---
title: "RGMQL: GenoMetric Query Language for R/Bioconductor"
author:
- "Simone Pallotta"
- "Marco Masseroli"
date: "`r Sys.Date()`"
bibliography: bibliography.bib
csl: american-medical-association-no-et-al.csl
output: BiocStyle::pdf_document
vignette: >
%\VignetteIndexEntry{RGMQL: GenoMetric Query Language for R/Bioconductor}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
link-citations: true
---
# Introduction
Recent years have seen a tremendous increase in the volume of data generated
in the life sciences, especially propelled by the rapid progress of
Next Generation Sequencing (NGS) technologies.
These high-throughput technologies can produce billions of short DNA or RNA
fragments in excess of a few terabytes of data in a single run.
Next-generation sequencing refers to the deep, in-parallel DNA sequencing
technologies providing massively parallel analysis and extremely
high-throughput from multiple samples at much reduced cost.
Improvement of sequencing technologies and data processing pipelines
is rapidly providing sequencing data, with associated high-level features,
of many individual genomes in multiple biological and clinical conditions.
To make effective use of the produced data, the design of big data algorithms
and their efficient implementation on modern high performance
computing infrastructures, such as clouds, CPU clusters
and network infrastructures, is required in order to achieve scalability
and performance.
For this purpose, the GenoMetric Query Language (GMQL) has been proposed
as high-level, declarative language to process, query,
and compare multiple and heterogeneous genomic datasets for biomedical
knowledge discovery [@Bioinformatics2015]
## Purpose
A very important emerging problem is to make sense of the enormous amount and
variety of NGS data becoming available, i.e., to discover how different genomic
regions and their products interact and cooperate with each other.
To this aim, the integration of several heterogeneous DNA feature data
is required.
Such big genomic feature data are collected within numerous and
heterogeneous files, usually distributed within different repositories,
lacking an attribute-based organization and a systematic description
of their metadata.
These heterogeneous data can contain the hidden answers to very important
biomedical questions.
To unveil them, standard tools already available for knowledge extraction
are too specialized or present powerful features, but have a rough interface
not well-suited for scientists/biologists.
GMQL addresses these aspects using cloud-based technologies
(including Apache Hadoop, mapReduce, and Spark), and focusing on genomic data
operations written as simple queries with implicit iterations over thousands
of heterogeneous samples, computed efficiently [@IEEE7484654].
This RGMQL package makes easy to take advantage of GMQL functionalities also
to scientists and biologists with limited knowledge of query and
programming languages, but used to the R/Bioconductor environment.
This package is built over a GMQL scalable data management engine
written in Scala programming language, released as Scala API [@githubrepo]
providing a set of functions to combine, manipulate, compare, and extract
genomic data from different data sources both from local and remote datasets.
These functions, built extending functionalities available in the
R/Bioconductor framework, allow performing complex GMQL processing and queries
without knowledge of GMQL syntax, but leveraging on R idiomatic paradigm
and logic.
# Genomic Data Model
The Genomic Data Model (GDM) is based on the notions of datasets
and samples [@modeling2016]
Datasets are collections of samples, and each sample consists of two parts,
the region data, which describe portions of the genome, and the metadata,
which describe sample general properties and how observations are collected.
In contrast to other data models, it clearly divides, and comprehensively
manages, observations about genomic regions and metadata.
GDM provides a flat attribute based organization, just requiring that
each dataset is associated with a given data schema, which specifies
the attributes and their type of region data.
The first attributes of such schema are fixed (chr, left, right, strand);
they represent the genomic region identifying coordinates.
In addition, metadata have free attribute-value pair format.
## Genomic Region
Genomic region data describe a broad variety of biomolecular aspects and are
very valuable for biomolecular investigation.
A genomic region is a portion of a genome, qualified by a quadruple of values
called region coordinates:
$$< chr, left, right, strand >$$
Regions can have associated an arbitrary number of attributes with their value,
according to the processing of DNA, RNA or epigenomic sequencing reads that
determined the region.
