---
title: "User Guide"
output:
    rmarkdown::html_vignette:
        number_sections: true
vignette: >
  %\VignetteIndexEntry{User Guide}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r options, include = FALSE}
knitr::opts_chunk$set(comment = NA, purl = FALSE, dpi = 96, fig.width = 0.5, fig.height = 0.7, dev = 'png')
```

```{r render-how-to, include = FALSE, eval = FALSE, echo = FALSE}
# Render vignette outside of build_vignettes() via rmarkdown:
# Render the HTML vignette
rmarkdown::render('vignettes/UserGuide.Rmd', output_format = 'rmarkdown::html_vignette')

# Render the PDF vignette. Needs to have a functional LaTeX installation.
rmarkdown::render('vignettes/UserGuide.Rmd', output_format = 'rmarkdown::pdf_document')
```

```{r setup}
library(ApplyPolygenicScore)
```


# Introduction
ApplyPolygenicScore provides utilities for applying a polygenic score model to genetic data.

This guide contains step-by-step instructions for how best to take advantage of the functions 
in this package to facilitate this process and transition smoothly into downstream analyses.

# Input data

The application of a polygenic score model to genetic data requires two basic inputs:

1. A VCF file containing genotype data for a set of individuals.
2. A polygenic score model weight file containing the SNP coordinates and weights for the polygenic score.

Optionally, a third data type, phenotype data, can be provided to several ApplyPolygenicScore functions.

VCF file requirements:

* VCF files must be in standard VCF format.
* All multiallelic sites must be merged into one line per variant.

PGS weight file requirements:

- PGS weight files provided by the PGS Catalog should be imported with the native package function `import.pgs.weight.file` to ensure proper formatting
- PGS weight files from other sources must be formatted according to PGS Catalog standards for compatibility with the import function.
- PGS weight files from other sources that are imported independently must have the required columns: `CHROM`, `POS`, `effect_allele`, `beta` and optionally `ID`, `other_allele`, and `allelefrequency_effect`.
- Weights for multiple alleles at the same site can be provided as separate rows in the file with differing alternative alleles specified. 

## Input importation

ApplyPolygenicScore provides functions for the importation of input data into R,
 and the verification of these data for compatibility with other functions in the package.

### Importing VCF data

Use the `import.vcf` function to import VCF data into R. This function is a wrapper around the `vcfR` package
that ensures the VCF data is in the format expected by other package functions.

The example below imports a VCF file from GIAB, included in the package as an example file.

```{r import-vcf}
vcf.file <- system.file(
    'extdata',
    'HG001_GIAB.vcf.gz',
    package = 'ApplyPolygenicScore',
    mustWork = TRUE
    )

vcf.data <- import.vcf(
    vcf.path = vcf.file,
    long.format = TRUE,
    info.fields = NULL,
    format.fields = NULL,
    verbose = TRUE
    )

str(vcf.data)
```


#### Imported VCF data structures

As we can see, `import.vcf` returns a list with two elements: `split.wide.vcf.matrices` and `combined.long.vcf.df`.
These two objects represent two ways of representing the imported data.

The "wide" format is the most memory-efficient.
It splits the VCF data into two objects: a table of per-variant data with only essential columns (CHROM, POS, ID, REF, ALT)
and a matrix of genotypes where each row corresponds to a variant and each column corresponds to an individual.
This format allows for efficient storage and manipulation of genotype data, especially for large VCF files.
The key limitation of this format is that it does not allow the importation of information (other than genotype)
 unique to a sample-variant pair, such as that stored in the `FORMAT` field of the VCF file.


The "long" format is optional and will only be returned if `long.format = TRUE` is specified. It allows for the importation of all
information in the VCF file, including the `FORMAT` field, however it requires a lot more memory to store in R. This is because a
row is created for each sample-variant pair, resulting in a data frame with many more rows than the wide format.
For most applications, the wide format is sufficient and preferred for its memory efficiency.

### Importing polygenic score data

Use the `import.pgs.weight.file` function to import PGS weight files formatted according to PGS Catalog specifications.
This function parses the header that is typically provided by PGS catalog files and differentiates between harmonized and 
not harmonized data columns.

The example below imports a PGS weight file from the PGS Catalog, included in the package as an example file.

