QFeatures object

All datasets in scpdata are stored in a QFeatures object (see intro vignette). The object is created following the scp data framework, as described in this short demo.

Experiment description

We also require the collection of experimental data that describes the dataset. This information is commonly retrieved from the publication associated with the dataset and provides a scientific context to the dataset. This information is used for building the dataset documentation.

Data source information

Finally, the ExperimentHub project, on which scpdata relies, requires every dataset to thoroughly provide a description of the data sources.

inst/scripts/

The folder contains all R scripts used to generate the QFeatures objects from the source files, one script for each dataset. Each script is named as follows: make-data_ + DATASET_NAME + .R.

Note the file called make-metadata.R. It generates a CSV table required by ExperimentHub where each line corresponds to a dataset and the columns contains the data source information. The table is stored in inst/extdata/metadata.csv, which should never be changed manually.

R/

The folder contains 3 R scripts, but new contributions should only consider the data.R and can safely ignore the other two. The data.R script contains the documentation for each dataset, formatted using roxygen2 markup.

man/

The folder contains the compiled documentation manuals, one for each dataset. These were automatically generated by roxygen2 and should never be changed manually.

1. Collect data

If you want to contribute an already published dataset, identify the data sources for all feature data and the sample annotations. This is generally provided in the article, but you may need to request additional information from the authors.

If you want to contribute with your own dataset, make sure that all feature data and the sample annotation table are available from a public repository (eg PRIDE, MASSive or Zenodo).

2. Create the QFeatures object

Create a new R script, inst/scripts/make-data_DATASET_NAME.R, which contains all the code to convert the data source data into the QFeatures object. Here are some tips and tricks for generating a high-quality dataset:

• Sample annotations are often cluttered, and spread over different tables or contained within sample names. Generating high-quality sample annotations may be time-consuming and frustrating. Don’t overlook this task, sample annotations are essential for rigourous and accurate downstream analysis.
• Converting feature data tables and annotation tables into QFeatures or SingleCellExperiment objects can be streamlined using scp::readSCP() and scp::readSingleCellExperiment(), respectively.
• Always start with the lowest feature level (eg PSMs). If available, you should add peptide and protein data using QFeatures::addAssay(). You should then add links between the assays. This is streamlined using QFeatures::addAssayLink().
• Make sure to add data with as little processing as possible. For instance, MaxQuant provides peptide intensities, but also iBAQ and MaxLFQ normalised values. You should favour the former over the latter two, which you could add as supplementary assays (for example, see here).

3. Document the dataset

Add the data documentation and the data collection procedure in scpdata/R/data.R. Use roxygen2 markup language. The documentation is structured as follows, but you can best use the documentation of an existing dataset as a template:

• Title: First authors et al. Year (Journal): minimal description.
• Description: short description of the data set. What and how many cells were acquired? What technology? What is the research question?
• Format: describe your QFeatures object. Describe each assay, namely what level features it contains, the number of features and the number of cells/samples
• Data acquisition: summarise the data acquisition protocol, namely the sample isolation, sample preparation, liquid chromatography, mass spectrometry and raw data processing.
• Data collection: summarise the steps you undertook to generate the QFeatures object, and where to find the script you created.
• Source: link the public repository with the source data
• References: if published, refer to the original work that acquired the data.
• Example: add an example to show how to retrieve the dataset. To avoid the associated overhead when testing the package, we recommend adding the example as follows:

##' \donttest{
##' dataset_name()
##' }

• Keywords: add the line ##' @keywords datasets
• "dataset_name": end the documentation with the name of your dataset, ensuring your data set is correctly exported.

4. Update metadata

Add the data source information in the inst/script/make-metadata.R script and run the complete script that will update the inst/extdata/metadata.csv. You can use a previous dataset as template. All fields are mandatory: Title, Description, BiocVersion, Genome, SourceType, SourceUrl, SourceVersion, Species, TaxonomyId, Coordinate_1_based, DataProvider, Maintainer, RDataClass, DispatchClass, PublicationDate, NumberAssays, PreprocessingSoftware, LabelingProtocol, PsmsAvailable, PeptidesAvailable, ProteinsAvailable, ContainsSingleCells, Notes. See ?ExperimentHubData::makeExperimentHubMetadata for a comprehensive description of the fields.

Next, ensure that your updated metadata.csv file is valid by running ExperimentHubData::makeExperimentHubMetadata("scpdata").

5. Create a pull request

Push any change you made to GitHub and open a pull request to notify us of your contribution. The pull request should include all the commits related to the dataset you want to contribute. Provide in the description where we can retrieve your QFeatures object, e.g. through Zenodo.

6. Almost done!

Once your pull request is submitted, we will take over and will proceed to the following steps:

1. We will review your changes to ensure you comply with the above guidelines. We may eventually request changes.
2. We will contact the Bioconductor team ([email protected]) to upload your Rda to Microsoft Azure, if needed, and to update the metadata.csv on their server. See the help page for more information.
3. We will compile the documentation with roxygen2 and check the package is still valid. We may eventually request changes.
4. We will update the NEWS.md file and bump package version
5. If this is your first contribution, we will add your name to the package authors.