bioLeak: Leakage-Safe Modeling and Auditing for Genomic and Clinical Data

bioLeak is an R package for detecting, quantifying, and diagnosing data leakage in biomedical machine-learning workflows. It focuses on leakage introduced by preprocessing, dependent samples, and resampling violations in cross-validation and related evaluation settings.

Purpose and scope

In scope:

• Preprocessing leakage: global imputation, scaling, filtering, or feature selection applied before resampling.
• Dependence leakage: repeated measures, subject-level grouping, batch/site/study effects, and near-duplicate samples.
• Resampling violations: group overlap, study holdout, and time-ordered evaluation.
• Diagnostic evidence: permutation-based performance gaps, batch/fold association tests, target leakage scans, and duplicate detection.

Out of scope:

• Proving the absence of leakage or guaranteeing unbiased performance.
• Production deployment tooling.
• Unsupervised learning (not currently supported).
• Calibration assessment and general data-quality diagnostics.

Why bioLeak is needed

Standard cross-validation assumes independent samples and exchangeable labels. Biomedical datasets often violate these assumptions due to repeated measures, site effects, batch structure, and temporal dependence. These violations can inflate performance metrics even when a model does not generalize. bioLeak enforces leakage-aware resampling and provides post-hoc diagnostics that estimate how much apparent performance could be driven by leakage or confounding.

Core functionality

• Leakage-aware splitting (make_split_plan): subject-grouped, batch-blocked, study leave-out, and time-series splits with reproducible metadata.
• Guarded preprocessing and fitting (fit_resample): train-only imputation, normalization, filtering, and feature selection; excludes split-defining columns from predictors; supports parsnip specs and built-in learners; returns per-fold metrics and predictions for instability checks.
• Multiclass and survival modeling support in fit_resample with task-appropriate metrics (accuracy/macro-F1/log-loss; C-index).
• Leak-safe hyperparameter tuning (tune_resample): nested CV with tidymodels tune/dials using leakage-aware splits.
• Guarded vs leaky comparisons: run the same model with an intentionally leaky comparator (for example, global preprocessing or leaky features) to estimate performance inflation risk.
• Leakage diagnostics (audit_leakage): permutation gap for signal vs permuted labels, batch/study association tests, target leakage scan on X_ref, and near-duplicate detection.
• Diagnostics polish: calibration checks (calibration_summary, plot_calibration) and confounder sensitivity (confounder_sensitivity, plot_confounder_sensitivity).
• Reporting (audit_report): HTML summary of audit results for sharing and review.

Installation

Requires R >= 4.3.

From CRAN:

install.packages("bioLeak")

Development version from GitHub:

install.packages("remotes")
remotes::install_github("selcukorkmaz/bioLeak")

Non-obvious dependencies:

• SummarizedExperiment and BiocGenerics are Bioconductor packages (installed automatically by remotes, but can be installed manually with BiocManager::install() if needed).
• Optional packages enable specific features: glmnet, ranger, pROC, PRROC, survival, future.apply, RANN, rmarkdown, tune, dials.

Minimal working example

library(bioLeak)

set.seed(1)
n_subject <- 40
rep_per_subject <- 3
n <- n_subject * rep_per_subject

subject <- rep(seq_len(n_subject), each = rep_per_subject)
batch <- rep(seq_len(6), length.out = n)

# Subject-level latent risk creates dependence across repeated measures
subj_risk <- rnorm(n_subject, sd = 1)
x1 <- subj_risk[subject] + rnorm(n, sd = 0.5)
x2 <- rnorm(n)
x3 <- rnorm(n)
p_subj <- stats::plogis(1.5 * subj_risk)
outcome <- factor(ifelse(runif(n) < p_subj[subject], "case", "control"),
                  levels = c("control", "case"))

df <- data.frame(subject, batch, outcome, x1, x2, x3)

# Leakage-aware splits (subjects do not cross folds)
splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "subject_grouped",
  group = "subject",
  v = 5,
  stratify = TRUE,
  seed = 1
)

# Guarded pipeline (train-only preprocessing)
spec <- parsnip::logistic_reg(mode = "classification") |>
  parsnip::set_engine("glm")

fit_guarded <- fit_resample(
  df,
  outcome = "outcome",
  splits = splits,
  learner = spec,
  metrics = "auc",
  preprocess = list(
    impute = list(method = "median"),
    normalize = list(method = "zscore"),
    filter = list(var_thresh = 0),
    fs = list(method = "none")
  ),
  refit = FALSE,
  seed = 1
)

