RejuvenationKit

RejuvenationKit is a typed Python toolkit for auditing and analyzing longitudinal preclinical rejuvenation studies. It sits above established assay-specific pipelines and helps research teams answer:

Is this study trustworthy, did the intervention produce a coherent response, when did it appear, and which biological systems drove it?

Phase 1 is complete and available as an alpha. It includes protocol-aware quality control, analysis-readiness profiling, experimental-confounding checks, held-out DSP change detection, sequential response monitoring, randomized longitudinal inference, and reproducible report bundles.

This project is for research use. It is not medical software and does not produce treatment recommendations.

Who this is for

RejuvenationKit is designed for:

• preclinical gene-therapy and longevity teams comparing constructs, doses, or vector lots;
• canine-aging and veterinary-trial researchers with repeated visits and heterogeneous endpoints;
• computational biologists who need a decision layer above RNA-seq, methylation, proteomics, histology, imaging, or clinical-assay pipelines; and
• collaborators reviewing whether a study is ready to support an efficacy claim.

It is especially useful when an experiment combines several noisy readouts and risks confusing biology with site, plate, assay run, operator, manufacturing lot, or visit timing.

What it catches

• Missing visits and missing features at expected visits
• Treatment, cohort, or timepoint confounding with experimental handling
• Batch shifts, replicate disagreement, distribution anomalies, and attrition bias
• Weak paired-analysis sample sizes hidden by apparently large enrollment
• Multichannel responses that emerge gradually or persist across visits
• Individual responders and dominant evidence modalities
• Randomized treatment effects calibrated without fitting the null model on treated subjects

Public canine validation

The one-command audit was run end to end on public Dog Aging Project longitudinal chemistry data:

Result	Observed
Dogs	972
Long-form observations	6,808
Complete-case retention to the second wave	75.5%
Held-out complete trajectories scored	213
Held-out trajectories crossing the nominal 5% threshold	11

The cohort is observational and contains no rapamycin assignment, so detections are not treatment effects. The case demonstrates real ingestion, missingness, retention, held-out calibration, reporting, and artifact integrity. See the DAP audit case study.

Evaluate it with a study

The most valuable feedback is a de-identified, simulated, or public dataset shaped like a real preclinical workflow. Open a study-evaluation request with the decision, visit schedule, modalities, and known complications. Do not attach confidential or identifiable data to a public issue.

Architecture

Study / subject / observation schemas
                 │
        ┌────────┴────────┐
        │ Phase 1: QC     │  longitudinal integrity, drift, outliers
        └────────┬────────┘
                 │
        ┌────────┴────────┐
        │ Phase 2: Fusion │  multimodal estimates + uncertainty
        └────────┬────────┘
                 │
        ┌────────┴────────┐
        │ Phase 3: State  │  latent biological-state tracking
        └────────┬────────┘
                 │
        ┌────────┴──────────────┐
        │ Phase 4: Full SDK     │  workflows, combinations, reporting
        └───────────────────────┘

The core schemas are assay-neutral. An observation identifies a subject, time point, modality, feature, value, unit, and optional uncertainty. Algorithms consume validated Study objects and return typed result objects rather than unstructured tables.

Roadmap

• Phase 1 — aging-qc (baseline implemented): subject- and visit-level missingness, absolute and subject-relative visit windows, input ordering, range and unit checks, batch mean shifts, treatment/batch/site/plate/lot/timepoint confounding, replicate consistency, visit coverage, longitudinal retention, paired-analysis readiness, robust outliers, attrition-bias diagnostics, and covariance-aware multivariate and sequential change detection, leakage-safe control calibration, and randomized longitudinal treatment-effect inference.
• Phase 2 — aging-fusion: fuse clocks, omics, pathology, imaging, and clinical biomarkers while preserving modality-level uncertainty and disagreement.
• Phase 3 — aging-state: longitudinal latent-state estimation, smoothing, change-point detection, and forecast validation.
• Phase 4 — full SDK: stable workflows, combination-therapy interaction analysis, reporting, adapters, documentation, and public benchmark datasets.

