Here we
- Compile all the information we have about TI methods
- Characterise the methods with regards to user and developer friendliness (method quality control)
- Characterise the methods with regards to prior information, underlying algorithm, possible detectable trajectory types, …
| # | script/folder | description |
|---|---|---|
| 1 | 📁gather_methods_information |
Gathering all the information we have about the methods |
| 2 | 📁tool_qc |
Tool quality control |
| 3 | 📁method_characterisation |
Method characterisation |
📁varia |
The results of this experiment are available here.
Most information of the methods are contained within their respective containers (see the dynmethods repository, https://github.com/dynverse/dynmethods). We gather additional information from our google sheets (https://docs.google.com/spreadsheets/d/1Mug0yz8BebzWt8cmEW306ie645SBh_tDHwjVw4OFhlE), which also contains the quality control for each methods.
| # | script/folder | description |
|---|---|---|
| 1 | 📄group_methods_into_tools.R |
Grouping methods into tools |
| 2 | 📄process_quality_control.R |
Downloading and processing the quality control worksheet |
| 3 | 📄add_quality_control.R |
Add QC scores to methods and tools tibble |
Here we compare the user and developer friendliness of the different trajectory inference tools
| # | script/folder | description |
|---|---|---|
| 1 | 📄qc_aspects_table.R |
Generate a table containing the qc scoresheet |
| 2 | 📄qc_scores_overview.R |
Create an overview figure of the quality control |
Here we have a look at the diversity of TI methods
| # | script/folder | description |
|---|---|---|
| 1 | 📄tool_characterisation.R |
Several figures for looking at the history and diversity of TI methods/tools |
| 2 | 📄tools_table.R |
Generate a table containing the methods |