## Metadata
Metadata describe the biological and clinical properties associated with
each sample.
They are usually collected in a broad variety of data structures and formats
that constitute barriers to their use and comparison. GDM models metadata
simply as arbitrary semi-structured attribute-value pairs,
where attributes may have multiple values.
## Genomic Sample
Formally, a sample $s$ is a collection of genomic regions modelled as
the following triple: $$< id, {< r_i,v_i >}, {m_j} >$$ where:
* $id$ is the sample identifier
* Each region is a pair of coordinates $r_i$ and values $v_i$
* Metadata $m_j$ are attribute-value pairs $< a_j,v_j >$
Note that the sample $id$ attribute provides a many-to-many connection between
regions and metadata of a sample.
Through the use of a data type system to express region data, and of arbitrary
attribute-value pairs for metadata, GDM provides interoperability across
datasets in multiple formats produced by different experimental techniques.
## Dataset
A dataset is a collection of samples uniquely identified, with the same region
schema and with each sample consisting of two parts:
* region data: describing characteristics and location of genomic portions
* metadata: expressing general properties of the sample
Each dataset is typically produced within the same project by using the same
or equivalent technology and tools, but with different experimental
conditions, described by metadata.
Datasets contain large number of information describing regions of a genome,
with data encoded in human readable format using plain text files.
GMQL datasets are materialized in a standard layout composed of three
types of files:
1. genomic region tab-delimited text files with extension .gdm, or .gtf
if in standard GTF format
2. metadata attribute-value tab-delimited text files with the same fullname
(name and extension) of the correspondent genomic region file and extension
.meta
3. schema XML file containing region attribute names and types
All these files reside in a unique folder called $files$.
<!--  -->
In RGMQL package, dataset files are considered read-only.
Once read, genomic information is represented in an abstract data structure
inside the package, mapped to a R GRanges data structure as needed for
optimal use and interoperability with all available R/Bioconductor functions.
# GenoMetric Query Language
The GenoMetric Query Language name stems from such language ability to deal
with genomic distances, which are measured as number of nucleotide bases
between genomic regions (aligned to the same reference genome) and computed
using arithmetic operations between region coordinates.
GMQL is a high-level, declarative language that allows expressing queries
easily over genomic regions and their metadata, in a way similar to what can
be done with the Structured Query Language (SQL) over a relational database.
GMQL approach exhibits two main differences with respect to other tools
based on Hadoop, mapReduce framework, and Spark engine technologies
to address similar biomedical problems:
* GMQL:
1. reads from processed datasets
2. supports metadata management
* Others:
1. read generally from raw or aligned data from NGS machines
2. provide no support for metadata management
GMQL is the appropriate tool for querying many genomic datasets and very many
samples of numerous processed genomic region data that are becoming available.
Note however that GMQL performs worse than some other available systems on a
small number of small-scale datasets, but these other systems are not
cloud-based; hence, they are not adequate for efficient big data processing
and, in some cases, they are inherently limited in their
data management capacity, as they only work as RAM memory resident processes.
## GMQL Query Structure
A GMQL query, or script, is expressed as a sequence of GMQL operations with
the following structure:
$$< variable > = operator(< parameters >) < variable >;$$
where each $< variable >$ stands for a GDM dataset
This RGMQL package brings GMQL functionalities into R environment,
allowing users to build directly a GMQL query without knowing the GMQL syntax,
but using R idiomatic expressions and available R functions suitably extended.
In RGMQL every GMQL operations is translated into a R function
and expressed as:
$$ variable = operator(variable, parameters)$$
It is very similar to the GMQL syntax for operation expression, although
expressed with the R idiomatic paradigm and logic, with parameters totally
built using R native data structures such as lists, matrices, vectors or
R logic conditions.
# Processing Environments
In this section, we show how GMQL processing is built in R, which operations
are available in RGMQL, and the difference between local
and remote dataset processing.
## Local Processing
RGMQL local processing consumes computational power directly from local
CPUs/system while managing datasets (both GMQL or generic text plain datasets).
