---
title: "How to use import functions"
author: 
  - name: Giulia Pais
    affiliation: | 
     San Raffaele Telethon Institute for Gene Therapy - SR-Tiget, 
     Via Olgettina 60, 20132 Milano - Italia
    email: giuliapais1@gmail.com, calabria.andrea@hsr.it
output: 
  BiocStyle::html_document:
    self_contained: yes
    toc: true
    toc_float: true
    toc_depth: 2
    code_folding: show
date: "`r doc_date()`"
package: "`r pkg_ver('ISAnalytics')`"
vignette: >
  %\VignetteIndexEntry{How to use import functions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}  
---
```{r style, echo = FALSE, results = 'asis'}
  BiocStyle::markdown()
```

```{r setup, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    crop = NULL 
    ## Related to 
    ## https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html
)
```


```{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")

## Write bibliography information
bib <- c(
    R = citation(),
    BiocStyle = citation("BiocStyle")[1],
    knitcitations = citation("knitcitations")[1],
    knitr = citation("knitr")[1],
    rmarkdown = citation("rmarkdown")[1],
    sessioninfo = citation("sessioninfo")[1],
    testthat = citation("testthat")[1],
    ISAnalytics = citation("ISAnalytics")[1]
)

write.bibtex(bib, file = "how_to_import_functions.bib")
```


# Introduction to `ISAnalytics` import functions family

In this vignette we're going to explain more in detail how functions of the 
import family should be used, the most common workflows to follow and more.

## How to install ISAnalytics

To install the package run the following code:

```{r installBioc, eval=FALSE}
if(!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ISAnalytics")
```

To install from GitHub:

```{r installGitHub, eval=FALSE}
if (!require(devtools)) {
  install.packages("devtools")
}
devtools::install_github("calabrialab/ISAnalytics", build_vignettes = TRUE)
```
```{r}
library(ISAnalytics)
```

## Setting options

`ISAnalytics` has a verbose option that allows some functions to print 
additional information to the console while they're executing. 
To disable this feature do:

```{r OptVerbose, eval=FALSE}
# DISABLE
options("ISAnalytics.verbose" = FALSE)

# ENABLE
options("ISAnalytics.verbose" = TRUE)

```

Some functions also produce report in a user-friendly HTML format, 
to set this feature:

```{r OptWidg, eval=FALSE}
# DISABLE HTML REPORTS
options("ISAnalytics.widgets" = FALSE)

# ENABLE HTML REPORTS
options("ISAnalytics.widgets" = TRUE)
```


## Designed to work with Vispa2 pipeline

The vast majority of the functions included in this package is designed to work 
in combination with Vispa2 pipeline. If you don't know what it is, we strongly 
recommend you to take a look at these links:  

* Article: [VISPA2: Article](
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702242/)  
* BitBucket Wiki: [VISPA2 Wiki](
https://bitbucket.org/andreacalabria/vispa2/wiki/Home)  

## File system structure generated

Vispa2 produces a standard file system structure starting from a folder you 
specify as your workbench or root. The structure always follows this schema:  

* root/
  * Optional intermediate folders
    * Projects (PROJECTID)
      * bam
      * bcmuxall
      * bed
      * iss
      * quality
      * quantification
        * Pools (concatenatePoolIDSeqRun)
      * report

We've included 2 examples of this structure in our package, one correct and the 
other one including errors or potential problems. They are both in .zip format, 
so you might want to unzip them if you plan to experiment with them.  
An example on how to access them:  

```{r}
root_correct <- system.file("extdata", "fs.zip", package = "ISAnalytics")
root_correct <- unzip_file_system(root_correct, "fs")
fs::dir_tree(root_correct)
```

# Importing a single integration matrix

If you want to import a single integration matrix you can do so by using the 
`import_single_Vispa2Matrix` function.  
This function reads the .tsv file and converts it into a tidy tibble.  
If you're not familiar with the "tidy" concept, we recommend to take a look at 
this link to get the basics:  

* [The importance of tidy data](
https://bookdown.org/rdpeng/RProgDA/the-importance-of-tidy-data.html)

This package is in fact based on the `tidyverse` and tries to follow its 
philosophy and guidelines as close as possible.  
Vispa2 pipeline and the associated Create Matrix tool produce matrices with a 
standard structure which we'll refer to as "messy", because different 
experimental data is divided in different columns and there are a lot of 
NA values.

```{r, echo = FALSE}
example_matrix_path <- system.file("extdata", "ex_annotated_ISMatrix.tsv.xz", 
                                   package = "ISAnalytics")
example_matrix <- read.csv(example_matrix_path, sep = "\t", header = TRUE, 
                           stringsAsFactors = FALSE, check.names = FALSE)
knitr::kable(head(example_matrix), 
             caption = "A simple example of messy matrix.", align = "l")
```

```{r}
example_matrix_path <- system.file("extdata", "ex_annotated_ISMatrix.tsv.xz", 
                                   package = "ISAnalytics")
imported_im <- import_single_Vispa2Matrix(example_matrix_path)
```

```{r, echo = FALSE}
knitr::kable(head(imported_im), caption = "Example of tidy integration matrix")
```

