Package {vennDiagramLab}

Type:	Package
Title:	Headless Venn Diagram Analysis and Rendering
Version:	2.0.5
Description:	Headless companion to the 'Venn Diagram Lab' web tool (https://www.venndiagramlab.org/). Build, render, and statistically analyze Venn / 'UpSet' diagrams from 'CSV' / 'TSV' / 'GMT' / 'GMX' inputs. Provides the same 44 SVG models, intersection / 'Jaccard' / hypergeometric statistics, and PDF report layout as the web tool, with byte-equivalent 'TSV' exports (parity-tested against the published Python package). Integrates with 'ggplot2', 'tidygraph', and 'broom'.
License:	MIT + file LICENSE
Language:	en-US
URL:	https://zoliqua.github.io/Venn-Diagram-Lab/r/, https://github.com/ZoliQua/Venn-Diagram-Lab, https://www.venndiagramlab.org/
BugReports:	https://github.com/ZoliQua/Venn-Diagram-Lab/issues
Depends:	R (≥ 4.2)
Imports:	methods, stats, utils, jsonlite, xml2, ggplot2, ComplexUpset, ggraph, tidygraph, grDevices, rsvg, patchwork, gridExtra, BiocGenerics
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, rprojroot, pdftools, broom, tibble, dplyr, covr, BiocCheck, lintr, pkgdown
VignetteBuilder:	knitr
biocViews:	Visualization, GeneSetEnrichment, Software
Encoding:	UTF-8
Config/testthat/edition:	3
Config/roxygen2/version:	8.0.0
RoxygenNote:	8.0.0
NeedsCompilation:	no
Packaged:	2026-05-12 19:27:29 UTC; Zoli
Author:	Zoltán Dul [aut, cre], Márton Ölbei [aut], N. Shaun B. Thomas [aut], Azeddine Si Ammour [aut], Attila Csikász-Nagy [aut]
Maintainer:	Zoltán Dul <zoltan.dul@gmail.com>
Repository:	CRAN
Date/Publication:	2026-05-18 18:10:02 UTC

RegionData: one region of a Venn diagram

Description

Returned as elements of 'RegionResult@regions'. Bitmask convention: bit 'i' set means "in set with index 'i'" in 'dataset@set_names'.

Slots

bitmask

Region bitmask (1 to 2^n - 1).

label

Human-readable label like '"AB"' or '"ABC"'.

set_indices

Integer vector of 0-based set indices in this region.

set_names

Names of the sets in this region.

exclusive_items

Items present in exactly these sets.

inclusive_items

Items present in at least these sets.

RegionResult: result of analyze()

Description

Bundles the input dataset, chosen model, region map, set sizes, and a lazy ['StatisticsResult-class'] accessible via 'statistics(result)'.

Slots

dataset

The input ['VennDataset-class'].

model

Resolved model name (e.g. '"venn-4-set"' or '"proportional"').

regions

Named list keyed by 'as.character(bitmask)', each value a ['RegionData-class']. Only non-empty regions are stored (sparse for high set counts with few overlaps).

set_sizes

Named integer vector: set name -> inclusive size.

is_approximate

'TRUE' for the proportional 3-set layout where exact areas can't be achieved with circles.

StatisticsResult: container for pairwise statistical metric tables

Description

Returned by [compute_pairwise()] and (lazily) by 'statistics()' on a 'RegionResult'. Holds five tables:

Slots

jaccard

NxN named matrix of Jaccard indices.

dice

NxN named matrix of Sorensen-Dice coefficients.

overlap_coefficient

NxN named matrix of Szymkiewicz-Simpson overlap coefficients.

fold_enrichment

NxN named matrix of fold-enrichment values.

hypergeometric

Long-form data.frame (one row per set pair) with columns: set_a, set_b, intersection, expected, p_value, p_adjusted, significant, highly_significant.

