.dir,
filename.prefix = 'vignette-example-basic',
file.extension = 'png',
width = 4,
height = 4
)NULL
4.3.2 Add phenotypes
If provided with phenotype column names, this function will plot individual boxplots for each category in a categorical phenotype.
- Non-categorical phenotypes are ignored.
- Colors are automatically assigned up to 12 categories.
- If more than 12 categories are present, all categories are plotted in black.
- When multiple PGS columns are present and/or multiple phenotype columns are specified, each combination is plotted in a separate panel.
- Panels are arranged in a grid with unique PGS inputs on the x-axis and unique phenotype inputs on the y-axis.
create.pgs.boxplot(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
output.dir = temp.dir,
filename.prefix = 'vignette-example-phenotype',
file.extension = 'png',
tidy.titles = TRUE,
add.stripplot = FALSE,
width = 5,
height = 11,
alpha = 0.9,
phenotype.columns = c('binary.factor.phenotype', 'categorical.phenotype', 'continuous.phenotype'),
titles.cex = 1.2
)NULL
4.4 PGS Correlation
create.pgs.with.continuous.phenotype.plot plots a
scatterplot between the PGS and a continuous phenotype, and reports a
correlation. Non-continuous phenotypes are ignored. This function can
only be used if phenotype data are provided.
4.4.1 Basic plot
create.pgs.with.continuous.phenotype.plot(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
output.dir = temp.dir,
filename.prefix = 'vignette-example-correlation',
file.extension = 'png',
tidy.titles = TRUE,
phenotype.columns = c('continuous.phenotype', 'categorical.phenotype'),
width = 5,
height = 5
)NULL
Unsurprisingly, our randomly generated phenotype data are not correlated with our fake PGS.
- When multiple PGS columns are present and/or multiple phenotype columns are specified, each combination is plotted in a separate panel.
- Panels are arranged in a grid with unique PGS inputs on the x-axis and unique phenotype inputs on the y-axis.
4.5 PGS Percentile Rank
create.pgs.rank.plot plots several panels of
per-individual data, each in the order of increasing PGS percentile
rank.
At the top, a barplot of missing PGS variant genotype counts or percents for each individual (only if at least one individual is missing a variant). Next, a barplot of the percentile rank of each individual. Next, a covariate bar of the PGS quartile and decile of each individual. Next, a covariate bar of categories for each provided categorical phenotype in each individual. Finally, a heatmap of continuous phenotype values for each provided continuous phenotype in each individual.
4.5.1 Basic plot
# Introducing some missing genotypes to demonstrate the missing genotype barplot
pgs.results.with.phenotype.analysis$pgs.output$n.missing.genotypes <- rep(c(0, 1, 0, 2, 1), 2)
create.pgs.rank.plot(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
output.dir = temp.dir,
filename.prefix = 'vignette-example-basic',
file.extension = 'png',
width = 5,
height = 6
)NULL
4.5.2 Add phenotypes
create.pgs.rank.plot(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
output.dir = temp.dir,
filename.prefix = 'vignette-example-phenotype',
file.extension = 'png',
phenotype.columns = c('binary.factor.phenotype', 'binary.phenotype', 'categorical.phenotype', 'continuous.phenotype'),
width = 6,
height = 8
)[1] "springgreen3"
[1] "darkturquoise"
[1] "deepskyblue"NULL
- Legend titles are inherited from phenotype column names.
- For cohorts exceeding 50 individuals, sample labels are automatically removed.
- If missing phenotypes exist, they are plotted in gray and accompanied by a missingness legend.
4.5.3 Optional arguments
This function comes with a handful of additional arguments to control the appearance of the plot.
- Use
missing.genotype.styleto toggle between displaying counts and percentages. - Use
categorical.paletteto specify a custom color palette for categorical phenotype bars. - Use
binary.palletteto specify a custom color palette for binary and continuous phenotype bars.
4.6 Case-control analysis
Another feature of ApplyPolygenicScore is the ability to perform a
case-control style analysis to assess the accuracy of a PGS in
predicting a binary phenotype. The
analyze.pgs.binary.predictiveness function takes the output
of apply.polygenic.score, fits a logistic regression to
predict the specified phenotype, plots a receiver-operator curve, and
returns the corresponding area under the curve (AUC) value.
4.6.1 Basic Plot
pgs.case.control.analysis <- analyze.pgs.binary.predictiveness(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
pgs.columns = 'PGS.with.replaced.missing',
phenotype.column = 'binary.phenotype',
output.dir = temp.dir,
filename.prefix = 'vignette-example-basic',
file.extension = 'png',
width = 5,
height = 5,
titles.cex = 1.2
);Warning in analyze.pgs.binary.predictiveness(pgs.data =
pgs.results.with.phenotype.analysis$pgs.output, : Phenotype column
'binary.phenotype' is not a factor. Attempting to convert to factor.
4.6.2 Binarize Continuous Phenotypes
If you wish to assess the predictiveness of a PGS in predicting a continuous phenotype, you can binarize the continuous phenotype by specifying a threshold that will split the continuous values into two categories.
pgs.case.control.analysis.continuous <- analyze.pgs.binary.predictiveness(
pgs.data = pgs.results.with.phenotype.analysis$pgs.output,
pgs.columns = 'PGS.with.replaced.missing',
phenotype.column = 'continuous.phenotype',
phenotype.type = 'continuous',
cutoff.threshold = 0, # binarize at 0
output.dir = temp.dir,
filename.prefix = 'vignette-example-continuous',
file.extension = 'png',
width = 5,
height = 5,
titles.cex = 1.2
);
- provide multiple pgs columns to plot multiple ROC curves in the same plot
- provide multiple phenotype columns to plot separate panels for each phenotype