---
title: "Introduction_to_metabolomicsWorkbenchR"
author:
- name: Gavin R Lloyd
  affiliation: Phenome Centre Birmingham, University of Birmingham, UK
  email: g.r.lloyd@bham.ac.uk
- name: Ralf J Weber
  affiliation: Phenome Centre Birmingham, University of Birmingham, UK
  email: r.j.weber@bham.ac.uk
output:
  BiocStyle::html_document:
    toc: yes
    toc_depth: 2
    number_sections: yes
    toc_float: yes
package: metabolomicsWorkbenchR
vignette: >
  %\VignetteIndexEntry{Introduction_to_metabolomicsWorkbenchR} 
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r echo = FALSE,include=FALSE}
suppressPackageStartupMessages(library(metabolomicsWorkbenchR))
```


# Introduction
Metabolomics Workbench [(link)](www.metabolomicsworkbench.org) hosts a metabolomics 
data repository. It contains over 1000 publicly available studies including raw data, 
processed data and metabolite/compound information.

The repository is searchable using a REST service API. The metabolomicsWorkbenchR
package makes the endpoints of this service available in R and provides functionality
to search the database and import datasets and metabolite information into commonly used 
formats such as data frames and SummarizedExperiment objects.

# Installation
To install this package enter:
```
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("metabolomicsWorkbenchR")
```
For older versions, please refer to the appropriate Bioconductor release.

# Running a query
The Metabolomics Workbench API has a number of endpoints that can be used to 
query several different databases. Complete details are provided in the API
documentation [(link)](https://www.metabolomicsworkbench.org/tools/MWRestAPIv1.0.pdf).

`metabolomicsWorkbenchR` provides a simple interface to all API endpoints via 
the `do_query` method. Four inputs are required:

* `context` -  The context determines the type of data to be accessed from the Metabolomics Workbench,
such as metadata or results related to the submitted studies, data from metabolites, genes/proteins and
analytical chemistry databases as well as other services related to mass spectrometry and metabolite
identification. Valid contexts are "study", "compound", "refmet", 
"gene", "protein", "moverz" and "exactmass".
* `input_item` - Input items direct the search towards a specific part of the database. 
If the database is a table, then `input_item` is a column in that table that will be searched for matching values.
* `input_value` - The value to search for in the named `input_item`.
* `output_item` - The type of output to be returned. Usually some data in the form of a table, but sometimes a file (e.g. png, mol).

By combining different context, input and output items a variety of information can be returned. In this first example, we query the study context for study titles containing the keyword "Diabetes" and request a summary of each matching study.

```{r}
# search for all studies with "Diabetes" in the title and return a summary
df = do_query(
    context = 'study',
    input_item = 'study_title',
    input_value = 'Diabetes',
    output_item = 'summary'
)
df[1:3,c(1,4)]
```
The result is a 14x12 data.frame with study titles, authors, descriptions etc.

In the next example we query the compound context for "regno" identifier 11 and request all available information for the matching compound.

```{r}
df = do_query(
    context = 'compound',
    input_item = 'regno',
    input_value = '11',
    output_item = 'compound_exact'
)

df[,1:3]
```

Valid contexts, input items and output items can be listed using the `names` function:

```{r}
# valid contexts
names(context) # context, input_item or output_item
```

Valid inputs and outputs for a context can be displayed by accessing the list of context objects. Valid inputs for a particular output can also be displayed by accessing the list of output item objects. Use of `metabolmicsWorkbenchR` objects is detailed in a later section.

More information about the different contexts can be found in the API documentation
[(link)](https://www.metabolomicsworkbench.org/tools/MWRestAPIv1.0.pdf)

# Special cases
`metabolomicsWorkBenchR` includes some output items in addition to those specified by the API documentation. These special cases are described here.

## `input_item` "ignored"
The input item is used with the "study" context and the "untarg_studies" input_item. The API ignores the input_item and the input_value when using this query and returns a list of studies with untargeted data.

```{r}
df = do_query(
  context = 'study',
  input_item = 'ignored',
  input_value = 'ignored',
  output_item = 'untarg_studies'
)

df[1:3,1:3]
```

## `output_item` "compound_exact", "protein_exact" and "gene_exact"
These outputs refer to compound, protein and gene context API outputs that can be used with exact matching. This means that only exact matches to the input_value will be returned. For these outputs all available output fields will be returned.

```{r}
df = do_query(
  context = 'compound',
  input_item = 'regno',
  input_value = '11',
  output_item = 'compound_exact'
)

df[,1:3]
```

## `output_item` "protein_partial" and "gene_partial"
These outputs refer to protein and gene contexts API outputs that can be used with partial matching. This means that all records with a partial match to the input_value will be returned. For these outputs all available output fields will be returned.

```{r}
df = do_query(
  context = 'gene',
  input_item = 'gene_name',
  input_value = 'acetyl-CoA',
  output_item = 'gene_partial'
)

df[1:3,1:3]
```

## `output_item` "SummarizedExperiment" and "DatasetExperiment"
This output refers to the study context and uses multiple queries to return a SummarizedExperiment or DatasetExperiment object for a study_id or analysis_id.

```{r}
SE = do_query(
    context = 'study',
    input_item = 'study_id',
    input_value = 'ST000001',
    output_item = 'SummarizedExperiment' # or 'DatasetExperiment'
)

SE

```

## `output_item` "MultiAssayExperiment"
This output refers to the study context and uses multiple queries to return a MultiAssayExperiment object for a study_id.

```{r}
MAE = do_query(
    context = 'study',
    input_item = 'study_id',
    input_value = 'ST000009',
    output_item = 'MultiAssayExperiment' 
)

MAE
```

## `output_item` "untarg_SummarizedExperiment" and "untarg_DatasetExperiment"
This output refers to the study context and uses multiple queries to return a SummarizedExperiment or DatasetExperiment object of untargeted data for an analysis_id.

```{r}
SE = do_query(
    context = 'study',
    input_item = 'analysis_id',
    input_value = 'AN000023',
    output_item = 'untarg_SummarizedExperiment' # or 'untarg_DatasetExperiment'
)

SE
```


# S4 classes
A number of classes have been defined in this package and for completeness they are described below. They are used to
implement access to the API endpoints and it is not expected that they will be used as objects by the user. The `do_query` function uses character strings to access predefined instances of these objects and simplify the query.

## Contexts
Each database is referred to as a 'context'. These contexts can be searched using
input/output pairs to search the database for matches and return the results.

In `metabolmicsWorkbenchR` a predefined list called `context` contains 
`mw_context` objects. These objects define which inputs and outputs are valid
options for a context.

The name of all valid contexts can be displayed:

```{r}
# list all context names
names(context)
```

Information about a specific context can be obtained using the show method for a
an `mw_context` object:

```{r}
# list valid inputs/outputs for the "study" context
context$study
```
## Input / Output Items
Once the context of the search has been decided upon valid inputs and outputs can 
be chosen. All input and output items have been predefined as lists called 
`input_item` and `output_item`.

The `input_item` list contains `mw_input_item` objects that specify valid pattern
matching of the input value using regex.

```{r}
# input item "study_id" info
input_item$study_id
```

The `output_item` list contains `mw_output_item` objects that specify valid inputs,
the expected return fields (if the return is a data.frame) and the type of input matching that is supported.

```{r}
# output item 'summary' info
output_item$summary
```

# Session Info
```{r,echo=FALSE}
sessionInfo()
```