VennDataset: in-memory representation of a Venn-diagram input

Description

Returned by the 'load_*()' family and consumed by [analyze()]. Holds the deduplicated set members, first-seen item ordering for byte-equivalent TSV output, and source metadata (path, format, optional hypergeometric universe size).

Slots

set_names

Ordered character vector of set identifiers (length 2-9).

items

Named list ('names(items) == set_names') of character vectors, each containing the deduplicated members of the corresponding set.

item_order

First-seen insertion order of all items across all sets, matching JS Set/Map semantics. Used by TSV writers (Phase 2) for byte-equivalent output to the web tool.

universe_size

Hypergeometric universe N (population size) from the source file, when known. Binary CSV/TSV loaders set this to the row count (matching the web tool's 'csv.rows.length'); other formats leave it 'NULL', signaling "compute as length(item_order)" downstream.

source_path

Original file path if loaded from disk; 'NULL' for in-memory datasets.

format

Source format: one of '"csv"', '"tsv"', '"gmt"', '"gmx"'.

Analyze a Venn diagram dataset

Description

Compute the Venn region map for a ['VennDataset-class'] and bind it to a model.

Usage

analyze(dataset, model = "auto")

Arguments

Value

A ['RegionResult-class'] with the per-region item membership, set sizes, and (lazily) 'statistics(result)'.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
result@model

ds <- load_sample("dataset_real_cancer_drivers_4")
result <- analyze(ds, model = "auto")
result@model

Augment method for RegionResult (broom-compatible)

Description

Returns one row per item in the dataset's universe, with boolean columns indicating set membership and a 'region_label' column naming the exact region (e.g. '"A"', '"AB"', '"ABC"') the item belongs to. Item ordering follows 'dataset@item_order' (first-seen across all sets, JS Set/Map semantics).

Usage

## S3 method for class 'RegionResult'
augment(x, ...)

Arguments

Details

Region labels use the package's positional letter convention (A-I), matching the labels in 'RegionResult@regions' and the bundled SVG models, regardless of the dataset's 'set_names'.

Value

A tibble (or data.frame fallback) with 'nrow(out) == length(x@dataset@item_order)' and columns: 'item' (character), one logical column per set (named after the set), 'region_label' (character).

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
if (requireNamespace("broom", quietly = TRUE)) broom::augment(result)

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
broom::augment(result)

Benjamini-Hochberg FDR adjustment

Description

Wraps 'stats::p.adjust(p, method = "BH")'. Returns adjusted p-values in the same order as the input. Empty input -> empty output.

Usage

bh_fdr(p_values)

Arguments

Value

Numeric vector of adjusted p-values, same length as input.

Examples

bh_fdr(c(0.001, 0.01, 0.05, 0.5))

Lens-shaped intersection area of two circles

Description

Mirrors 'src/utils/proportionalLayout.ts circleIntersectionArea' (web tool) and Python 'circle_intersection_area' byte-for-byte.

Usage

circle_intersection_area(r1, r2, d)

Arguments

Value

Numeric: 0 if circles are disjoint, pi * min(r1,r2)^2 if fully nested, else the lens-shaped intersection area.

Examples

circle_intersection_area(1, 1, 1)   # ~ 1.228
circle_intersection_area(1, 1, 3)   # 0 (disjoint)

Compute all 5 pairwise statistical tables

Description

Orchestrator that returns a ['StatisticsResult-class'] populated with Jaccard, Dice, Overlap Coefficient, Fold Enrichment (square NxN matrices) plus a long-form hypergeometric table with BH-FDR adjustment.

Usage

compute_pairwise(
  set_names,
  inclusive_sizes,
  pairwise_intersections,
  universe_size
)

Arguments

Value

A ['StatisticsResult-class'] object.

Examples

compute_pairwise(
    set_names = c("A", "B"),
    inclusive_sizes = c(A = 10L, B = 8L),
    pairwise_intersections = list("A|B" = 5L),
    universe_size = 100L
)

Sorensen-Dice coefficient

Description

Computes 2 * |A intersection B| / (|A| + |B|). Returns 0 if both sets are empty (matches web tool convention).