We will refer to the structure generated by `import_single_Vispa2Matrix` as 
"integration matrix" for convenience.  
To be considered an integration matrix the tibble must contain the mandatory
variables, which are "chr" (chromosome), "integration_locus" and "strand". 
It might also contain annotation variables if the matrix was annotated during 
the Vispa2 pipeline run.  
You can access these names by using two functions:

```{r}
# Displays the mandatory vars, can be called also for manipulation purposes 
# on tibble instead of calling individual variables
mandatory_IS_vars()

# Displays the annotation variables
annotation_IS_vars()
```

You can of course operate on the integration matrices as you would on any other 
tibble, but some functions will check the presence of specific columns because 
they're needed in that context.

# Importing the association file

While you can import single matrices for brief analysis, what you would like to 
do most of the times is import multiple matrices at once, based on certain 
parameters. To do that you must first import the association file, which is the 
file that holds all associated metadata and information about every project, 
pool and single experiment.  
The function that imports this file does not simply read it into your R 
environment, but performs an alignment check with your file system, so you 
have to specify the path to the root folder where your Vispa2 runs produce 
output (see the previous section).
To import the association file do:  

```{r R.options=options(ISAnalytics.widgets = FALSE)}
options(ISAnalytics.widgets = FALSE)
path_as_file <- system.file("extdata", "ex_association_file.tsv",
                            package = "ISAnalytics")
association_file <- import_association_file(path_as_file, root_correct)
```

If you have the "widgets" option active, this will produce a visual HTML report 
of the results of the alignment check, either in Rstudio or in your browser.  
If projects or pools are missing you will be notified: until you fix those 
problems, those elements will be ignored until you re-import the association 
file.

# Importing multiple matrices in parallel

There are 2 different functions for importing multiple matrices in parallel:  

* `import_parallel_Vispa2Matrices_interactive`
* `import_parallel_Vispa2Matrices_auto`  

The interactive version will ask you to input your choices directly into the 
console, the automatic version will not, but has some limitations.  
Both functions rely on the association file and some basic parameters, most 
notably:  

* `quantification_type`: this is a string or a vector of characters indicating 
which quantification types you want the function to look for. The possible 
values are `r quantification_types()`
* `matrix_type`: tells the function if it should consider annotated or not 
annotated matrices. The only possible options are "annotated" and 
"not_annotated"
* `workers`: indicates the number of parallel workers to instantiate when 
importing. Keep in mind that the higher is the number, the faster the process 
is, but also higher is the RAM peak, so you should be aware of this especially 
if you're dealing with really big matrices. Set this parameter according to 
your needs and according to your hardware specifications.  

Both the versions will produce several HTML reports after all key steps, 
such as:  

* Report on which files were found, reporting eventual anomalies 
(missing files or duplicates)
* Report on which files were chosen for import after interactive selection or 
after automatic filtering
* Report on which files were actually imported, signaling potential errors 
during the import phase  

Both the functions, by default, return a named list which names are the
quantification types specified in input and the elements are 
integration matrices, obtained by binding rows of all imported matrices 
for the same quantification type.
If the parameter `multi_quant_matrix` is set to TRUE, the function will 
automatically produce a single multi-quantification matrix via 
`comparison_matrix` (see `?comparison_matrix` for details).

## Interactive version

As stated before, with the interactive version you have more control and you 
can directly choose:  

* Which projects to import
* Which pools to import
* If duplicates files are found which ones are to be kept

If you haven't imported the association file yet, you can directly pass the 
path to the association file and the path to the root folder into the function:
in this way the association file will automatically be imported.

