plsdo 0.1.1

PLS covariance analysis with statistical testing and visualisation

plsdo

Partial Least Squares (PLS) covariance analysis with permutation testing, bootstrap reliability, and publication-ready visualisation — from the command line.

(Pronounced: "please do")

plsdo was built out of necessity for project-specific neuroscience and neuroimaging pipelines, then generalised to handle flexible, diverse datasets beyond its origins. It implements two PLS variants used in neuroimaging and cognitive neuroscience research:

• Correlational PLS — finds latent variables that maximise covariance between two continuous data matrices (e.g. brain measures and behaviour scores).
• Discriminatory PLS — finds latent variables that maximise covariance between a continuous data matrix and a dummy-coded group matrix (i.e. group differences).

Statistical validity is built in: every analysis runs a permutation test on singular values and bootstraps loading stability. Only latent variables that pass both tests appear in the output.

Early alpha. The API and output format may change before the first stable release. Feedback and bug reports are very welcome — please open an issue.

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Installation

Requires Python ≥ 3.10.

uv pip install plsdo

For discriminatory PLS with cross-validation (requires scikit-learn):

uv pip install "plsdo[cv]"

For development:

git clone https://github.com/braincentrekcl/plsdo.git
cd plsdo
uv venv .venv && source .venv/bin/activate
uv pip install -e ".[dev]"

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Quick start

Correlational PLS

plsdo run --method c \
  --x brain_measures.csv \
  --y behaviour_scores.csv \
  --demographics participants.csv \
  --group-col treatment \
  --subject-id participant_id \
  --output results/

Discriminatory PLS

plsdo run --method d \
  --y mri_features.csv \
  --demographics participants.csv \
  --group-col drug_group \
  --subject-id participant_id \
  --output results/

Cross-validation (discriminatory only)

Requires plsdo[cv] — see Installation above.

plsdo cross-validate \
  --y mri_features.csv \
  --demographics participants.csv \
  --group-col drug_group \
  --subject-id participant_id \
  --output cv_results/

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Output

Each run writes to the output directory:

results/
  figures/     cross-correlation heatmap, permutation test, loading bar plots, score plots
  data/        singular values, p-values, loadings, bootstrap ratios, subject scores (CSV)
  log.txt      parameters and version stamp

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Documentation

Page	Contents
Usage guide	Full CLI options, multiple grouping variables, all flags
Input format	How to structure X, Y, demographics, and metadata files
Interpreting output	What each plot and CSV means
Statistical methods	Design matrix encoding, p-value correction, LV filtering, bootstrap alignment
Missing data	Why plsdo does not impute, and what to do instead

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Contributing

Issues and pull requests are welcome. Please open an issue before starting significant work.

Contact: eilidh [dot] macnicol [at] kcl [dot] ac [dot] uk

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Citation

If you use plsdo in your research, please cite it. GitHub will show a "Cite this repository" prompt from the CITATION.cff file, or you can use the BibTeX entry in docs/references.bib.

Licence

BSD 3-Clause. See LICENSE.