Usage

dice(size_a, size_b, intersection)

Arguments

Value

Numeric in [0, 1].

Examples

dice(10, 10, 5)

Effective hypergeometric universe size for a RegionResult

Description

Returns the universe N consistent with the web tool. Binary CSV/TSV datasets get 'dataset@universe_size' (= csv.rows.length, includes all-zero rows); aggregated/GMT/GMX datasets fall back to 'length(item_order)' (= |union of items|).

Usage

effective_universe(result)

## S4 method for signature 'RegionResult'
effective_universe(result)

Arguments

Value

Integer, the universe size N.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
effective_universe(result)

ds <- load_sample("dataset_real_cancer_drivers_4")
result <- analyze(ds)
effective_universe(result)   # 20000 for binary cancer drivers sample

Fold enrichment (observed / expected ratio)

Description

Computes (k * N) / (K * n). Returns 0.0 if any denominator is zero (matches web tool convention).

Usage

fold_enrichment(N, K, n, k)

Arguments

Value

Numeric (>= 0; can exceed 1 for over-representation).

Examples

fold_enrichment(20000, 138, 581, 126)

Generate an area-proportional SVG for a 2- or 3-set RegionResult

Description

Circle sizes and inter-circle distances are solved analytically (2-set, exact) or by triangulation (3-set, approximate) so that overlap areas match the requested intersection counts. The returned SVG matches the 44-model schema: ShapeA-I, NameA-I, Count_*, CountSUM_*, Bullet* elements are all present and addressable via xml2.

Usage

generate_proportional_svg(
  result,
  width = .PROP_DEFAULT_WIDTH,
  height = .PROP_DEFAULT_HEIGHT
)

Arguments

Value

A 'character' (length 1) with the raw SVG.

Examples

tmp <- tempfile(fileext = ".tsv")
writeLines(c("Gene\tSetA\tSetB",
             "GENE1\t1\t0",
             "GENE2\t1\t1",
             "GENE3\t0\t1"), tmp)
ds  <- load_tsv(tmp, binary = TRUE)
res <- analyze(ds, model = "proportional")
svg <- generate_proportional_svg(res)
nchar(svg) > 0

Embed a rendered Venn diagram as a ggplot2 layer

Description

Returns a list of ggplot2 layers that draw 'data' (a ['RegionResult-class']) as a rasterized Venn diagram on a unit-square coordinate system, ready to compose with other ggplot2 elements (titles, themes, additional annotations).

Usage

geom_venn(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  width_px = .GEOM_VENN_DEFAULT_WIDTH
)

Arguments

Details

This is a NEW capability – the Python package has no equivalent. It uses the same rasterization pipeline as [to_pdf_report()]: render the SVG via [render_venn_svg()], rasterize via 'rsvg::rsvg_nativeraster()', and wrap as a 'grid::rasterGrob()' inside 'ggplot2::annotation_custom()'.

Value

A list of ggplot2 layers: an 'annotation_custom' carrying the rasterized Venn, a 'geom_blank' establishing '[0, 1] x [0, 1]' limits, and a 'coord_fixed(ratio = 1)' so the diagram remains square. Note that 'coord_fixed' will override any coordinate system the user has already added; add 'geom_venn()' before other coord layers to avoid a warning.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
if (getRversion() >= "4.6") {
  p <- ggplot2::ggplot() + geom_venn(data = result) + ggplot2::theme_void()
  inherits(p, "ggplot")
}

if (getRversion() >= "4.6") {
  result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
  library(ggplot2)
  ggplot() +
      geom_venn(data = result) +
      theme_void() +
      ggtitle("Cancer driver overlap (4 sources)")
}

Glance method for RegionResult (broom-compatible)

Description

Returns a 1-row tibble summarizing the analysis: number of sets, number of non-empty regions, total unique items, hypergeometric universe size, resolved model name, whether the layout is approximate (proportional 3-set), and the count of statistically significant / highly significant pairs (FDR-adjusted q < 0.05 / < 0.001).

