From 9a7454f4b00c96c2c31cf2422910d44cd0049410 Mon Sep 17 00:00:00 2001 From: Ian Date: Sat, 1 Aug 2026 15:56:33 +0200 Subject: [PATCH 01/13] feat(spatial): lattice neighbour graphs for Visium and Visium HD MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Spots and bins sit on a known grid, so neighbours are integer arithmetic — no spatial index, no distances, nothing approximate. Sort by (row, col), build a row directory, and each neighbour is a binary search inside one row. Offsets verified against 10x's Space Ranger docs and by replicating squidpy's algorithm: Visium is doubled coordinates, (row + col) constant parity, six neighbours at (row, col±2) and (row±1, col±1). Documented capture area (78 rows x 64 spots = 4992) is a test case. Two traps this defends against: - squidpy ignores the lattice and runs Euclidean kNN on pixel coordinates with a median*1.3 cutoff. Exact on contiguous tissue, but the cutoff is global, so at 50% lattice occupancy about half its edges are not lattice-adjacent. Working from the integers has no such failure mode. - raw doubled coordinates are anisotropic (in-row 2 units, diagonal sqrt 2), so metric methods silently drop the in-row neighbours and return a diagonal-only graph. visium_isometric_coords() rescales so all six sit at the pitch, and passing offset coordinates instead of doubled is now a hard error rather than a wrong graph. Builds CSR directly in two passes; an edge list first would cost ~700 MB at HD scale. 10.5M bins / 21.1M edges in 954ms, 591 MB. 18 tests: exact adjacency on small grids, closed-form edge counts, interior and hand-computed boundary degrees, parity, tissue holes, permutation invariance, symmetry, u32::MAX coordinates. Plus the load-bearing one — Leiden on a lattice produces zero disconnected communities, i.e. spatially contiguous domains. --- src/error.rs | 48 +++ src/lib.rs | 1 + src/spatial/lattice.rs | 705 +++++++++++++++++++++++++++++++++++++++++ src/spatial/mod.rs | 14 + 4 files changed, 768 insertions(+) create mode 100644 src/spatial/lattice.rs create mode 100644 src/spatial/mod.rs diff --git a/src/error.rs b/src/error.rs index e741dbc..32cff75 100644 --- a/src/error.rs +++ b/src/error.rs @@ -54,6 +54,35 @@ pub enum ClusteringError { }, /// The supplied CSR arrays were not a well-formed sparse matrix. InvalidCsr(String), + /// Two observations claimed the same lattice position. + DuplicateLatticePosition { + /// Lattice row. + row: u32, + /// Lattice column. + col: u32, + /// The two node indices sharing it. + nodes: (usize, usize), + }, + /// Visium hex positions did not all share the same `(row + col)` parity. + /// + /// In doubled coordinates every spot has the same parity, so mixed parity means the + /// coordinates are not doubled — most often offset coordinates (`col / 2`), which would + /// silently produce a wrong graph rather than an empty one. + InconsistentHexParity { + /// A node with the majority parity. + expected_node: usize, + /// The first node found disagreeing with it. + node: usize, + /// The offending position. + position: (u32, u32), + }, + /// Parallel coordinate arrays had different lengths. + CoordinateLengthMismatch { + /// Length of the first array. + got: usize, + /// Expected length, from the first array supplied. + expected: usize, + }, /// The adjacency is not symmetric, so `Σ strength != 2 · total_weight`. /// /// Most often means an un-symmetrised k-NN graph was passed to @@ -100,6 +129,25 @@ impl fmt::Display for ClusteringError { crate::network::MAX_NODES ), Self::InvalidCsr(msg) => write!(f, "malformed CSR input: {msg}"), + Self::DuplicateLatticePosition { row, col, nodes } => write!( + f, + "nodes {} and {} both sit at lattice position ({row}, {col})", + nodes.0, nodes.1 + ), + Self::CoordinateLengthMismatch { got, expected } => { + write!(f, "coordinate array has length {got}, expected {expected}") + } + Self::InconsistentHexParity { + expected_node, + node, + position, + } => write!( + f, + "node {node} at ({}, {}) disagrees in (row + col) parity with node \ + {expected_node}. Visium doubled coordinates give every spot the same parity; \ + mixed parity usually means offset coordinates were passed instead", + position.0, position.1 + ), Self::AsymmetricGraph { degree_sum, expected, diff --git a/src/lib.rs b/src/lib.rs index 1a2c419..139351e 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -5,6 +5,7 @@ pub mod error; #[cfg(feature = "knn")] pub mod neighborhood; pub mod network; +pub mod spatial; #[cfg(test)] pub(crate) mod testdata; diff --git a/src/spatial/lattice.rs b/src/spatial/lattice.rs new file mode 100644 index 0000000..0e6eb6f --- /dev/null +++ b/src/spatial/lattice.rs @@ -0,0 +1,705 @@ +//! Neighbour graphs for observations on a regular lattice. +//! +//! Visium spots and Visium HD bins sit on a known grid, so their neighbours follow from +//! integer arithmetic — no spatial index, no distance computation, nothing approximate. +//! At HD scale (millions of bins) that is the difference between seconds and minutes. +//! +//! # Why not distance-based +//! +//! `squidpy.gr.spatial_neighbors` ignores the lattice and runs Euclidean kNN on the float +//! pixel coordinates, keeping edges under `median(dist) * 1.3`. On contiguous tissue that +//! reproduces the lattice exactly, but the cutoff is global, so on a sparse or fragmented +//! lattice it bridges the gaps: at 50% occupancy roughly half the edges it produces are not +//! lattice-adjacent, and at 20% it is three quarters. Working from the integers is exact +//! everywhere and needs no heuristic. +//! +//! # The anisotropy trap +//! +//! Never run Euclidean kNN on raw doubled coordinates. In-row neighbours are 2 units apart +//! while diagonal ones are only √2, so a metric method silently drops the two in-row +//! neighbours and returns a diagonal-only graph — wrong, and wrong in a way that still looks +//! like a plausible clustering. [`visium_isometric_coords`] rescales to a space where all six +//! neighbours are equidistant; use it for anything distance-based. + +use crate::error::{ClusteringError, Result}; +use crate::network::{CSRNetwork, MAX_NODES}; + +/// The lattice the coordinates describe. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum Lattice { + /// Visium's hex grid in doubled coordinates: `col` steps by 2 within a row, and odd rows + /// are offset by 1, so a spot exists only where `(row + col)` is even. Six neighbours: + /// `(row, col ± 2)` and `(row ± 1, col ± 1)`. + VisiumHex, + /// Square grid, edge-adjacent only. Four neighbours: `(row ± 1, col)`, `(row, col ± 1)`. + Square4, + /// Square grid including diagonals. Eight neighbours. + Square8, +} + +impl Lattice { + /// Half the neighbour offsets — enough that each undirected edge is generated exactly + /// once, by its lower endpoint in (row, col) order. The other half are these negated. + const fn forward_offsets(self) -> &'static [(i64, i64)] { + match self { + Self::VisiumHex => &[(0, 2), (1, -1), (1, 1)], + Self::Square4 => &[(0, 1), (1, 0)], + Self::Square8 => &[(0, 1), (1, 0), (1, 1), (1, -1)], + } + } + + /// Neighbours an interior position has. Only used for documentation and tests. + pub const fn degree(self) -> usize { + match self { + Self::VisiumHex => 6, + Self::Square4 => 4, + Self::Square8 => 8, + } + } +} + +/// Where each distinct row sits in the sorted node order. +struct RowIndex { + /// Distinct row values, ascending. + rows: Vec, + /// `starts[i]..starts[i + 1]` is row `rows[i]`'s slice of the sorted order. + starts: Vec, +} + +impl RowIndex { + /// Slice of the sorted order holding `row`, or `None` if the row is empty. + #[inline] + fn range(&self, row: u32) -> Option> { + let i = self.rows.binary_search(&row).ok()?; + Some(self.starts[i] as usize..self.starts[i + 1] as usize) + } +} + +/// Builds a neighbour graph from integer lattice coordinates. +/// +/// `rows` and `cols` give each observation's position; every edge has weight 1.0. Positions +/// that are absent — tissue-masked spots, trimmed bins — simply have no edge there, so a +/// partial capture area needs no special handling. +/// +/// Since the graph is exactly the lattice adjacency, Leiden's internal-connectivity guarantee +/// becomes spatial contiguity: no community can be split across disconnected regions. +/// +/// # Errors +/// +/// If the arrays differ in length, two observations share a position, or there are more +/// nodes than `u32` adjacency can address. +pub fn lattice_graph(rows: &[u32], cols: &[u32], lattice: Lattice) -> Result { + let n = rows.len(); + if cols.len() != n { + return Err(ClusteringError::CoordinateLengthMismatch { + got: cols.len(), + expected: n, + }); + } + if n > MAX_NODES { + return Err(ClusteringError::TooManyNodes { n_nodes: n }); + } + if n == 0 { + return CSRNetwork::from_csr_parts(vec![0], Vec::new(), Vec::new(), None); + } + + // Sorted by (row, col), so a row is contiguous and its columns ascend — which is what + // lets a neighbour lookup be a binary search inside one row rather than over everything. + let mut order: Vec = (0..n as u32).collect(); + order.sort_unstable_by_key(|&v| key(rows[v as usize], cols[v as usize])); + + // Columns in sorted order, held separately so the binary search reads contiguous memory. + // Going through `cols[order[i]]` gathers randomly on every comparison instead: 1.21s vs + // 954ms on 10.5M bins. + let sorted_cols: Vec = order.iter().map(|&v| cols[v as usize]).collect(); + + for (i, w) in order.windows(2).enumerate() { + let (a, b) = (w[0] as usize, w[1] as usize); + if rows[a] == rows[b] && sorted_cols[i] == sorted_cols[i + 1] { + return Err(ClusteringError::DuplicateLatticePosition { + row: rows[a], + col: cols[a], + nodes: (a.min(b), a.max(b)), + }); + } + } + + if lattice == Lattice::VisiumHex { + check_hex_parity(rows, cols)?; + } + + let index = build_row_index(&order, rows); + let offsets = lattice.forward_offsets(); + + // Two passes so the CSR arrays are allocated once at their final size. Materialising an + // edge list first would cost ~700 MB at HD scale before the graph even exists. + let mut degree = vec![0u32; n]; + visit_edges(&order, rows, cols, &sorted_cols, &index, offsets, |v, u| { + degree[v] += 1; + degree[u] += 1; + }); + + let mut node_ptrs = Vec::with_capacity(n + 1); + node_ptrs.push(0usize); + let mut total = 0usize; + for &d in °ree { + total += d as usize; + node_ptrs.push(total); + } + + let mut neighbors = vec![0u32; total]; + let mut weights = vec![1.0f32; total]; + let mut cursor: Vec = node_ptrs[..n].to_vec(); + visit_edges(&order, rows, cols, &sorted_cols, &index, offsets, |v, u| { + neighbors[cursor[v]] = u as u32; + cursor[v] += 1; + neighbors[cursor[u]] = v as u32; + cursor[u] += 1; + }); + weights.truncate(total); + + CSRNetwork::from_csr_parts(node_ptrs, neighbors, weights, None) +} + +/// Packs a position into one sortable integer. `i64` keeps the arithmetic honest for offsets +/// that would take a `u32` below zero. +#[inline] +const fn key(row: u32, col: u32) -> u64 { + ((row as u64) << 32) | col as u64 +} + +/// Rejects coordinates that are not doubled. +/// +/// Real Visium data is always `(row + col)` even, but only *consistency* is required here — +/// a shifted origin is fine, offset coordinates are not. Without this, passing `col / 2` +/// produces a graph that is wrong rather than empty, since the offsets still find something. +fn check_hex_parity(rows: &[u32], cols: &[u32]) -> Result<()> { + let parity = (rows[0] + cols[0]) % 2; + for v in 1..rows.len() { + if (rows[v] + cols[v]) % 2 != parity { + return Err(ClusteringError::InconsistentHexParity { + expected_node: 0, + node: v, + position: (rows[v], cols[v]), + }); + } + } + Ok(()) +} + +/// Rescales Visium doubled coordinates so all six neighbours sit at the same distance. +/// +/// Doubled coordinates are anisotropic — in-row neighbours are 2 units apart, diagonal ones +/// √2 — so any distance-based method must be given these instead. `pitch` is the +/// centre-to-centre spot spacing, 100.0 µm on standard Visium; the output is in the same +/// units, and every one of the six neighbours lands exactly `pitch` away. +/// +/// # Errors +/// +/// If the arrays differ in length. +pub fn visium_isometric_coords( + rows: &[u32], + cols: &[u32], + pitch: f64, +) -> Result<(Vec, Vec)> { + if cols.len() != rows.len() { + return Err(ClusteringError::CoordinateLengthMismatch { + got: cols.len(), + expected: rows.len(), + }); + } + // col carries the doubling, so it scales by pitch/2; row spacing is the hex height. + let (sx, sy) = (pitch / 2.0, pitch * f64::sqrt(3.0) / 2.0); + Ok(( + cols.iter().map(|&c| c as f64 * sx).collect(), + rows.iter().map(|&r| r as f64 * sy).collect(), + )) +} + +fn build_row_index(order: &[u32], rows: &[u32]) -> RowIndex { + let mut index = RowIndex { + rows: Vec::new(), + starts: Vec::new(), + }; + for (pos, &v) in order.iter().enumerate() { + let r = rows[v as usize]; + if index.rows.last() != Some(&r) { + index.rows.push(r); + index.starts.push(pos as u32); + } + } + index.starts.push(order.len() as u32); + index +} + +/// Calls `f(v, u)` once per undirected edge, with both node indices in the caller's +/// numbering. Runs identically in both passes, so degrees and fill can never disagree. +#[inline] +fn visit_edges( + order: &[u32], + rows: &[u32], + cols: &[u32], + sorted_cols: &[u32], + index: &RowIndex, + offsets: &[(i64, i64)], + mut f: impl FnMut(usize, usize), +) { + for &v in order { + let v = v as usize; + let (r, c) = (rows[v] as i64, cols[v] as i64); + for &(dr, dc) in offsets { + let (tr, tc) = (r + dr, c + dc); + if tr < 0 || tc < 0 || tr > u32::MAX as i64 || tc > u32::MAX as i64 { + continue; + } + if let Some(u) = lookup(order, sorted_cols, index, tr as u32, tc as u32) { + f(v, u); + } + } + } +} + +/// Node at `(row, col)`, or `None` if nothing sits there. +#[inline] +fn lookup( + order: &[u32], + sorted_cols: &[u32], + index: &RowIndex, + row: u32, + col: u32, +) -> Option { + let range = index.range(row)?; + // The row is short — 128 columns on Visium, ~3250 on HD — so this stays in L1. + let i = sorted_cols[range.clone()].binary_search(&col).ok()?; + Some(order[range.start + i] as usize) +} + +#[cfg(test)] +mod tests { + use super::*; + use rand::SeedableRng; + use rand::seq::SliceRandom; + use std::collections::HashSet; + + /// Every position in an R x C block. + fn full_square(r: u32, c: u32) -> (Vec, Vec) { + let mut rows = Vec::new(); + let mut cols = Vec::new(); + for i in 0..r { + for j in 0..c { + rows.push(i); + cols.push(j); + } + } + (rows, cols) + } + + /// Visium's layout: `(row + col)` even, `col` stepping by 2 within a row. + fn full_hex(r: u32, c: u32) -> (Vec, Vec) { + let mut rows = Vec::new(); + let mut cols = Vec::new(); + for i in 0..r { + for j in 0..c { + if (i + j) % 2 == 0 { + rows.push(i); + cols.push(j); + } + } + } + (rows, cols) + } + + /// Undirected edges as coordinate pairs, so results can be compared across node orderings. + fn edge_set(g: &CSRNetwork, rows: &[u32], cols: &[u32]) -> HashSet<((u32, u32), (u32, u32))> { + let mut out = HashSet::new(); + for v in 0..g.node_count() { + for (u, _) in g.neighbors(v) { + out.insert(canon((rows[v], cols[v]), (rows[u], cols[u]))); + } + } + out + } + + /// Orders an edge's endpoints so the two directions collapse to one entry. + fn canon(a: (u32, u32), b: (u32, u32)) -> ((u32, u32), (u32, u32)) { + if a <= b { (a, b) } else { (b, a) } + } + + #[test] + fn square4_3x3_has_exactly_the_lattice_edges() { + let (rows, cols) = full_square(3, 3); + let g = lattice_graph(&rows, &cols, Lattice::Square4).unwrap(); + + let mut expected = HashSet::new(); + for r in 0..3u32 { + for c in 0..3u32 { + if c + 1 < 3 { + expected.insert(canon((r, c), (r, c + 1))); + } + if r + 1 < 3 { + expected.insert(canon((r, c), (r + 1, c))); + } + } + } + assert_eq!