Example:  

```{r, eval = FALSE}
matrices <- import_parallel_Vispa2Matrices_interactive(
  association_file = path_as_file,
  root = root_correct,
  quantification_type = c("fragmentEstimate", "seqCount"),
  matrix_type = "annotated",
  workers = 2)
```

If you've already imported the association file you can instead call the 
function like this:

```{r, eval = FALSE}
matrices <- import_parallel_Vispa2Matrices_interactive(
  association_file = association_file,
  root = NULL,
  quantification_type = c("fragmentEstimate", "seqCount"),
  matrix_type = "annotated",
  workers = 2)
```

You can simply access the data frames by doing:  

```{r, eval=FALSE}
matrices$fragmentEstimate
matrices$seqCount
```


## Automatic version

If you choose to opt for the automatic version you should keep in mind that 
the function automatically considers everything included in the association 
file, so if you want to import only a subset of projects and/or pools you 
should filter the association file according to your criteria before calling 
the function:

```{r}
library(magrittr)
refined_af <- association_file %>% dplyr::filter(.data$ProjectID == "CLOEXP")
```

**NOTE: do not drop any columns when filtering the association file or the file 
will be flagged as malformed**  

In automatic version there is no way of discriminating duplicates, so there is 
the possibility to specify additional patterns to look for in file names to 
mitigate this problem. However, if after matching of the additional patterns 
duplicates are still found they're simply discarded.  

There are 2 additional parameters to set:  

* `patterns`: a string or a character vector containing regular expressions to 
be matched on file names. If you're not familiar with regular expressions, 
I suggest you to start 
from here [stringr cheatsheet](
https://github.com/rstudio/cheatsheets/blob/master/strings.pdf)
* `matching_opt`: a single string that tells the function how to match the 
patterns. The possible values for this parameter are `r matching_options()`:  
  * ANY : looks for files that match at least one of the patterns specified 
  in `patterns`
  * ALL: looks for files that match all the patterns specified in `patterns`
  * OPTIONAL: looks for files that preferentially match all the patterns, if 
  none are found looks for files that match any of the patterns and finally 
  if none are found simply looks for files present that match the 
  quantification type.  
  
You can call the function with `patterns` set to NULL if you don't wish to 
match anything:

```{r R.options=options(ISAnalytics.widgets = FALSE)}
matrices_auto <- import_parallel_Vispa2Matrices_auto(
  association_file = refined_af,
  root = NULL,
  quantification_type = c("fragmentEstimate","seqCount"),
  matrix_type = "annotated",
  workers = 2,
  patterns = NULL,
  matching_opt = "ANY" # Same if you choose "ALL" or "OPTIONAL"
)
matrices_auto
```

Let's do an example with a file system where there are issues, such as 
duplicates:  
```{r R.options=options(ISAnalytics.widgets = FALSE)}
root_err <- system.file("extdata", "fserr.zip", package = "ISAnalytics")
root_err <- unzip_file_system(root_err, "fserr")
fs::dir_tree(root_err)
association_file_fserr <- import_association_file(path_as_file, root_err)
refined_af_err <- association_file_fserr %>% 
  dplyr::filter(.data$ProjectID == "CLOEXP")
matrices_auto2 <- import_parallel_Vispa2Matrices_auto(
  association_file = refined_af_err,
  root = NULL,
  quantification_type = c("fragmentEstimate","seqCount"),
  matrix_type = "annotated",
  workers = 2,
  patterns = "NoMate",
  matching_opt = "ANY" # Same if you choose "ALL" or "OPTIONAL"
)
matrices_auto
```

As you can see, in the file system with issues we have more than one file for 
quantification type, duplicates have "NoMate" suffix in their file name. 
By specifying this pattern in the function, we're only going to import 
those files.  

As for the interactive version, you can call the function with path to the 
association file and root if you want to simply import everything without 
filtering.

# Reproducibility

The `r Biocpkg("ISAnalytics")` package `r citep(bib[["ISAnalytics"]])` was 
made possible thanks to:

* R `r citep(bib[["R"]])`
* `r Biocpkg("BiocStyle")` `r citep(bib[["BiocStyle"]])`
* `r CRANpkg("knitcitations")` `r citep(bib[["knitcitations"]])`
* `r CRANpkg("knitr")` `r citep(bib[["knitr"]])`
* `r CRANpkg("rmarkdown")` `r citep(bib[["rmarkdown"]])`
* `r CRANpkg("sessioninfo")` `r citep(bib[["sessioninfo"]])`
* `r CRANpkg("testthat")` `r citep(bib[["testthat"]])`

This package was developed using 
`r BiocStyle::Githubpkg("lcolladotor/biocthis")`.

`R` session information.

```{r reproduce3, echo=FALSE}
## Session info
library("sessioninfo")
options(width = 120)
session_info()
```


# Bibliography

This vignette was generated using `r Biocpkg("BiocStyle")` 
`r citep(bib[["BiocStyle"]])`
with `r CRANpkg("knitr")` `r citep(bib[["knitr"]])` and 
`r CRANpkg("rmarkdown")` `r citep(bib[["rmarkdown"]])` 
running behind the scenes.

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

```{r results = "asis", echo = FALSE, warning = FALSE, message = FALSE}
## Print bibliography
bibliography()
```
