---
title: "Running the mdp package"
author: "Melissa Lever"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Running the mdp package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

## About

The Molecular Degree of Perturbation allows you to quantify the heterogeneity of transcriptome data samples. The `mdp` takes data containing at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. Gene perturbation scores are calculated for each gene within each class. The algorithm is based on the Molecular Distance to Health which was first implemented in Pankla et al. 2009. 
It expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes.

## Basic usage

Load expression and pheno data and run:

```{r}
library(mdp)
data(example_data) # expression data has gene names in the rows
data(example_pheno) # pheno data needs a Sample and Class column
mdp.results <- mdp(data=example_data, pdata=example_pheno, control_lab = "baseline")
```



### Sample scores
The sample scores can be accessed from the `sample_scores` element of the mdp results.
```{r}
sample_scores_list <- mdp.results$sample_scores
# select sample scores calculated using the perturbed genes
sample_scores <- sample_scores_list[["perturbedgenes"]]
head(sample_scores)
sample_plot(sample_scores,control_lab = "baseline", title="perturbed")
```

### Z-score 
The `mdp` works by calulating the z-score relative to the control samples, taking the absolute value of this matrix and setting all vlaues below a threshold (2 as a default) to 0. Expression values that are not 0 are *perturbed*. You can access this thresholded z-score matrix by,
```{r}
zscore <- mdp.results$zscore
```


### Gene scores
For each gene in each class, a gene score is calculated, which is the average thresholded z-score value for that gene. A gene frequency is also calculated, which is the frequency that the gene is perturbed in a class.
```{r}
gene_scores <- mdp.results$gene_scores
gene_freq <- mdp.results$gene_freq
head(gene_scores)
```


### Perturbed genes
The `mdp` ranks genes according to the difference between their gene score in the test versus the control samples. The `fraction_genes` option for the `mdp` function allows you to control what top fraction of these ranked genes will count as the `perturbed_genes`. You can obtain a list of the perturbed genes from the mdp results,
```{r}
perturbed_genes <- mdp.results$perturbed_genes
```

## Further usage

### Adding pathways
Sample scores can also be calculated using genes that are within certain genesets. The `mdp` will accept genesets that are in the form of a list (see example below). You can read in a .gmt file of genesets using the `fgsea::gmtPathways` function from the `fgsea` package.


```{r results='hide',fig.show = 'hide'}
file_address <- system.file("extdata", "ReactomePathways.gmt", package = "mdp")
pathways <- fgsea::gmtPathways(file_address)
mdp.results <- mdp(data=example_data, pdata=example_pheno, control_lab = "baseline",pathways=pathways)
```

For each pathway, the signal-to-noise ratio of the test versus control sample scores will be calculated. You can access these results in the `pathways` element of the `mdp` results.
```{r}
head(mdp.results$pathways)
sample_scores <- mdp.results$sample_scores[["Interferon alpha/beta signaling"]]
sample_plot(sample_scores,control_lab = "baseline", title="Interferon a/b")

```

### Z-score calculation options

As a default, the `mdp` z-score normalises the expression data using the median as the averaging statistic. The standard deviation is estimated using the median absolute deviation `mad` function from the `Stats` package. If you would like to use the mean instead, select "mean". 
```{r results='hide',fig.show = 'hide'}
mdp.results <- mdp(data=example_data, pdata=example_pheno, control_lab = "baseline", measure = "mean")
```

You can calculate the thresholded z-score using the `compute_zscore` function. A vector of control sample names must be provided.
```{r}
control_samples <- example_pheno[example_pheno$Class == "baseline","Sample"] 
zscore <- compute_zscore(data = example_data,control_samples = control_samples,measure = "mean",std = 2)
```