(edge_set(&g, &rows, &cols), expected); + assert_eq!(g.edge_count(), 12, "3x3 grid: 6 horizontal + 6 vertical"); + } + + #[test] + fn square8_3x3_adds_exactly_the_diagonals() { + let (rows, cols) = full_square(3, 3); + let four = lattice_graph(&rows, &cols, Lattice::Square4).unwrap(); + let eight = lattice_graph(&rows, &cols, Lattice::Square8).unwrap(); + let (a, b) = ( + edge_set(&four, &rows, &cols), + edge_set(&eight, &rows, &cols), + ); + assert!(a.is_subset(&b)); + assert_eq!(b.len() - a.len(), 8, "2x2 blocks x 2 diagonals each"); + } + + /// Closed form for a fully occupied grid, checked over a range of shapes. + #[test] + fn square_edge_counts_match_the_closed_form() { + for (r, c) in [(1, 1), (1, 7), (7, 1), (2, 2), (5, 9), (16, 16)] { + let (rows, cols) = full_square(r, c); + let g4 = lattice_graph(&rows, &cols, Lattice::Square4).unwrap(); + assert_eq!( + g4.edge_count() as u32, + r * (c - 1) + (r - 1) * c, + "Square4 {r}x{c}" + ); + let g8 = lattice_graph(&rows, &cols, Lattice::Square8).unwrap(); + assert_eq!( + g8.edge_count() as u32, + r * (c - 1) + (r - 1) * c + 2 * (r - 1) * (c - 1), + "Square8 {r}x{c}" + ); + } + } + + /// The property that would break if a forward offset were double-counted: duplicate + /// entries are summed on construction, so a double emission shows up as weight 2.0. + #[test] + fn every_edge_has_weight_exactly_one() { + for lattice in [Lattice::Square4, Lattice::Square8, Lattice::VisiumHex] { + let (rows, cols) = match lattice { + Lattice::VisiumHex => full_hex(9, 13), + _ => full_square(9, 13), + }; + let g = lattice_graph(&rows, &cols, lattice).unwrap(); + for v in 0..g.node_count() { + for (u, w) in g.neighbors(v) { + assert_eq!(w, 1.0, "{lattice:?}: edge {v}-{u} has weight {w}"); + } + } + assert!(!g.has_self_loops(), "{lattice:?} produced a self-loop"); + } + } + + #[test] + fn interior_degree_matches_the_lattice() { + for lattice in [Lattice::Square4, Lattice::Square8, Lattice::VisiumHex] { + let (r, c) = (11u32, 15u32); + let (rows, cols) = match lattice { + Lattice::VisiumHex => full_hex(r, c), + _ => full_square(r, c), + }; + let g = lattice_graph(&rows, &cols, lattice).unwrap(); + // Hex reaches two columns sideways, so its interior starts one further in. + let margin = if lattice == Lattice::VisiumHex { 2 } else { 1 }; + let mut checked = 0; + for v in 0..g.node_count() { + let (vr, vc) = (rows[v], cols[v]); + if vr >= margin && vr < r - margin && vc >= margin && vc < c - margin { + assert_eq!( + g.degree(v), + lattice.degree(), + "{lattice:?}: interior spot ({vr}, {vc})" + ); + checked += 1; + } + } + assert!(checked > 10, "{lattice:?}: only {checked} interior spots"); + } + } + + /// Visium's parity: neighbours of an even-parity spot are also even-parity, which is what + /// makes the doubled-coordinate scheme self-consistent. + #[test] + fn hex_neighbours_preserve_parity_and_are_the_six_offsets() { + let (rows, cols) = full_hex(9, 13); + let g = lattice_graph(&rows, &cols, Lattice::VisiumHex).unwrap(); + let expected: HashSet<(i64, i64)> = + [(0, 2), (0, -2), (1, 1), (1, -1), (-1, 1), (-1, -1)].into(); + + for v in 0..g.node_count() { + assert_eq!((rows[v] + cols[v]) % 2, 0, "spot {v} has odd parity"); + for (u, _) in g.neighbors(v) { + let d = ( + rows[u] as i64 - rows[v] as i64, + cols[u] as i64 - cols[v] as i64, + ); + assert!(expected.contains(&d), "unexpected hex offset {d:?}"); + assert_eq!((rows[u] + cols[u]) % 2, 0); + } + } + } + + /// Boundary spots, worked out by hand on the layout in the module docs. + #[test] + fn hex_boundary_degrees_are_exact() { + let (rows, cols) = full_hex(5, 9); + let g = lattice_graph(&rows, &cols, Lattice::VisiumHex).unwrap(); + let at = |r: u32, c: u32| { + (0..g.node_count()) + .find(|&v| rows[v] == r && cols[v] == c) + .unwrap_or_else(|| panic!("no spot at ({r}, {c})")) + }; + assert_eq!(g.degree(at(2, 4)), 6, "interior"); + assert_eq!(g.degree(at(0, 4)), 4, "top edge: no row above"); + assert_eq!(g.degree(at(2, 0)), 3, "left edge: nothing to the left"); + assert_eq!(g.degree(at(0, 0)), 2, "corner"); + assert_eq!(g.degree(at(4, 8)), 2, "opposite corner"); + } + + /// Tissue masking removes spots; the remaining ones must simply lose those edges. + #[test] + fn missing_positions_leave_holes_rather_than_shifting_neighbours() { + let (rows, cols) = full_square(5, 5); + let keep: Vec = (0..rows.len()) + .filter(|&i| !(rows[i] == 2 && cols[i] == 2)) + .collect(); + let r: Vec = keep.iter().map(|&i| rows[i]).collect(); + let c: Vec = keep.iter().map(|&i| cols[i]).collect(); + + let g = lattice_graph(&r, &c, Lattice::Square4).unwrap(); + let full = lattice_graph(&rows, &cols, Lattice::Square4).unwrap(); + assert_eq!(g.node_count(), 24); + assert_eq!(g.edge_count(), full.edge_count() - 4, "the hole's 4 edges"); + + for v in 0..g.node_count() { + for (u, _) in g.neighbors(v) { + let d = (r[u] as i64 - r[v] as i64).abs() + (c[u] as i64 - c[v] as i64).abs(); + assert_eq!(d, 1, "a hole must not join spots two apart"); + } + } + } + + /// Node order is the caller's; the graph must not depend on it. + #[test] + fn result_is_invariant_under_input_permutation() { + let (rows, cols) = full_hex(9, 13); + let reference = { + let g = lattice_graph(&rows, &cols, Lattice::VisiumHex).unwrap(); + edge_set(&g, &rows, &cols) + }; + + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(7); + for _ in 0..8 { + let mut perm: Vec = (0..rows.len()).collect(); + perm.shuffle(&mut rng); + let r: Vec = perm.iter().map(|&i| rows[i]).collect(); + let c: Vec = perm.iter().map(|&i| cols[i]).collect(); + let g = lattice_graph(&r, &c, Lattice::VisiumHex).unwrap(); + assert_eq!(edge_set(&g, &r, &c), reference); + } + } + + #[test] + fn adjacency_is_symmetric() { + for lattice in [Lattice::Square4, Lattice::Square8, Lattice::VisiumHex] { + let (rows, cols) = match lattice { + Lattice::VisiumHex => full_hex(12, 17), + _ => full_square(12, 17), + }; + let g = lattice_graph(&rows, &cols, lattice).unwrap(); + g.validate_symmetry() + .unwrap_or_else(|e| panic!("{lattice:?}: {e}")); + } + } + + /// Real Visium geometry: 78 rows, `array_col` 0..126 even / 1..127 odd, 64 spots per row. + /// Verified against 10x's Space Ranger spatial-outputs documentation. + #[test] + fn matches_the_documented_visium_capture_area() { + let mut rows = Vec::new(); + let mut cols = Vec::new(); + for r in 0..78u32 { + for i in 0..64u32 { + rows.push(r); + cols.push(2 * i + (r % 2)); + } + } + assert_eq!(rows.len(), 4992, "10x documents 4992 spots"); + + let g = lattice_graph(&rows, &cols, Lattice::VisiumHex).unwrap(); + assert_eq!(g.node_count(), 4992); + // Interior spots: rows 1..77 with both in-row neighbours present. + let interior = (0..g.node_count()) + .filter(|&v| rows[v] > 0 && rows[v] < 77 && cols[v] >= 2 && cols[v] <= 125) + .count(); + assert!(interior > 4000); + for v in 0..g.node_count() { + if rows[v] > 0 && rows[v] < 77 && cols[v] >= 2 && cols[v] <= 125 { + assert_eq!(g.degree(v), 6, "spot ({}, {})", rows[v], cols[v]); + } + } + } + + /// The trap: offset coordinates look like valid input but describe a different lattice. + #[test] + fn rejects_offset_coordinates_masquerading_as_doubled() { + let (rows, doubled) = full_hex(9, 13); + let offset: Vec = doubled.iter().map(|&c| c / 2).collect(); + assert!( + matches!( + lattice_graph(&rows, &offset, Lattice::VisiumHex), + Err(ClusteringError::InconsistentHexParity { .. }) + ), + "offset coordinates must not be silently accepted" + ); + // Square lattices have no parity constraint, so they stay unaffected. + assert!(lattice_graph(&rows, &offset, Lattice::Square4).is_ok()); + } + + /// A shifted origin is still doubled, so it must be allowed. + #[test] + fn accepts_a_shifted_hex_origin() { + let (rows, cols) = full_hex(9, 13); + let shifted: Vec = cols.iter().map(|&c| c + 1).collect(); + let g = lattice_graph(&rows, &shifted, Lattice::VisiumHex).unwrap(); + let reference = lattice_graph(&rows, &cols, Lattice::VisiumHex).unwrap(); + assert_eq!(g.edge_count(), reference.edge_count()); + } + + /// All six neighbours must land at exactly `pitch` in the rescaled space — the property + /// that makes distance-based methods safe on Visium. + #[test] + fn isometric_coords_equalise_all_six_neighbours() { + let (rows, cols) = full_hex(9, 13); + let g = lattice_graph(&rows, &cols, Lattice::VisiumHex).unwrap(); + let (x, y) = visium_isometric_coords(&rows, &cols, 100.0).unwrap(); + + for v in 0..g.node_count() { + for (u, _) in g.neighbors(v) { + let d = ((x[u] - x[v]).powi(2) + (y[u] - y[v]).powi(2)).sqrt(); + assert!( + (d - 100.0).abs() < 1e-9, + "neighbour distance {d}, expected the 100 µm pitch" + ); + } + } + + // And nothing else is that close, so a radius query at 1.1x pitch is exactly the + // lattice — which is what makes the isometric form usable for kNN. + let v = (0..g.node_count()) + .find(|&v| rows[v] == 4 && cols[v] == 6) + .unwrap(); + let within = (0..g.node_count()) + .filter(|&u| u != v && ((x[u] - x[v]).powi(2) + (y[u] - y[v]).powi(2)).sqrt() < 110.0) + .count(); + assert_eq!(within, 6); + } + + #[test] + fn rejects_bad_input() { + assert!(matches!( + lattice_graph(&[0, 1], &[0], Lattice::Square4), + Err(ClusteringError::CoordinateLengthMismatch { + got: 1, + expected: 2 + }) + )); + assert!(matches!( + lattice_graph(&[3, 3], &[7, 7], Lattice::Square4), + Err(ClusteringError::DuplicateLatticePosition { row: 3, col: 7, .. }) + )); + } + + #[test] + fn handles_degenerate_input() { + let empty = lattice_graph(&[], &[], Lattice::Square4).unwrap(); + assert_eq!(empty.node_count(), 0); + assert_eq!(empty.edge_count(), 0); + + let single = lattice_graph(&[5], &[9], Lattice::VisiumHex).unwrap(); + assert_eq!(single.node_count(), 1); + assert_eq!(single.edge_count(), 0); + + // Far apart, so nothing is adjacent. + let scattered = lattice_graph(&[0, 100, 200], &[0, 100, 200], Lattice::Square8).unwrap(); + assert_eq!(scattered.edge_count(), 0); + } + + /// Leiden guarantees internally-connected communities, so on a lattice every domain must + /// be a spatially contiguous region. This is the property the whole approach rests on. + #[test] + fn leiden_domains_are_spatially_contiguous() { + use crate::community_search::leiden::{LeidenConfig, ObjectiveKind, leiden}; + + let (rows, cols) = full_square(40, 40); + let g = lattice_graph(&rows, &cols, Lattice::Square4).unwrap(); + for resolution in [0.05, 0.1, 0.5] { + let c = leiden( + &g, + &LeidenConfig { + objective: ObjectiveKind::Cpm { resolution }, + ..Default::default() + }, + ) + .unwrap(); + assert_eq!( + crate::testdata::disconnected_community_count(&g, c.labels()), + 0, + "resolution {resolution} produced a spatially split domain" + ); + assert!( + c.n_clusters() > 1, + "resolution {resolution} found one domain" + ); + } + } + + /// Coordinates at the top of the `u32` range must not wrap when an offset goes negative. + #[test] + fn extreme_coordinates_do_not_wrap() { + let rows = [0u32, 0, u32::MAX, u32::MAX]; + let cols = [0u32, u32::MAX, 0, u32::MAX]; + let g = lattice_graph(&rows, &cols, Lattice::Square8).unwrap(); + assert_eq!(g.edge_count(), 0, "corners of the space are not adjacent"); + } + + /// Visium HD at 2 µm. Sources disagree on the grid: 10x's row count implies 3350², while + /// spatialdata documents 3250². Only a size for the benchmark, so either does — but the + /// builder never assumes an extent, and callers shouldn't either. + /// + /// ```text + /// cargo test --release --no-default-features visium_hd_scale -- --ignored --nocapture + /// ``` + #[test] + #[ignore = "takes a few seconds and allocates ~1 GB"] + fn visium_hd_scale() { + let side = 3250u32; + let t = std::time::Instant::now(); + let (rows, cols) = full_square(side, side); + let coords = t.elapsed(); + + let t = std::time::Instant::now(); + let g = lattice_graph(&rows, &cols, Lattice::Square4).unwrap(); + let build = t.elapsed(); + + println!( + "{}x{} = {} bins, {} edges\n coords {:.2?}, graph {:.2?}, {:.1} MB", + side, + side, + g.node_count(), + g.edge_count(), + coords, + build, + g.memory_bytes() as f64 / 1e6 + ); + assert_eq!(g.node_count(), (side * side) as usize); + assert_eq!(g.edge_count() as u32, 2 * side * (side - 1)); + } +} diff --git a/src/spatial/mod.rs b/src/spatial/mod.rs new file mode 100644 index 0000000..5cdfb8c --- /dev/null +++ b/src/spatial/mod.rs @@ -0,0 +1,14 @@ +//! Spatial neighbour graphs. +//! +//! Spatial domain detection is mostly a graph-construction problem: build the right graph and +//! the clustering is the same Leiden used everywhere else. Because refinement guarantees +//! internally-connected communities, running it on a spatial graph gives spatially contiguous +//! domains for free. +//! +//! Use [`ObjectiveKind::Cpm`](crate::community_search::leiden::ObjectiveKind::Cpm) rather than +//! RB for spatial domains — modularity's resolution limit bites harder here, where domains +//! tend to be numerous and small. + +pub mod lattice; + +pub use lattice::{Lattice, lattice_graph, visium_isometric_coords}; From c48e6fe658169bffe29b7f82922a7b5dc0734901 Mon Sep 17 00:00:00 2001 From: Ian Date: Sat, 1 Aug 2026 16:50:46 +0200 Subject: [PATCH 02/13] feat(spatial): radius and kNN neighbour graphs on a uniform grid MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Point-based assays (Xenium, MERFISH, CosMx, Slide-seq) position cells rather than slotting them into a grid, so neighbours have to be searched for. Both builders run over a uniform-grid index rather than a kd-tree. That started as a performance choice — tissue is close to uniformly dense, so bucketing beats traversal — but became a correctness one. kiddo 5.2.2 is unusable here on two counts, both found by tests in this commit: - KdTree panics outright past 32 points sharing a coordinate on one axis. Every Visium row is 64 spots at one y; every HD row is thousands. - ImmutableKdTree returns neighbour ids that do not match the coordinates it measured. Querying a collinear run, it reported distance 0 for a point five units away: the distances are right for the true neighbours, the ids are not. A graph built from it joins each cell to someone else's neighbours — wrong, and wrong in a way that still looks like a plausible clustering. The grid has no such failure modes. Duplicates and collinear runs are ordinary inputs, everything is exact, and it needs no dependency at all, so the spatial builders are unconditional rather than feature-gated. Radius is symmetric by construction. kNN is not, so Symmetry::Union (scanpy's convention, degree >= k) or Intersection (mutual only, degree <= k) reconciles it explicitly — emitting both directions and letting construction merge them would silently give mutual pairs twice the weight. Ties break on the lower index, so pixel-rounded coordinates still give a reproducible graph. Verified against O(n^2) brute force across seven degenerate point sets (collinear, all-identical, duplicated pairs, dense spike, far clumps) x three radii and three k. 500k cells: knn(6) 299ms, radius(30) 80ms. --- Cargo.toml | 2 + src/spatial/grid.rs | 180 +++++++++++ src/spatial/mod.rs | 17 + src/spatial/points.rs | 738 ++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 937 insertions(+) create mode 100644 src/spatial/grid.rs create mode 100644 src/spatial/points.rs diff --git a/Cargo.toml b/Cargo.toml index a14e921..a05f580 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -15,6 +15,8 @@ default = ["knn"] # clustering core can be built and tested without the HNSW/kd-tree stack, which does not # compile on every target. knn = ["dep:hnsw_rs", "dep:kiddo", "dep:ndarray"] +# Spatial neighbour graphs are always available: the uniform-grid index they use has no +# dependencies, so there is no build to opt out of. [dependencies] anyhow = "1.0.100" diff --git a/src/spatial/grid.rs b/src/spatial/grid.rs new file mode 100644 index 0000000..4961618 --- /dev/null +++ b/src/spatial/grid.rs @@ -0,0 +1,180 @@ +//! Uniform-grid spatial index. +//! +//! Tissue is close to uniformly dense, so bucketing points into fixed cells beats a tree: +//! queries are O(points nearby) with no traversal, duplicates and collinear runs are ordinary +//! inputs rather than pathological ones, and there is nothing approximate anywhere. +//! +//! It also replaces a dependency that turned out to be wrong. `kiddo` 5.2.2's `KdTree` panics +//! once more than 32 points share a coordinate on one axis — which every Visium row does — +//! and its `ImmutableKdTree` returns neighbour *ids* that do not match the coordinates it +//! measured, so a graph built from it joins each cell to someone else's neighbours. + +/// Points bucketed into cells of a fixed size, stored CSR-style. +pub(crate) struct Grid { + cell: f64, + min: [f64; D], + dims: [usize; D], + /// `starts[c]..starts[c + 1]` is cell `c`'s slice of `items`. + starts: Vec, + items: Vec, +} + +impl Grid { + /// Buckets `points` into cells of roughly `target_cell`. + /// + /// The cell size is enlarged if it would need more cells than points, so a handful of + /// far-flung outliers cannot make the index enormous. + pub(crate) fn new(points: &[[f64; D]], target_cell: f64) -> Self { + debug_assert!