Dataset Dependency Graphs for Leakage-Aware Evaluation.

splitGraph: Dataset Dependency Graphs for Leakage-Aware Evaluation

splitGraph is an R package for representing biomedical dataset structure as a typed dependency graph so that leakage-relevant relationships can be made explicit, validated, queried, and converted into deterministic split constraints.

It does not fit models, run preprocessing pipelines, or generate resamples by itself. Its job is to encode dataset structure before evaluation so that overlap, provenance, and time-ordering assumptions are inspectable instead of implicit.

The plot above shows six samples (blue) that share three subjects (orange), two batches (green), two timepoints (red), and two outcome classes (brown). A plain vfold_cv on this dataset would violate subject, batch, and time structure at the same time — and that is exactly what the graph is designed to make visible.

Why It Exists

In biomedical evaluation workflows, leakage often comes from dataset structure rather than obvious coding mistakes. Samples may share:

• the same subject
• the same batch
• the same study
• the same collection timepoint
• the same assay provenance
• the same derived feature set
• the same outcome definition

If those relationships are not modeled explicitly, a train/test split can look correct while still violating the intended scientific separation.

splitGraph makes those dependencies first-class objects.

What It Does (and Does Not Do)

Does:

• metadata ingestion with canonical ID normalization
• one-shot graph construction from canonical metadata via graph_from_metadata()
• typed node and edge constructors backed by igraph
• structural, semantic, and leakage-relevant validation, with a documented validation_overrides mechanism for explicit exceptions
• typed query and traversal helpers (with a safety cap on query_paths())
• projected sample-dependency detection
• split-constraint derivation for subject, batch, study, time, and composite modes
• translation of constraints into a stable, tool-agnostic split_spec
• split-spec preflight validation and leakage summary helpers
• JSON serialization for dependency_graph and split_spec so handoff objects are portable across sessions and languages (write_*() / read_*(), requires jsonlite)
• typed layered plot() method with per-type colors and a node-type legend
• print(), summary(), and as.data.frame() on all core S3 objects

Does not:

• fit models or run preprocessing pipelines
• generate resamples (rsample does that)
• implement leakage-aware training workflows
• provide a general-purpose graph analytics toolkit

The package is intentionally narrow: dataset dependency structure for leakage-aware evaluation design.

Installation

From GitHub:

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

To use the JSON serialization API, also install jsonlite:

install.packages("jsonlite")

Quick Start

The fastest path is graph_from_metadata(), which auto-detects canonical columns in a metadata frame and assembles a validated dependency_graph:

library(splitGraph)

meta <- data.frame(
  sample_id    = c("S1", "S2", "S3", "S4", "S5", "S6"),
  subject_id   = c("P1", "P1", "P2", "P2", "P3", "P3"),
  batch_id     = c("B1", "B2", "B1", "B2", "B1", "B2"),
  timepoint_id = c("T0", "T1", "T0", "T1", "T0", "T1"),
  time_index   = c(0, 1, 0, 1, 0, 1),
  outcome_id   = c("ctrl", "case", "ctrl", "case", "case", "ctrl")
)

g <- graph_from_metadata(meta, graph_name = "demo")
plot(g)

validation <- validate_graph(g)
subject_constraint <- derive_split_constraints(g, mode = "subject")
spec <- as_split_spec(subject_constraint, graph = g)
validate_split_spec(spec)
summarize_leakage_risks(g, constraint = subject_constraint, split_spec = spec)

# Persist the spec for a downstream consumer (R or non-R):
path <- tempfile(fileext = ".json")
write_split_spec(spec, path)
spec2 <- read_split_spec(path)

For full control over node labels, attribute columns, and the feature-set provenance edges, use create_nodes() / create_edges() / build_dependency_graph() directly. graph_from_metadata() auto-builds the six sample-rooted canonical edges, timepoint_precedes, and the appropriate outcome edge (sample_has_outcome by default, or subject_has_outcome when outcome_scope = "subject"). The featureset_generated_from_study and featureset_generated_from_batch edges always require the explicit constructor path.

Downstream Handoff

split_spec is the tool-agnostic handoff object produced by as_split_spec(). splitGraph does not know about any particular resampling package — downstream consumers are expected to provide their own adapters so that splitGraph stays neutral and has no runtime dependency on them.