Usage

## S3 method for class 'RegionResult'
glance(x, ...)

Arguments

Value

A 1-row tibble (or data.frame fallback) with columns: 'n_sets', 'n_regions', 'n_items', 'universe_size', 'model', 'is_approximate', 'n_significant', 'n_highly_significant'.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
if (requireNamespace("broom", quietly = TRUE)) broom::glance(result)

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
broom::glance(result)

One-sided hypergeometric p-value (over-representation)

Description

Computes P(X >= k) where X ~ Hypergeometric(N, K, n). Returns 1.0 for invalid inputs so the metric is safe to feed into BH-FDR without filtering.

Usage

hypergeometric_p_value(N, K, n, k)

Arguments

Details

Maps to R's ‘phyper(k - 1, K, N - K, n, lower.tail = FALSE)'. Note that R’s phyper parameter convention differs from Python's scipy: R uses 'm' for success-in-population and 'n' for failure-in-population (= N - K), where Python uses 'N' for total population.

Value

Numeric in [0, 1].

Examples

hypergeometric_p_value(20000, 138, 581, 126)

Jaccard similarity index

Description

Computes |A intersection B| / |A union B|. Matches the web tool's convention of returning 0 when both sets are empty (NaN-safe).

Usage

jaccard(size_a, size_b, intersection)

Arguments

Value

Numeric in [0, 1].

Examples

jaccard(10, 10, 5)
jaccard(0, 0, 0)

List all bundled Venn diagram models

Description

Returns metadata for the 44 bundled SVG model templates plus the 'proportional' synthetic generator (added in Phase 3). Read from JSON region files in 'inst/extdata/models/json/'.

Usage

list_models()

Value

A 'data.frame' with columns 'name' (filename stem), 'set_count' (2-9), and 'display_name' (from the JSON 'name' field). Sorted by '(set_count, name)'.

Examples

head(list_models())

List bundled sample dataset names

Description

Returns the names of the 5 bundled sample datasets, sorted alphabetically. Use [load_sample()] to load one.

Usage

list_samples()

Value

Character vector of 5 sample identifiers.

Examples

list_samples()

Load a delimited file (CSV/TSV) into a ['VennDataset-class']

Description

Supports two layouts: * Binary mode (default): one row per item, with 0/1 columns marking membership in each set. The first 'prefix_cols' columns are item metadata; remaining columns are sets. * Aggregated mode ('binary = FALSE'): each column is a set, and cells contain item identifiers. Empty cells are ignored.

Usage

load_csv(path, binary = TRUE, delimiter = NULL, prefix_cols = 1L)

Arguments

Value

A ['VennDataset-class'].

Examples

tmp <- tempfile(fileext = ".csv")
writeLines(c("Gene,SetA,SetB", "G1,1,0", "G2,1,1", "G3,0,1"), tmp)
ds <- load_csv(tmp, binary = TRUE)
ds@set_names

Load a GMT (Gene Matrix Transposed) file into a ['VennDataset-class']

Description

Each line is one set: 'set_name<TAB>description<TAB>item1<TAB>item2<TAB>...'. Lines with fewer than 3 tab-separated columns or empty set names are skipped.

Usage

load_gmt(path)

Arguments

Value

A ['VennDataset-class'].

Examples

tmp <- tempfile(fileext = ".gmt")
writeLines(c("SetA\tdesc\tGENE1\tGENE2\tGENE3",
             "SetB\tdesc\tGENE2\tGENE3\tGENE4"), tmp)
ds <- load_gmt(tmp)
ds@set_names

Load a GMX file (transposed GMT) into a ['VennDataset-class']

Description

Row 0 = set names, row 1 = descriptions, rows 2+ = items column-aligned.