(!points.is_empty()); + let mut min = [f64::INFINITY; D]; + let mut max = [f64::NEG_INFINITY; D]; + for p in points { + for d in 0..D { + min[d] = min[d].min(p[d]); + max[d] = max[d].max(p[d]); + } + } + + // Cap total cells at ~2 per point: sparse data gets coarser cells rather than a + // gigantic mostly-empty index. + let mut cell = target_cell.max(f64::MIN_POSITIVE); + let mut dims = [0usize; D]; + loop { + let mut total = 1usize; + let mut overflow = false; + for d in 0..D { + let span = max[d] - min[d]; + dims[d] = ((span / cell).floor() as usize).saturating_add(1); + match total.checked_mul(dims[d]) { + Some(t) => total = t, + None => { + overflow = true; + break; + } + } + } + if !overflow && total <= points.len().saturating_mul(2).max(64) { + break; + } + cell *= 2.0; + } + + let n_cells: usize = dims.iter().product(); + let mut starts = vec![0u32; n_cells + 1]; + let index_of = |p: &[f64; D]| -> usize { + let mut idx = 0usize; + for d in 0..D { + let c = (((p[d] - min[d]) / cell).floor() as usize).min(dims[d] - 1); + idx = idx * dims[d] + c; + } + idx + }; + + for p in points { + starts[index_of(p) + 1] += 1; + } + for c in 0..n_cells { + starts[c + 1] += starts[c]; + } + let mut items = vec![0u32; points.len()]; + let mut cursor: Vec = starts[..n_cells].to_vec(); + for (i, p) in points.iter().enumerate() { + let c = index_of(p); + items[cursor[c] as usize] = i as u32; + cursor[c] += 1; + } + + Self { + cell, + min, + dims, + starts, + items, + } + } + + #[inline] + pub(crate) fn cell_size(&self) -> f64 { + self.cell + } + + /// Which cell a point falls in, per axis. + #[inline] + pub(crate) fn cell_of(&self, p: &[f64; D]) -> [usize; D] { + let mut out = [0usize; D]; + for d in 0..D { + out[d] = (((p[d] - self.min[d]) / self.cell).floor() as usize).min(self.dims[d] - 1); + } + out + } + + /// Largest Chebyshev ring that could still hold a point, from `centre`. + pub(crate) fn max_ring(&self, centre: &[usize; D]) -> usize { + centre + .iter() + .zip(&self.dims) + .fold(0usize, |r, (&c, &dim)| r.max(c).max(dim - 1 - c)) + } + + /// Calls `f` for every point in a cell at Chebyshev distance exactly `ring` from `centre`. + /// + /// Walking one ring at a time is what lets a k-nearest search stop as soon as its current + /// k-th distance is provably better than anything left unvisited. + pub(crate) fn for_each_in_ring( + &self, + centre: &[usize; D], + ring: usize, + mut f: impl FnMut(u32), + ) { + // Clamp each axis to the grid, then walk the box with an odometer, skipping the + // interior — those cells belong to a smaller ring and were visited already. + let mut lo = [0usize; D]; + let mut hi = [0usize; D]; + for d in 0..D { + lo[d] = centre[d].saturating_sub(ring); + hi[d] = (centre[d] + ring).min(self.dims[d] - 1); + } + + let mut at = lo; + loop { + let on_surface = ring == 0 + || (0..D).any(|d| { + (at[d] == centre[d].wrapping_sub(ring) && centre[d] >= ring) + || at[d] == centre[d] + ring + }); + if on_surface { + let idx = at + .iter() + .zip(&self.dims) + .fold(0usize, |acc, (&a, &dim)| acc * dim + a); + for &item in &self.items[self.starts[idx] as usize..self.starts[idx + 1] as usize] { + f(item); + } + } + + // Odometer step over the clamped box. + let mut d = D; + loop { + if d == 0 { + return; + } + d -= 1; + if at[d] < hi[d] { + at[d] += 1; + break; + } + at[d] = lo[d]; + } + } + } +} + +#[inline] +pub(crate) fn distance_sq(a: &[f64; D], b: &[f64; D]) -> f64 { + let mut s = 0.0; + for d in 0..D { + let diff = a[d] - b[d]; + s += diff * diff; + } + s +} diff --git a/src/spatial/mod.rs b/src/spatial/mod.rs index 5cdfb8c..345c7cf 100644 --- a/src/spatial/mod.rs +++ b/src/spatial/mod.rs @@ -9,6 +9,23 @@ //! RB for spatial domains — modularity's resolution limit bites harder here, where domains //! tend to be numerous and small. +//! # Reading Visium data +//! +//! Two things to know before wiring up a loader, both verified against the upstream source: +//! +//! * Space Ranger ≥ 2.0 renamed `tissue_positions_list.csv` to `tissue_positions.csv` **and +//! added a header row**. Parse the header rather than assuming column positions — scanpy +//! branches on the filename to decide. +//! * `scanpy.read_visium` labels the two pixel columns the wrong way round, then selects them +//! in the wrong order. The errors cancel, so `obsm["spatial"]` really is `(x, y)`, but the +//! names in `obs` are swapped. Don't trust `pxl_row_in_fullres` / `pxl_col_in_fullres` if +//! you read those columns directly. +//! +//! Neither affects `array_row` / `array_col`, which is what [`lattice_graph`] wants. + +pub(crate) mod grid; pub mod lattice; +pub mod points; pub use lattice::{Lattice, lattice_graph, visium_isometric_coords}; +pub use points::{SpatialWeight, Symmetry, knn_graph, radius_graph}; diff --git a/src/spatial/points.rs b/src/spatial/points.rs new file mode 100644 index 0000000..eacf2bd --- /dev/null +++ b/src/spatial/points.rs @@ -0,0 +1,738 @@ +//! Neighbour graphs from point coordinates, for assays that are not on a lattice. +//! +//! Xenium, MERFISH, CosMx and Slide-seq give each cell a position rather than a grid slot, so +//! neighbours have to be searched for. Both builders here are exact, over the uniform grid in +//! [`grid`](super::grid) — no approximation, so no recall gaps to make a graph +//! irreproducible. Both are checked against brute force on degenerate inputs. +//! +//! On Visium data, pass [`visium_isometric_coords`](super::visium_isometric_coords) rather +//! than raw lattice coordinates; see the module docs for why. + +use rayon::prelude::*; + +use crate::error::{ClusteringError, Result}; +use crate::network::{CSRNetwork, MAX_NODES}; +use crate::spatial::grid::{Grid, distance_sq}; + +/// How to reconcile the two directions of a k-nearest-neighbour relation. +/// +/// kNN is directed — `j` being among `i`'s nearest does not make `i` among `j`'s — but the +/// quality functions need a symmetric graph, so one of these has to be chosen. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)] +pub enum Symmetry { + /// Keep an edge if either endpoint chose the other. Degrees come out at or above `k`. + /// This is what scanpy and squidpy do. + #[default] + Union, + /// Keep an edge only if both endpoints chose each other. Degrees come out at or below + /// `k`, and the graph is sparser and more conservative — hub cells stop dragging in + /// distant neighbours. + Intersection, +} + +/// Weight to give an edge, as a function of its length. +#[derive(Debug, Clone, Copy, PartialEq)] +pub enum SpatialWeight { + /// Every edge weighs 1.0. Adjacency alone carries the signal. + Uniform, + /// `exp(-d² / (2σ²))`, so nearby cells count for more. `sigma` is in the same units as + /// the coordinates. + Gaussian { + /// Kernel bandwidth. + sigma: f64, + }, +} + +impl SpatialWeight { + #[inline] + fn apply(self, distance_sq: f64) -> f64 { + match self { + Self::Uniform => 1.0, + Self::Gaussian { sigma } => (-distance_sq / (2.0 * sigma * sigma)).exp(), + } + } + + fn validate(self) -> Result<()> { + match self { + Self::Gaussian { sigma } if !sigma.is_finite() || sigma <= 0.0 => { + Err(ClusteringError::InvalidConfig(format!( + "gaussian sigma must be finite and positive, got {sigma}" + ))) + } + _ => Ok(()), + } + } +} + +/// A neighbour and the square of its distance, kept squared to avoid a pointless `sqrt`. +type Candidate = (u32, f64); + +fn validate_points(points: &[[f64; D]]) -> Result<()> { + if points.len() > MAX_NODES { + return Err(ClusteringError::TooManyNodes { + n_nodes: points.len(), + }); + } + for (i, p) in points.iter().enumerate() { + if let Some(bad) = p.iter().find(|c| !c.is_finite()) { + return Err(ClusteringError::InvalidConfig(format!( + "point {i} has a non-finite coordinate {bad}" + ))); + } + } + Ok(()) +} + +/// Joins every pair of points closer than `radius`. +/// +/// The relation is symmetric by construction, so no reconciliation step is needed and the +/// result does not depend on any tie-breaking. `radius` is in coordinate units — for spatial +/// assays that means it is directly interpretable, e.g. 30 µm. +/// +/// Degree is unbounded: in dense tissue a generous radius can give a cell thousands of +/// neighbours, and cost downstream is linear in degree. `max_degree` caps it by keeping only +/// the nearest, but note the cap is applied **before** symmetrisation, so a node can still +/// finish slightly above it when a neighbour kept an edge it dropped. +/// +/// # Errors +/// +/// If `radius` is not finite and positive, a coordinate is not finite, or there are more +/// points than `u32` adjacency can address. +pub fn radius_graph( + points: &[[f64; D]], + radius: f64, + weight: SpatialWeight, + max_degree: Option, +) -> Result { + if !radius.is_finite() || radius <= 0.0 { + return Err(ClusteringError::InvalidConfig(format!( + "radius must be finite and positive, got {radius}" + ))); + } + validate_points(points)?; + weight.validate()?; + if points.is_empty() { + return CSRNetwork::from_csr_parts(vec![0], Vec::new(), Vec::new(), None); + } + + // Cells the size of the radius, so everything in range sits in the 3^D block around a + // point and one ring is all that ever needs visiting. + let grid = Grid::new(points, radius); + let r2 = radius * radius; + + let mut adjacency: Vec> = points + .par_iter() + .enumerate() + .map(|(i, p)| { + let centre = grid.cell_of(p); + let reach = (radius / grid.cell_size()).ceil() as usize; + let mut found: Vec = Vec::new(); + for ring in 0..=reach.min(grid.max_ring(¢re)) { + grid.for_each_in_ring(¢re, ring, |j| { + if j as usize != i { + let d2 = distance_sq(p, &points[j as usize]); + if d2 <= r2 { + found.push((j, d2)); + } + } + }); + } + // Sorted by (distance, id) so a capped result never depends on visit order — + // points at equal distance are the norm on regular tissue. + found.sort_unstable_by(|a, b| a.1.partial_cmp(&b.1).unwrap().then(a.0.cmp(&b.0))); + if let Some(cap) = max_degree { + found.truncate(cap); + } + found + }) + .collect(); + + // Capping breaks symmetry, so restore it. Without a cap this is already a no-op. + if max_degree.is_some() { + symmetrise(&mut adjacency, Symmetry::Union); + } + assemble(adjacency, weight) +} + +/// Joins every point to its `k` nearest neighbours, then makes the relation symmetric. +/// +/// Ties are broken on the lower node index, so a fixed input gives a fixed graph even where +/// many points sit at identical coordinates — which happens whenever coordinates have been +/// rounded to a pixel grid. +/// +/// # Errors +/// +/// If `k` is zero, a coordinate is not finite, or there are more points than `u32` adjacency +/// can address. +pub fn knn_graph( + points: &[[f64; D]], + k: usize, + symmetry: Symmetry, + weight: SpatialWeight, +) -> Result { + if k == 0 { + return Err(ClusteringError::InvalidConfig( + "k must be at least 1".into(), + )); + } + validate_points(points)?; + weight.validate()?; + if points.is_empty() { + return CSRNetwork::from_csr_parts(vec![0], Vec::new(), Vec::new(), None); + } + + // Aim for a handful of points per cell, so the first ring or two usually suffices. + let grid = Grid::new(points, typical_spacing(points, k)); + + let mut adjacency: Vec> = points + .par_iter() + .enumerate() + .map(|(i, p)| { + let centre = grid.cell_of(p); + let limit = grid.max_ring(¢re); + let mut found: Vec = Vec::with_capacity(k * 2); + + for ring in 0..=limit { + grid.for_each_in_ring(¢re, ring, |j| { + if j as usize != i { + found.push((j, distance_sq(p, &points[j as usize]))); + } + }); + if found.len() < k { + continue; + } + // Anything still unvisited sits at Chebyshev distance >= ring + 1, so it is + // at least `ring * cell` away — a point can be anywhere inside its own cell, + // which costs one ring of slack. Once the k-th best beats that, stop. + found.sort_unstable_by(|a, b| a.1.partial_cmp(&b.1).unwrap().then(a.0.cmp(&b.0))); + found.truncate(k.max(1).min(found.len())); + let safe = ring as f64 * grid.cell_size(); + if found[found.len() - 1].1 <= safe * safe { + break; + } + } + + found.sort_unstable_by(|a, b| a.1.partial_cmp(&b.1).unwrap().then(a.0.cmp(&b.0))); + found.truncate(k); + found + }) + .collect(); + + symmetrise(&mut adjacency, symmetry); + assemble(adjacency, weight) +} + +/// A cell size that puts roughly `k` points in each cell. +fn typical_spacing(points: &[[f64; D]], k: usize) -> f64 { + let mut min = [f64::INFINITY; D]; + let mut max = [f64::NEG_INFINITY; D]; + for p in points { + for d in 0..D { + min[d] = min[d].min(p[d]); + max[d] = max[d].max(p[d]); + } + } + let mut volume = 1.0f64; + for d in 0..D { + volume *= (max[d] - min[d]).max(f64::MIN_POSITIVE); + } + let per_cell = (k.max(1) as f64).max(1.0); + let cells = (points.len() as f64 / per_cell).max(1.0); + (volume / cells).powf(1.0 / D as f64).max(f64::MIN_POSITIVE) +} + +/// Rewrites `adjacency` in place so `j ∈ adj[i]` exactly when `i ∈ adj[j]`. +/// +/// Done explicitly rather than by emitting both directions and letting construction merge +/// them: duplicates are *summed* there, which would quietly give mutual pairs twice the +/// weight of one-sided ones. +fn symmetrise(adjacency: &mut [Vec], symmetry: Symmetry) { + let n = adjacency.len(); + + // Reverse lists via counting sort — O(total edges), and no per-node allocation. + let mut counts = vec![0u32; n + 1]; + for list in adjacency.iter() { + for &(u, _) in list { + counts[u as usize + 1] += 1; + } + } + for i in 0..n { + counts[i + 1] += counts[i]; + } + let mut reverse = vec![(0u32, 0.0f64); counts[n] as usize]; + let mut cursor: Vec = counts[..n].to_vec(); + for (v, list) in adjacency.iter().enumerate() { + for &(u, d) in list { + reverse[cursor[u as usize] as usize] = (v as u32, d); + cursor[u as usize] += 1; + } + } + + for (v, list) in adjacency.iter_mut().enumerate() { + let incoming = &reverse[counts[v] as usize..counts[v + 1] as usize]; + match symmetry { + Symmetry::Union => { + list.extend_from_slice(incoming); + list.sort_unstable_by_key(|&(u, _)| u); + list.dedup_by_key(|&mut (u, _)| u); + } + Symmetry::Intersection => { + let mut mutual: Vec = incoming.iter().map(|&(u, _)| u).collect(); + mutual.sort_unstable(); + list.retain(|&(u, _)| mutual.binary_search(&u).is_ok()); + list.sort_unstable_by_key(|&(u, _)| u); + } + } + } +} + +/// Flattens symmetric adjacency lists into a graph. +fn assemble(adjacency: Vec>, weight: SpatialWeight) -> Result { + let n = adjacency.len(); + let mut node_ptrs = Vec::with_capacity(n + 1); + node_ptrs.push(0usize); + let mut total = 0usize; + for list in &adjacency { + total += list.len(); + node_ptrs.push(total); + } + + let mut neighbors = Vec::with_capacity(total); + let mut weights = Vec::with_capacity(total); + for list in &adjacency { + for &(u, d2) in list { + neighbors.push(u); + weights.push(weight.apply(d2) as f32); + } + } + + CSRNetwork::from_csr_parts(node_ptrs, neighbors, weights, None) +} + +#[cfg(test)] +mod tests { + use super::*; + use rand::{Rng, SeedableRng}; + use std::collections::HashSet; + + fn grid(side: usize) -> Vec<[f64; 2]> { + (0..side) + .flat_map(|r| (0..side).map(move |c| [c as f64, r as f64])) + .collect() + } + + fn edge_set(g: &CSRNetwork) -> HashSet<(usize, usize)> { + let mut out = HashSet::new(); + for v in 0..g.node_count() { + for (u, _) in g.neighbors(v) { + out.insert((v.min(u), v.max(u))); + } + } + out + } + + /// On a unit grid the radius picks out a known connectivity, so this is exact. + #[test] + fn radius_on_a_unit_grid_is_exactly_the_expected_connectivity() { + let pts = grid(6); + let four = radius_graph(&pts, 1.01, SpatialWeight::Uniform, None).unwrap(); + let eight = radius_graph(&pts, 1.5, SpatialWeight::Uniform, None).unwrap(); + + // 6x6 grid: 4-connected has 2*6*5 = 60 edges, 8-connected adds 2*5*5 = 50 diagonals. + assert_eq!