### Initialization
Load and attach the RGMQL package in a R session using library function:
```{r, initialization}
library('RGMQL')
```
RGMQL depends on another packaage *RGMQLlib* automatically installed and
loaded once install and loaded *RGMQL*; if not, you can install it from
Bioconductor and load it using:
```{r, initialization_RGMQLlib}
library('RGMQLlib')
```
Before starting using any GMQL operation we need to initialise the GMQL
context with the following code:
```{r, init}
init_gmql()
```
The function *init_gmql()* initializes the context of scalable data management
engine laid upon Spark and Hadoop, and the format of materialized result
datasets. Details on this and all other functions are provided in the R
documentation for this package (i.e., help(RGMQL)).
### Read Dataset
After initialization, we need to read datasets.
We already defined above the formal definition of dataset and the power of
GMQL to deal with data in a variety of standard tab-delimited text formats.
In the following, we show how to get data from different sources.
We distinguish two different cases:
1. Local dataset:\newline
A local dataset is a folder with sample files (region files and correspondent
metadata files) on the user computer.
As data are already in the user computer, we simply execute:
```{r, read GMQL dataset}
gmql_dataset_path <- system.file("example", "EXON", package = "RGMQL")
data_out = read_gmql(gmql_dataset_path)
```
In this case we are reading a GMQL dataset specified by the path of
its folder "EXON" within the subdirectory "example" of the package "RGMQL".
It does not matter what kind of format the data are, *read_gmql()* reads
many standard tab-delimited text formats without the need of specifying any
additional input parameter.
2. GRangesList:\newline
For better integration in the R environment and with other R packages,
we provide the *read_GRangesList()* function to read directly from R memory
using GRangesList as input.
```{r, read GRangesList}
library("GenomicRanges")
# Granges Object with one region: chr2 and two metadata columns: score = 5
# and GC = 0.45
gr1 <- GRanges(seqnames = "chr2",
ranges = IRanges(103, 106), strand = "+", score = 5L, GC = 0.45)
# Granges Object with two regions both chr1 and two metadata columns: score = 3
# for the fist region and score = 4 for the second one, GC = 0.3 and 0.5
# for the first and second region, respectively
gr2 <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(107, 113), width = 3), strand = c("+", "-"),
score = 3:4, GC = c(0.3, 0.5))
grl <- GRangesList("txA" = gr1, "txB" = gr2)
data_out <- read_GRangesList(grl)
```
In this example we show how versatile the RGMQL package is.
As specified above, we can directly read a list of GRanges previously created
starting from two GRanges.
Both *read_GRangesList()* and *read_gmql()* functions return a result object,
in this case *data_out*: an instance of GMQLDataset class used as input
for executing the subsequent GMQL operation.\newline
NOTE: if *metadata(grl)* is empty, the function provides two default metadata:
* "provider" = "PoliMi"
* "application" = "RGMQL"
### Queries
GMQL is not a traditional query language:
With "query" we intend a group of operations that together produce a result;
in this sense GMQL queries are more similar to SQL scripts.
GMQL programming consists of a series of select, union, project,
difference (and so on ...) commands.
Let us see a short example:
Find somatic mutations in exons.
Consider mutation data samples of human breast cancer cases.
For each sample, quantify the mutations in each exon and select the exons
with at least one mutation. Return the list of samples ordered by
the number of such exons.