Usage

load_gmx(path)

Arguments

Value

A ['VennDataset-class'].

Examples

tmp <- tempfile(fileext = ".gmx")
writeLines(c("SetA\tSetB",
             "desc_A\tdesc_B",
             "GENE1\tGENE2",
             "GENE2\tGENE3"), tmp)
ds <- load_gmx(tmp)
length(ds@items)

Load a bundled sample dataset by name

Description

Sample datasets ship inside the package under 'inst/extdata/samples/' and cover biological (cancer drivers, MSigDB pathways) and mock (streaming platforms, gene sets) use cases. Use [list_samples()] to enumerate.

Usage

load_sample(name)

Arguments

Value

A ['VennDataset-class'] with the appropriate format and mode applied.

Examples

ds <- load_sample("dataset_mock_gene_sets")
length(ds@set_names)

ds <- load_sample("dataset_real_cancer_drivers_4")
analyze(ds)@model

Load a tab-separated file into a ['VennDataset-class']

Description

Equivalent to 'load_csv(path, binary = binary, delimiter = "\t", prefix_cols = prefix_cols)'.

Usage

load_tsv(path, binary = TRUE, prefix_cols = 1L)

Arguments

Value

A ['VennDataset-class'].

Examples

tmp <- tempfile(fileext = ".tsv")
writeLines(c("Gene\tSetA\tSetB", "G1\t1\t0", "G2\t1\t1", "G3\t0\t1"), tmp)
ds <- load_tsv(tmp, binary = TRUE)
ds@universe_size

Szymkiewicz-Simpson overlap coefficient

Description

Computes |A intersection B| / min(|A|, |B|). Useful when one set is much smaller than the other.

Usage

overlap_coefficient(size_a, size_b, intersection)

Arguments

Value

Numeric in [0, 1].

Examples

overlap_coefficient(10, 5, 3)

Render a force-directed network plot from a RegionResult

Description

Builds a ggraph plot where nodes are sets (sized by inclusive cardinality) and edges are pairwise overlaps (thickness proportional to the chosen metric; blue for FDR-significant edges below 'significance_threshold', grey otherwise). Layout uses the deterministic 'stress' algorithm from graphlayouts.

Usage

render_network(
  result,
  edge_metric = "intersection",
  seed = 42L,
  significance_threshold = 0.05,
  node_color_map = NULL
)

Arguments

Details

Idiomatic R port of Python 'render_network' – same parameter contract, but renders via ggraph + tidygraph instead of networkx + matplotlib.

Value

A 'ggplot' (ggraph subclass).

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
p <- render_network(result)
inherits(p, "ggplot")

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
p <- render_network(result, edge_metric = "jaccard")
ggplot2::ggsave(tempfile(fileext = ".png"), p, width = 7, height = 7)

Render an UpSet plot from a RegionResult

Description

Builds a ComplexUpset ggplot showing intersection sizes (top bars), set membership matrix (middle dot grid), and per-set sizes (left bars). Idiomatic R port of Python 'render_upset' – same parameter contract, but renders via ComplexUpset (ggplot2) instead of matplotlib (not a 1:1 port).

Usage

render_upset(
  result,
  max_columns = 20L,
  sort_by = c("size", "degree"),
  threshold = 0L,
  color_mode = c("depth", "heatmap", "custom"),
  colors = NULL
)

Arguments

Value

A 'ggplot' object (saveable via 'ggplot2::ggsave()').

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
if (getRversion() >= "4.6") {
  p <- render_upset(result)
  inherits(p, "ggplot")
}

if (getRversion() >= "4.6") {
  result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
  p <- render_upset(result, sort_by = "degree", color_mode = "heatmap")
  ggplot2::ggsave(tempfile(fileext = ".png"), p, width = 8, height = 5)
}

Render a RegionResult onto its model SVG and return the raw SVG string

Description

Loads the bundled SVG template for 'result@model' (or the explicit 'model' override), walks the DOM via xml2 to overwrite text content ('Name*', 'Count_*', 'CountSUM_*', 'Title') and inline 'fill:' colors ('Shape*', 'Shape*2' for Euler extras, 'Bullet*'), and serializes back to a string.

