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 | 📄generate_data.R |
Generate up- and downscaled datasets |
| 1 | 📄submit_jobs.R |
Run the methods on the cluster |
| 2 | 📄retrieve_results.R |
Retrieve the results and generate the scalability models |
| 3 | 📄generate_figures.R |
Classify the models and generate scalability figures |
| 3a | 📄summary_figure.R |
|
| 3b | 📄individual_example.R |
|
| 3c | 📄individual_overview.R |
|
| 3d | 📄error_logs.R |
|
📄compare_model_object_size.R |
||
📄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.