---
title: "HoloFoodR: interface to HoloFoodR database"
date: "`r Sys.Date()`"
package: HoloFoodR
output:
    BiocStyle::html_document:
        fig_height: 7
        fig_width: 10
        toc: yes
        toc_depth: 2
        number_sections: true
vignette: >
    %\VignetteIndexEntry{HoloFoodR}
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteEncoding{UTF-8}
---

```{r}
#| label: setup
#| include: false

library(knitr)
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    cache = TRUE
)
```

# Introduction

`HoloFoodR` is a package designed to ease access to the EBI's
[HoloFoodR](https://www.holofooddata.org/) resource, allowing searching and
retrieval of multiple datasets for downstream analysis.

The HoloFood database does not encompass metagenomics data; however,
such data is stored within the [MGnify](https://www.ebi.ac.uk/metagenomics)
database. Both packages offer analogous functionalities, streamlining the
integration of data and enhancing accessibility.

`r BiocStyle::Biocpkg("TreeSummarizedExperiment")`

# Installation

`HoloFoodR` is hosted on Bioconductor, and can be installed using via
`BiocManager`.

```{r}
#| label: install
#| eval: false

BiocManager::install("HoloFoodR")
```

# Load the package

Once installed, `HoloFoodR` is made available in the usual way.

```{r}
#| label: load_package

library(HoloFoodR)
```

# Functionalities

`HoloFoodR` offers three functions `doQuery()`, `getResult()` and `getData()`
which can be utilized to search and fetch data from HoloFood database.

In this tutorial, we demonstrate how to search animals, subset animals based
on whether they have specific sample type, and finally fetch the data on
samples. Note that this same can be done with `doQuery()` and `getResult()`
(or `getData()` and `getResult()`) only by utilizing query filters. This
tutorial is for demonstrating the functionality of the package.

Additionally, the package includes `getMetaboLights()` function which can be
utilized to retrieve metabolomic data from MetaboLights database.

## Search data

To search animals, genome catalogues, samples or viral catalogues, you can use
`doQuery()` function. You can also use `getData()` but `doQuery()` is optimized
for searching these datatypes. For example, instead of nested list of sample
types `doQuery()` returns sample types as presence/absence table which is more
convenient.

Here we search animals, and subset them based on whether they include
histological samples. Note that this same can be done also by using query
filters.

```{r}
#| label: search_animals

animals <- doQuery("animals", max.hits = 100)
animals <- animals[ animals[["histological"]], ]

colnames(animals) |> head()
```

`doQuery()` returns a `data.frame` including information on type of data being
searched.

## Get data

Now we have information on which animal has histological samples. Let's get
data on those animals to know the sample IDs to fetch.

```{r}
#| label: get_animal_data

animal_data <- getData(
    accession.type = "animals", accession = animals[["accession"]])
```

The returned value of `getData()` function is a `list`. We can have the data
also as a `data.frame` when we specify `flatten = TRUE`. The data has
information on animals including samples that have been drawn from them. 

```{r}
#| label: get_samples1

samples <- animal_data[["samples"]]

colnames(samples) |> head()
```

The elements of the `list` are `data.frames`. For example, "samples" table
contains information on samples drawn from animals that were specified in input.

Now we can collect sample IDs.

```{r}
#| label: get_samples2

sample_ids <- unique(samples[["accession"]])
```

## Get data on samples

To get data on samples, we can utilize `getResult()` function. It returns the
data in `r BiocStyle::Biocpkg("MultiAssayExperiment")` (`MAE`) format.

```{r}
#| label: get_mae

mae <- getResult(sample_ids)
mae
```

`MAE` object stores individual omics as
`r BiocStyle::Biocpkg("TreeSummarizedExperiment")` (`TreeSE`) objects.

```{r}
#| label: show_mae

mae[[1]]
```

In `TreeSE`, each column represents a sample and rows represent features.

## Incorporate with MGnify data

`MGnifyR` is a package that can be utilized to fetch metagenomics data from
MGnify database. From the `MGnifyR` package, we can use
`MGnifyR::searchAnalysis()` function to search analyses based on sample IDs
that we have.

```{r}
#| label: search_analyses1
#| eval: false

library(MGnifyR)

mg <- MgnifyClient(useCache = TRUE)

# Get those samples that are metagenomic samples
metagenomic_samples <- samples[
    samples[["sample_type"]] == "metagenomic_assembly", ]

# Get analysis IDs based on sample IDs
analysis_ids <- searchAnalysis(
    mg, type = "samples", metagenomic_samples[["accession"]])
```

```{r}
#| label: search_analyses2
#| include: false

path <- system.file("extdata", "analysis_ids.rds", package = "HoloFoodR")
analysis_ids <- readRDS(path)
```

```{r}
#| label: search_analyses3

head(analysis_ids)
```

Then we can fetch data based on accession IDs.

```{r}
#| label: get_metageomic_data1
#| eval: false

mae_metagenomic <- MGnifyR::getResult(mg, analysis_ids)
```

```{r}
#| label: get_metageomic_data2
#| include: false

path <- system.file("extdata", "mae_metagenomic.rds", package = "HoloFoodR")
mae_metagenomic <- readRDS(path)
```

```{r}
#| label: get_metageomic_data3

mae_metagenomic
```

`MGnifyR::getResult()` returns `MAE` object just like `HoloFoodR`. However,
metagenomic data points to individual analyses instead of samples. We can
harmonize the data by replacing analysis IDs with sample IDs, and then we can
combine the data to single `MAE`.

```{r}
#| label: combine_data
#| eval: false

# Get experiments from metagenomic data
exps <- experiments(mae_metagenomic)
# Convert analysis names to sample names
exps <- lapply(exps, function(x){
    # Get corresponding sample ID
    sample_id <- names(analysis_ids)[ match(colnames(x), analysis_ids) ]
    # Replace analysis ID with sample ID
    colnames(x) <- sample_id
    return(x)
})

# Add to original MultiAssayExperiment
mae <- c(experiments(mae), exps)
mae
```

Now, with the `MAE` object linking samples from various omics together,
compatibility is ensured as the single omics datasets are in
`(Tree)SummarizedExperiment` format. This compatibility allows us to harness
cutting-edge downstream analytics tools like
[miaverse framework](https://microbiome.github.io/) that support these data
containers seamlessly.

## Extra: Get data from MetaboLights database

The HoloFood database exclusively contains targeted metabolomic data. However,
it provides URL addresses linking to the MetaboLights database, where untargeted
metabolomics data can be accessed. To retrieve this data, you can utilize the
getMetaboLights() function to retrieve information on available data. Moreover,
it returns processed metabolomic data (for processed data, you can also use
`getReturn(x, get.metabolomic=TRUE)`). Below, we retrieve all the processed
(mapped) metabolomic data associated with HoloFood.

```{r}
#| label: get_metabolomics
#| eval: false

# Get untargeted metabolomic samples
samples <- doQuery("samples", sample_type = "metabolomic")
# Get the data
metabolomic <- getMetaboLights(samples[["metabolomics_url"]])

# Show names of data.frames
names(metabolomic)
```

The result is a list that includes three data.frames:

    - study metadata
    - assay metadata
    - assay that includes abundance table and feature metadata

For spectra data, you can either fetch files using `getMetaboLightsFile()`,
or follow this
[vignette](https://rformassspectrometry.github.io/MsIO/articles/MsIO.html#loading-data-from-metabolights)
for guidance on loading data directly into an \code{MsExperiment} object,
which is tailored for metabolomics spectra data.

# Session info

```{r}
#| label: session_info

sessionInfo()
```