(four.edge_count(), 60); + assert_eq!(eight.edge_count(), 110); + assert!(edge_set(&four).is_subset(&edge_set(&eight))); + + for (v, p) in pts.iter().enumerate() { + if p[0] > 0.0 && p[0] < 5.0 && p[1] > 0.0 && p[1] < 5.0 { + assert_eq!(four.degree(v), 4); + assert_eq!(eight.degree(v), 8); + } + } + } + + /// A radius relation is symmetric before anything is done to it, so this must hold with + /// no reconciliation step at all. + #[test] + fn radius_is_symmetric_without_reconciliation() { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(4); + let pts: Vec<[f64; 2]> = (0..400) + .map(|_| [rng.random::() * 20.0, rng.random::() * 20.0]) + .collect(); + let g = radius_graph(&pts, 2.0, SpatialWeight::Uniform, None).unwrap(); + g.validate_symmetry().unwrap(); + assert!(g.edge_count() > 0); + } + + /// The cross-check that matters: the two builders must agree on Visium. + #[test] + fn isometric_radius_reproduces_the_exact_hex_lattice() { + use crate::spatial::lattice::{Lattice, lattice_graph, visium_isometric_coords}; + + let (mut rows, mut cols) = (Vec::new(), Vec::new()); + for r in 0..20u32 { + for i in 0..16u32 { + rows.push(r); + cols.push(2 * i + (r % 2)); + } + } + let exact = lattice_graph(&rows, &cols, Lattice::VisiumHex).unwrap(); + + let (x, y) = visium_isometric_coords(&rows, &cols, 100.0).unwrap(); + let pts: Vec<[f64; 2]> = x.iter().zip(&y).map(|(&a, &b)| [a, b]).collect(); + // Anything past the pitch but short of the next shell at pitch*sqrt(3). + let searched = radius_graph(&pts, 110.0, SpatialWeight::Uniform, None).unwrap(); + + assert_eq!(edge_set(&exact), edge_set(&searched)); + } + + #[test] + fn knn_union_contains_intersection_and_both_are_symmetric() { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(11); + let pts: Vec<[f64; 2]> = (0..300) + .map(|_| [rng.random::() * 50.0, rng.random::() * 50.0]) + .collect(); + + let u = knn_graph(&pts, 6, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + let i = knn_graph(&pts, 6, Symmetry::Intersection, SpatialWeight::Uniform).unwrap(); + u.validate_symmetry().unwrap(); + i.validate_symmetry().unwrap(); + + assert!(edge_set(&i).is_subset(&edge_set(&u))); + assert!( + i.edge_count() < u.edge_count(), + "random points give some one-sided pairs" + ); + + for v in 0..pts.len() { + assert!(u.degree(v) >= 6, "union degree is at least k"); + assert!(i.degree(v) <= 6, "intersection degree is at most k"); + } + } + + /// Coordinates rounded to a pixel grid produce many exact ties; those must not make the + /// result depend on kd-tree traversal order. + #[test] + fn duplicate_points_still_give_a_deterministic_graph() { + let mut pts = Vec::new(); + for i in 0..40 { + // Four coincident points at each of ten sites. + for _ in 0..4 { + pts.push([(i % 10) as f64, 0.0]); + } + } + let first = knn_graph(&pts, 5, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + for _ in 0..8 { + let again = knn_graph(&pts, 5, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + assert_eq!(edge_set(&first), edge_set(&again)); + } + assert!( + !first.has_self_loops(), + "a point must not be its own neighbour" + ); + } + + #[test] + fn gaussian_weights_fall_off_with_distance() { + let pts = vec![[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]]; + let g = radius_graph(&pts, 2.5, SpatialWeight::Gaussian { sigma: 1.0 }, None).unwrap(); + + let w = |a: usize, b: usize| g.neighbors(a).find(|&(u, _)| u == b).unwrap().1; + // exp(-1/2) and exp(-4/2), to f32 precision. + assert!((w(0, 1) - (-0.5f64).exp()).abs() < 1e-6, "got {}", w(0, 1)); + assert!((w(0, 2) - (-2.0f64).exp()).abs() < 1e-6, "got {}", w(0, 2)); + assert!(w(0, 1) > w(0, 2)); + assert_eq!(w(0, 1), w(1, 0), "the kernel must be symmetric"); + } + + #[test] + fn max_degree_bounds_the_hubs() { + // One dense clump: without a cap, every point sees every other. + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(2); + let pts: Vec<[f64; 2]> = (0..200) + .map(|_| [rng.random::(), rng.random::()]) + .collect(); + + let uncapped = radius_graph(&pts, 5.0, SpatialWeight::Uniform, None).unwrap(); + let capped = radius_graph(&pts, 5.0, SpatialWeight::Uniform, Some(10)).unwrap(); + assert_eq!(uncapped.degree(0), 199, "everything is within the radius"); + capped.validate_symmetry().unwrap(); + assert!(capped.edge_count() < uncapped.edge_count() / 5); + // Documented as a soft cap: symmetrisation can push a node back over it. + for v in 0..pts.len() { + assert!(capped.degree(v) >= 10, "each node keeps its own 10"); + } + } + + #[test] + fn works_in_three_dimensions() { + let pts: Vec<[f64; 3]> = (0..4) + .flat_map(|z| { + (0..4).flat_map(move |y| (0..4).map(move |x| [x as f64, y as f64, z as f64])) + }) + .collect(); + let g = radius_graph(&pts, 1.01, SpatialWeight::Uniform, None).unwrap(); + g.validate_symmetry().unwrap(); + // 4x4x4 grid, 6-connected: 3 * 4 * 4 * 3 = 144 edges. + assert_eq!(g.edge_count(), 144); + let interior = (0..pts.len()) + .find(|&v| pts[v].iter().all(|&c| c > 0.0 && c < 3.0)) + .unwrap(); + assert_eq!(g.degree(interior), 6); + } + + #[test] + fn node_order_does_not_change_the_graph() { + use rand::seq::SliceRandom; + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(9); + let pts: Vec<[f64; 2]> = (0..250) + .map(|_| [rng.random::() * 30.0, rng.random::() * 30.0]) + .collect(); + let reference: HashSet<(u64, u64)> = { + let g = knn_graph(&pts, 5, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + edge_set(&g) + .into_iter() + .map(|(a, b)| (a as u64, b as u64)) + .collect() + }; + + for _ in 0..5 { + let mut perm: Vec = (0..pts.len()).collect(); + perm.shuffle(&mut rng); + let shuffled: Vec<[f64; 2]> = perm.iter().map(|&i| pts[i]).collect(); + let g = knn_graph(&shuffled, 5, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + // Map back through the permutation and compare as sets. + let mapped: HashSet<(u64, u64)> = edge_set(&g) + .into_iter() + .map(|(a, b)| { + let (a, b) = (perm[a] as u64, perm[b] as u64); + (a.min(b), a.max(b)) + }) + .collect(); + assert_eq!(mapped, reference); + } + } + + /// `KdTree` panics past 32 items sharing a value on one axis. Spatial data does that as a + /// matter of course, so this covers the shapes that actually occur. + #[test] + fn survives_heavy_axis_alignment() { + // A full Visium row is 64 spots at one y; a capture area is 78 of them. + let mut visium = Vec::new(); + for r in 0..78 { + for i in 0..64 { + visium.push([(2 * i + (r % 2)) as f64 * 50.0, r as f64 * 86.6]); + } + } + let g = knn_graph(&visium, 6, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + g.validate_symmetry().unwrap(); + assert_eq!(g.node_count(), 4992); + + // A whole HD-sized row at a single y. + let row: Vec<[f64; 2]> = (0..3250).map(|i| [i as f64, 0.0]).collect(); + let g = radius_graph(&row, 1.5, SpatialWeight::Uniform, None).unwrap(); + assert_eq!(g.edge_count(), 3249, "a path along the row"); + + // Every point identical — the worst case for any tree. + let same = vec![[7.0, 7.0]; 200]; + for sym in [Symmetry::Union, Symmetry::Intersection] { + let g = knn_graph(&same, 5, sym, SpatialWeight::Uniform).unwrap(); + g.validate_symmetry() + .unwrap_or_else(|e| panic!("{sym:?}: {e}")); + assert!(!g.has_self_loops()); + } + } + + /// Point sets covering the shapes that break spatial indices: duplicates, collinear runs, + /// tight clumps, near-empty space. + fn awkward_point_sets(seed: u64) -> Vec<(String, Vec<[f64; 2]>)> { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(seed); + let mut out: Vec<(String, Vec<[f64; 2]>)> = Vec::new(); + + out.push(( + "uniform".into(), + (0..300) + .map(|_| [rng.random::() * 40.0, rng.random::() * 40.0]) + .collect(), + )); + out.push(( + "collinear".into(), + (0..200).map(|i| [i as f64, 0.0]).collect(), + )); + out.push(( + "grid_with_ties".into(), + (0..20) + .flat_map(|r| (0..20).map(move |c| [c as f64, r as f64])) + .collect(), + )); + out.push(("all_identical".into(), vec![[3.0, 4.0]; 150])); + out.push(( + "duplicated_pairs".into(), + (0..150).map(|i| [(i / 2) as f64, 0.0]).collect(), + )); + out.push(( + "two_far_clumps".into(), + (0..200) + .map(|i| { + let off = if i < 100 { 0.0 } else { 1000.0 }; + [off + rng.random::(), rng.random::()] + }) + .collect(), + )); + out.push(( + "one_dense_spike".into(), + (0..250) + .map(|i| { + if i < 200 { + [rng.random::() * 0.001, rng.random::() * 0.001] + } else { + [rng.random::() * 100.0, rng.random::() * 100.0] + } + }) + .collect(), + )); + out + } + + /// The radius search must agree with O(n²) brute force exactly, on every shape. + #[test] + fn radius_matches_brute_force() { + for (name, pts) in awkward_point_sets(21) { + for &radius in &[0.5f64, 1.5, 7.0] { + let g = radius_graph(&pts, radius, SpatialWeight::Uniform, None).unwrap(); + let got = edge_set(&g); + + let r2 = radius * radius; + let mut want = HashSet::new(); + for i in 0..pts.len() { + for j in (i + 1)..pts.len() { + let d2 = (pts[i][0] - pts[j][0]).powi(2) + (pts[i][1] - pts[j][1]).powi(2); + if d2 <= r2 { + want.insert((i, j)); + } + } + } + assert_eq!(got, want, "{name} at radius {radius}"); + } + } + } + + /// The k-nearest search must pick exactly the same neighbours brute force would, + /// including how it breaks ties. + #[test] + fn knn_matches_brute_force() { + for (name, pts) in awkward_point_sets(22) { + for &k in &[1usize, 3, 8] { + // Compare pre-symmetrisation, which is where the search itself is decided. + let mut want: Vec> = Vec::with_capacity(pts.len()); + for i in 0..pts.len() { + let mut all: Vec<(f64, u32)> = (0..pts.len()) + .filter(|&j| j != i) + .map(|j| { + let d2 = + (pts[i][0] - pts[j][0]).powi(2) + (pts[i][1] - pts[j][1]).powi(2); + (d2, j as u32) + }) + .collect(); + all.sort_unstable_by(|a, b| a.0.partial_cmp(&b.0).unwrap().then(a.1.cmp(&b.1))); + all.truncate(k); + let mut ids: Vec = all.into_iter().map(|(_, j)| j).collect(); + ids.sort_unstable(); + want.push(ids); + } + + // Intersection keeps only mutual pairs, so recover the directed lists by + // asking for the union and checking containment both ways instead. + let g = knn_graph(&pts, k, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + for (i, truth) in want.iter().enumerate() { + let neigh: HashSet = g.neighbors(i).map(|(u, _)| u).collect(); + for &j in truth { + assert!( + neigh.contains(&(j as usize)), + "{name} k={k}: node {i} lost its true neighbour {j}" + ); + } + } + // And every union edge must be justified by one side's true k-nearest. + for (i, truth) in want.iter().enumerate() { + for (u, _) in g.neighbors(i) { + assert!( + truth.contains(&(u as u32)) || want[u].contains(&(i as u32)), + "{name} k={k}: edge {i}-{u} is in neither node's k-nearest" + ); + } + } + } + } + } + + /// Xenium-scale: a few hundred thousand cells over a tissue section. + /// + /// ```text + /// cargo test --release --no-default-features xenium_scale -- --ignored --nocapture + /// ``` + #[test] + #[ignore = "takes a few seconds"] + fn xenium_scale() { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(1); + let n = 500_000; + // ~12 mm square at roughly Xenium cell density. + let pts: Vec<[f64; 2]> = (0..n) + .map(|_| { + [ + rng.random::() * 12_000.0, + rng.random::() * 12_000.0, + ] + }) + .collect(); + + let t = std::time::Instant::now(); + let g = knn_graph(&pts, 6, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + let knn = t.elapsed(); + + let t = std::time::Instant::now(); + let r = radius_graph(&pts, 30.0, SpatialWeight::Uniform, None).unwrap(); + let rad = t.elapsed(); + + println!( + "{n} cells\n knn(6) {:.2?} {} edges\n radius(30) {:.2?} {} edges", + knn, + g.edge_count(), + rad, + r.edge_count() + ); + g.validate_symmetry().unwrap(); + r.validate_symmetry().unwrap(); + } + + #[test] + fn rejects_bad_input() { + let pts = grid(3); + assert!(radius_graph(&pts, 0.0, SpatialWeight::Uniform, None).is_err()); + assert!(radius_graph(&pts, f64::NAN, SpatialWeight::Uniform, None).is_err()); + assert!(knn_graph(&pts, 0, Symmetry::Union, SpatialWeight::Uniform).is_err()); + assert!(radius_graph(&pts, 1.0, SpatialWeight::Gaussian { sigma: 0.0 }, None).is_err()); + let bad = vec![[0.0, 0.0], [f64::NAN, 1.0]]; + assert!(radius_graph(&bad, 1.0, SpatialWeight::Uniform, None).is_err()); + } + + #[test] + fn handles_degenerate_input() { + let none: Vec<[f64; 2]> = Vec::new(); + assert_eq!( + radius_graph(&none, 1.0, SpatialWeight::Uniform, None) + .unwrap() + .node_count(), + 0 + ); + let one = vec![[1.0, 2.0]]; + let g = knn_graph(&one, 5, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + assert_eq!(g.node_count(), 1); + assert_eq!(g.edge_count(), 0); + + // Nothing within the radius. + let far = vec![[0.0, 0.0], [1000.0, 0.0]]; + let g = radius_graph(&far, 1.0, SpatialWeight::Uniform, None).unwrap(); + assert_eq!(g.edge_count(), 0); + } +} From 448f927a272ab2e4462c887b6bf67f7cc1e2ca0a Mon Sep 17 00:00:00 2001 From: Ian Date: Sat, 1 Aug 2026 18:01:05 +0200 Subject: [PATCH 03/13] feat(spatial): Delaunay neighbour graphs with adaptive pruning MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Parameter-free neighbourhoods: no radius, no k, and the triangulation adapts to local density on its own. Verified exactly via Euler — a triangulation of n points with h on the convex hull has precisely 3n - 3 - h edges. A triangulation covers the convex hull, so it invents edges across ventricles, concave boundaries and between detached fragments. Joining two sides of a hole is exactly the error that yields a plausible-looking meaningless domain, so pruning is not optional. HullPruning::Adaptive drops an edge longer than `factor` times the local edge scale at *both* endpoints. Three details, each measured rather than assumed: - local scale is the 25th percentile of incident edge lengths, not the median. A node on a hole rim has several edges spanning the void, and those inflate its median until the artefact looks normal — the statistic hides what it is meant to expose. At the median, 6 of 42 fragment bridges survived at any factor. - compares against max of the two scales, not min. With min, a genuine boundary between a dense nest and loose stroma keeps only 19-40% of its edges; with max, 92-95%. - factor defaults to 2.0: the largest value that fully separates two detached fragments, the one unambiguous requirement. Costs ~5% of edges on uniform points, less on real tissue. It will not clear a hole 2-3 cells across; that needs ~1.5 and costs 16% everywhere, and at that width it is arguable whether the cells either side are neighbours anyway. Gabriel and relative-neighbourhood graphs are not the answer despite being parameter-free: their emptiness tests pass for a hole, so Gabriel leaves 19 of 42 fragment bridges and drops 21% of real edges, RNG drops 52%. Degenerate input (collinear, all-coincident) is a hard error rather than an edgeless graph, which would cluster into singletons and look like a result. 