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01-gather_methods_information
02-tool_qc
03-method_characterisation
varia
README.Rmd
README.md

README.md

Trajectory inference methods

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 πŸ“gather_methods_information Gathering all the information we have about the methods
2 πŸ“tool_qc Tool quality control
3 πŸ“method_characterisation Method characterisation
πŸ“varia

The results of this experiment are available here.

Gathering all the information we have about the methods

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 πŸ“„group_methods_into_tools.R Grouping methods into tools
2 πŸ“„process_quality_control.R Downloading and processing the quality control worksheet
3 πŸ“„add_quality_control.R Add QC scores to methods and tools tibble

Tool quality control

Here we compare the user and developer friendliness of the different trajectory inference tools

# script/folder description
1 πŸ“„qc_aspects_table.R Generate a table containing the qc scoresheet
2 πŸ“„qc_scores_overview.R Create an overview figure of the quality control

Method characterisation

Here we have a look at the diversity of TI methods

# script/folder description
1 πŸ“„tool_characterisation.R Several figures for looking at the history and diversity of TI methods/tools
2 πŸ“„tools_table.R Generate a table containing the methods
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