---
title: "Data Sanitization"
shorttitle: "Data Sanitization"
package: sesame
output: rmarkdown::html_vignette
fig_width: 8
fig_height: 6
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{"4. data sanitization"}
  %\VignetteEncoding{UTF-8} 
---

## What is the purpose of Data Sanitization? 

Probe masking is important to prevent privacy data leakage. The goal of
data sanitization is to modifiy IDAT files in place, so they can be released
to public domain without privacy leak. This will be achieved by
deIdentification. The following function requires the R package seSAMe. 

```{r message=FALSE, warning=FALSE, include=FALSE}
library(sesame)
```

Let's take DNA methylation data from the HM450 platform for example.
```{r, eval=TRUE}
dest_dir = tempdir()
res_grn = sesameDataDownload("3999492009_R01C01_Grn.idat", dest_dir=dest_dir)
res_red = sesameDataDownload("3999492009_R01C01_Red.idat", dest_dir=dest_dir)
```
                                                   
## deIdentify 

### Method 1

This first method of deIdentification masks SNP probe intensity mean by zero.
As a consequence, the allele frequency will be 0.5. 

```{r, eval=TRUE}

deIdentify(res_grn$dest_file, sprintf("%s/deidentified_Grn.idat", dest_dir))
deIdentify(res_red$dest_file, sprintf("%s/deidentified_Red.idat", dest_dir))

betas1 = getBetas(readIDATpair(sprintf("%s/3999492009_R01C01", dest_dir)))
betas2 = getBetas(readIDATpair(sprintf("%s/deidentified", dest_dir)))

head(betas1[grep('rs',names(betas1))]) 
head(betas2[grep('rs',names(betas2))])
```

Note that before deIdentify, the rs values will all be different. After
deIdentify, the rs values will all be masked at an intensity of 0.5. 

### Method 2 

This second method of deIdentification will scramble the intensities using
a secret key to help formalize a random number. Therefore, randomize needs
to be set to TRUE. 

```{r, eval=TRUE}

my_secret <- 13412084
set.seed(my_secret)

deIdentify(res_grn$dest_file,
    sprintf("%s/deidentified_Grn.idat", dest_dir), randomize=TRUE)

my_secret <- 13412084
set.seed(my_secret)
deIdentify(res_red$dest_file,
    sprintf("%s/deidentified_Red.idat", dest_dir), randomize=TRUE)

betas1 = getBetas(readIDATpair(sprintf("%s/3999492009_R01C01", dest_dir)))
betas2 = getBetas(readIDATpair(sprintf("%s/deidentified", dest_dir)))

head(betas1[grep('rs',names(betas1))]) 
head(betas2[grep('rs',names(betas2))]) 

```
Note that the rs values are scrambled after deIdentify.  

## reIdentify

To restore order of the deIdentified intensities, one can re-identify IDATs.
The reIdentify function can thus restore the scrambled SNP intensities. 

```{r, eval=TRUE}

my_secret <- 13412084
set.seed(my_secret)

reIdentify(sprintf("%s/deidentified_Grn.idat", dest_dir),
    sprintf("%s/reidentified_Grn.idat", dest_dir))

my_secret <- 13412084
set.seed(my_secret)
reIdentify(sprintf("%s/deidentified_Red.idat", dest_dir),
    sprintf("%s/reidentified_Red.idat", dest_dir))

betas1 = getBetas(readIDATpair(sprintf("%s/3999492009_R01C01", dest_dir)))
betas2 = getBetas(readIDATpair(sprintf("%s/reidentified", dest_dir)))

head(betas1[grep('rs',names(betas1))]) 
head(betas2[grep('rs',names(betas2))]) 

```
Note that reIdentify restored the values. Subsequently, they are the same as
betas1.