The typical end-to-end flow is:

1. graph_from_metadata(meta) → typed dependency_graph
2. derive_split_constraints(g, mode = ...) → split_constraint
3. as_split_spec(constraint, graph = g) → split_spec
4. (optional) write_split_spec(spec, path) → JSON, for cross-session or cross-language handoff
5. adapter in the downstream package → native resamples

The sample_data frame carried by split_spec exposes exactly what an adapter needs: sample_id for joining against the observation frame, group_id for grouped resampling, batch_group / study_group for blocking, and order_rank for ordered evaluation. An adapter can be built on top of, for example, rsample::group_vfold_cv() (grouped CV keyed to group_id) or rsample::rolling_origin() (ordered evaluation keyed to order_rank).

For three small, self-contained adapter examples (a base-R LOGO adapter, plus illustrative rsample::group_vfold_cv() and rsample::rolling_origin() adapters), see the Adapter cookbook vignette:

vignette("adapter-cookbook", package = "splitGraph")

Core Concepts

Node types

• Sample, Subject, Batch, Study, Timepoint, Assay, FeatureSet, Outcome

Canonical edge types

• sample_belongs_to_subject
• sample_processed_in_batch
• sample_from_study
• sample_collected_at_timepoint
• sample_measured_by_assay
• sample_uses_featureset
• sample_has_outcome
• subject_has_outcome
• timepoint_precedes
• featureset_generated_from_study
• featureset_generated_from_batch

Main S3 objects

graph_node_set, graph_edge_set, dependency_graph, depgraph_validation_report, graph_query_result, split_constraint, split_spec, split_spec_validation, leakage_risk_summary.

Main Functions

Example Queries

query_node_type(g, "Subject")
query_edge_type(g, "sample_processed_in_batch")
query_neighbors(g, node_ids = "sample:S1", edge_types = "sample_belongs_to_subject")
detect_shared_dependencies(g, via = "Batch")
detect_dependency_components(g, via = c("Subject", "Batch"))

Example Split Designs

subject_constraint <- derive_split_constraints(g, mode = "subject")
batch_constraint   <- derive_split_constraints(g, mode = "batch")
study_constraint   <- derive_split_constraints(g, mode = "study")
time_constraint    <- derive_split_constraints(g, mode = "time")

strict_composite <- derive_split_constraints(
  g, mode = "composite", strategy = "strict",
  via = c("Subject", "Batch")
)

rule_based_composite <- derive_split_constraints(
  g, mode = "composite", strategy = "rule_based",
  priority = c("batch", "study", "subject", "time")
)

Serialization (JSON)

Both core handoff objects can be written to a stable, schema-versioned JSON format and read back, so a dependency_graph or split_spec is portable across R sessions and across language boundaries.

graph_path <- tempfile(fileext = ".json")
spec_path  <- tempfile(fileext = ".json")

write_dependency_graph(g, graph_path)
write_split_spec(spec, spec_path)

g2    <- read_dependency_graph(graph_path)
spec2 <- read_split_spec(spec_path)

The JSON schema is documented under ?write_dependency_graph and ?write_split_spec. Each file carries a schema_version field; reading a file written under a different schema version emits a warning but still loads. NA values in sample_data round-trip as JSON null. The jsonlite package (a Suggests dep) must be installed.

Plot Method

plot(g) renders a typed, layered layout with per-type node colors and an auto-generated node-type legend. Layers: Sample (top), peer dependencies (Subject / Batch / Study / Timepoint) in the middle band, Assay / FeatureSet next, Outcome (bottom).

plot(g)                              # typed layered layout (default)
plot(g, layout = "sugiyama")         # alternative hierarchical layout
plot(g, show_labels = FALSE)         # hide node labels on dense graphs
plot(g, legend = FALSE)              # suppress the legend
plot(g, legend_position = "bottomright")
plot(g, node_colors = c(Sample = "#000000"))  # override type colors

Citation

citation("splitGraph")

produces:

Korkmaz S (2026). splitGraph: Dataset Dependency Graphs for Leakage-Aware Evaluation. R package version 0.2.0. https://github.com/selcukorkmaz/splitGraph

License

MIT. See LICENSE.

Appendix: Design Guarantees

The package prefers explicit failure over silent guessing. In particular:

• unknown sample IDs, ambiguous direct assignments, and conflicting duplicate nodes or edges are rejected rather than silently resolved
• contradictory time-order metadata are rejected rather than reconciled arbitrarily
• validation truth is not changed by severity filters, and generated split specs are re-validated against the package's own preflight rules
• documented exceptions go through validation_overrides (e.g. allow_multi_subject_samples); the same override is honored by both validate_graph() and derive_split_constraints(mode = "subject")
• query_paths() defaults to a finite path-length cap so traversal cannot explode on dense graphs; pass max_length = Inf to opt out
• the on-disk JSON format is schema-versioned, and loading a file with a different schema_version warns rather than failing silently.