```{r import-pgs}
pgs.file <- system.file(
    'extdata',
    'PGS003378_hmPOS_GRCh38.txt.gz',
    package = 'ApplyPolygenicScore',
    mustWork = TRUE
    )

pgs.data <- import.pgs.weight.file(
    pgs.weight.path = pgs.file,
    use.harmonized.data = TRUE
    )

str(pgs.data)
```

The output of the `import.pgs.weight.file` function is a list with one data frame containing the SNP coordinates and weights for the polygenic score, 
with standardized column names expected by other package functions, and a second data frame containing the header information from the PGS catalog file.

### Importing phenotype data

Providing phenotype data is an optional feature of several ApplyPolygenicScore functions. Phenotype data must be imported as a data frame
and must contain a column named `Indiv` corresponding to the individual IDs in the VCF file.

```{r import-phenotype}

# Isolating the individual IDs from the VCF data
vcf.individuals <- colnames(vcf.data$split.wide.vcf.matrices$genotyped.alleles)

# Simulating phenotype data
set.seed(123)
phenotype.data <- data.frame(
    Indiv = vcf.individuals,
    continuous.phenotype = rnorm(length(vcf.individuals)),
    binary.phenotype = rbinom(length(vcf.individuals), 1, 0.5)
    )

head(phenotype.data)
```

## Creating a BED-formatted coordinate file

VCF files can be very large. Sometimes they are too large to be imported into R. In these cases, it is useful to first filter the VCF file to just the variants
that are included in the PGS you wish to calculate and reduce file size. This is best done using command line tools designed for VCF file manipulation. For filtering, they typically 
require a BED file containing the coordinates of the variants you wish to keep. To simplify this process, ApplyPolygenicScore provides functions for converting PGS weight files
to BED-formatted coordinate files.

### Conversion of PGS weight files to a coordinate file in BED format

BED format requires the following first three columns: chromosome name, start position, and end position.
PGS weight files only contain the chromosome name and end position of each variant, so must be reformatted
with an additional column for the start position, and with the correct column order.

> Note: When writing BED formatted data frames to files, make sure to use tab-separated values and not include row names.
Additionally, most tools do not accept BED files with column names. If you wish to maintain a header, you may need to add
a comment character to the first line of the file: `# chr start end`

Use the `convert.pgs.to.bed` function to convert a PGS weight file to a BED-formatted coordinate data frame.

```{r convert-pgs-to-bed}

pgs.coordinate.info <- pgs.data$pgs.weight.data

pgs.bed.format <- convert.pgs.to.bed(
    pgs.weight.data = pgs.coordinate.info,
    chr.prefix = TRUE,
    numeric.sex.chr = FALSE,
    slop = 10
    )

head(pgs.bed.format)
```

Coordinate files must match the coordinate style used in the VCF file you wish to filter. The `convert.pgs.to.bed` function provides options for
formatting chromosome names, as these can vary between human genome reference files that originate from different databases.

- Use `chr.prefix = TRUE` to add 'chr' to the chromosome name (UCSC style)
- Use `chr.prefix = FALSE` to remove 'chr' from the chromosome name (Ensembl style)
- Use `numeric.sex.chr = FALSE` to format the X and Y chromosomes as 'X' and 'Y' respectively
- Use `numeric.sex.chr = TRUE` to format the X and Y chromosomes as '23' and '24' respectively

The `slop` option imitates `bedtools` nomenclature for adding base pairs to the start and end of a set of coordinates. `slop = 10` adds 10 base pairs to the start and end of each variant coordinate.

Here is an example of genomic coordinates in BED file format for a variant on chromosome 1 at the 20th base pair.

No slop:

|chr|start|end|
|---|---|---|
|chr1|19|20|

With slop of 10 base pairs:

|chr|start|end|
|---|---|---|
|chr1|9|30|

### Merging coordinates from multiple polygenic scores

What if you want to apply multiple polygenic scores to the same VCF file?
Instead of filtering the VCF file multiple times, you can use the `combine.pgs.bed` function to merge multiple BED-formatted data frames
into a single set of coordinates, and filter your VCF just once for the union of all variants in multiple PGSs.

