Get data on samples from HoloFood database

Usage

getResult(accession, ...)

Arguments

accession

Character vector specifying the accession IDs of type samples.

...

optional arguments:

use.cache Logical scalar specifying whether to use cache. Note that when get.metabolomic = TRUE is specified, the file from the MetaboLights is stored in the local system to the location specified by cache.dir despite of the value of use.cache. (Default: FALSE)
cache.dir Character scalar specifying cache directory. (Default: tempdir())
clear.cache Logical scalar specifying whether to use.cache (Default: FALSE)
assay.type Character scalar specifying the name of assay in resulting TreeSummarizedExperiment object. (Default: "counts")
get.metabolomic Logical scalar specifying whether to retrieve processed metabolomic data from MetaboLights database. For retrieving spectra data, refer to getMetaboLights documentation. (Default: FALSE)

Value

MultiAssayExperiment

Details

With getResult, you can fetch data on samples from the HoloFood database. Compared to getData, this function is more convenient for fetching the samples data because it converts the data to MultiAssayExperiment where different omics are stored as TreeSummarizedExperiment objects which are optimized for downstream analytics. Columns of returned MultiAssayExperiment are individual animals. These columns are linked with individual samples that are stored in TreeSummarizedExperiment objects.

The HoloFood database lacks non-targeted metabolomic data but they can be fetched from MetaboLights resource. Certain datasets include processed features. Those datasets can be retrieved with the function getResult which integrates metabolomic data with other datasets from HoloFood.

Furthermore, while the HoloFoodR database does not include metagenomic assembly data, users can access such data from the MGnify database. The MGnifyR package provides a convenient interface for accessing this database. By employing MGnifyR::getResult(), users can obtain data formatted as a MultiAssayExperiment object, containing multiple TreeSummarizedExperiment objects. Consequently, data from both HoloFood and MGnify databases are inherently compatible for subsequent downstream analysis.

Examples


# Find samples on certain animal
samples <- doQuery("samples", animal_accession = "SAMEA112904746")

# Get the data
mae <- getResult(samples[["accession"]])
#> 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.):
#> 'SAMEA10130039', 'SAMEA13389496', 'SAMEA13389497', 'SAMEA13901618', 'SAMEA13901619', 'SAMEA13929785', 'SAMEA7571815'
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 2 columns
#>  [2] FATTY ACIDS: TreeSummarizedExperiment with 19 rows and 2 columns
#>  [3] HISTOLOGY: TreeSummarizedExperiment with 20 rows and 2 columns
#>  [4] INFLAMMATORY MARKERS: TreeSummarizedExperiment with 14 rows and 2 columns
#>  [5] metagenomic_assembly: TreeSummarizedExperiment with 0 rows and 2 columns
#>  [6] host_genomic: TreeSummarizedExperiment with 0 rows and 2 columns
#>  [7] transcriptomic: TreeSummarizedExperiment with 0 rows and 2 columns
#>  [8] metatranscriptomic: TreeSummarizedExperiment with 0 rows and 1 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