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Introduction to `derfinderData`
===============================

If you wish, you can view this vignette online [here](http://lcolladotor.github.io/derfinderData/).

```{r vignetteSetup, echo=FALSE, message=FALSE, warning = FALSE}
## Track time spent on making the vignette
startTime <- Sys.time()

## Bib setup
library('knitcitations')

## Load knitcitations with a clean bibliography
cleanbib()
cite_options(hyperlink = 'to.doc', citation_format = 'text', style = 'html')
# Note links won't show for now due to the following issue
# https://github.com/cboettig/knitcitations/issues/63

## Write bibliography information
write.bibtex(c(knitcitations = citation('knitcitations'),
    brainspan = RefManageR::BibEntry(bibtype = 'Unpublished', key = 'brainspan', title = 'Atlas of the Developing Human Brain [Internet]. Funded by ARRA Awards 1RC2MH089921-01, 1RC2MH090047-01, and 1RC2MH089929-01.', author = 'BrainSpan', year = 2011, url = 'http://developinghumanbrain.org'),
    knitrBootstrap = citation('knitrBootstrap'), 
    knitr = citation('knitr')[3],
    rmarkdown = citation('rmarkdown')),
    file = 'derfinderDataRef.bib')
bib <- read.bibtex('derfinderDataRef.bib')

## Assign short names
names(bib) <- c('knitcitations', 'brainspan', 'knitrBootstrap',
    'knitr', 'rmarkdown')
```


# Overview

`derfinderData` is a small data package with information extracted from _BrainSpan_ (see [here](http://download.alleninstitute.org/brainspan/MRF_BigWig_Gencode_v10/bigwig/)) `r citep(bib[['brainspan']])` for 24 samples restricted to chromosome 21. The BigWig files in this package can then be used by other packages for examples, such as in `derfinder` and `derfinderPlot`. 

While you could download the data from _BrainSpan_ `r citep(bib[['brainspan']])`, this package is helpful for scenarios where you might encounter some difficulties such as the one described in this [thread](https://stat.ethz.ch/pipermail/bioc-devel/2014-September/006329.html).


# Data

The following code builds the phenotype table included in `derfinderData`. For two randomly selected structures, 12 samples were chosen with 6 of them being fetal samples and the other 6 coming from adult individuals. For the fetal samples, the age in PCW is transformed into age in years by

    age_in_years = (age_in_PCW - 40) / 52

In other data sets you might want to subtract 42 instead of 40 if some observations have PCW up to 42.

```{r 'pheno'}
## Construct brainspanPheno table
brainspanPheno <- data.frame(
    gender = c('F', 'M', 'M', 'M', 'F', 'F', 'F', 'M', 'F', 'M', 'M', 'F', 'M', 'M', 'M', 'M', 'F', 'F', 'F', 'M', 'F', 'M', 'M', 'F'),
    lab = c('HSB97.AMY', 'HSB92.AMY', 'HSB178.AMY', 'HSB159.AMY', 'HSB153.AMY', 'HSB113.AMY', 'HSB130.AMY', 'HSB136.AMY', 'HSB126.AMY', 'HSB145.AMY', 'HSB123.AMY', 'HSB135.AMY', 'HSB114.A1C', 'HSB103.A1C', 'HSB178.A1C', 'HSB154.A1C', 'HSB150.A1C', 'HSB149.A1C', 'HSB130.A1C', 'HSB136.A1C', 'HSB126.A1C', 'HSB145.A1C', 'HSB123.A1C', 'HSB135.A1C'),
    Age = c(-0.442307692307693, -0.365384615384615, -0.461538461538461, -0.307692307692308, -0.538461538461539, -0.538461538461539, 21, 23, 30, 36, 37, 40, -0.519230769230769, -0.519230769230769, -0.461538461538461, -0.461538461538461, -0.538461538461539, -0.519230769230769, 21, 23, 30, 36, 37, 40)
)
brainspanPheno$structure_acronym <- rep(c('AMY', 'A1C'), each = 12)
brainspanPheno$structure_name <- rep(c('amygdaloid complex', 'primary auditory cortex (core)'), each = 12)
brainspanPheno$file <- paste0('http://download.alleninstitute.org/brainspan/MRF_BigWig_Gencode_v10/bigwig/', brainspanPheno$lab, '.bw')
brainspanPheno$group <- factor(ifelse(brainspanPheno$Age < 0, 'fetal', 'adult'), levels = c('fetal', 'adult'))
```