```{r, query}
# These statements define the paths to the folders "EXON" and "MUT" in the
# subdirectory "example" of the package "RGMQL"
exon_path <- system.file("example", "EXON", package = "RGMQL")
mut_path <- system.file("example", "MUT", package = "RGMQL")
# Read EXON folder as a GMQL dataset named "exon_ds" containing a single
# sample with exon regions, and MUT folder as a GMQL dataset named "mut_ds"
# containing multiple samples with mutation regions
exon_ds <- read_gmql(exon_path)
mut_ds <- read_gmql(mut_path)
# Filter out mut_ds based on predicate
mut = filter(mut_ds, manually_curated__dataType == 'dnaseq' &
clinical_patient__tumor_tissue_site == 'breast')
# Filter out exon_ds based on predicate
exon = filter(exon_ds, annotation_type == 'exons' &
original_provider == 'RefSeq')
# For each mutation sample, count mutations within each exon while
# mapping the mutations to the exon regions using the map() function
exon1 <- map(exon, mut)
# Remove exons in each sample that do not contain mutations
exon2 <- filter(exon1, count_left_right >= 1)
# Using the extend() function, count how many exons remain in each sample and
# store the result in the sample metadata as a new attribute-value pair,
# with exon_count as attribute name
exon3 <- extend(exon2, exon_count = COUNT())
# Order samples in descending order of the added metadata exon_count
exon_res = arrange(exon3, list(DESC("exon_count")))
```
If you want to store persistently the result, you can materialize it into
specific path defined as input parameter.
```{r, materialize}
# Materialize the result dataset on disk
collect(exon_res)
```
By default *collect()* has R working directory as storing path and *ds1* as
name of resulted dataset folder.
### Execution
RGMQL processing does not generate results until you invoke the *execute()*
function.
```{r, execute, eval = FALSE}
execute()
```
*execute()* can be issued only if at least one *read()* and at least one
*collect()* are present in the RGMQL query, otherwise an error is generated.
Data are saved in the path specified in every *collect()* present
in the query.
After the execution, the context of scalable data management engine is stopped
and a new invocation of *init_gmql()* is needed.
Beside *execute()* we can use:
```{r, take,eval=FALSE}
g <- take(exon_res, rows = 45)
```
to execute all *collect()* commands in the RGMQL query and extract data
as GRangesList format, a GRangesList for each *collect()* and a GRanges for
each sample.
NOTE: GRangesList are contained in the R environment and are not saved on disk.
With the *rows* parameter it is possible to specify how many rows, for each
sample inside the input dataset, are extracted; by default, the *rows*
parameter value is $0$, that means all rows are extracted.
Note that, since we are working with big data, to extract all rows could be
very time and space consuming.
## Remote Processing
RGMQL remote processing consumes computational power from remote
cluster/system while managing GMQL datasets.\newline
Remote processing exists in two flavour:
- REST web services: \newline
User can write GMQL queries (using original GMQL syntax) to be executed
remotely on remote data (or local data previously uploaded).
- Batch execution: \newline
Similar to local execution; user reads data and the system automatically
uploads them on the remote system if they are not already there; once
loaded, RGMQL functions can be issued to manage and process remote data.
### REST Web Services
This RGMQL package allows invoking REST services that implement the
commands specified at [link](http://genomic.deib.polimi.it/gmql-rest/swagger/).
#### Initialization
GMQL REST services require login; so, the first step is to perform logon
with user and password, or as guest.
Upon successful logon, you get a request token that you must use
in every subsequent REST call.
Login can be performed using the *login_gmql()* function:
```{r, init with login}
test_url = "http://www.gmql.eu/gmql-rest/"
login_gmql(test_url)
```
It saves the token in the Global R environment within the variable
named *authToken*. With this token you can call all the functions
in the GMQL REST web services suite.
#### Execution
User can write a GMQL query as in the following example, and run it as
second parameter of the *run_query()* function:
```{r, run, eval = FALSE}
job <- run_query(test_url, "query_1", "DNA = SELECT() Example_Dataset_1;
MATERIALIZE DNA INTO RESULT_DS;", output_gtf = FALSE)
```
Or the user can execute a query reading it directly from file (e.g., the
"query1.txt" file in the "example" subdirectory of the RGMQL package):
```{r, run_from_file, eval = FALSE}
query_path <- system.file("example", "query1.txt", package = "RGMQL")
job <- run_query_fromfile(test_url, query_path, output_gtf = FALSE)
```
Once run, query continues on the remote server while *run_query()*
or *run_query_fromfile()* returns immediately.