```{r merge-pgs-bed}
# convert your PGS weight files with no added slop
pgs.bed1 <- convert.pgs.to.bed(pgs.coordinate.info, slop = 0)

# simulating a second PGS with all coordinates shifted by 20 base pairs.
pgs.bed2 <- pgs.bed1
pgs.bed2$start <- pgs.bed2$start + 20
pgs.bed2$end <- pgs.bed2$end + 20

# Input must be a named list
pgs.bed.list <- list(PGS1 = pgs.bed1, PGS2 = pgs.bed2)

merged.pgs.bed <- combine.pgs.bed(
    pgs.bed.list = pgs.bed.list,
    add.annotation.data = TRUE,
    annotation.column.index = which(colnames(pgs.bed1) == 'rsID') # keep information from the rsID column during merge
    )

str(merged.pgs.bed)
```

`combine.pgs.bed` automatically annotates each interval (in a new column) with the name of the origin PGS.
It provides the option of adding information from one additional column from the inputs to the annotation in the merged output.
In the cases of overlapping intervals (e.g. the same variant is included in multiple PGSs), overlapping annotations are concatenated
with a comma.

## Input data validation

Both the `import.vcf` and `import.pgs.weight.file` functions perform some basic validation of the input data during import.
For example, PGS files are checked for duplicate variants, and VCF files are checked for unmerged multiallelic sites.
If you have not used the native import functions, you may wish to check that your data is compatible with the polygenic score application functions in this package.

The `apply.polygenic.score` function performs extensive input validation prior to starting the PGS application process. It can be
configured to just run the validation step to check your inputs first.

```{r check-pgs-columns}

# By default, apply.polygenic.score expects the vcf data to be in wide format.
apply.polygenic.score(
    vcf.data = vcf.data$split.wide.vcf.matrices,
    vcf.long.format = FALSE,
    pgs.weight.data = pgs.data$pgs.weight.data,
    phenotype.data = phenotype.data,
    validate.inputs.only = TRUE
    )

# If you have imported a VCF file in long format, you can specify that with the vcf.long.format argument.
apply.polygenic.score(
    vcf.data = vcf.data$combined.long.vcf.df$dat,
    vcf.long.format = TRUE,
    pgs.weight.data = pgs.data$pgs.weight.data,
    phenotype.data = phenotype.data,
    validate.inputs.only = TRUE
    )

```

# Polygenic Score Application

## Basic usage

The main function of ApplyPolygenicScore is `apply.polygenic.score`. It comes with some bells and whistles, but
the basic usage is simple. Provide the VCF data and the PGS weight data, and specify how you wish the algorithm
to handle missing genotypes.

```{r apply-pgs}

pgs.results <- apply.polygenic.score(
    vcf.data = vcf.data$split.wide.vcf.matrices,
    pgs.weight.data = pgs.data$pgs.weight.data,
    correct.strand.flips = FALSE, # no strand flip check to avoid warnings
    missing.genotype.method = 'none'
    )

str(pgs.results)
```

We see several things. First, a warning was printed by `combine.vcf.with.pgs`. This is a function called by `apply.polygenic.score` to merge vcf and pgs data by genomic coordinates.
The warning indicates that some variants from the PGS weight data were not found in the VCF data. These variants were not called in any individual. `apply.polygenic.score` does not
apply any weights to these variants, which effectively assumes their dosage as homozygous reference for all individuals. Checkout our discussion on [missing genotype data](https://github.com/uclahs-cds/package-ApplyPolygenicScore/discussions/17)
for more details on how missing variants come to be.


Next, we see the output of the function. The output is a list with two elements: `pgs.output`, and `regression.output`. `regression.output` is empty because we did not provide the optional `phenotype.data`, more on that later.
`pgs.output` is a data frame with the individual IDs from the VCF and the calculated polygenic score in the `PGS` column for each individual. As we will see later, each missing genotype method creates a uniquely named column of PGS values.


Next, the `percentile`, `decile`, and `quartile` columns report the respective percentile information for each individual's PGS among the distribution of the entire cohort in the VCF data.
These values look a bit strange in this example because our [example VCF](https://github.com/uclahs-cds/package-ApplyPolygenicScore/discussions/6) contains only two individuals which have identical genotypes. In a real-world scenario, these values would be more informative.


The next two columns report the number and percentage of missing PGS genotypes for each individual. This number should be equal to or greater than the number reported in the warning message from `combine.vcf.with.pgs`.


The final column reports the number of non-missing alleles for each individual. This value is equivalent to the number of non-missing variants multiplied by 2 (2 alleles per genotype in a diploid genome).
It is used to normalize the PGS values for missing genotypes when the `normalize` missing genotype method is used.

### `combine.vcf.with.pgs`

A quick aside about this function. While it is internal to `apply.polygenic.score`, it is also available for use on its own. The output includes a list of the variants that were not found in the VCF data,
which may be useful for troubleshooting missing genotype data.

