Reference

associations

• visualize$associations$forestPublication-style forest plot of the effect estimates in a PolyGeniusAssociation, laid out as names | table.left | forest | table.right.
• visualize$associations$heatmapDraws one cell per (row, column) combination of an association table, filled by color.by and starred by asterisk.by.
• visualize$associations$landscapeOne point per PRS-level association, grouped along the x-axis by group.by and placed at the -\log_{10} of the p-value column pval names.
• visualize$associations$survivalDraws the survival-curve artifacts carried on a summary-mode PolyGeniusAssociation from associate$regression.
• visualize$associations$variants.qqQuantile-quantile plot of a GWAS scan from associate$singleVariant: observed against expected -\log_{10}(p) under the null, with the 1:1 reference line and the genomic-inflation factor \lambda_{GC} annotated.

data

• visualize$data$embedding$samplesinternalScatters two dimensions of a stored embedding, one point per sample or per model.
• visualize$data$scores$distributionDraws one data$scores layer as a per-model distribution -- violin, density, histogram or boxplot -- optionally grouped and faceted by sample phenotypes.
• visualize$data$scores$distribution.heatmapReduces one score layer to a model-by-distribution heatmap: one row per model, one column per density grid point or histogram bin, on a score grid shared by every row so rows are comparable.
• visualize$data$scores$heatmapDraws one matrix from data$scores as a samples-by-models heatmap using ComplexHeatmap, or models-by-samples when flip.orientation = TRUE.
• visualize$data$similarity$samplesinternalDraws one stored pairwise matrix as a clustered heatmap, using ComplexHeatmap and circlize.

evaluate

• visualize$evaluate$associationForest plot of evaluate$association()'s per-model effect -- odds ratio for a binary outcome, beta for a continuous one -- faceted by outcome, with split.by strata dodged and colored within each model's row.
• visualize$evaluate$compareForest plot of evaluate$compare()'s per-model contrast against its reference model, faceted by outcome (each panel naming its reference).
• visualize$evaluate$incrementalForest plot of evaluate$incremental()'s deltas -- delta AUC, delta liability R-squared, delta R-squared -- one row per model, faceted by outcome and metric (columns) and stratum (rows).
• visualize$evaluate$performanceRenders evaluate$performance()'s curves from its own artifacts: a binary outcome draws ROC or precision-recall from the confusion counts, a continuous outcome draws binned score against outcome from...
• visualize$evaluate$profilePlots an evaluate$profile() result: one row per model, one column per metric, each point the stored estimate with its interval where one exists.
• visualize$evaluate$redundancyModel-by-model geom_tile of one evaluate$redundancy() method.
• visualize$evaluate$stratificationPlots evaluate$stratification()'s per-band contrast -- an odds ratio for a binary outcome, a mean difference for a continuous one -- by band, one point-and-interval series per model, faceted by outcome (columns) and...

execution

• visualize$execution$dashboardinternalRenders a self-contained HTML dashboard for one execution from its structured <exec_id>.jsonl event stream: run summary, a time-scrubber, the collapsed dynamic execution graph (type -> rule -> type), per-rule...
• visualize$execution$performanceinternalRenders the execution dashboard's performance charts as live ggplot2 objects from one execution's <exec_id>.jsonl event stream.

genome

• visualize$genome$attributioninternalRenders an attribution PolyGeniusGenomeSignal as a per-trait lane heatmap: one row per model, genome on x, each cell filled with the summed weight x single-variant effect that model attributes to the bin, on a...
• visualize$genome$concordanceinternalRenders a concordance PolyGeniusGenomeSignal as a signed genome track: one point per variant at the consensus S = value.num / value.den in [-1, 1], coloured on a diverging ramp and sized by n.models.
• visualize$genome$convergenceinternalRenders a convergence PolyGeniusGenomeSignal as a binned area track: per display bin, the summed |weight x outcome effect| the contributing models place there.
• visualize$genome$coverageCompanion coverage track to visualize$genome$effects: per genomic bin, the number of distinct models contributing a variant there.
• visualize$genome$cumulativeWeightinternalRenders a cumulative-weight PolyGeniusGenomeSignal as a binned area track: per display bin, the summed |beta| the model library places there.
• visualize$genome$effectsMiami-style genome track for exploring candidate pleiotropy across a model set.
• visualize$genome$lociA thin track of labelled rectangles, one per interval, for marking hotspots or candidate regions above the data tracks of a visualize$genome$stack.
• visualize$genome$manhattanManhattan-style genome track of per-variant association strength from associate$singleVariant.
• visualize$genome$overviewCompute the default genome signals for a model library and stack their tracks in one call: the applied shortcut for the compute-then-stack workflow, readable at any library size.
• visualize$genome$prsGenome track relating each model's variants to its PRS-level association with outcome.
• visualize$genome$reuseManhattan-style genome track of variant reuse: each point is a harmonized variant at its genomic position, its height the number of models carrying it, on a pseudo-log10 y-axis.
• visualize$genome$stackCompose genome tracks vertically onto one shared genome x-axis.

models

• visualize$models$reuseHistogram of how many models each harmonized variant appears in, one bar per integer count.
• visualize$models$sizesHistogram of the number of distinct variants per model across a PGS library, on a log10 x-axis.
• visualize$models$top.variantsBar chart of the variants that appear in the most models, most-reused first.
• visualize$models$uniquenessHistogram of the fraction of each model's variants that appear in no other model.

generate

• generate-pgs-catalog-iointernalRead and write PGS objects as PGS Catalog scoring files.
• GenerateModelsEngineinternalInternal engine that combines sources and algorithms into model specs.

visualize

• rowOrderReads back the row order a ComplexHeatmap clustering produced, for both a single Heatmap and a multi-panel HeatmapList.
• visualize-shared-paramsinternalDocumentation-only topic.

workspace

• ResourceStoreinternalResourceStore owns the cache layout for generated resources.

PGS Library

• as.PGSCrosses from the shared-backbone side back to the owned side, explicitly: no code path materializes a view implicitly, so a caller always knows when it is paying for a copy of a model's variant table.
• comparePGSLibrariesCompares two libraries' model identity -- the ordered position -> (name, build) map -- without touching variant data.

Data Classes

• loadPolyGeniusReads a .pgd file and rebuilds the object it holds through real constructors, never by readRDS()-ing a live R6 object.
• PolyGenius-backendssavePolyGenius() and loadPolyGenius() write and read one kind of file: a saveRDS() of a version-stamped plain payload, suffix .pgd, whose class field is what the load side dispatches on.
• savePolyGeniusWrites study to a file (suffix .pgd) that loadPolyGenius() can read back, on this machine or any other.

Results

• PolyGeniusProvenanceConstructs one operation-provenance record for attachment to a PolyGenius object: what was run, what the caller supplied, and what the operation resolved or produced.
• PolyGeniusSchemaConstructs the column and artifact contract for the result table produced by one analysis type.
• schema-evaluationinternalEvaluate has no PolyGeniusSchema object.

Statistics

Execution Engine

Utilities

• genome-chromosomesinternalOne place where chromosome codes are interpreted.
• liftoverConverts variant coordinates from one genome build to another.