User can extract from result (job) the job_id and status.
jod_id can then be used to continuously invoke log and trace calls,
both in this RGMQL package, to check for job completed status.
```{r, trace, eval = FALSE}
jod_id <- job$id
trace_job(test_url, jod_id)
```
Then, results materialized on the remote repository can by downloaded
locally and imported in GRangesList using the functions
in this RGMQL package [(see Import/Export)](#utilities).
The returned *job* contains also the name of the created datasets,
one for each materialize in the GMQL query run, in the same order;
the first can be downloaded locally with:
```{r, download, eval = FALSE}
name_dataset <- job$datasets[[1]]$name
download_dataset(test_url, name_dataset)
```
By default *download_dataset()* has R working directory as local storing path.
Once download is done, we can logout from remote repository using:
```{r, logout}
logout_gmql(test_url)
```
*logout_gmql()* deletes the $authToken$ from R environment.
Downloaded result dataset can be then imported and used in the R environment
as GRangesList [(see Import/Export)](#utilities).
Alternatively, the remote result dataset can be directly downloaded and
imported in the R environment as GRangesList using the function
*download_as_GRangesList()*:
```{r, download_as_GRangesList, eval=FALSE}
name_dataset <- job$datasets[[1]]$name
grl = download_as_GRangesList(remote_url, name_dataset)
```
### Batch Execution
This execution type is similar to local processing (syntax, functions, and
so on ...) except that materialized data are stored only on the remote
repository, from where they can be downloaded locally and imported in
GRangesList using the functions in this RGMQL package
[(see Import/Export)](#utilities).
Before starting with an example, note that we have to log into remote
infrastructure with login function:
```{r, login remote, eval = FALSE}
login_gmql(test_url)
```
Otherwise, we can initialize the data engine with a remote url:
```{r, initialize remote}
init_gmql(url = test_url)
```
In this way login is automatically performed as specified above.
After initialization we have to change to remote processing:
```{r, change processing mode}
remote_processing(TRUE)
```
or alternatively, instead of switching mode, we can initilize the data engine
setting remote processing as TRUE:
```{r, init remote processing }
init_gmql(url = test_url, remote_processing = TRUE)
```
Once done, we can start building our query:
```{r, remote query}
## Read the remote dataset Example_Dataset_1
## Read the remote dataset Example_Dataset_2
TCGA_dnaseq <- read_gmql("public.Example_Dataset_1", is_local = FALSE)
HG19_bed_ann <- read_gmql("public.Example_Dataset_2", is_local = FALSE)
## Filter out TCGA_dnaseq based on predicate
mut = filter(TCGA_dnaseq, manually_curated__dataType == 'dnaseq' &
clinical_patient__tumor_tissue_site == 'breast')
# Filter out HG19_bed_ann based on predicate
exon = filter(HG19_bed_ann, annotation_type == 'exons' &
original_provider == 'RefSeq')
# For each mutation sample, count mutations within each exon while
# mapping the mutations to the exon regions using the map() function
exon1 <- map(exon, mut)
# Remove exons in each sample that do not contain mutations
exon2 <- filter(exon1, count_left_right >= 1)
# Using the extend() function, count how many exons remain in each sample and
# store the result in the sample metadata as a new attribute-value pair,
# with exon_count as attribute name
exon3 <- extend(exon2, exon_count = COUNT())
# Order samples in descending order of the added metadata exon_count
exon_res = arrange(exon3, list(DESC("exon_count")))
```
N.B.
in case of remote processing we have to specify the name of the output
dataset (not necessary the same as imput) as the second parameter in order to
correctly process the qeury on the remote GMQL system.
Here is the R code we use:
```{r, remote materialize, eval = FALSE}
collect(exon_res,"exon_res")
```
```{r, remote execute, eval = FALSE}
execute()
```
In this case R processing continues until remote processing ends.
With remote processing, after the execution, the context of scalable
data management engine is not stopped, and can be used for further queries.
