Software
Tools we build and maintain for the community
CoDa Stereo
CoDa Stereo is an open-source Shiny application that brings the Aitchison geometry to the daily workflow of stereological and histomorphometric studies — fields where measurements are naturally compositional (proportions of tissue types, cell-line fractions, organelle volume densities) and therefore must be analysed with the appropriate log-ratio methods rather than off-the-shelf parametric tools.
The app implements the full PPDAC cycle (Problem → Plan → Data → Analysis → Conclusion) so that students and collaborators without a strong statistical background can move from a raw spreadsheet to a defensible permutational MANOVA with diagnostics, pairwise post-hoc tests, and publication-ready plots — all in the browser.
Features
| Module | Capabilities |
|---|---|
| Data upload | CSV and Excel (.xlsx/.xls) with sheet selector; smart parts selector that auto-excludes ID columns |
| Data inspection | Zero/NA heatmap (observed zero vs. structural zero vs. missing); naniar::vis_miss plot; counts per part |
| Zero handling | Three methods via zCompositions (CZM, multRepl, lrEM); NA handling via listwise deletion or geometric-mean imputation |
| Compositional analysis | Manual ternary plot (pure ggplot2, no ggtern dependency); amalgamation of D > 3 parts into 3 groups; clr-PCA biplot via FactoMineR + factoextra |
| Statistical modelling | Factorial permutational MANOVA via RRPP::lm.rrpp; pairwise post-hoc via RRPP::pairwise; residual diagnostics; formula preview before fitting |
| Downloads | Ternary and biplot as PDF; imputed data as CSV; ANOVA and pairwise tables as CSV |
Why CoDa Stereo
Stereological datasets are routinely analysed as if cell-fraction or tissue-fraction percentages were independent variables — they’re not. The unit-sum constraint induces spurious correlations and false significance in standard MANOVA. CoDa Stereo handles the geometry correctly throughout, without requiring users to write log-ratio transforms by hand.
Install and run
# Run locally
# install.packages("devtools")
devtools::install_github("diogoprov/CoDaStereo")
CoDaStereo::run_app()A deployment on Posit Connect Cloud (free tier) is also supported — see the README for step-by-step instructions.
Citation
CoDa Stereo was developed as supplementary material for:
Provete, D.B., Severgnini, M.R., Fernandes, C.E., Franco-Belussi, L. (2026). A practical guide to compositional data analysis in tissue stereology and blood cell profile. Histochemistry and Cell Biology (in review). Preprint on Research Square.
The companion paper covers the methodological rationale, simulations comparing CoDa with naïve approaches, and worked examples.
A CITATION.cff and BibTeX snippets are available on the repository.
Issues, feedback, and contributions
Bug reports, feature requests, and pull requests are very welcome on GitHub Issues. PRs should target the dev branch.