---
title: "5. Tutorial: Export Human Protein Atlas (HPA) data as JSON"
author:
- name: Anh N. Tran
  affiliation: Northwestern University, Illinois, USA
  email: trannhatanh89@gmail.com
date: 6/8/2019
output: 
    BiocStyle::html_document:
        toc: true
        toc_depth: 2
        toc_float: true
        number_sections: true
vignette: >
  %\VignetteIndexEntry{"5. Tutorial: Export Human Protein Atlas (HPA) data as JSON"}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r library, message=FALSE, warning=FALSE, error=FALSE}
library(BiocStyle)
library(HPAanalyze)
library(dplyr)
library(jsonlite)
```

# The case 
In certain situation, users may want to export HPA downloaded data into JavaScript Object Notation (JSON) format to use for purposed such as asynchronous, real-time server-to-browser communication. To reduce package dependencies, `HPAanalyze` does not support exporting to JSON via the `hpaExport` function. However, this can be done using a short script as described below.

# The solution
Exporting data to JSON can be achieved by converting dataframes resulting from `hpaDownload`/`hpaSubset` to JSON format using the `jsonlite` package and write the files to `.json` file.

## Download and subset data
There is no special processing needed to the datasets. You can download and subset data as usual. The resulting object is a list of dataframes.
```{r}
data <- hpaDownload(downloadList = "histology", version = "example")
data_subset <-
  hpaSubset(data,
            targetGene = c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1'))
```

## Convert dataframes to JSON
The list of dataframes will then be converted to a list of `json` using `jsonlite::toJSON`.

```{r}
data_json <- lapply(data_subset, jsonlite::toJSON)

str(data_json)

# List of 3
#  $ normal_tissue       : 'json' chr "[{\"ensembl\":\"ENSG00000026508\",\"gene\":\"CD44\",\"tissue\":\"adrenal gland\",\"cell_type\":\"glandular cell"| __truncated__
#  $ pathology           : 'json' chr "[{\"ensembl\":\"ENSG00000026508\",\"gene\":\"CD44\",\"cancer\":\"breast cancer\",\"high\":1,\"medium\":6,\"low\"| __truncated__
#  $ subcellular_location: 'json' chr "[{\"ensembl\":\"ENSG00000026508\",\"gene\":\"CD44\",\"reliability\":\"Enhanced\",\"enhanced\":\"Golgi apparatus"| __truncated__
```

## Write JSON file
Finally, the `.json` file can be saved to your working folder using the follow code. Notice that there will be one `.json` file for each dataset.

```{r}
for (i in seq_along(data_json)) {
  write(data_json[[i]], 
        file = paste0("hpa_data_", names(data_json[i]), ".json"))
}
```

# In one function
If you routinely export HPA data into JSON format, the following function allow you to do so with the same syntax as `hpaExport`.

```{r}
## The function (note that you don't need to put .json into the file name)
hpaExportJSON <- function(data, fileName) {
  data_json <- lapply(data, jsonlite::toJSON)
  for (i in seq_along(data_json)) {
    write(data_json[[i]],
          file = paste0(fileName, "_", names(data_json[i]), ".json"))
  }
}

## Export data subset
hpaExportJSON(data_subset, fileName = "hpa_data")
```


# Copyright

__Anh Tran, 2018-2021 __

Please cite: __Tran, A.N., Dussaq, A.M., Kennell, T. et al. HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data. BMC Bioinformatics 20, 463 (2019) https://doi.org/10.1186/s12859-019-3059-z __