Materialized datasets are stored only remotely, from where they can be
downloaded and directly imported into the R environment as GRangesList using
the function *download_as_GRangesList()*:
```{r, download_as_GRangesList_2, eval=FALSE}
name_dataset <- job$datasets[[1]]$name
grl = download_as_GRangesList(remote_url, name_dataset)
```
Alternatively, the remote materialized dataset can be first downloaded and
stored locally:
```{r, download_2, eval = FALSE}
name_dataset <- job$datasets[[1]]$name
download_dataset(test_url, name_dataset)
```
and then imported in the R environment as GRangesList
[(see Import/Export)](#utilities).
In any case, once download is done, we can logout from remote repository using:
```{r, logout_2, eval=FALSE}
logout_gmql(test_url)
```
## Mixed Processing
As said before, the processing flavour can be switched using the function:
```{r, switch mode}
remote_processing(TRUE)
```
An user can switch processing mode until the first *collect()* has been
performed.
This kind of processing comes from the fact that the *read_gmql()* function can
accept either a local dataset or a remote repository dataset,
even in the same query as in the following example:
```{r, mixed query}
# This statement defines the path to the folder "MUT" in the subdirectory
# "example" of the package "RGMQL"
mut_path <- system.file("example", "MUT", package = "RGMQL")
# Read MUT folder as a GMQL dataset named "mut_ds" containing a single
# sample with mutation regions
mut_ds <- read_gmql(mut_path, is_local = TRUE)
# Read the remote dataset Example_Dataset_2
HG19_bed_ann <- read_gmql("public.Example_Dataset_2", is_local = FALSE)
# Filter out based on predicate
mut = filter(mut_ds, manually_curated__dataType == 'dnaseq' &
clinical_patient__tumor_tissue_site == 'breast')
exon = filter(HG19_bed_ann, annotation_type == 'exons' &
original_provider == 'RefSeq')
# For each mutation sample, count mutations within each exon while
# mapping the mutations to the exon regions using the map() function
exon1 <- map(exon, mut)
# Remove exons in each sample that do not contain mutations
exon2 <- filter(exon1, count_left_right >= 1)
# Using the extend() function, count how many exons remain in each sample and
# store the result in the sample metadata as a new attribute-value pair,
# with exon_count as attribute name
exon3 <- extend(exon2, exon_count = COUNT())
# Order samples in descending order of the added metadata exon_count
exon_res = arrange(exon3, list(DESC("exon_count")))
```
Materialize result:
```{r, mixed materialize, eval = FALSE}
collect(exon_res,"exon_res")
```
Execute processing:
```{r, mixed execute, eval = FALSE}
execute()
```
As we can see, the two *read_gmql()* functions above read from different
sources: *mut_ds* from local dataset, *HG19_bed_ann* from remote
repository.
If we set remote processing to false (*remote_processing(FALSE)*),
the execution is performed locally first downloading all needed datasets from
remote repositories, otherwise all local datasets are automatically uploaded
to the remote GMQL repository associated with the remote system where the
processing is performed.
In the latter case, materialized datasets are stored on remote repository,
from where they can be downloaded and imported into the R environment as in
the remote processing scenario [(see Remote Processing)](#remote-processing).
NOTE: The public datasets cannot be downloaded from a remote GMQL repository
by design.
# Utilities
The RGMQL package contains functions that allow the user to interface with
other packages available in R/Bioconductor repository, e.g., GenomicRanges,
and TFARM. These functions return GRangesList or GRanges with metadata
associated, if present, as data structure suitable to further processing
in other R/Bioconductor packages.
## Import/Export
We can import a GMQL dataset into R environment as follows:
```{r, import}
# This statement defines the path to the folder "EXON" in the subdirectory
# "example" of the package "RGMQL"
dataset_path <- system.file("example", "EXON", package = "RGMQL")
# Import the GMQL dataset EXON as GRangesList
imported_data <- import_gmql(dataset_path, is_gtf = FALSE)
imported_data
# and its metadata
metadata(imported_data)
```
The second parameter *is_gtf* must specify the file format: .GTF or .GDM.
