ARStat v1.2.1

Live app: https://arstat-jm7varr6fck8uajj4lgs6t.streamlit.app/

ARStat is a Streamlit web application and scriptable Python backend for reproducible statistical analysis and visualization of anthelmintic dose-response assays.

ARStat supports four assay workflows:

• Egg hatch assay
• Larval development assay
• Motility assay
• Survival/mortality assay

The app starts from raw assay measurements or normalized XY replicate tables, calculates assay-specific response variables, fits four-parameter dose-response curves, estimates IC50 values, calculates fold-resistance versus a selected reference group, performs exploratory dose-level tests with multiple-testing correction, and exports analysis tables, plots, Excel workbooks, and methods text.

Hookworm-style demonstration data

This release includes synthetic demonstration datasets using Ancylostoma caninum labels:

Assay	Isolates	Drug	Raw endpoint
Egg hatch	WMD vs KGR	Thiabendazole	Eggs and L1 larvae
Larval development	WMD vs KGR	Ivermectin	Developed and undeveloped larvae
Motility	WMD vs KGR	Ivermectin	Continuous activity units
Survival/mortality	WMD vs KGR	Ivermectin	Alive and dead counts

The bundled datasets are synthetic demonstration datasets for testing the interface and workflow. They are not primary experimental measurements and should not be cited as biological results.

Quick start

Use the live app

No installation is required. Open the app directly in a browser:

https://arstat-jm7varr6fck8uajj4lgs6t.streamlit.app/

Run locally

conda create -n arstat python=3.11 -y
conda activate arstat
cd ARStat
pip install -r requirements.txt
streamlit run app.py

Then open the local URL shown by Streamlit, usually http://localhost:8501.

Supported input layouts

Raw assay measurements

Use assay-specific count or measurement columns. ARStat calculates the biological response before fitting the dose-response model.

For motility assays, the selected column may contain continuous activity measurements from WormLab or another tracking system, mean velocity, distance moved, activity counts, or a consistently defined manual motility score. Raw activity values are normalized within each fitted drug-by-group combination to the mean zero-dose control. Each fitted group must therefore contain a positive zero-dose control mean. ARStat then models motility inhibition while displaying relative motility as a descending curve when the traditional plot mode is selected.

Motility values may also be supplied already normalized as percentages (0–100) or fractions (0–1). The user specifies whether those values represent retained motility or motility inhibition.

Normalized XY replicate table

Use the wide XY layout commonly used by dose-response applications:

dose,replicate_1,replicate_2,replicate_3
0,100,100,100
0.5,92,89,91
2.5,78,82,80
5,48,43,46

The dose/X column and individual replicate/Y columns are required. Optional experimental-group and drug columns allow several strains, isolates, genetic backgrounds, populations, treatments, time points, or other groups to be analyzed in one file:

Group,Drug,Dose,Rep1,Rep2,Rep3
WMD,TBZ,0,100,99,101
WMD,TBZ,0.5,91,89,90
KGR,TBZ,0,100,100,99
KGR,TBZ,0.5,98,96,97

ARStat maps the selected experimental-group column to its backward-compatible internal strain field and fits one curve per drug-by-group combination. Values may be entered as percentages (0–100) or fractions (0–1). Values below 0% or above 100% are flagged but retained rather than clipped, because control normalization, background correction, or replicate variation can legitimately produce out-of-range observations. ARStat reshapes the table internally and calculates the mean, standard deviation, and sample size. Do not import precomputed means, medians, standard deviations, or sample sizes. CSV and XLSX files are supported.

Reproducibility and validation

Run the unit tests:

pip install pytest
pytest -q

Generate simulated benchmark datasets with known IC50 values:

python scripts/generate_simulated_benchmarks.py

Run all bundled example datasets through the full workflow:

python scripts/run_all_examples.py

Benchmark outputs are written to benchmarks/. Example validation outputs are written to validation_outputs/.

What ARStat outputs

• analyzed row-level data
• dose summaries
• fitted IC50 table
• bootstrap IC50 confidence intervals when enabled
• fold-resistance versus a selected reference group
• fold-resistance confidence intervals when available
• per-dose pairwise tests with raw, Benjamini-Hochberg, and Bonferroni p-values
• downloadable Excel workbook
• export-ready PNG plot
• methods text describing the selected workflow

Important statistical notes

• Zero-dose controls: log-scaled plots cannot display x=0, so ARStat shows zero-dose controls at a symbolic left-edge tick labelled 0. These observations remain included in calculations and summaries.
• IC50 interpretation: IC50 is the midpoint between the fitted lower and upper asymptotes of the four-parameter logistic model. If the fitted maximum response is below 100%, IC50 is not necessarily the dose giving 50% absolute response.
• Motility interpretation: the reported value is a motility-inhibition IC50 under the selected activity definition, exposure time, parasite stage, and normalization. It should not be interpreted automatically as a lethal concentration.
• Pairwise tests: pairwise dose-level p-values are exploratory. ARStat reports both raw and adjusted p-values.
• Count assays: Fisher exact tests pool replicate counts at each dose and do not model replicate-to-replicate overdispersion.
• Continuous assays: motility and normalized XY workflows use replicate-level Mann-Whitney U tests by default.

Required columns

For raw assay measurements, the minimum required fields are a strain/isolate or other group column, a dose column, and the assay response measurements. Drug, dose-unit, and replicate/well identifiers are optional interface mappings or metadata.

For normalized XY input, only a dose column and individual replicate response columns are required. Experimental-group and drug columns are optional.

Assay-specific raw columns:

• Egg hatch: eggs, L1
• Larval development: developed, undeveloped; conventional L3 and L1 headings are suggested as developed and undeveloped, respectively, but must be confirmed against the protocol
• Motility: one continuous activity or motility column, such as motility
• Survival/mortality: alive, dead

Repository structure

app.py                         Streamlit app
arstat_core.py                 Reusable analysis backend
sample_data/                   Synthetic hookworm-style examples
templates/                     Blank CSV templates
tests/                         Unit tests
scripts/                       Benchmark and validation scripts
benchmarks/                    Simulated benchmark outputs
docs/                          User, validation, and statistical documentation

License

MIT License.

Citation

Please cite the archived software release listed in CITATION.cff. If no DOI has been minted yet, cite the GitHub repository and release tag.