11 tests including Euler exactness, hole clearance, fragment separation, density-boundary preservation, permutation invariance, and contiguity of Leiden domains on the result. --- Cargo.lock | 16 + Cargo.toml | 1 + src/error.rs | 14 + src/spatial/delaunay.rs | 666 ++++++++++++++++++++++++++++++++++++++++ src/spatial/mod.rs | 2 + src/spatial/points.rs | 6 + 6 files changed, 705 insertions(+) create mode 100644 src/spatial/delaunay.rs diff --git a/Cargo.lock b/Cargo.lock index e6eae2d..6c06d49 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -347,6 +347,15 @@ version = "0.2.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "43da5946c66ffcc7745f48db692ffbb10a83bfe0afd96235c5c2a4fb23994929" +[[package]] +name = "delaunator" +version = "1.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3e1ee323c1275374f7e612d3724d12707079fb6a2117349fe144def656f5a880" +dependencies = [ + "robust", +] + [[package]] name = "divrem" version = "1.0.0" @@ -1131,6 +1140,12 @@ version = "0.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "2b15c43186be67a4fd63bee50d0303afffcef381492ebe2c5d87f324e1b8815c" +[[package]] +name = "robust" +version = "1.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "4e27ee8bb91ca0adcf0ecb116293afa12d393f9c2b9b9cd54d33e8078fe19839" + [[package]] name = "rustversion" version = "1.0.20" @@ -1237,6 +1252,7 @@ version = "0.7.0" dependencies = [ "anyhow", "criterion", + "delaunator", "hnsw_rs", "kiddo", "nalgebra-sparse", diff --git a/Cargo.toml b/Cargo.toml index a05f580..e4e9a5b 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -30,6 +30,7 @@ rand_chacha = { version = "0.9.0" } kiddo = { version = "5.2.2", optional = true } ndarray = { version = "0.16.1", features = ["rayon"], optional = true } hnsw_rs = { version = "0.3.2", features = ["simdeez_f"], optional = true } +delaunator = "1.1.0" [dev-dependencies] serde_json = "1.0" diff --git a/src/error.rs b/src/error.rs index 32cff75..1dc995b 100644 --- a/src/error.rs +++ b/src/error.rs @@ -76,6 +76,15 @@ pub enum ClusteringError { /// The offending position. position: (u32, u32), }, + /// No triangulation exists for the given points — they are collinear, or fewer than + /// three distinct positions. + /// + /// Reported rather than returning an edgeless graph, which would cluster into singletons + /// and look like a result. + DegenerateTriangulation { + /// How many points were supplied. + n_points: usize, + }, /// Parallel coordinate arrays had different lengths. CoordinateLengthMismatch { /// Length of the first array. @@ -134,6 +143,11 @@ impl fmt::Display for ClusteringError { "nodes {} and {} both sit at lattice position ({row}, {col})", nodes.0, nodes.1 ), + Self::DegenerateTriangulation { n_points } => write!( + f, + "no Delaunay triangulation exists for these {n_points} points: they are \ + collinear, or fewer than three distinct positions" + ), Self::CoordinateLengthMismatch { got, expected } => { write!(f, "coordinate array has length {got}, expected {expected}") } diff --git a/src/spatial/delaunay.rs b/src/spatial/delaunay.rs new file mode 100644 index 0000000..a01ddec --- /dev/null +++ b/src/spatial/delaunay.rs @@ -0,0 +1,666 @@ +//! Delaunay neighbour graphs. +//! +//! Parameter-free: no radius, no `k`. Each cell is joined to the ones it actually borders, +//! and the triangulation adapts to local density on its own — dense regions get short edges, +//! sparse regions long ones, with no threshold to tune. Average degree is just under 6, since +//! a triangulation of `n` points with `h` on the convex hull has exactly `3n - 3 - h` edges. +//! +//! # Why pruning is still needed +//! +//! A triangulation covers the *convex hull* of the points, so it invents edges wherever the +//! tissue is not convex: across ventricles and vessels, around concave boundaries, and +//! between physically separate fragments on the same section. Those edges are long, and +//! joining two sides of a hole is exactly the kind of error that produces a plausible-looking +//! but meaningless domain. +//! +//! Note that the parameter-free subgraphs — Gabriel, relative-neighbourhood — do **not** fix +//! this. Their tests ask whether other points lie near the edge, and the whole point of a +//! hole is that nothing does, so a hole-spanning edge passes. Measured on two detached +//! fragments, Gabriel leaves 19 of 42 bridges standing and drops 21% of genuine edges +//! elsewhere; the relative-neighbourhood graph drops 52%. Removing an artefact edge needs a +//! rule about length. See [`HullPruning`]. +//! +//! # What other tools do +//! +//! Worth knowing, because the defaults differ sharply: +//! +//! * **squidpy** applies no pruning at all — `delaunay=True` returns the raw triangulation, +//! convex hull included. Its `radius` and `percentile` filters both default to off, and in +//! 1.6.x a scalar `radius` is *silently ignored* on the Delaunay path. +//! * **Giotto** is the only one that derives a cutoff automatically, using a global Tukey +//! whisker over all edge lengths. Being global, it fails exactly where tissue density +//! varies: on a dense block beside 24x sparser stroma it shatters the sparse side into +//! ~570 components while barely touching the dense one. +//! +//! That is the case for a local rule, and why [`HullPruning::Adaptive`] is the default here. + +use crate::error::{ClusteringError, Result}; +use crate::network::{CSRNetwork, MAX_NODES}; +use crate::spatial::points::SpatialWeight; + +/// How to drop the edges a triangulation invents across empty space. +#[derive(Debug, Clone, Copy, PartialEq)] +pub enum HullPruning { + /// Keep the whole triangulation. Correct only when the tissue really is convex and + /// gap-free, which is rare. + None, + /// Drop edges longer than this, in coordinate units. Use when the tissue has a known + /// scale and roughly uniform density. + MaxDistance(f64), + /// Drop the longest `fraction` of all edges. + /// + /// One global cutoff, so it over-prunes sparse regions and under-prunes dense ones + /// whenever density varies — which in tissue it always does. + Percentile { + /// Share of edges to drop, in `[0, 1)`. + fraction: f64, + }, + /// Drop an edge longer than `factor` times the local edge scale at **both** its + /// endpoints, where local scale is a low quantile of a node's incident edge lengths. + /// + /// Compares against `max` of the two scales rather than `min`, so an edge that is long + /// only because one end sits in sparse tissue survives — a real boundary between a dense + /// nest and loose stroma should stay connected. A hole-spanning edge is long relative to + /// both ends and goes. + /// + /// The default of 2.0 is measured, by `calibrate_adaptive_factor`: it is the largest + /// factor that fully separates two detached tissue fragments, which is the one + /// unambiguous requirement — separate pieces must never be joined. It costs about 5% of + /// edges on uniform-random points, and less on real tissue, whose cells pack more + /// regularly than a Poisson process. + /// + /// What it will *not* do is clear a hole only two or three cells across. Removing those + /// needs a factor around 1.5, which drops ~16% of genuine edges everywhere. That is a + /// resolution limit rather than a tuning failure: at two cells wide it is genuinely + /// arguable whether the cells either side are neighbours. + Adaptive { + /// Multiple of the local scale above which an edge is dropped. + factor: f64, + }, +} + +impl Default for HullPruning { + fn default() -> Self { + Self::Adaptive { factor: 2.0 } + } +} + +impl HullPruning { + fn validate(self) -> Result<()> { + match self { + Self::MaxDistance(d) if !d.is_finite() || d <= 0.0 => { + Err(ClusteringError::InvalidConfig(format!( + "max distance must be finite and positive, got {d}" + ))) + } + Self::Percentile { fraction } if !(0.0..1.0).contains(&fraction) => { + Err(ClusteringError::InvalidConfig(format!( + "fraction must be in [0, 1), got {fraction}" + ))) + } + Self::Adaptive { factor } if !factor.is_finite() || factor <= 0.0 => { + Err(ClusteringError::InvalidConfig(format!( + "factor must be finite and positive, got {factor}" + ))) + } + _ => Ok(()), + } + } +} + +/// Builds a Delaunay neighbour graph from 2D coordinates. +/// +/// 2D only — the 3D equivalent is a tetrahedralisation, which is a different algorithm and a +/// much heavier dependency. For 3D use [`radius_graph`](super::radius_graph) or +/// [`knn_graph`](super::knn_graph). +/// +/// # Errors +/// +/// If the points are degenerate (all collinear, or fewer than three distinct positions), so +/// no triangulation exists. That is reported rather than returning an empty graph, since an +/// edgeless graph would cluster into singletons and look like a result. +pub fn delaunay_graph( + points: &[[f64; 2]], + pruning: HullPruning, + weight: SpatialWeight, +) -> Result { + pruning.validate()?; + if points.len() > MAX_NODES { + return Err(ClusteringError::TooManyNodes { + n_nodes: points.len(), + }); + } + for (i, p) in points.iter().enumerate() { + if !p[0].is_finite() || !p[1].is_finite() { + return Err(ClusteringError::InvalidConfig(format!( + "point {i} has a non-finite coordinate" + ))); + } + } + + let n = points.len(); + if n < 2 { + return CSRNetwork::from_csr_parts(vec![0; n + 1], Vec::new(), Vec::new(), None); + } + // Two points have no triangle but do border each other. + if n == 2 { + let d2 = distance_sq(points[0], points[1]); + return build(n, &[(0, 1, d2)], weight); + } + + let coords: Vec = points + .iter() + .map(|p| delaunator::Point { x: p[0], y: p[1] }) + .collect(); + let tri = delaunator::triangulate(&coords); + if tri.triangles.is_empty() { + return Err(ClusteringError::DegenerateTriangulation { n_points: n }); + } + + // Each triangle contributes three edges; neighbouring triangles share them, so collect + // into a set keyed on the ordered pair. + let mut edges: Vec<(u32, u32, f64)> = Vec::with_capacity(tri.triangles.len()); + for t in tri.triangles.chunks_exact(3) { + for &(a, b) in &[(t[0], t[1]), (t[1], t[2]), (t[2], t[0])] { + if a < b { + edges.push((a as u32, b as u32, distance_sq(points[a], points[b]))); + } else { + edges.push((b as u32, a as u32, distance_sq(points[a], points[b]))); + } + } + } + edges.sort_unstable_by_key(|e| (e.0, e.1)); + edges.dedup_by_key(|e| (e.0, e.1)); + + let kept = prune(&edges, n, pruning); + build(n, &kept, weight) +} + +#[inline] +fn distance_sq(a: [f64; 2], b: [f64; 2]) -> f64 { + let (dx, dy) = (a[0] - b[0], a[1] - b[1]); + dx * dx + dy * dy +} + +fn prune(edges: &[(u32, u32, f64)], n: usize, pruning: HullPruning) -> Vec<(u32, u32, f64)> { + match pruning { + HullPruning::None => edges.to_vec(), + HullPruning::MaxDistance(d) => { + let limit = d * d; + edges.iter().copied().filter(|e| e.2 <= limit).collect() + } + HullPruning::Percentile { fraction } => { + let mut lengths: Vec = edges.iter().map(|e| e.2).collect(); + lengths.sort_unstable_by(|a, b| a.partial_cmp(b).unwrap()); + let keep = ((edges.len() as f64) * (1.0 - fraction)).ceil() as usize; + if keep >= edges.len() { + return edges.to_vec(); + } + let limit = lengths[keep.saturating_sub(1).min(lengths.len() - 1)]; + edges.iter().copied().filter(|e| e.2 <= limit).collect() + } + HullPruning::Adaptive { factor } => { + let scale = local_scale(edges, n); + let f2 = factor * factor; + edges + .iter() + .copied() + .filter(|&(a, b, d2)| { + // Squared throughout, so the factor is squared too. + let s = scale[a as usize].max(scale[b as usize]); + d2 <= f2 * s + }) + .collect() + } + } +} + +/// Quantile of incident edge length used as a node's local spacing. +/// +/// Deliberately below the median. A node on the rim of a hole has several long edges reaching +/// across it, and those drag its median up until the spurious edges look normal — the median +/// hides exactly what we are trying to find. A lower quantile reads the genuine local +/// spacing, since at least a quarter of any node's edges go to real neighbours. +const SCALE_QUANTILE: f64 = 0.25; + +/// Squared local length scale per node. One small sort each, since Delaunay degree averages +/// just under 6. +fn local_scale(edges: &[(u32, u32, f64)], n: usize) -> Vec { + let mut incident: Vec> = vec![Vec::new(); n]; + for &(a, b, d2) in edges { + incident[a as usize].push(d2); + incident[b as usize].push(d2); + } + incident + .iter_mut() + .map(|lengths| { + if lengths.is_empty() { + return f64::INFINITY; + } + lengths.sort_unstable_by(|a, b| a.partial_cmp(b).unwrap()); + let idx = ((lengths.len() - 1) as f64 * SCALE_QUANTILE).round() as usize; + lengths[idx] + }) + .collect() +} + +/// Turns an undirected edge list into a symmetric graph. +fn build(n: usize, edges: &[(u32, u32, f64)], weight: SpatialWeight) -> Result { + let mut degree = vec![0u32; n]; + for &(a, b, _) in edges { + degree[a as usize] += 1; + degree[b as usize] += 1; + } + let mut node_ptrs = Vec::with_capacity(n + 1); + node_ptrs.push(0usize); + let mut total = 0usize; + for &d in °ree { + total += d as usize; + node_ptrs.push(total); + } + + let mut neighbors = vec![0u32; total]; + let mut weights = vec![0.0f32; total]; + let mut cursor: Vec = node_ptrs[..n].to_vec(); + for &(a, b, d2) in edges { + let w = weight.of(d2) as f32; + neighbors[cursor[a as usize]] = b; + weights[cursor[a as usize]] = w; + cursor[a as usize] += 1; + neighbors[cursor[b as usize]] = a; + weights[cursor[b as usize]] = w; + cursor[b as usize] += 1; + } + + CSRNetwork::from_csr_parts(node_ptrs, neighbors, weights, None) +} + +#[cfg(test)] +mod tests { + use super::*; + use rand::{Rng, SeedableRng}; + use std::collections::HashSet; + + fn uniform(n: usize, span: f64, seed: u64) -> Vec<[f64; 2]> { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(seed); + (0..n) + .map(|_| [rng.random::() * span, rng.random::() * span]) + .collect() + } + + /// A square of tissue with a circular hole punched out of the middle. + fn tissue_with_hole(seed: u64) -> (Vec<[f64; 2]>, [f64; 2], f64) { + tissue_with_hole_radius(seed, 15.0) + } + + fn tissue_with_hole_radius(seed: u64, hole: f64) -> (Vec<[f64; 2]>, [f64; 2], f64) { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(seed); + let centre = [50.0, 50.0]; + let pts = (0..3000) + .map(|_| [rng.random::() * 100.0, rng.random::() * 100.0]) + .filter(|p: &[f64; 2]| { + (p[0] - centre[0]).powi(2) + (p[1] - centre[1]).powi(2) > hole * hole + }) + .collect(); + (pts, centre, hole) + } + + /// Tissue in two pieces with a gap between, as a torn or folded section gives. + fn two_fragments(seed: u64) -> Vec<[f64; 2]> { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(seed); + (0..2000) + .map(|i| { + let off = if i % 2 == 0 { 0.0 } else { 75.0 }; + [off + rng.random::() * 50.0, rng.random::() * 50.0] + }) + .collect() + } + + /// Does the segment a-b pass through the disc? + fn crosses(a: [f64; 2], b: [f64; 2], centre: [f64; 2], r: f64) -> bool { + let (dx, dy) = (b[0] - a[0], b[1] - a[1]); + let len2 = dx * dx + dy * dy; + if len2 == 0.0 { + return false; + } + let t = (((centre[0] - a[0]) * dx + (centre[1] - a[1]) * dy) / len2).clamp(0.0, 1.0); + let (px, py) = (a[0] + t * dx, a[1] + t * dy); + (px - centre[0]).powi(2) + (py - centre[1]).powi(2) < r * r + } + + fn edge_set(g: &CSRNetwork) -> HashSet<(usize, usize)> { + let mut out = HashSet::new(); + for v in 0..g.node_count() { + for (u, _) in g.neighbors(v) { + out.insert((v.min(u), v.max(u))); + } + } + out + } + + /// Euler's formula: a Delaunay triangulation of `n` points with `h` on the convex hull + /// has exactly `3n - 3 - h` edges. Nothing approximate about it. + #[test] + fn unpruned_edge_count_matches_eulers_formula() { + for (n, seed) in [(50usize, 1u64), (500, 2), (2000, 3)] { + let pts = uniform(n, 100.0, seed); + let g = delaunay_graph(&pts, HullPruning::None, SpatialWeight::Uniform).unwrap(); + + let coords: Vec = pts + .iter() + .map(|p| delaunator::Point { x: p[0], y: p[1] }) + .collect(); + let h = delaunator::triangulate(&coords).hull.len(); + + assert_eq!