```{r merge-vcf-with-pgs}

merged.vcf.pgs.data <- combine.vcf.with.pgs(
    vcf.data = vcf.data$split.wide.vcf.matrices$vcf.fixed,
    pgs.weight.data = pgs.data$pgs.weight.data
    )

names(merged.vcf.pgs.data)
head(merged.vcf.pgs.data$missing.snp.data)[, 1:6]
```

## Allele matching

After PGS and VCF data are merged by coordinates, you may also wish to check that the alleles in the VCF data variant record match the corresponding alleles in the PGS weight data.
By convention, the `other_allele` (non-effect allele) in the PGS weight data should match the reference (REF) allele in the VCF data, and the `effect_allele` should match the alternate (ALT) allele in the VCF data.
Checkout our discussion on [allele matching](https://github.com/uclahs-cds/package-ApplyPolygenicScore/discussions/68) for more details on the various ways in which this matching may fail.

`apply.polygenic.score` can perform an allele match check (internally using `assess.pgs.vcf.allele.match`). This functionality can be customized with the following arguments:

- `correct.strand.flips` to automatically correct mismatches caused by strand flips by flipping the PGS weight data alleles.
- `remove.ambiguous.allele.matches` to remove variants with mismatched alleles that cannot be corrected by flipping. The variant will be treated as a cohort-wide missing variant and handled according to missing variant rules described below.
- `max.strand.flips` to specify the number of unambiguous strand flips that needs to be present for removal of ambiguous strand flips to occur (convenient for cases where you have reason to believe that all ambiguous strand flips are actually effect size flips) 
- `remove.mismatched.indels` to remove variants with mismatched INDEL alleles. These variants will be treated as cohort-wide missing variants and handled according to missing variant rules described below.

```{r allele-matching, eval = FALSE}

# Most strict allele match handling
strict.allele.match.result <- apply.polygenic.score(
    vcf.data = vcf.data$split.wide.vcf.matrices,
    pgs.weight.data = pgs.data$pgs.weight.data,
    missing.genotype.method = 'none',
    correct.strand.flips = TRUE,
    remove.ambiguous.allele.matches = TRUE,
    remove.mismatched.indels = TRUE
    );

# Less strict allele match handling
less.strict.allele.match.result <- apply.polygenic.score(
    vcf.data = vcf.data$split.wide.vcf.matrices,
    pgs.weight.data = pgs.data$pgs.weight.data,
    missing.genotype.method = 'none',
    correct.strand.flips = TRUE,
    remove.ambiguous.allele.matches = TRUE,
    max.strand.flips = 1, # don't remove ambiguous allele matches unless there is at least one unambiguous strand flip
    remove.mismatched.indels = FALSE
    );

```

## Missing genotype methods

Let's explore all the different options for handling missing genotype data. The `missing.genotype.method` argument can be set to one of the following options:
`none`, `normalize`, and `mean.dosage`. Any combination of these methods can be applied in the same call to `apply.polygenic.score`, producing the corresponding number
of polygenic score output columns. For details on how each of these methods work, head back to our [missing genotype data](https://github.com/uclahs-cds/package-ApplyPolygenicScore/discussions/17)
discussion post.

Briefly:

- `none` ignores missing genotypes, effectively assuming them to be homozygous reference calls
- `normalize` computes the score the same was as `none`, then divides the score by the number of non-missing alleles
- `mean.dosage` replaces the dosage of genotypes missing in some but not all individuals with the mean dosage of the variant across the cohort; genotypes missing in all individuals are assumed homozygous reference