# Leaky comparator: add a leakage feature computed on the full dataset
df_leaky <- within(df, {
  leak_subject <- ave(as.numeric(outcome == "case"), subject, FUN = mean)
})

fit_leaky <- fit_resample(
  df_leaky,
  outcome = "outcome",
  splits = splits,
  learner = spec,
  metrics = "auc",
  preprocess = list(
    impute = list(method = "none"),
    normalize = list(method = "none"),
    filter = list(var_thresh = 0),
    fs = list(method = "none")
  ),
  refit = FALSE,
  seed = 1
)

# Use unstratified permutations here to avoid warnings in small grouped data
audit_guarded <- audit_leakage(
  fit_guarded,
  metric = "auc",
  B = 30,
  perm_stratify = FALSE,
  X_ref = df[, c("x1", "x2", "x3")]
)

audit_leaky <- audit_leakage(
  fit_leaky,
  metric = "auc",
  B = 30,
  perm_stratify = FALSE,
  X_ref = df_leaky[, c("x1", "x2", "x3", "leak_subject")]
)

summary(fit_guarded)
summary(fit_leaky)
summary(audit_guarded)
summary(audit_leaky)

Interpretation notes:

• If the leaky comparator shows higher AUC and leak_subject ranks near the top of the target leakage scan, the performance gap is likely inflated by leakage.
• Similar guarded and leaky results do not prove the absence of leakage; they only reduce specific risks tested by the audit.

Tidymodels interoperability

bioLeak supports several tidymodels abstractions for resampling, preprocessing, and metrics:

• fit_resample() accepts rsample rset/rsplit objects as splits.
• as_rsample() converts LeakSplits to an rsample rset.
• preprocess can be a recipes::recipe (prepped on training folds, baked on test folds).
• learner can be a workflows::workflow.
• metrics accepts yardstick::metric_set objects. Note: When using recipes/workflows, the built-in guarded preprocessing list is not applied; ensure your recipe is leakage-safe.

Example (rsample + recipes + yardstick):

if (requireNamespace("rsample", quietly = TRUE) &&
    requireNamespace("recipes", quietly = TRUE) &&
    requireNamespace("yardstick", quietly = TRUE)) {
  rs <- rsample::vfold_cv(df, v = 5)
  rec <- recipes::recipe(outcome ~ ., data = df) |>
    recipes::step_normalize(recipes::all_numeric_predictors())
  ys <- yardstick::metric_set(yardstick::roc_auc, yardstick::accuracy)

  fit_rs <- fit_resample(
    df,
    outcome = "outcome",
    splits = rs,
    learner = spec,
    preprocess = rec,
    metrics = ys,
    refit = FALSE
  )
}

Methodological assumptions

• The split mode matches the true dependence structure (subject, batch, study, or time).
• Leakage is inferred from performance gaps and diagnostic signals, not proven or ruled out.
• Permutation gaps assume the chosen resampling scheme reflects the intended evaluation setting.
• By default, permutation gaps use refit-based permutations when refit data are available (auto mode), falling back to fixed-prediction shuffles when they are not.
• Target leakage scans include univariate associations plus a multivariate/interaction check by default for supported tasks; proxies outside X_ref can still pass undetected.

Interpretation guidance

• Permutation gap: large positive gaps indicate non-random signal; they do not by themselves indicate or refute leakage.
• Batch/study association warnings indicate that folds align with metadata; this can reflect leakage or study design constraints.
• Target leakage flags identify features overly aligned with the outcome; inspect data provenance before removing them.
• Duplicate detection flags near-identical samples across train/test by default; use duplicate_scope = "all" to include within-fold duplicates and review for data-quality issues.

Common misinterpretations:

• "Non-significant permutation test means no leakage": false.
• "High AUC implies good generalization": false if resampling is violated.
• "No flagged features means no leakage": false; audits are limited to available metadata and X_ref.

Project status and intended audience

Status: experimental (version 0.1.0). APIs and defaults may change. Intended audience: biomedical ML researchers, biostatisticians, and methodologists reviewing cross-validation and leakage risk.

Citation and reproducibility

• Use citation("bioLeak") after installation or cite the GitHub repository with version and commit hash.
• Report split mode, grouping columns, random seeds, preprocessing steps, learner specification, metrics, and audit settings (B, target threshold, similarity method).
• Include both guarded and leaky comparator results when used.

License

MIT