Usage

render_venn_svg(
  result,
  model = NULL,
  set_names = NULL,
  colors = NULL,
  title = NULL,
  show_names = TRUE,
  show_counts = TRUE
)

Arguments

Details

For 'model = "proportional"', delegates to [generate_proportional_svg()].

Mirrors Python 'render_venn_svg' byte-for-byte except for: (a) the return type is 'character' instead of an 'SvgImage' wrapper class; (b) xml2 may emit slightly different whitespace/attribute ordering than lxml. Functional content (text, fill colors, structure) is identical.

Value

A 'character' (length 1) with the raw SVG.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
svg <- render_venn_svg(result)
nchar(svg) > 0

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
svg <- render_venn_svg(result)
nchar(svg) > 0

Area-proportional 2-set circle layout

Description

Solves for two circles whose areas equal 'a_only + ab' and 'b_only + ab' and whose intersection area equals 'ab', by analytical bisection on the inter-center distance. Always exact (returns 'is_approximate = FALSE').

Usage

solve_2set(a_only, b_only, ab)

Arguments

Details

Mirrors Python 'solve_2set' byte-for-byte.

Value

A named list with elements:

circles

Length-2 list of 'list(cx, cy, r)' named lists.

error

Relative error of the achieved area fit (typically < 1e-4).

is_approximate

Always 'FALSE' for 2-set.

Examples

solve_2set(a_only = 30L, b_only = 30L, ab = 10L)

Area-proportional 3-set circle layout (Wilkinson 2012-style triangulation)

Description

Computes pairwise inter-center distances via bisection on the lens intersection area, then places circle C via barycentric triangulation against AB. Always sets 'is_approximate = TRUE' because perfect 3-circle area-proportional fits don't always exist mathematically.

Usage

solve_3set(regions)

Arguments

Details

Mirrors Python 'solve_3set' byte-for-byte (including the 'error = NaN' deliberate sentinel - 3-set fit error is not measured in v0.1).

Value

A named list with elements 'circles' (length 3), 'error' (always NaN in v0.1), 'is_approximate' (always TRUE).

Examples

solve_3set(list("1" = 100L, "2" = 80L, "3" = 30L,
                 "4" = 60L,  "5" = 20L, "6" = 15L, "7" = 5L))

Lazy pairwise statistics for a RegionResult

Description

Computes (and on subsequent calls re-computes) the ['StatisticsResult-class'] for the pairwise metric tables. R has no built-in 'cached_property' equivalent for S4 slots, so this is recomputed each call. Cache externally via 'stats <- statistics(result)' if you need to access it many times.

Usage

statistics(result)

## S4 method for signature 'RegionResult'
statistics(result)

Arguments

Value

A ['StatisticsResult-class'].

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
stats <- statistics(result)
stats@jaccard["A", "B"]

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
stats <- statistics(result)
stats@jaccard
stats@hypergeometric

Tidy method for RegionResult (broom-compatible)

Description

Returns a long-form table with one row per ordered set pair, combining the five pairwise statistical metrics (Jaccard, Dice, overlap coefficient, fold enrichment, hypergeometric p-value + BH-FDR-adjusted q-value). Pair ordering is '(set_a, set_b)' with 'set_a' appearing earlier in 'result@dataset@set_names'.

Usage

## S3 method for class 'RegionResult'
tidy(x, ...)

Arguments

Value

A tibble (or data.frame if 'tibble' is not installed) with columns 'set_a', 'set_b', 'intersection', 'expected', 'jaccard', 'dice', 'overlap_coefficient', 'fold_enrichment', 'p_value', 'p_adjusted', 'significant', 'highly_significant'. One row per unordered pair, so 'n*(n-1)/2' rows for an 'n'-set dataset.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
if (requireNamespace("broom", quietly = TRUE)) broom::tidy(result)

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
broom::tidy(result)

Write the Item Matrix TSV

Description

Mirrors the React webapp's "Export Matrix" button + Python's 'RegionResult.to_matrix_tsv()' byte-for-byte.