(g.edge_count(), 3 * n - 3 - h, "n={n}, hull={h}"); + // Which puts average degree just under 6. + let avg = 2.0 * g.edge_count() as f64 / n as f64; + assert!(avg < 6.0 && avg > 5.0, "average degree {avg} at n={n}"); + } + } + + /// The reason pruning exists: a triangulation spans holes, and the default must not. + #[test] + fn adaptive_pruning_clears_a_tissue_hole() { + let (pts, centre, hole) = tissue_with_hole(7); + let count_crossing = |g: &CSRNetwork| { + edge_set(g) + .iter() + .filter(|&&(a, b)| crosses(pts[a], pts[b], centre, hole * 0.9)) + .count() + }; + + let raw = delaunay_graph(&pts, HullPruning::None, SpatialWeight::Uniform).unwrap(); + let pruned = delaunay_graph(&pts, HullPruning::default(), SpatialWeight::Uniform).unwrap(); + + let before = count_crossing(&raw); + let after = count_crossing(&pruned); + println!("hole-crossing edges: {before} raw -> {after} pruned"); + assert!( + before >= 5, + "the hole should be bridged before pruning: {before}" + ); + assert_eq!(after, 0, "{after} edges still cross the hole"); + // And it must not gut the rest of the graph. The default costs ~5% on uniform-random + // points, which are less regularly packed than real cells, so this is the pessimistic + // end of the range. + assert!( + pruned.edge_count() as f64 > 0.92 * raw.edge_count() as f64, + "pruning removed {} of {} edges", + raw.edge_count() - pruned.edge_count(), + raw.edge_count() + ); + } + + /// Why the rule compares against `max` of the two local scales, not `min`: a genuine + /// boundary between dense and sparse tissue has to survive. + #[test] + fn adaptive_pruning_keeps_a_dense_to_sparse_boundary() { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(5); + let mut pts: Vec<[f64; 2]> = Vec::new(); + // Dense nest on the left, loose stroma on the right, touching at x = 50. + for _ in 0..1500 { + pts.push([rng.random::() * 50.0, rng.random::() * 50.0]); + } + for _ in 0..150 { + pts.push([ + 50.0 + rng.random::() * 50.0, + rng.random::() * 50.0, + ]); + } + + let g = delaunay_graph(&pts, HullPruning::default(), SpatialWeight::Uniform).unwrap(); + let spanning = edge_set(&g) + .iter() + .filter(|&&(a, b)| (pts[a][0] - 50.0).signum() != (pts[b][0] - 50.0).signum()) + .count(); + assert!( + spanning > 10, + "only {spanning} edges cross the density boundary; the two regions are severed" + ); + } + + /// Sweeps the one tuned constant across every artefact it has to remove and everything it + /// must not, over several seeds, so the default is a measurement rather than a guess. + /// + /// A large hole is easy and tells you little. The discriminating cases are a + /// capillary-scale hole a few cell spacings across, and two separated fragments. + /// + /// ```text + /// cargo test --release --no-default-features calibrate_adaptive -- --ignored --nocapture + /// ``` + #[test] + #[ignore = "diagnostic sweep, not an assertion"] + fn calibrate_adaptive_factor() { + println!( + "{:>6} | {:>9} | {:>8} | {:>9} | {:>6} | {:>8}", + "factor", "hole r=15", "hole r=4", "fragments", "kept", "boundary" + ); + + for factor in [1.5, 2.0, 2.5, 3.0, 4.0, 6.0] { + let p = HullPruning::Adaptive { factor }; + let (mut big, mut small, mut bridges, mut kept, mut boundary) = + (0usize, 0usize, 0usize, 0.0f64, 0usize); + + for seed in 0..4u64 { + for (radius, counter) in [(15.0, &mut big), (4.0, &mut small)] { + let (pts, centre, hole) = tissue_with_hole_radius(seed, radius); + let g = delaunay_graph(&pts, p, SpatialWeight::Uniform).unwrap(); + *counter += edge_set(&g) + .iter() + .filter(|&&(a, b)| crosses(pts[a], pts[b], centre, hole * 0.9)) + .count(); + if radius == 15.0 { + let raw = delaunay_graph(&pts, HullPruning::None, SpatialWeight::Uniform) + .unwrap(); + kept += 100.0 * g.edge_count() as f64 / raw.edge_count() as f64; + } + } + + let frag = two_fragments(seed); + let g = delaunay_graph(&frag, p, SpatialWeight::Uniform).unwrap(); + bridges += edge_set(&g) + .iter() + .filter(|&&(a, b)| (frag[a][0] < 60.0) != (frag[b][0] < 60.0)) + .count(); + + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(100 + seed); + let mut mixed: Vec<[f64; 2]> = Vec::new(); + for _ in 0..1500 { + mixed.push([rng.random::() * 50.0, rng.random::() * 50.0]); + } + for _ in 0..150 { + mixed.push([ + 50.0 + rng.random::() * 50.0, + rng.random::() * 50.0, + ]); + } + let m = delaunay_graph(&mixed, p, SpatialWeight::Uniform).unwrap(); + boundary += edge_set(&m) + .iter() + .filter(|&&(a, b)| { + (mixed[a][0] - 50.0).signum() != (mixed[b][0] - 50.0).signum() + }) + .count(); + } + + println!( + "{factor:>6.1} | {big:>9} | {small:>8} | {bridges:>9} | {:>5.1}% | {boundary:>8}", + kept / 4.0 + ); + } + } + + /// Detached tissue pieces must never be joined — the criterion the default is set by. + #[test] + fn detached_fragments_are_never_bridged() { + for seed in 0..4 { + let pts = two_fragments(seed); + let g = delaunay_graph(&pts, HullPruning::default(), SpatialWeight::Uniform).unwrap(); + let bridges = edge_set(&g) + .iter() + .filter(|&&(a, b)| (pts[a][0] < 60.0) != (pts[b][0] < 60.0)) + .count(); + assert_eq!(bridges, 0, "seed {seed} left {bridges} bridging edges"); + + // And each fragment must survive intact rather than being shattered. + let c = crate::testdata::disconnected_community_count( + &g, + &pts.iter() + .map(|p| usize::from(p[0] >= 60.0)) + .collect::>(), + ); + assert_eq!(c, 0, "seed {seed}: a fragment came apart"); + } + } + + #[test] + fn pruning_modes_are_ordered_by_aggressiveness() { + let pts = uniform(1000, 100.0, 11); + let raw = delaunay_graph(&pts, HullPruning::None, SpatialWeight::Uniform).unwrap(); + + let mut previous = raw.edge_count(); + for fraction in [0.01, 0.05, 0.2] { + let g = delaunay_graph( + &pts, + HullPruning::Percentile { fraction }, + SpatialWeight::Uniform, + ) + .unwrap(); + assert!(g.edge_count() < previous, "fraction {fraction} kept more"); + previous = g.edge_count(); + } + + let mut previous = raw.edge_count(); + for factor in [4.0, 2.0, 1.2] { + let g = delaunay_graph( + &pts, + HullPruning::Adaptive { factor }, + SpatialWeight::Uniform, + ) + .unwrap(); + assert!(g.edge_count() <= previous, "factor {factor} kept more"); + previous = g.edge_count(); + } + } + + #[test] + fn is_symmetric_and_order_independent() { + use rand::seq::SliceRandom; + let pts = uniform(600, 80.0, 13); + let g = delaunay_graph(&pts, HullPruning::default(), SpatialWeight::Uniform).unwrap(); + g.validate_symmetry().unwrap(); + assert!(!g.has_self_loops()); + + let reference: HashSet<(usize, usize)> = edge_set(&g); + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(3); + for _ in 0..4 { + let mut perm: Vec = (0..pts.len()).collect(); + perm.shuffle(&mut rng); + let shuffled: Vec<[f64; 2]> = perm.iter().map(|&i| pts[i]).collect(); + let h = + delaunay_graph(&shuffled, HullPruning::default(), SpatialWeight::Uniform).unwrap(); + let mapped: HashSet<(usize, usize)> = edge_set(&h) + .into_iter() + .map(|(a, b)| (perm[a].min(perm[b]), perm[a].max(perm[b]))) + .collect(); + assert_eq!(mapped, reference); + } + } + + #[test] + fn max_distance_is_exact() { + let pts = uniform(500, 50.0, 17); + let limit = 3.0; + let g = delaunay_graph( + &pts, + HullPruning::MaxDistance(limit), + SpatialWeight::Uniform, + ) + .unwrap(); + for (a, b) in edge_set(&g) { + let d = distance_sq(pts[a], pts[b]).sqrt(); + assert!(d <= limit + 1e-9, "kept an edge of length {d}"); + } + } + + #[test] + fn degenerate_input_is_reported_not_silently_empty() { + // Collinear points have no triangulation. + let line: Vec<[f64; 2]> = (0..50).map(|i| [i as f64, 0.0]).collect(); + assert!(matches!( + delaunay_graph(&line, HullPruning::None, SpatialWeight::Uniform), + Err(ClusteringError::DegenerateTriangulation { .. }) + )); + + // As do coincident ones. + let same = vec![[1.0, 1.0]; 40]; + assert!(matches!( + delaunay_graph(&same, HullPruning::None, SpatialWeight::Uniform), + Err(ClusteringError::DegenerateTriangulation { .. }) + )); + } + + #[test] + fn handles_small_inputs() { + let none: Vec<[f64; 2]> = Vec::new(); + assert_eq!( + delaunay_graph(&none, HullPruning::None, SpatialWeight::Uniform) + .unwrap() + .node_count(), + 0 + ); + let one = vec![[0.0, 0.0]]; + let g = delaunay_graph(&one, HullPruning::None, SpatialWeight::Uniform).unwrap(); + assert_eq!((g.node_count(), g.edge_count()), (1, 0)); + + // Two points border each other even though there is no triangle. + let two = vec![[0.0, 0.0], [1.0, 0.0]]; + let g = delaunay_graph(&two, HullPruning::None, SpatialWeight::Uniform).unwrap(); + assert_eq!((g.node_count(), g.edge_count()), (2, 1)); + + let three = vec![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0]]; + let g = delaunay_graph(&three, HullPruning::None, SpatialWeight::Uniform).unwrap(); + assert_eq!(g.edge_count(), 3, "a single triangle"); + } + + #[test] + fn rejects_bad_config() { + let pts = uniform(50, 10.0, 1); + for bad in [ + HullPruning::MaxDistance(0.0), + HullPruning::MaxDistance(f64::NAN), + HullPruning::Percentile { fraction: 1.0 }, + HullPruning::Percentile { fraction: -0.1 }, + HullPruning::Adaptive { factor: 0.0 }, + ] { + assert!( + delaunay_graph(&pts, bad, SpatialWeight::Uniform).is_err(), + "{bad:?} should be rejected" + ); + } + let bad = vec![[0.0, 0.0], [f64::NAN, 1.0], [1.0, 1.0]]; + assert!(delaunay_graph(&bad, HullPruning::None, SpatialWeight::Uniform).is_err()); + } + + /// Leiden on a Delaunay graph must still give spatially contiguous domains. + #[test] + fn leiden_domains_stay_contiguous() { + use crate::community_search::leiden::{LeidenConfig, ObjectiveKind, leiden}; + let pts = uniform(2000, 100.0, 23); + let g = delaunay_graph(&pts, HullPruning::default(), SpatialWeight::Uniform).unwrap(); + let c = leiden( + &g, + &LeidenConfig { + objective: ObjectiveKind::Cpm { resolution: 0.2 }, + ..Default::default() + }, + ) + .unwrap(); + assert_eq!( + crate::testdata::disconnected_community_count(&g, c.labels()), + 0 + ); + assert!(c.n_clusters() > 1); + } +} diff --git a/src/spatial/mod.rs b/src/spatial/mod.rs index 345c7cf..4440286 100644 --- a/src/spatial/mod.rs +++ b/src/spatial/mod.rs @@ -23,9 +23,11 @@ //! //! Neither affects `array_row` / `array_col`, which is what [`lattice_graph`] wants. +pub mod delaunay; pub(crate) mod grid; pub mod lattice; pub mod points; +pub use delaunay::{HullPruning, delaunay_graph}; pub use lattice::{Lattice, lattice_graph, visium_isometric_coords}; pub use points::{SpatialWeight, Symmetry, knn_graph, radius_graph}; diff --git a/src/spatial/points.rs b/src/spatial/points.rs index eacf2bd..042e1dd 100644 --- a/src/spatial/points.rs +++ b/src/spatial/points.rs @@ -44,6 +44,12 @@ pub enum SpatialWeight { } impl SpatialWeight { + /// Weight for an edge of the given squared length. + #[inline] + pub(crate) fn of(self, distance_sq: f64) -> f64 { + self.apply(distance_sq) + } + #[inline] fn apply(self, distance_sq: f64) -> f64 { match self { From a36aad77fd9967fd7f2f716edace86781c8dff24 Mon Sep 17 00:00:00 2001 From: Ian Date: Sat, 1 Aug 2026 18:07:14 +0200 Subject: [PATCH 04/13] docs(spatial): ground the Delaunay pruning rule in prior art MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The adaptive rule turns out to be Zahn's 1971 inconsistent-edge criterion: delete an edge "significantly larger than the average of nearby edge weights on both sides", which is the same both-endpoints shape used here. He reports "a factor of 2 usually means the separation is quite apparent", with worked examples from 1.3 to 2.6 — so the default of 2.0, arrived at by measurement, is also the canonical value. Records his limitation too, since it carries over unchanged: the criterion "doesn't detect one-way gradients however steep", so a smoothly ramping density gives it no discontinuity to find. Also notes what it would take to add Gabriel/RNG later: Lingas (1994) extracts either from an existing triangulation in O(n), and the exclusion region must be open, since the closed variant disconnects on ties and gridded coordinates are made of ties. --- src/spatial/delaunay.rs | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/src/spatial/delaunay.rs b/src/spatial/delaunay.rs index a01ddec..c6a6625 100644 --- a/src/spatial/delaunay.rs +++ b/src/spatial/delaunay.rs @@ -20,6 +20,13 @@ //! elsewhere; the relative-neighbourhood graph drops 52%. Removing an artefact edge needs a //! rule about length. See [`HullPruning`]. //! +//! They may still be worth adding as an option one day — MST ⊆ RNG ⊆ GG ⊆ Delaunay, both are +//! always connected, and Lingas (1994) extracts either from an existing triangulation in +//! O(n), so they are nearly free once the Delaunay exists. Two things to get right if so: +//! use the **open** exclusion region, since the closed variant disconnects on ties and +//! gridded coordinates are made of ties; and expect mean degree ~4 for Gabriel and ~2.56 for +//! RNG against Delaunay's 6. +//! //! # What other tools do //! //! Worth knowing, because the defaults differ sharply: @@ -69,10 +76,17 @@ pub enum HullPruning { /// edges on uniform-random points, and less on real tissue, whose cells pack more /// regularly than a Poisson process. /// - /// What it will *not* do is clear a hole only two or three cells across. Removing those - /// needs a factor around 1.5, which drops ~16% of genuine edges everywhere. That is a - /// resolution limit rather than a tuning failure: at two cells wide it is genuinely - /// arguable whether the cells either side are neighbours. + /// It also happens to be the canonical value. Zahn's 1971 inconsistent-edge criterion is + /// this same shape — an edge "significantly larger than the average of nearby edge + /// weights on **both sides** of the edge" — and he reports that "a factor of 2 usually + /// means the separation is quite apparent", with worked examples spanning 1.3 to 2.6. + /// + /// Two things it will not do. It cannot clear a hole only two or three cells across: + /// that needs a factor near 1.5, which drops ~16% of genuine edges everywhere, and at + /// that width it is arguable whether the cells either side are neighbours at all. And, + /// as Zahn notes of the same criterion, it "doesn't detect one-way gradients however + /// steep" — a tissue whose density ramps smoothly across the section has no local + /// discontinuity for the rule to find. Adaptive { /// Multiple of the local scale above which an edge is dropped. factor: f64, From 4426206d6b7ce8a8c370edf32a01a95f78b204d4 Mon Sep 17 00:00:00 2001 From: Ian Date: Sat, 1 Aug 2026 20:32:33 +0200 Subject: [PATCH 05/13] test(spatial): stability and validity of the neighbour graphs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The existing tests prove each builder produces the graph it claims to. These ask the different question: does what comes out survive what real data does to you — cells missed by segmentation, centroids off by a fraction of a cell, a parameter picked slightly differently. Writing them turned up a limitation worth stating plainly. A bare spatial graph carries no expression, so the only thing it can recover is spatial structure. Physical separation it gets exactly: detached fragments and masked-apart tissue come back as connected components, with no resolution to choose, unchanged by 30% subsampling, by coordinate jitter, and across every parameter tried. Boundaries *inside* continuous tissue it does not get. There is nothing for the objective to anchor to, so it returns a tiling whose blob size the resolution sets. The tiling is spatially contiguous and looks exactly like a result, and it is arbitrary — resample and the boundaries move. My first attempt at these tests assumed otherwise and asserted domain recovery on planted density nests; it came back at ARI 0.09, which is the correct answer to the wrong question. So the file now asserts the limitation too: on homogeneous tissue the tiling must *fail* to reproduce under resampling. If that ever starts holding up, the assertion fires and says the docs need revisiting. 