# HoloFoodR: interface to HoloFoodR database

# Introduction

`HoloFoodR` is a package designed to ease access to the EBI’s HoloFoodR 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 database. Both packages offer analogous functionalities, streamlining the integration of data and enhancing accessibility.

TreeSummarizedExperiment

# Installation

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

    BiocManager::install("HoloFoodR")

# Load the package

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

    library(HoloFoodR)
    #> Loading required package: MultiAssayExperiment
    #> Loading required package: SummarizedExperiment
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# 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.

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

    colnames(animals) |> head()
    #> [1] "accession"            "system"               "canonical_url"
    #> [4] "histological"         "host_genomic"         "inflammatory_markers"

`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.

    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.

    samples <- animal_data[["samples"]]

    colnames(samples) |> head()
    #> [1] "accession"        "title"            "sample_type"      "animal"
    #> [5] "canonical_url"    "metagenomics_url"

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.

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

## Get data on samples

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

    mae <- getResult(sample_ids)
    #> Warning: Data for the following samples cannot be found. The sample types are metagenomic_assembly, host_genomic, transcriptomic and metatranscriptomic (Note that metagenomic assemblies can be found from the MGnify database. See MGnifyR package.):
    #> 'SAMEA10130025', 'SAMEA13389405', 'SAMEA13389406', 'SAMEA13901590', 'SAMEA13901591', 'SAMEA13929779', 'SAMEA7697591', 'SAMEA10130091', 'SAMEA13389692', 'SAMEA13389693', 'SAMEA13901708', 'SAMEA7571845', 'SAMEA10158030', 'SAMEA13389419', 'SAMEA13389420', 'SAMEA13901594', 'SAMEA13901595', 'SAMEA13929781', 'SAMEA7697592', 'SAMEA10130039', 'SAMEA13389496', 'SAMEA13389497', 'SAMEA13901618', 'SAMEA13901619', 'SAMEA13929785', 'SAMEA7571815', 'SAMEA10130112', 'SAMEA13389794', 'SAMEA13389795', 'SAMEA13901758', 'SAMEA13901759', 'SAMEA13929811', 'SAMEA7571864', 'SAMEA10158022', 'SAMEA13389146', 'SAMEA13389147', 'SAMEA13901511', 'SAMEA13901512', 'SAMEA13929767', 'SAMEA7571777', 'SAMEA10130019', 'SAMEA13389353', 'SAMEA13389354', 'SAMEA13389355', 'SAMEA13901574', 'SAMEA13901575', 'SAMEA14095991', 'SAMEA7722475', 'SAMEA10130101', 'SAMEA13389738', 'SAMEA13389739', 'SAMEA13901730', 'SAMEA13901731', 'SAMEA13929802', 'SAMEA7571856', 'SAMEA10455480', 'SAMEA13389220', 'SAMEA10129993', 'SAMEA13389183', 'SAMEA13389184', 'SAMEA13901520', 'SAMEA13901521', 'SAMEA7697579', 'SAMEA10130017', 'SAMEA13389345', 'SAMEA13389346', 'SAMEA13901571', 'SAMEA13901572', 'SAMEA13929772', 'SAMEA7571801', 'SAMEA10130113', 'SAMEA13389807', 'SAMEA13389808', 'SAMEA13901762', 'SAMEA13901763', 'SAMEA13929813', 'SAMEA7571866', 'SAMEA10455481', 'SAMEA13389227', 'SAMEA10455479', 'SAMEA13389169', 'SAMEA10130020', 'SAMEA13389357', 'SAMEA13389358', 'SAMEA13901576', 'SAMEA13901577', 'SAMEA13929773', 'SAMEA7697587', 'SAMEA10130100', 'SAMEA13389734', 'SAMEA13389735', 'SAMEA13901728', 'SAMEA13901729', 'SAMEA13929801', 'SAMEA7697622', 'SAMEA10130016', 'SAMEA13389342', 'SAMEA13389343', 'SAMEA13901569', 'SAMEA13901570', 'SAMEA13929771', 'SAMEA7571800', 'SAMEA10130040', 'SAMEA13389503', 'SAMEA13389504', 'SAMEA13901620', 'SAMEA13901621', 'SAMEA13929786', 'SAMEA7571816', 'SAMEA10129979', 'SAMEA13389081', 'SAMEA13389082', 'SAMEA13901489', 'SAMEA13901490', 'SAMEA7571769', 'SAMEA10130002', 'SAMEA13389243', 'SAMEA13389244', 'SAMEA13901540', 'SAMEA13901541', 'SAMEA7697582', 'SAMEA10129985', 'SAMEA13389133', 'SAMEA13389134', 'SAMEA13901505', 'SAMEA13901506', 'SAMEA13929764', 'SAMEA7571775', 'SAMEA10455476', 'SAMEA13389110', 'SAMEA10130031', 'SAMEA13389443', 'SAMEA13389444', 'SAMEA13901602', 'SAMEA13901603', 'SAMEA7571811', 'SAMEA10130023', 'SAMEA13389395', 'SAMEA13389396', 'SAMEA13901586', 'SAMEA13901587', 'SAMEA13929777', 'SAMEA7571806', 'SAMEA10130090', 'SAMEA13389687', 'SAMEA13389688', 'SAMEA13901707', 'SAMEA7571844', 'SAMEA10130119', 'SAMEA13389832', 'SAMEA13389833', 'SAMEA13901773', 'SAMEA13929818', 'SAMEA7697633', 'SAMEA10129996', 'SAMEA13389204', 'SAMEA13389205', 'SAMEA13901529', 'SAMEA13901530', 'SAMEA7697580', 'SAMEA10130088', 'SAMEA13389677', 'SAMEA13389678', 'SAMEA13901704', 'SAMEA13929799', 'SAMEA7571843'
    mae
    #> A MultiAssayExperiment object of 8 listed
    #>  experiments with user-defined names and respective classes.
    #>  Containing an ExperimentList class object of length 8:
    #>  [1] BIOGENIC AMINES: TreeSummarizedExperiment with 7 rows and 35 columns
    #>  [2] FATTY ACIDS: TreeSummarizedExperiment with 19 rows and 57 columns
    #>  [3] HISTOLOGY: TreeSummarizedExperiment with 20 rows and 57 columns
    #>  [4] INFLAMMATORY MARKERS: TreeSummarizedExperiment with 14 rows and 58 columns
    #>  [5] metagenomic_assembly: TreeSummarizedExperiment with 0 rows and 53 columns
    #>  [6] host_genomic: TreeSummarizedExperiment with 0 rows and 53 columns
    #>  [7] transcriptomic: TreeSummarizedExperiment with 0 rows and 44 columns
    #>  [8] metatranscriptomic: TreeSummarizedExperiment with 0 rows and 16 columns
    #> Functionality:
    #>  experiments() - obtain the ExperimentList instance
    #>  colData() - the primary/phenotype DataFrame
    #>  sampleMap() - the sample coordination DataFrame
    #>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
    #>  *Format() - convert into a long or wide DataFrame
    #>  assays() - convert ExperimentList to a SimpleList of matrices
    #>  exportClass() - save data to flat files

