---
title: "Run and Report BRAID Analyses"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Run and Report BRAID Analyses}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, include=FALSE}
library(braidReports)
set.seed(20240805)
```

## Introduction

This vignette gives a basic rundown of the two centerpiece functions of the 
`braidReports` package, `runBraidAnalysis()` and `makeBraidReport()`.  Of 
course, the term "centerpiece" here is a bit misleading, given that in a sense
these are also the two most superfluous functions in the BRAID package suite.
In reality, they're nothing more than convenient wrappers for a large set of
functionalities that users can, and in many cases should, delve into themselves,
as doing so will allow them more freedom, more flexibility, and more control.
But the tasks performed by these functions are so commonly required, and so
convenient to package, that we felt that it was necessary to build them.

The first and simpler of these two functions is `runBraidAnalysis()`.  Taking
the same parameter set as `findBestBraid()` from `braidrm`, it uses that function to perform
a BRAID fit (with model selection) of the given data.  It also performs
individual Hill dose-response fits of both included drugs by fitting the data
points where the partner drug has a concnetration of zero.  This results in a
`braidAnalysis` object, containing a BRAID fit of class `bradirm`, and up to
two Hill dose response fits describing the dose response behavior of each
individual drug. (If either drug was not measured in isolation for any of the
provided data points, the corresponding Hill fit will be omitted.)  The
`braidAnalysis` object can then be passed to `makeBraidReport()` to visualize
the completed analysis.

```{r}
surface <- synergisticExample
synergisticAnalysis <- runBraidAnalysis(measure ~ concA + concB,
										surface,
										defaults = c(0,2))

names(synergisticAnalysis)
```

There may be cases in which the user would prefer not to use the results of
`findBestBraid()` in their analysis, but still produce a report.  It is
therefore possible to pass any BRAID fit object of class `bradirm` to the
`makeBraidReport()` function using the `basicBraidAnalysis()` function, which
simply wraps its input into a `braidAnalysis` object.

```{r}
otherSurface <- antagonisticExample
antagonisticFit <- braidrm(measure ~ concA + concB,
						   otherSurface, model="kappa2")
antagonisticAnalysis <- basicBraidAnalysis(antagonisticFit)

names(antagonisticAnalysis)

```

## BRAID Report Pages

The `makeBraidReport()` function takes a fully analyzed combination surface
(respresented by a `braidAnalysis` object) and produces a one page report with
a range of plots and tables summarizing many aspects of the analysis. These 
reports are assembled into a single grid, designed for a standard 8.5 by 11
page, using the `cowplot` package.  The report includes response surface
plots of the raw and fitted data, tables summarizing the best-fit parameters
and metrics, as well as other plots such as potentiation plots and additive
comparison plots if desired.  Here is the default appearance:

```{r, fig.width=8,fig.height=10}
report <- makeBraidReport(synergisticAnalysis,c("A Drug","B Drug"),
						  c(0.5,0.9),c(5,5))
print(report)
```

The `control` parameter of the function allows the user to specify a long list
of customizations that alter the appearance of the final report.  This includes
the abbreviations of drugs used in plots and charts, drug units, color scales,
and more.

```{r, fig.width=8, fig.height=10}
syncontrol <- list(abbs=c("A","B"),units=c("\u00B5M"),leveltext=c("50","90"),
				   xscale=scale_x_log10(breaks=c(0.1,0.5,2,10),
				   					 labels=as.character),
				   fillscale=scale_fill_viridis_c(option="A"),
				   colorscale=scale_color_brewer(palette="Set1"),
				   title="Example Analysis")
nextReport <- makeBraidReport(synergisticAnalysis,c("A Drug","B Drug"),
						  c(0.5,0.9),c(5,5),control=syncontrol)
print(nextReport)
```

The `control` parameter can also be used to change the overall layout of the
report page (options are "simple", "standard", and "dense"), and add additional
combination metrics to the included tables:

```{r, fig.width=8, fig.height=10}
concs <- cbind(otherSurface$concA,otherSurface$concB)
act <- otherSurface$measure

otherSurface$bliss <- deviationSurface(concs,act,"Bliss",range=c(0,1))
otherSurface$zip <- deviationSurface(concs,act,"ZIP",range=c(0,1))
comboRows <- otherSurface$concA>0 & otherSurface$concB>0

ufit <- fitUrsaModel(measure ~ concA + concB, otherSurface)

metrics <- character()
metrics[["V[Bliss]"]] <- signif(mean(otherSurface$bliss[comboRows]),3)
metrics[["ZIP~delta"]] <- signif(mean(otherSurface$zip[comboRows]),3)
metrics[["URSA~alpha"]] <- signif(coef(ufit)[["alpha"]],3)

finalReport <- makeBraidReport(antagonisticAnalysis,
							   compounds=c("First Drug","Second Drug"),
							   levels = c(0.5,0.9), limits=c(8,8),
							   control=list(metrics=metrics,layout="simple"))
print(finalReport)
```

Note that the names of metrics are parsed into more formatted expressions using 
the base `parse()` function; this allows the use of subscripts and certain
symbols, but necessitates using the tilde (`~`) to include spaces.

See the documentation of `makeBraidReport()` for a more complete listing of 
the possible customization options.