Usage

to_matrix_tsv(result, path)

## S4 method for signature 'RegionResult'
to_matrix_tsv(result, path)

Arguments

Details

Columns: Item, <SetName1>, <SetName2>, ..., Region. Rows: one per item. Iteration order: mask 1..(2^n - 1); within each mask, items in 'dataset@item_order'.

Value

Invisibly returns 'path'.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
to_matrix_tsv(result, tempfile(fileext = ".tsv"))

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
to_matrix_tsv(result, tempfile(fileext = ".tsv"))

Compose a multi-page PDF report from a RegionResult

Description

Writes a US Letter landscape PDF with overview, venn+upset, statistics tables, and (by default) network and methodology pages. Each page has a footer with package version, generation timestamp, and page number.

Usage

to_pdf_report(
  result,
  path,
  title = NULL,
  include_network = TRUE,
  include_about = TRUE
)

Arguments

Value

Invisibly returns 'NULL'. The PDF is written to 'path'.

Examples

if (getRversion() >= "4.6") {
  result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
  to_pdf_report(result, tempfile(fileext = ".pdf"))
}

Write the Region Summary TSV

Description

Mirrors the React webapp's "Export Region Summary" button + Python's 'RegionResult.to_region_summary_tsv()' byte-for-byte.

Usage

to_region_summary_tsv(result, path)

## S4 method for signature 'RegionResult'
to_region_summary_tsv(result, path)

Arguments

Details

Columns: Region, Sets, Depth, Exclusive_Count, Inclusive_Count, Exclusive_Pct, Items. Rows: every region (1..2^n - 1). Sorted by (Depth ASC, Region label ASC). Items: semicolon-joined, ordered by 'dataset@item_order'. Cells starting with '='/'+'/'-'/'@' (after optional leading whitespace) are escape-prefixed with a single quote.

Value

Invisibly returns 'path'.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
to_region_summary_tsv(result, tempfile(fileext = ".tsv"))

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
to_region_summary_tsv(result, tempfile(fileext = ".tsv"))

Write the pairwise Statistics TSV

Description

Mirrors the React webapp's DataSummaryPanel "Export Statistics" button + Python's 'RegionResult.to_statistics_tsv()' byte-for-byte.

Usage

to_statistics_tsv(result, path)

## S4 method for signature 'RegionResult'
to_statistics_tsv(result, path)

Arguments

Details

Columns: Set_A, Set_B, Name_A, Name_B, Size_A, Size_B, Intersection, Union, Jaccard, Overlap_Coeff, Dice, Expected, Fold_Enrichment, P_value, FDR, Significant. Float formatting: * Jaccard / Overlap_Coeff / Dice: 4 decimals via [.js_to_fixed()] * Expected: 2 decimals * Fold_Enrichment: 3 decimals * P_value / FDR: scientific (JS toExponential(2)) if '< 0.001', else 6 decimals * Significant: one of '"***"', '"**"', '"*"', '"ns"' keyed off FDR thresholds (0.001, 0.01, 0.05).

Rows are sorted by P_value ascending (matches the underlying StatisticsResult).

Value

Invisibly returns 'path'.

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
to_statistics_tsv(result, tempfile(fileext = ".tsv"))

result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
to_statistics_tsv(result, tempfile(fileext = ".tsv"))

Get the vennDiagramLab package version

Description

Returns the installed version of vennDiagramLab as a character string. This trivial function exists so the Phase 0 skeleton has one public export; Phase 1 introduces the real analyze() / load_*() API.

Usage

vdl_version()

Value

Character string, the package version (e.g. "2.0.0").

Examples

vdl_version()