```{r missing-genotype-methods}

all.missing.methods.pgs.results <- apply.polygenic.score(
    vcf.data = vcf.data$split.wide.vcf.matrices,
    pgs.weight.data = pgs.data$pgs.weight.data,
    correct.strand.flips = FALSE, # no strand flip check to avoid warnings
     missing.genotype.method = c('none', 'normalize', 'mean.dosage')
    )

head(all.missing.methods.pgs.results$pgs.output)
```

We can see that the output now contains three columns of PGS values, one for each missing genotype method.

|missing genotype method|column name|
|---|---|
|`none`|`PGS`|
|`normalize`|`PGS.with.normalized.missing`|
|`mean.dosage`|`PGS.with.replaced.missing`|

The `PGS.with.normalized.missing` contains the `PGS` values divided by 124: the number of non-missing alleles in the cohort.
As we can see from the `n.missing.genotypes` column, the number of missing variants is 66. The number of variants in the example PGS
weight file is 128, so the number of non-missing genotypes is 128 - 66 = 62; multiply by two for a diploid genome (2 alleles per genotype): 62 * 2 = 124.

The `PGS.with.replaced.missing` column is identical to the `PGS` column in this case, since all missing genotypes were missing from all
individuals in the cohort, all such dosages were assumed as homozygous reference, and no change from the `none` method was made.

What if you have a list of population allele frequencies for the variants in your PGS weight file? Surely, when implementing the `mean.dosage` method, 
values derived from a massive public dataset would be more accurate than inferring the population dosage from your cohort! No problem, simply add these
frequencies to your pgs weight file, in PGS Catalog format, and use the `use.external.effect.allele.frequency` argument to tell `apply.polygenic.score` to use them instead.

## Custom percentiles

Curious to see how your PGS values distribute among quintiles? No problem. Specify a custom number of percentiles with `n.percentiles`.

```{r custom-percentiles}

custom.percentiles.pgs.results <- apply.polygenic.score(
    vcf.data = vcf.data$split.wide.vcf.matrices,
    pgs.weight.data = pgs.data$pgs.weight.data,
    correct.strand.flips = FALSE, # no strand flip check to avoid warnings
    n.percentiles = 5
    )

head(custom.percentiles.pgs.results$pgs.output)

```

## Phenotype analysis

Let's explore the extra features provided for handling phenotype data.

Since these features are much more useful in larger cohorts, let's start by loading a larger VCF file.
The following example file contains fake genotype data for 10 fake individuals, along with some simulated
phenotype data and a fake set of PGS weights.

```{r load-large-vcf}

phenotype.test.data.path <- system.file(
    'extdata',
    'phenotype.test.data.Rda',
    package = 'ApplyPolygenicScore',
    mustWork = TRUE
    )

load(phenotype.test.data.path)
str(phenotype.test.data)

```

When provided with phenotype data, `apply.polygenic.score` merges the phenotype data with the PGS output.

```{r phenotype-merge}

pgs.results.with.phenotype <- apply.polygenic.score(
    vcf.data = phenotype.test.data$vcf.data,
    vcf.long.format = TRUE,
    pgs.weight.data = phenotype.test.data$pgs.weight.data,
    phenotype.data = phenotype.test.data$phenotype.data
    )

head(pgs.results.with.phenotype$pgs.output)
```

Here we see that the `pgs.output` data frame now contains the phenotype data columns in addition to the PGS columns.

When provided with a vector of `phenotype.analysis.columns`, `apply.polygenic.score` automatically detects the data type
of each column (continuous or binary), and performs a regression (linear or logistic) between the PGS and each phenotype column.
This is particularly useful to quickly see how well your PGS predicts the trait it is intended to predict, or whether it is associated
with any other variables of interest.