9 tests. The meaningful guarantee across resolutions is that a domain may subdivide an island but never spans two. --- tests/common/mod.rs | 30 ++++ tests/spatial_stability.rs | 334 +++++++++++++++++++++++++++++++++++++ 2 files changed, 364 insertions(+) create mode 100644 tests/spatial_stability.rs diff --git a/tests/common/mod.rs b/tests/common/mod.rs index 7f2cc17..4bce6bf 100644 --- a/tests/common/mod.rs +++ b/tests/common/mod.rs @@ -129,6 +129,36 @@ pub fn test_graphs() -> Vec<(String, CSRNetwork)> { out } +/// Adjusted Rand Index: agreement between two labellings, corrected for chance. +/// +/// 1.0 is exact agreement, 0.0 is what random labelling scores. Unlike NMI it does not +/// reward splitting, so a partition that shatters one of the two is penalised. +pub fn adjusted_rand_index(a: &[usize], b: &[usize]) -> f64 { + let n = a.len() as f64; + if a.len() < 2 { + return 1.0; + } + let ka = a.iter().max().map_or(0, |m| m + 1); + let kb = b.iter().max().map_or(0, |m| m + 1); + let mut joint = std::collections::HashMap::new(); + let (mut sa, mut sb) = (vec![0.0f64; ka], vec![0.0f64; kb]); + for i in 0..a.len() { + *joint.entry((a[i], b[i])).or_insert(0.0f64) += 1.0; + sa[a[i]] += 1.0; + sb[b[i]] += 1.0; + } + let comb2 = |x: f64| x * (x - 1.0) / 2.0; + let sum_ij: f64 = joint.values().map(|&c| comb2(c)).sum(); + let sum_a: f64 = sa.iter().map(|&c| comb2(c)).sum(); + let sum_b: f64 = sb.iter().map(|&c| comb2(c)).sum(); + let expected = sum_a * sum_b / comb2(n); + let max = 0.5 * (sum_a + sum_b); + if (max - expected).abs() < 1e-12 { + return 1.0; + } + (sum_ij - expected) / (max - expected) +} + /// Normalized mutual information between two labellings, in `[0, 1]`. pub fn nmi(a: &[usize], b: &[usize]) -> f64 { let n = a.len() as f64; diff --git a/tests/spatial_stability.rs b/tests/spatial_stability.rs new file mode 100644 index 0000000..5df7d70 --- /dev/null +++ b/tests/spatial_stability.rs @@ -0,0 +1,334 @@ +//! Stability and validity of the spatial neighbour graphs. +//! +//! The unit tests prove each builder produces the graph it claims to. That is necessary but +//! not sufficient: what matters on real data is whether what you get out survives what real +//! data does to you — cells missed by segmentation, coordinates off by a fraction of a cell, +//! a parameter chosen slightly differently. +//! +//! # What a bare spatial graph can and cannot do +//! +//! It carries no expression, so the only structure it encodes is spatial: which cells are +//! near which. Two consequences, and both are asserted here. +//! +//! It reliably recovers **physical separation** — detached fragments, tissue masked apart, +//! anything with a real gap. That shows up as connected components, needs no resolution +//! choice, and is stable under every perturbation tried below. +//! +//! It does **not** find boundaries inside continuous tissue. There is nothing for the +//! objective to anchor to, so it returns a tiling whose blob size is set by the resolution. +//! The tiling is spatially contiguous and looks exactly like a result, but it is arbitrary, +//! and therefore unstable: resample and the boundaries move. That is asserted too, so the +//! limitation cannot quietly stop being true. +//! +//! So the resolution-free question — "which cells could possibly be in the same domain" — is +//! the one a spatial graph answers. Choosing among the domains inside a connected region +//! needs expression, which arrives with neighbourhood feature averaging. + +mod common; + +use common::{adjusted_rand_index, disconnected_community_count}; +use rand::{Rng, SeedableRng}; +use rand_chacha::ChaCha8Rng; +use single_clustering::community_search::leiden::{LeidenConfig, ObjectiveKind, leiden}; +use single_clustering::network::CSRNetwork; +use single_clustering::spatial::{ + HullPruning, Lattice, SpatialWeight, Symmetry, delaunay_graph, knn_graph, lattice_graph, + radius_graph, +}; + +/// Four tissue islands with clear space between them. +fn separated_tissue(seed: u64) -> (Vec<[f64; 2]>, Vec) { + let mut rng = ChaCha8Rng::seed_from_u64(seed); + let centres = [[20.0, 20.0], [80.0, 20.0], [20.0, 80.0], [80.0, 80.0]]; + let radius = 18.0; + + let mut pts = Vec::new(); + let mut truth = Vec::new(); + for (i, c) in centres.iter().enumerate() { + for _ in 0..700 { + let a = rng.random::() * std::f64::consts::TAU; + let r = radius * rng.random::().sqrt(); + pts.push([c[0] + r * a.cos(), c[1] + r * a.sin()]); + truth.push(i); + } + } + (pts, truth) +} + +/// Featureless tissue at one density — nothing for a spatial objective to find. +fn homogeneous_tissue(seed: u64) -> Vec<[f64; 2]> { + let mut rng = ChaCha8Rng::seed_from_u64(seed); + (0..2500) + .map(|_| [rng.random::() * 100.0, rng.random::() * 100.0]) + .collect() +} + +/// Connected component of each node — the resolution-free structure a spatial graph encodes. +fn components(graph: &CSRNetwork) -> Vec { + let n = graph.node_count(); + let mut label = vec![usize::MAX; n]; + let mut next = 0; + for start in 0..n { + if label[start] != usize::MAX { + continue; + } + let mut stack = vec![start]; + label[start] = next; + while let Some(v) = stack.pop() { + for (u, _) in graph.neighbors(v) { + if label[u] == usize::MAX { + label[u] = next; + stack.push(u); + } + } + } + next += 1; + } + label +} + +fn cluster(graph: &CSRNetwork, resolution: f64) -> Vec { + let config = LeidenConfig { + objective: ObjectiveKind::Cpm { resolution }, + ..Default::default() + }; + leiden(graph, &config).unwrap().labels().to_vec() +} + +const BUILDERS: [&str; 3] = ["radius", "knn", "delaunay"]; + +fn build(name: &str, pts: &[[f64; 2]]) -> CSRNetwork { + match name { + "radius" => radius_graph(pts, 4.0, SpatialWeight::Uniform, None).unwrap(), + "knn" => knn_graph(pts, 8, Symmetry::Union, SpatialWeight::Uniform).unwrap(), + _ => delaunay_graph(pts, HullPruning::default(), SpatialWeight::Uniform).unwrap(), + } +} + +/// Validity: separated tissue must come back as separate components — exactly, and with no +/// resolution to choose. +#[test] +fn every_builder_separates_disconnected_tissue() { + let (pts, truth) = separated_tissue(1); + for name in BUILDERS { + let agreement = adjusted_rand_index(&components(&build(name, &pts)), &truth); + assert!( + (agreement - 1.0).abs() < 1e-9, + "{name}: components do not match the islands (ARI {agreement:.4})" + ); + } +} + +/// Validity: a domain may subdivide an island, but must never span two. That holds at every +/// resolution, which is what makes it the meaningful guarantee. +#[test] +fn no_domain_ever_spans_separated_tissue() { + let (pts, truth) = separated_tissue(7); + for name in BUILDERS { + let graph = build(name, &pts); + for resolution in [0.01, 0.05, 0.2, 1.0] { + let labels = cluster(&graph, resolution); + let mut owner = std::collections::HashMap::new(); + for (v, &c) in labels.iter().enumerate() { + let island = *owner.entry(c).or_insert(truth[v]); + assert_eq!( + island, truth[v], + "{name} at resolution {resolution}: a domain spans two islands" + ); + } + } + } +} + +/// Stability: segmentation misses cells, so dropping some must not change the structure. +#[test] +fn structure_survives_subsampling() { + let (pts, _) = separated_tissue(2); + for name in BUILDERS { + let full = components(&build(name, &pts)); + + for drop in [0.05, 0.15, 0.3] { + let mut rng = ChaCha8Rng::seed_from_u64(99); + let keep: Vec = (0..pts.len()) + .filter(|_| rng.random::() > drop) + .collect(); + let sub: Vec<[f64; 2]> = keep.iter().map(|&i| pts[i]).collect(); + let restricted: Vec = keep.iter().map(|&i| full[i]).collect(); + + let agreement = adjusted_rand_index(&components(&build(name, &sub)), &restricted); + assert!( + agreement > 0.99, + "{name}: dropping {:.0}% of cells changed the structure ({agreement:.4})", + drop * 100.0 + ); + } + } +} + +/// Stability: segmentation centroids are not exact, so jitter below cell spacing must not +/// move anything. +#[test] +fn structure_survives_coordinate_jitter() { + let (pts, _) = separated_tissue(3); + for name in BUILDERS { + let reference = components(&build(name, &pts)); + + let mut rng = ChaCha8Rng::seed_from_u64(7); + let jittered: Vec<[f64; 2]> = pts + .iter() + .map(|p| { + [ + p[0] + (rng.random::() - 0.5) * 0.5, + p[1] + (rng.random::() - 0.5) * 0.5, + ] + }) + .collect(); + + let agreement = adjusted_rand_index(&components(&build(name, &jittered)), &reference); + assert!( + agreement > 0.99, + "{name}: coordinate jitter changed the structure ({agreement:.4})" + ); + } +} + +/// The builders define "neighbour" differently, but must agree about real structure. +#[test] +fn builders_agree_on_real_structure() { + let (pts, _) = separated_tissue(5); + let labels: Vec<(&str, Vec)> = BUILDERS + .iter() + .map(|&name| (name, components(&build(name, &pts)))) + .collect(); + + for i in 0..labels.len() { + for j in (i + 1)..labels.len() { + let agreement = adjusted_rand_index(&labels[i].1, &labels[j].1); + assert!( + agreement > 0.99, + "{} and {} disagree ({agreement:.4})", + labels[i].0, + labels[j].0 + ); + } + } +} + +/// Stability: neighbouring parameter values must give the same structure. A cliff would mean +/// the parameter is load-bearing in a way users cannot reason about. +#[test] +fn parameters_do_not_change_the_structure() { + let (pts, truth) = separated_tissue(4); + + for radius in [2.5f64, 3.0, 3.5, 4.0, 5.0] { + let g = radius_graph(&pts, radius, SpatialWeight::Uniform, None).unwrap(); + let agreement = adjusted_rand_index(&components(&g), &truth); + assert!( + agreement > 0.99, + "radius {radius} broke the structure ({agreement:.4})" + ); + } + for k in [4usize, 6, 8, 12, 16] { + let g = knn_graph(&pts, k, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + let agreement = adjusted_rand_index(&components(&g), &truth); + assert!( + agreement > 0.99, + "k {k} broke the structure ({agreement:.4})" + ); + } + for factor in [1.5f64, 2.0, 2.5, 3.0] { + let g = delaunay_graph( + &pts, + HullPruning::Adaptive { factor }, + SpatialWeight::Uniform, + ) + .unwrap(); + let agreement = adjusted_rand_index(&components(&g), &truth); + assert!( + agreement > 0.99, + "factor {factor} broke the structure ({agreement:.4})" + ); + } +} + +/// The documented limitation, asserted so it cannot quietly stop being true. +/// +/// On uniform tissue there is nothing spatial to find, so the result is an arbitrary tiling: +/// contiguous, plausible-looking, and not reproducible under resampling. Anyone clustering a +/// bare spatial graph on continuous tissue is reading tea leaves, and this is the evidence. +#[test] +fn homogeneous_tissue_gives_an_arbitrary_unstable_tiling() { + let pts = homogeneous_tissue(4); + let graph = build("delaunay", &pts); + let reference = cluster(&graph, 0.1); + + assert_eq!( + components(&graph).iter().max(), + Some(&0), + "one tissue region" + ); + assert!( + reference.iter().max().unwrap() > &3, + "yet the clustering carves it into many blobs" + ); + assert_eq!( + disconnected_community_count(&graph, &reference), + 0, + "each of them contiguous, which is what makes the tiling look like a result" + ); + + let mut rng = ChaCha8Rng::seed_from_u64(31); + let keep: Vec = (0..pts.len()) + .filter(|_| rng.random::() > 0.1) + .collect(); + let sub: Vec<[f64; 2]> = keep.iter().map(|&i| pts[i]).collect(); + let restricted: Vec = keep.iter().map(|&i| reference[i]).collect(); + let agreement = adjusted_rand_index(&cluster(&build("delaunay", &sub), 0.1), &restricted); + + assert!( + agreement < 0.8, + "a tiling of featureless tissue reproduced at ARI {agreement:.3}; if that now holds \ + up, the limitation documented in this file no longer applies and the module docs \ + need revisiting" + ); +} + +/// Whatever the builder or resolution, a domain must be one connected region of tissue. +#[test] +fn domains_are_always_spatially_contiguous() { + let (pts, _) = separated_tissue(6); + for name in BUILDERS { + let graph = build(name, &pts); + for resolution in [0.01, 0.05, 0.1, 0.3, 1.0] { + let labels = cluster(&graph, resolution); + assert_eq!( + disconnected_community_count(&graph, &labels), + 0, + "{name} at resolution {resolution} split a domain across space" + ); + } + } +} + +/// The lattice path: tissue detection masking a band must split the capture area. +#[test] +fn lattice_masking_splits_the_capture_area_cleanly() { + let (mut rows, mut cols, mut truth) = (Vec::new(), Vec::new(), Vec::new()); + for r in 0..78u32 { + if (30..48).contains(&r) { + continue; + } + for i in 0..64u32 { + rows.push(r); + cols.push(2 * i + (r % 2)); + truth.push(usize::from(r >= 48)); + } + } + let g = lattice_graph(&rows, &cols, Lattice::VisiumHex).unwrap(); + let agreement = adjusted_rand_index(&components(&g), &truth); + assert!( + (agreement - 1.0).abs() < 1e-9, + "the masked-apart regions were not separated ({agreement:.4})" + ); + assert_eq!(disconnected_community_count(&g, &cluster(&g, 0.05)), 0); +} From 93f143f78ce0ca5c7f578f21dab01dd0bfd62348 Mon Sep 17 00:00:00 2001 From: Ian Date: Sat, 1 Aug 2026 21:10:08 +0200 Subject: [PATCH 06/13] docs: pre-register the DLPFC benchmark target MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Written before implementing spatial domain detection so the target cannot drift to meet the result. Target: median ARI 0.46-0.52 across the 12 sections, against a non-spatial baseline of 0.38-0.43. The range is a real disagreement, not imprecision — BANKSY self-reports 0.518 from its own deposited artifacts, while the Genome Biology benchmark puts it near 0.46 running the same method on the same sections. Take the lower one. Records the independent medians for ten methods, the protocol rule that decides comparability (median ARI over resolutions yielding the correct cluster count, not best-of-sweep), and two traps: BANKSY's published ARIs are on smoothed labels via a SmoothLabels call visible only in its deposited code and never mentioned in the paper, and configuration can swamp method differences — DeepST scores 0.538 and 0.229 on the same section in two independent benchmarks. Also pins BANKSY's Visium parameters, including that lambda is 0.2 for Visium domain segmentation rather than the 0.8 used elsewhere, and that use_agf=TRUE means mean + gradient, so a mean-only implementation is not a faithful replication. --- docs/spatial-benchmark.md | 116 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 docs/spatial-benchmark.md diff --git a/docs/spatial-benchmark.md b/docs/spatial-benchmark.md new file mode 100644 index 0000000..4c707ad --- /dev/null +++ b/docs/spatial-benchmark.md @@ -0,0 +1,116 @@ +# DLPFC benchmark target + +Written **before** implementing spatial domain detection, so the target cannot drift to meet +the result. Everything here is sourced; anything unverified is marked. + +Dataset: 10x Visium human dorsolateral prefrontal cortex, Maynard et al. 2021. 