Keep calculation choices visible
Mouse, rat and custom filament sets expose the reference and calibration choices behind a threshold.
Project case study
Von Frey analysis: from response sequences to interpretable results.
Explore Raquel Sandoval Ortega’s Python tool for von Frey thresholds, longitudinal and pre-post analysis, statistical comparisons and editable figures.
My roleDeveloper
AvailabilityOpen source
Built withPython · PyQt6 · pandas · matplotlib
A GUI and command-line workflow for calculating withdrawal thresholds and organising preclinical assay results into figures, data tables and statistical summaries.
- 01
Import
Connect measurements with animal metadata.
- 02
Calculate
Select the filament set and threshold options.
- 03
Compare
Inspect groups, timepoints and statistical choices.
- 04
Export
Save figures, tables and session configuration.
The problem
Make the workflow usable.
A withdrawal-threshold calculation is only one part of an assay workflow. Researchers also need to connect animal metadata, experimental groups and timepoints, make calculation choices explicit, and preserve the analysis configuration.
My contribution
Connect science and software.
I developed a Python analysis tool that connects threshold calculation with longitudinal and factorial pre-post workflows. The GUI supports inspection and figure preparation; the command-line interface supports batch threshold computation.
Design decisions
Built around the work researchers do.
Respect the experimental structure
Longitudinal and pre-post workflows organise repeated observations and group comparisons.
Make outputs reusable
Editable figures, Excel/CSV tables and saved sessions support reporting and later review.
Evidence
What is available to inspect.
The public repository provides documentation and example datasets for longitudinal and pre-post designs. Documented outputs include PDF, PNG and SVG figures, data/statistics tables and saved analysis sessions. Statistical options include repeated-measures analysis, mixed-effects models and multiple-comparison correction.
Scope & limitations
What to check before using it.
Results depend on the filament calibration, response sequence, endpoint policy and statistical design. The documentation explains legacy calibration differences and boundary observations. Review those choices before interpreting a new experiment; a software-generated result is not a substitute for a suitable analysis plan.
For hiring teams
Experience behind the project.
Scientific requirements, statistical workflow design, Python interfaces and transparent reporting.