We can export a GRangesList as GMQL dataset as follows:
```{r, export}
# This statement defines the path to the subdirectory "example" of the
# package "RGMQL"
dir_out <- system.file("example", package = "RGMQL")
# Export the GRangesList 'data' as GMQL dataset called 'example' at destination
# path
export_gmql(imported_data, dir_out, is_gtf = TRUE)
```
The second parameter *is_gtf* must specify the file format: .GTF or .GDM.
## Filter and Extract
We can also import only a part of a GMQL dataset into R environment,
by filtering its content as follows:
```{r, filter_extract}
# This statement defines the path to the folder "TEAD" in the subdirectory
# "example" of the package "RGMQL"
data_in <- system.file("example", "TEAD", package = "RGMQL")
matrix <- filter_and_extract(data_in, metadata = NULL, region_attributes = c("count"))
matrix
```
*filter_and_extract()* filters the samples in the input dataset based on their
specified *metadata*, and then extracts as metadata columns of GRanges the
vector of region attributes you specify to retrieve from each filtered sample
from the input dataset.
If the $metadata$ argument is NULL, all samples are taken.
The number of obtained columns is equal to the number of samples left after
filtering, multiplied by the number of specified region attributes.
If *region_attributes* is not specified, only the fundamental elements of
GRanges are extracted, i.e., the genomic coordinates.
Note that this function works only if every sample in dataset includes the
same number of regions with the same coordinates.
Each metadata column is named using *region_attributes* concatenated with
the function input parameter *suffix*.
By default *suffix* correspond to a metadata: *antibody_target*.
## Metadata
Each sample of a GMQL dataset has its own metadata associated and generally
every metadata attribute has a single value. The case with distinct values
for the same metadata attribute is shown in the figure below for the
\emph{disease} metadata attribute.
\newline\newline
\newline\newline
In this case GMQL automatically handles this situation. In the Import/export
paragraph, we showed that a GMQL dataset can be imported into R environment
as a GRangesList, and so its metadata too.
```{r, metadata}
# This statement defines the path to the folder "DATASET_META" in the
# subdirectory "example" of the package "RGMQL"
dataset_path <- system.file("example", "DATASET_META", package = "RGMQL")
# Import the GMQL dataset DATASET_META as GRangesList
grl_data <- import_gmql(dataset_path, is_gtf = FALSE)
grl_data
# and its metadata
metadata(grl_data)
```
The metadata are stored as simple list in the form key-values and it does not
matter if mutiple values for the same metadata attribute are present;
all values are stored and shown.
Difficulties can arise when we need to get all the metadata values; normally,
since the metadata list is in the form key-value, we can extract the metadata
values using:
```{r, retrieve_value}
# store metadata on variable a
a = metadata(grl_data)
# get disease value of sample S_00000
a$S_00000$disease
```
Yet, in this case only the first disease value is shown. If we want to retrieve
all disease values, we should use instead:
```{r, retrieve_values}
# get all disease values of sample S_00000
a$S_00000[which(names(a$S_00000) %in% "disease")]
```
# Windows OS Issues
Be aware that during a [local processing](#local-processing) execution an error message
```{
Error in .jcall("RJavaTools", "Ljava/lang/Object;", "invokeMethod", cl,
: org.apache.spark.SparkException: Job aborted due to stage failure:
Task 0 in stage 7.0 failed 1 times, most recent failure:
Lost task 0.0 in stage 7.0 (TID 59, localhost, executor driver):
java.io.IOException: (null) entry in command string: null chmod 0644
```
may arise.
This happens because some Hadoop binary files are missing in Windows 64Bits.
In this case we need to:
* Go to [https://github.com/steveloughran/winutils](https://github.com/steveloughran/winutils), download the repository
* Create a directory (for example at ```C:\Program Files\hadoop\bin```. Use a path you wish
* Copy the files from the repository folder **hadoop-2.8.1** into the folder earlier created.
* Create environment variable HADOOP_HOME with value equal to the folder path you copied the binaries.
# References