`MAE` object stores individual omics as TreeSummarizedExperiment (`TreeSE`) objects.

    mae[[1]]
    #> class: TreeSummarizedExperiment
    #> dim: 7 35
    #> metadata(0):
    #> assays(1): counts
    #> rownames(7): Cadaverin Gesamtamine (Total biogenic amines) ... Spermin
    #>   Tyramin
    #> rowData names(4): marker.name marker.type marker.canonical_url units
    #> colnames(35): SAMEA112906114 SAMEA112906592 ... SAMEA112906002
    #>   SAMEA112906785
    #> colData names(13): accession sample_type ... Project Sample code
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):
    #> rowLinks: NULL
    #> rowTree: NULL
    #> colLinks: NULL
    #> colTree: NULL

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.

    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"]])

    head(analysis_ids)
    #>  SAMEA10130025   SAMEA7697591  SAMEA10130091   SAMEA7571845  SAMEA10158030
    #> "MGYA00606535" "MGYA00616692" "MGYA00606528" "MGYA00616689" "MGYA00606518"
    #>   SAMEA7697592
    #> "MGYA00615947"

Then we can fetch data based on accession IDs.

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

    mae_metagenomic
    #> A MultiAssayExperiment object of 6 listed
    #>  experiments with user-defined names and respective classes.
    #>  Containing an ExperimentList class object of length 6:
    #>  [1] microbiota: TreeSummarizedExperiment with 675 rows and 52 columns
    #>  [2] go-slim: TreeSummarizedExperiment with 116 rows and 52 columns
    #>  [3] go-terms: TreeSummarizedExperiment with 3264 rows and 52 columns
    #>  [4] interpro-identifiers: TreeSummarizedExperiment with 19681 rows and 52 columns
    #>  [5] taxonomy: TreeSummarizedExperiment with 1438 rows and 52 columns
    #>  [6] taxonomy-lsu: TreeSummarizedExperiment with 1856 rows and 52 columns
    #> Functionality:
    #>  experiments() - obtain the ExperimentList instance
    #>  colData() - the primary/phenotype DataFrame
    #>  sampleMap() - the sample coordination DataFrame
    #>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
    #>  *Format() - convert into a long or wide DataFrame
    #>  assays() - convert ExperimentList to a SimpleList of matrices
    #>  exportClass() - save data to flat files

`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`.

    # 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 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.

    # 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 for guidance on loading data directly into an object, which is tailored for metabolomics spectra data.

# Session info

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