Hands-on sessions - omniclustering

Resources

Choose a module to add to the Omniclustering benchmark. Alternatively, you can also work on the Iris example from the step-by-step guide.

• Downloadable single-cell datasets

• List of wrappers of methods that perform single-cell clustering

Projects assignments

To avoid overlaps, please specify which projects you are working on:

https://docs.google.com/spreadsheets/d/1nnPSALl6up0-yjXiWLPe0R7gSUyveozUQdVmZLu2fno/edit?usp=sharing

Project creation

See the Create a module - Clustering Omnibenchmark links to prefilled projects.

Warning

Please use an explicit name for easier recognition and also to avoid names overlap. E.g. method_[method name]_clustering.

Project set up

See the dedicated guides for Data modules, Methods modules or Metric modules.

Project population

You can find information about the required metadata (keywords, inputs, outputs, etc) in two ways:

1. Copy the structure of existing modules

Warning

Please do not fork those to avoid transferring unintended metadata from the original project; rather, copy the module structure manually

• Example Dataset

• Example Method

• Example Metric

2. Use the omniValidator inside your session

The requirements for a given omnibenchmark and keyword can be displayed by running the following lines:

import omniValidator as ov
ov.display_requirements(
    benchmark='BENCHMARK_NAME', 
    keyword='STEP_KEYWORD')

where BENCHMARK_NAME is the name of the benchmark (omniclustering) and STEP_KEYWORD is the keyword associated to the type of module you are working on (omniclustering_dataset, omniclustering_method or omniclustering_metric).

Info

omniValidator is ported with all projects and doesn't need to be installed.

Output / Omnibenchmark status

An overview of the Omnibenchmark components is available on the Omnibenchmark webpage

The output of the benchmark is available on our shiny app server.