```{r phenotype-analysis}

pgs.results.with.phenotype.analysis <- apply.polygenic.score(
    vcf.data = phenotype.test.data$vcf.data,
    vcf.long.format = TRUE,
    pgs.weight.data = phenotype.test.data$pgs.weight.data,
    phenotype.data = phenotype.test.data$phenotype.data,
    phenotype.analysis.columns = c('continuous.phenotype', 'binary.phenotype')
    )

head(pgs.results.with.phenotype.analysis$regression.output)

```

We finally see a data frame in the `regression.output` element of the output list. This data frame contains the results of the regression analysis
for each compatible phenotype. An accuracy metric in the form of an r-squared value is computed for linear regression, and an Area Under the (Receiver Operator) Curve (AUC) value is computed for logistic regression.

If multiple missing genotype methods are being used at once, producing multiple PGS columns, the regression analysis is only performed on one of these and defaults to the first method specified in `missing.genotype.method`.
If you wish to use one of the other PGSs for analysis, the source PGS column for regression (and percentile) analysis can be specified using the `analysis.source.pgs` argument.

# Data Visualization

ApplyPolygenicScore provides four plotting functions designed to operate on the output of `apply.polygenic.score`:

1. `create.pgs.density.plot`
2. `create.pgs.boxplot`
3. `create.pgs.with.continuous.phenotype.plot`
4. `create.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.

## Common plotting arguments

Plotting functions are built on the [BoutrosLab.plotting.general](https://github.com/uclahs-cds/package-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.


## PGS Density

`create.pgs.density.plot` plots the PGS distribution in the cohort.

### Basic plot

```{r plotting-dir, echo = FALSE}
temp.dir <- tempdir();

basic.density.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-basic',
    file.core = 'pgs-density',
    extension = 'png'
    );

phenotype.density.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-phenotype',
    file.core = 'pgs-density',
    extension = 'png'
    );

basic.boxplot.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-basic',
    file.core = 'pgs-boxplot',
    extension = 'png'
    );

phenotype.boxplot.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-phenotype',
    file.core = 'pgs-boxplot',
    extension = 'png'
    );

correlation.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-correlation',
    file.core = 'pgs-scatter',
    extension = 'png'
    );

basic.rank.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-basic',
    file.core = 'pgs-rank-plot',
    extension = 'png'
    );

phenotype.rank.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-phenotype',
    file.core = 'pgs-rank-plot',
    extension = 'png'
    );

roc.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-basic',
    file.core = 'pgs-roc-curves',
    extension = 'png'
    );

roc.continuous.filename <- ApplyPolygenicScore:::generate.filename(
    project.stem = 'vignette-example-continuous',
    file.core = 'pgs-roc-curves',
    extension = 'png'
    );

```

```{r pgs-density, eval = TRUE}

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
    )

```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, basic.density.filename));
```

### 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.


```{r pgs-density-phenotype, eval = TRUE}

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
    )

```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, phenotype.density.filename));
```

## 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.

### Basic plot

```{r pgs-boxplot, eval = TRUE}

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
    )

```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, basic.boxplot.filename));
```

### 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.

```{r pgs-boxplot-phenotype, eval = TRUE}

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
    )

```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, phenotype.boxplot.filename));
```

## 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.

### Basic plot

```{r pgs-correlation, eval = TRUE}

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
    )

```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, correlation.filename));
```

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.

## 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.

### Basic plot

```{r pgs-rank, eval = TRUE}

# 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
    )

```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, basic.rank.filename));
```

### Add phenotypes

```{r pgs-rank-phenotype, eval = TRUE}

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
    )

```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, phenotype.rank.filename));
```

- 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.

### Optional arguments
This function comes with a handful of additional arguments to control the appearance of the plot.

- Use `missing.genotype.style` to toggle between displaying counts and percentages.
- Use `categorical.palette` to specify a custom color palette for categorical phenotype bars.
- Use `binary.pallette` to specify a custom color palette for binary and continuous phenotype bars.

## 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.

### Basic Plot

```{r pgs-case-control, eval = TRUE}
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
    );
```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, roc.filename));
```

### 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.

```{r pgs-case-control-continuous, eval = TRUE, results = 'hide'}
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
    );
```

```{r out.width = '50%', echo = FALSE}
knitr::include_graphics(file.path(temp.dir, roc.continuous.filename));
```


- provide multiple pgs columns to plot multiple ROC curves in the same plot
- provide multiple phenotype columns to plot separate panels for each phenotype
