---
title: "How to use PANACEA"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{How to use PANACEA}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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


# Overview

Identification of the most appropriate pharmacotherapy for each patient based on genomic alterations is a major challenge in personalized oncology. `PANACEA` is a collection of personalized anti-cancer drug prioritization approaches utilizing network methods. The methods utilize personalized "driverness" scores from [`driveR`](https://egeulgen.github.io/driveR/) to rank drugs, mapping these onto a protein-protein interaction network (PIN). The "distance-based" method scores each drug based on these scores and distances between drugs and genes to rank given drugs. The "RWR" method propagates these scores via a random-walk with restart framework to rank the drugs.

The wrapper function `score_drugs()` can be used to score and rank drugs for an individual tumor sample via the "distance-based" or "RWR" method. The required inputs are:

- `driveR_res`: data frame of [driveR](https://egeulgen.github.io/driveR/) results. Details on how to obtain `driveR` output are provided in [this vignette](https://egeulgen.github.io/driveR/articles/how_to_use.html)
- `drug_interactions_df`: data frame of drug-gene interactions (defaults to interactions from DGIdb expert-curated sources)
- `W_mat`: (symmetric) adjacency matrix for the PIN (defaults to STRING v11.5 interactions with combined score > .4)
- `method`: scoring method (one of "distance-based" or "RWR")

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

# Example Input

In this vignette, driveR results for a lung adenocarcinoma case, `example_driveR_res`, is used as the example input dataset. Details on how to obtain `driveR` output are provided in [this vignette](https://egeulgen.github.io/driveR/articles/how_to_use.html).

```{r}
head(example_driveR_res)
```

# "Distance-based" Method

For this method, the score between a drug, d, and an altered gene, g, is defined as:

$$score(g, d) = \frac{1}{(dist(g,d) + 1)^2} driver\_prob_g$$
where $dist(g,d)$ is the distance between g and d within the PIN, and $driver\_prob_g$ is the driverness probability obtained from `driveR`.

The final score for a drug is calculated as the average of the scores between each altered gene and d:

$$score(d) = \sum_{g \in G} \frac{1}{|G|} score(g,d)$$
where G is the set of all altered genes.

Scoring of drugs for the example lung adenocarcinoma case `example_driveR_res` via this "distance-based" method can be performed as follows:

```{r dst_based, eval=FALSE}
example_scores_dist <- score_drugs(example_driveR_res, method = "distance-based")
```

```{r save1, echo=FALSE, eval=FALSE}
usethis::use_data(example_scores_dist)
```

This scores and ranks drugs via the "distance-based" method using drug-gene interactions from DGIdb expert-curated sources and the STRING v11.5 PIN with combined score > 700. Below, top 10 drugs are printed:

```{r dist_based2}
head(example_scores_dist, 10)
```

# "RWR" Method

For this method, a random-walk with restart framework is used to propagate the driverness probabilities. A drug's final score is its final propagation score.

Scoring of drugs for the example lung adenocarcinoma case `example_driveR_res` via this "RWR" method can be performed as follows:

```{r RWR, eval=FALSE}
example_scores_RWR <- score_drugs(example_driveR_res, method = "RWR")
```

```{r save2, echo=FALSE, eval=FALSE}
usethis::use_data(example_scores_RWR)
```

This scores and ranks drugs via the "RWR" method using drug-gene interactions from DGIdb expert-curated sources and the STRING v11.5 PIN with combined score > 700. Below, top 10 drugs are printed:

```{r RWR2}
head(example_scores_RWR, 10)
```
