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0-generate_data.R
1-submit_jobs.R
2-retrieve_results.R
3-generate_figures.R
3a-summary_figure.R
3b-individual_example.R
3c-individual_overview.R
3d-error_logs.R
README.Rmd
README.md
compare_model_object_size.R
generate_dataset.R

README.md

Scalability of methods

Here we test how well a method scales with increasing number of features (genes) and/or cells.

Each method is run on down- and upscaled datasets with increasing gene set and cell set sizes, and the execution times and memory usages are modelled using thin plate regressions splines within a generalised additivate model.

# script/folder description
0 πŸ“„generate_data.R Generate up- and downscaled datasets
1 πŸ“„submit_jobs.R Run the methods on the cluster
2 πŸ“„retrieve_results.R Retrieve the results and generate the scalability models
3 πŸ“„generate_figures.R Classify the models and generate scalability figures
3a πŸ“„summary_figure.R
3b πŸ“„individual_example.R
3c πŸ“„individual_overview.R
3d πŸ“„error_logs.R
πŸ“„compare_model_object_size.R
πŸ“„generate_dataset.R Helper to generate an up- and downscaled dataset which looks similar to the original datasets

The results of this experiment are available here.

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