Milestones and acceptance criteria live in docs/roadmap.md.

Quick start

python -m venv .venv
source .venv/bin/activate
python -m pip install "rejuvenationkit[visualization]"

Until 0.2.0a2 is published to PyPI, install the current source release from GitHub as described in the publishing guide.

from datetime import datetime, timezone

from rejuvenationkit.schemas import Modality, Observation, Study, Subject

study = Study(
    study_id="demo",
    subjects=[Subject(subject_id="mouse-001", cohort="treated")],
    observations=[
        Observation(
            subject_id="mouse-001",
            timestamp=datetime.now(timezone.utc),
            modality=Modality.CLINICAL,
            feature="body_mass",
            value=31.2,
            unit="g",
        )
    ],
)

Run run_phase1_audit(...) with a QCConfig and output directory to create the JSON, CSV, Markdown, manifest, and visualization bundle. The complete workflow is documented in the study-audit guide.

For development:

python -m pip install -e ".[dev,docs,visualization]"
pre-commit install
pytest

See examples/minimal_study.py and examples/phase_1_qc.py, with sample data in examples/data/longitudinal_observations.csv. For a complete report bundle, call run_phase1_audit(...) or run examples/public_dog_phase1_audit.py. The audit writes its configuration, input fingerprint, QC findings, readiness tables, summary, and overview figure in one operation. Optional plans add held-out multivariate detection or randomized treatment inference without changing the underlying study.

The examples/rapamycin_phase_1_qc.py example demonstrates staggered dosing anchors and balanced treatment batches. The examples/public_gse131754_rapamycin.py workflow downloads a real public mouse RNA-seq dataset, converts it into typed study data, runs QC, and produces explicitly exploratory rapamycin-versus-control expression contrasts. The examples/public_dog_aging_project.py workflow downloads real longitudinal blood chemistry measurements from pet dogs, checks expected-visit missingness, and summarizes paired changes. It has no treatment assignment and is not a rapamycin-effectiveness analysis. The examples/public_dog_multimodal.py workflow combines aligned clinical chemistry and metabolomics, then applies held-out covariance-aware change detection across both modalities. Install .[public-data] to read the source R-data object and .[visualization] to export covariance, detection-score, whitening, and decomposition figures. The examples/gene_therapy_confounding.py workflow demonstrates how a superficially complete canine gene-therapy study can still be unusable because site, vector lot, and visit-specific plates overlap the biological contrasts. The examples/randomized_rapamycin_effect.py workflow demonstrates out-of-fold control calibration, covariance-aware randomization testing, and feature-level longitudinal effect intervals in a synthetic 60-dog trial. The examples/public_dog_sequential.py workflow learns reference dynamics over three Precision waves, reports onset and persistence of unusual held-out trajectories, and exports sequential evidence and modality plots. It is an observational monitoring demonstration, not a treatment-effect analysis. The examples/triad_like_rapamycin_sequential.py workflow generates a clearly labeled synthetic 580-dog, seven-visit trial shaped like the public TRIAD protocol. It demonstrates responder onset, persistence, transient effects, and modality-localized evidence; it contains no DAP treatment outcomes.

Contribution workflow

1. Open an issue describing the scientific decision and expected validation.
2. Create a focused branch and add tests before or with the implementation.
3. Run ruff check ., ruff format --check ., mypy, and pytest.
4. Open a pull request using the template and document assumptions, validation data, and limits.

New algorithms should expose uncertainty, accept deterministic random seeds when applicable, and include a synthetic or public-data validation case. See CONTRIBUTING.md.

Scope boundaries

RejuvenationKit will not replace base calling, alignment, differential-expression software, clinical judgment, or regulatory validation. It is designed as a transparent decision layer over curated assay outputs. No therapy recommendations are produced.

License

Apache License 2.0. See LICENSE.

Citation

If RejuvenationKit contributes to research, cite the archived software release used in the analysis. Citation metadata are available in CITATION.cff; a DOI will be added after Zenodo archives the release.