We can then save the phenotype information, which is included in `derfinderData`.

```{r 'savePheno', eval = FALSE}
## Save pheno table
save(brainspanPheno, file = 'brainspanPheno.RData')
```

Here is how the data looks like:

```{r 'explorePheno', results = 'asis', bootstrap.show.code = FALSE}
library('knitr')

## Explore pheno
p <- brainspanPheno[, -which(colnames(brainspanPheno) %in% c('structure_acronym', 'structure_name', 'file'))]
kable(p, format = 'html', row.names = TRUE)
```

We can verify that this is indeed the information included in `derfinderData`.


```{r 'verifyPheno'}
## Rename our newly created pheno data
newPheno <- brainspanPheno

## Load the included data
library('derfinderData')

## Verify
identical(newPheno, brainspanPheno)
```

Using the phenotype information, you can use `derfinder` to extract the base-level coverage information for chromosome 21 from these samples. Then, you can export the data to BigWig files.

```{r 'getData', eval = FALSE}
library('derfinder')

## Determine the files to use and fix the names
files <- brainspanPheno$file
names(files) <- gsub('.AMY|.A1C', '', brainspanPheno$lab)

## Load the data
system.time(fullCovAMY <- fullCoverage(
    files = files[brainspanPheno$structure_acronym == 'AMY'], chrs = 'chr21'))
#user  system elapsed 
#4.505   0.178  37.676 
system.time(fullCovA1C <- fullCoverage(
    files = files[brainspanPheno$structure_acronym == 'A1C'], chrs = 'chr21'))
#user  system elapsed 
#2.968   0.139  27.704
    
## Write BigWig files
dir.create('AMY')
system.time(createBw(fullCovAMY, path = 'AMY', keepGR = FALSE))
#user  system elapsed 
#5.749   0.332   6.045
dir.create('A1C')
system.time(createBw(fullCovA1C, path = 'A1C', keepGR = FALSE))
#user  system elapsed 
#5.025   0.299   5.323 

## Check that 12 files were created in each directory
all(c(length(dir('AMY')), length(dir('A1C'))) == 12)
#TRUE

## Save data for examples running on Windows
save(fullCovAMY, file = 'fullCovAMY.RData')
save(fullCovA1C, file = 'fullCovA1C.RData')
```

These BigWig files are available under _extdata_ as shown below:

```{r 'findData'}
## Find AMY BigWigs
dir(system.file('extdata', 'AMY', package = 'derfinderData'))

## Find A1C BigWigs
dir(system.file('extdata', 'A1C', package = 'derfinderData'))
```




# Reproducibility

Code for creating the vignette

```{r createVignette, eval=FALSE, bootstrap.show.code=FALSE}
## Create the vignette
library('knitrBootstrap') 

knitrBootstrapFlag <- packageVersion('knitrBootstrap') < '1.0.0'
if(knitrBootstrapFlag) {
    ## CRAN version
    library('knitrBootstrap')
    system.time(knit_bootstrap('derfinderData.Rmd', chooser=c('boot',
        'code'), show_code = TRUE))
    unlink('derfinderData.md')
} else {
    ## GitHub version
    library('rmarkdown')
    system.time(render('derfinderData.Rmd',
        'knitrBootstrap::bootstrap_document'))
}
## Note: if you prefer the knitr version use:
# library('rmarkdown')
# system.time(render('derfinder.Rmd', 'html_document'))

## Extract the R code
library('knitr')
knit('derfinderData.Rmd', tangle = TRUE)

## Clean up
file.remove('derfinderDataRef.bib')
```

Date the vignette was generated.

```{r reproducibility1, echo=FALSE, bootstrap.show.code=FALSE}
## Date the vignette was generated
Sys.time()
```

Wallclock time spent generating the vignette.

```{r reproducibility2, echo=FALSE, bootstrap.show.code=FALSE}
## Processing time in seconds
totalTime <- diff(c(startTime, Sys.time()))
round(totalTime, digits=3)
```

`R` session information.

```{r reproducibility3, echo=FALSE, bootstrap.show.code=FALSE, bootstrap.show.message=FALSE}
## Session info
library('sessioninfo')
session_info()
```

# Bibliography

This vignette was generated using `knitrBootstrap` `r citep(bib[['knitrBootstrap']])`
with `knitr` `r citep(bib[['knitr']])` and `rmarkdown` `r citep(bib[['rmarkdown']])` running behind the scenes.

Citations made with `knitcitations` `r citep(bib[['knitcitations']])`.

```{r vignetteBiblio, results='asis', echo=FALSE}
## Print bibliography
bibliography()
```