12 sections, +3431–4788 spots each, manual annotation of cortical layers L1–L6 + white matter. + +## The target + +**Median ARI 0.46–0.52 across the 12 sections**, against a **non-spatial baseline of +0.38–0.43**. + +The range on the target is not imprecision — it is a real disagreement: + +| Source | BANKSY median ARI | +|---|---| +| Self-reported (authors' deposited artifacts) | 0.518 | +| Independent (Hu et al., Genome Biology 2024) | ~0.46 | + +Take the lower number as the bar. Method papers grade their own homework. + +## Where everyone else lands + +Independent, all 12 sections, computed from the BenchmarkST repo's per-section ARI files +(Hu et al., Genome Biology 2024;25(1):212): + +| Method | median | mean | +|---|---|---| +| GraphST | 0.559 | 0.557 | +| STAGATE | 0.523 | 0.505 | +| ADEPT | 0.519 | 0.531 | +| **BANKSY** | **~0.46** | — | +| CCST | 0.434 | 0.459 | +| SEDR | 0.426 | 0.414 | +| ConGI | 0.419 | 0.419 | +| SpaGCN | 0.412 | 0.392 | +| conST | 0.394 | 0.395 | +| DeepST | 0.262 | 0.300 | + +GraphST is the ceiling set by heavy deep-learning methods. It is **not** our bar — a +feature-augmentation approach that then runs ordinary Leiden should be measured against +BANKSY, which is the same shape. + +## The bar that justifies existing + +Non-spatial clustering on expression alone, section 151673: + +| | ARI | +|---|---| +| Seurat (expression only) | 0.430 | +| BANKSY's own λ=0 arm, same pipeline | 0.382 | +| Leiden / Louvain | **unconfirmed**, stated as below Seurat's 0.430 | + +If we do not clear ~0.43 by a clear margin, the spatial machinery is decoration. BANKSY's +claimed gain over its own baseline is ~0.58 vs ~0.38 on that section, i.e. **+0.184**. + +## Protocol — the part that decides comparability + +**Cluster count.** Sweep Leiden resolution 0.1–1.5. Keep only runs whose cluster count +exactly matches the section's annotated layer count. Report the **median ARI over all +qualifying resolutions** — not the best. Reporting best-of-sweep makes the number +incomparable with every published figure. Fallback when nothing matches: widen to k±1; if +still nothing, report nothing. + +**Exclude unannotated spots.** 28 of 3639 on 151673. Small effect (~0.004) but it shifts +everything. + +**5 vs 7 clusters.** Most sections have 7 layers; some have 5 (L3, L4, L5, L6, WM). Reported +as the 151669–151672 group — *unverified*, check each section's annotation set directly. + +**ARI implementation** is not a source of difference: `sklearn.adjusted_rand_score` and +`mclust::adjustedRandIndex` agree, and an independent from-scratch implementation reproduced +published values to three decimals. + +## Two traps + +**BANKSY's published ARIs are on smoothed labels, and the paper never says so.** The +deposited script calls `SmoothLabels(k = 6)` before scoring — visible only in code. Comparing +our raw labels against 0.518 would understate us. Either replicate the smoothing or state +plainly that the comparison is unsmoothed-to-smoothed. + +**Configuration can swamp method differences.** DeepST scores 0.538 in one independent +benchmark and 0.229 in another, on the same section — a swing of 0.31, far larger than the +gaps between most methods. GraphST, by contrast, reproduces to three decimals across groups. +Budget for this before concluding anything from a single number. + +## Matching BANKSY, if we replicate it + +Parameters for Visium domain segmentation, confirmed from the benchmark run script and a +quoted reply from BANKSY's author: + +- `lambda = 0.2` — for Visium v1/v2 domain segmentation. **Not 0.8**; that is for Visium HD + and other technologies. +- `k_geom = c(18, 18)` for DLPFC +- `use_agf = TRUE` — the feature matrix is neighbourhood **mean + azimuthal Gabor gradient**. + A mean-only implementation is not a faithful replication and should not be compared + against BANKSY's number. +- 2000 HVGs, 20 PCs, SNN graph at k_expr = 50 + +## Getting the data + +`zenodo.org/records/15114362` → `Benchmark_ST_analysis-master.zip`, **18.8 MB**. Contains a +complete section 151673 (`filtered_feature_bc_matrix.h5`, `metadata.tsv`, `spatial/`) **plus +predicted labels from 14 competing methods**, so a single-section comparison costs one small +download and no reimplementation of anyone else's method. + +Full source is spatialLIBD, `http://spatial.libd.org/spatialLIBD/`; annotations ship with the +data as `layer_guess` / `layer_guess_reordered` columns in `metadata.tsv`. + +## Unconfirmed + +Self-reported numbers from the BayesSpace / SpaGCN / STAGATE / GraphST papers (so no +self-vs-independent gap for any of them); exact Leiden/Louvain DLPFC ARI; BANKSY's λ=0 arm +across all 12 sections and whether it too was smoothed; SDMBench (Yuan et al., Nature Methods +2024) numbers, which is the most likely source of a clean independent Leiden figure. From fc8329c2707d944376fbf54449740c00df03a696 Mon Sep 17 00:00:00 2001 From: Ian Date: Sat, 1 Aug 2026 21:21:46 +0200 Subject: [PATCH 07/13] docs: which DLPFC numbers actually reproduce MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fills in the self-reported vs independent comparison, which is the part that decides what to trust: GraphST 151673 self 0.635 indep 0.633/0.638 gap ~0.00 BayesSpace 151673 self 0.55 indep 0.550 gap 0.00 STAGATE 151676 self 0.60 indep 0.493 gap 0.11 BANKSY 12-slice self 0.518 indep 0.469 gap 0.05 Two of four survive independent replication. BayesSpace also disagrees between two independent benchmarks (0.550 vs ~0.40 on the same section), so independence alone does not settle it either. Verifies from code what no paper states: sample 2 (151669-151672) is annotated L3-L6 + WM, so 5 clusters, the other eight sections 7. Also that the benchmark drops unannotated spots before clustering rather than before scoring — GraphST's own tutorial does the opposite, letting them train the model and vote in spatial refinement, and hardcodes 7 clusters for every section including the 5-cluster ones. Expands the independent table to 15 methods and marks which are machine-readable from the benchmark's raw per-run files versus recovered from the figure. BANKSY is figure-derived and new in the published version, so 0.469 carries +/-0.01 and is not an exact published value. --- docs/spatial-benchmark.md | 98 ++++++++++++++++++++++++++++++--------- 1 file changed, 77 insertions(+), 21 deletions(-) diff --git a/docs/spatial-benchmark.md b/docs/spatial-benchmark.md index 4c707ad..bb3f28d 100644 --- a/docs/spatial-benchmark.md +++ b/docs/spatial-benchmark.md @@ -22,26 +22,68 @@ Take the lower number as the bar. Method papers grade their own homework. ## Where everyone else lands -Independent, all 12 sections, computed from the BenchmarkST repo's per-section ARI files -(Hu et al., Genome Biology 2024;25(1):212): +Independent, all 12 sections, mean across slices of each slice's 20-run mean. Computed from +the BenchmarkST repo's raw per-run ARI files (Hu et al., Genome Biology 2024;25(1):212). -| Method | median | mean | +| Method | mean over 12 slices | source | |---|---|---| -| GraphST | 0.559 | 0.557 | -| STAGATE | 0.523 | 0.505 | -| ADEPT | 0.519 | 0.531 | -| **BANKSY** | **~0.46** | — | -| CCST | 0.434 | 0.459 | -| SEDR | 0.426 | 0.414 | -| ConGI | 0.419 | 0.419 | -| SpaGCN | 0.412 | 0.392 | -| conST | 0.394 | 0.395 | -| DeepST | 0.262 | 0.300 | +| GraphST | 0.557 | raw data | +| ADEPT | 0.530 | raw data | +| STAGATE | 0.503 | raw data | +| BASS | ~0.49 | figure | +| SpatialPCA | ~0.48 | figure | +| **BANKSY** | **0.469** | figure, ±0.01 | +| CCST | 0.451 | raw data | +| PRECAST | ~0.46 | figure | +| ConGI | 0.422 | raw data | +| SEDR | 0.411 | raw data | +| SpaGCN | 0.392 | raw data | +| conST | 0.385 | raw data | +| BayesSpace | ~0.37 | figure | +| DeepST | 0.322 | raw data | +| DR.SC | ~0.33 | figure | + +Nine methods have machine-readable per-run results in `ari_results/*.txt`; the rest exist +only inside the Fig. 2a raster and were recovered by colourmap inversion cross-checked +against the bioRxiv preprint's vector figures. **BANKSY is one of the figure-derived ones** +— it is new in the published version, so there is no vector cross-check. Treat 0.469 as +±0.01, not an exact published value. + +This benchmark contains **no non-spatial baseline** — no Seurat, Leiden, Louvain or k-means +anywhere in it. It cannot answer the question that matters most to us. GraphST is the ceiling set by heavy deep-learning methods. It is **not** our bar — a feature-augmentation approach that then runs ordinary Leiden should be measured against BANKSY, which is the same shape. +## Self-reported vs independent + +Which published numbers survive someone else running them. Self-reported values are the +stored outputs in each tool's own tutorial notebooks. + +| Method | section | self-reported | independent | gap | +|---|---|---|---|---| +| GraphST | 151673 | 0.635 | 0.633 (Kang), 0.638 (Hu) | **~0.00** | +| BayesSpace | 151673 | 0.55 | 0.550 (Kang) | **0.00** | +| STAGATE | 151676 | 0.60 | 0.493 (Hu) | **0.11** | +| BANKSY | 12-slice | 0.518 | 0.469 (Hu) | **0.05** | + +GraphST and BayesSpace reproduce essentially exactly. STAGATE and BANKSY do not. That is the +single most useful fact here: a published number is not evidence unless someone independent +has reproduced it, and two of the four checked did not survive. + +BayesSpace also disagrees *between* independent benchmarks — 0.550 (Kang) vs ~0.40 (Hu) on +the same section — so "independent" is not automatically decisive either. + +Two protocol details found in the tools' own tutorials, both of which change results: + +* **GraphST's own tutorial hardcodes `n_clusters = 7` for every section.** Run as-published + on 151669–151672, it asks for 7 clusters against 5-layer truth. The 5/7 split exists only + in third-party benchmark repos. +* **GraphST filters unannotated spots *after* clustering and spatial refinement**, so those + spots still train the model and still vote in the refinement step. Hu's benchmark drops + them before the graph is built. Same nominal rule, different graph. + ## The bar that justifies existing Non-spatial clustering on expression alone, section 151673: @@ -63,11 +105,23 @@ qualifying resolutions** — not the best. Reporting best-of-sweep makes the num incomparable with every published figure. Fallback when nothing matches: widen to k±1; if still nothing, report nothing. -**Exclude unannotated spots.** 28 of 3639 on 151673. Small effect (~0.004) but it shifts -everything. +**Exclude unannotated spots — before clustering, not just before scoring.** 28 of 3639 on +151673. The benchmark drops them in `load_DLPFC` (`ad.obs.dropna()`) so they never enter the +graph at all. Dropping them only at scoring time gives a different graph and a different +answer. + +**5 vs 7 clusters — verified from code, and not stated in the paper.** Hard-coded +byte-identically across every run script in the benchmark: + +```python +[[7,'151507'],[7,'151508'],[7,'151509'],[7,'151510'], + [5,'151669'],[5,'151670'],[5,'151671'],[5,'151672'], + [7,'151673'],[7,'151674'],[7,'151675'],[7,'151676']] +``` -**5 vs 7 clusters.** Most sections have 7 layers; some have 5 (L3, L4, L5, L6, WM). Reported -as the 151669–151672 group — *unverified*, check each section's annotation set directly. +So **sample 2 (151669–151672) is 5 clusters**, annotated L3–L6 + WM only — no L1 or L2. The +other eight sections are 7. Getting this wrong makes every affected section incomparable, +and no paper states it. **ARI implementation** is not a source of difference: `sklearn.adjusted_rand_score` and `mclust::adjustedRandIndex` agree, and an independent from-scratch implementation reproduced @@ -80,10 +134,12 @@ deposited script calls `SmoothLabels(k = 6)` before scoring — visible only in our raw labels against 0.518 would understate us. Either replicate the smoothing or state plainly that the comparison is unsmoothed-to-smoothed. -**Configuration can swamp method differences.** DeepST scores 0.538 in one independent -benchmark and 0.229 in another, on the same section — a swing of 0.31, far larger than the -gaps between most methods. GraphST, by contrast, reproduces to three decimals across groups. -Budget for this before concluding anything from a single number. +**Configuration can swamp method differences.** On section 151673, across two independent +benchmarks: DeepST scores 0.538 and 0.229 — a swing of 0.31, far larger than the gaps +between most methods on the list. BayesSpace scores 0.550 and ~0.40. GraphST, by contrast, +reproduces to three decimals (0.633 vs 0.638). So a method's number says as much about who +ran it as about the method, unless it is one of the reproducible ones. Budget for that before +concluding anything from a single figure — including ours. ## Matching BANKSY, if we replicate it From 1bcbcf875d17844d55d3a3be6263e3534fb57660 Mon Sep 17 00:00:00 2001 From: Ian Date: Sun, 2 Aug 2026 12:11:37 +0200 Subject: [PATCH 08/13] feat(spatial): BANKSY feature augmentation, checked against the reference MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Augments each cell's expression with a summary of its neighbourhood so ordinary Leiden finds spatial domains. Verified differentially against banksy-py's own functions via committed fixtures, not against a reading of the paper — which matters, because four details are wrong if you transcribe from the paper, and three of them I got wrong first time. - The lambda budget is not split evenly across harmonics. Each successive harmonic gets half the weight of the one before, so at max_m=1 the mean takes 2/3 of lambda and the gradient 1/3, not half each. - Every block is z-scored per column before scaling, so lambda mixes standardised blocks. Without it the block with larger variance dominates regardless of lambda. - The gradient term subtracts its own neighbourhood mean before the phase sum. This lives in the reference's matrix builder, not its weight construction, so a matmul against the weights silently omits it — my first fixtures did exactly that and would have passed against a wrong reference. - The neighbourhood size differs per harmonic, and the two references disagree about how: R uses k_geom[m] (18 for both on DLPFC), Python multiplies internally as k*(m+1) (so 18 and 36). R produced the published numbers, so k is explicit per harmonic here rather than derived. Also documents, from source: the scaled_gaussian kernel has no factor of 2 in its exponent; R and Python disagree on the ranked kernel, which additionally crashes for m>0 in Python; and the reference stores azimuths as float32, which is why the fixtures agree to 1e-6 rather than tighter. 5 differential tests over 5 decay kernels, harmonics 0-2, lambda 0 to 1. --- src/spatial/banksy.rs | 460 +++++++++++++++++++++++++++ src/spatial/mod.rs | 1 + tests/banksy_reference.rs | 291 +++++++++++++++++ tests/fixtures/banksy_reference.json | 1 + tools/gen_banksy_fixtures.py | 118 +++++++ 5 files changed, 871 insertions(+) create mode 100644 src/spatial/banksy.rs create mode 100644 tests/banksy_reference.rs create mode 100644 tests/fixtures/banksy_reference.json create mode 100644 tools/gen_banksy_fixtures.py diff --git a/src/spatial/banksy.rs b/src/spatial/banksy.rs new file mode 100644 index 0000000..71bae20 --- /dev/null +++ b/src/spatial/banksy.rs @@ -0,0 +1,460 @@ +//! BANKSY-style feature augmentation. +//! +//! Turns spatial domain detection into ordinary clustering: augment each cell's expression +//! with a summary of its neighbourhood, then run Leiden on the result exactly as you would +//! without coordinates. Nothing here knows about clustering. +//! +//! Every formula below was read from the reference implementation (`banksy-py`, +//! `banksy/main.py` and `banksy/embed_banksy.py`), not from the paper, and is pinned by +//! differential fixtures in `tests/fixtures/banksy_reference.json`. Two details are easy to +//! get wrong from the paper alone: +//! +//! * The λ budget is **not** split evenly across harmonics. Each successive harmonic gets +//! half the weight of the previous one, so at `max_m = 1` the mean takes ⅔ of λ and the +//! gradient ⅓ — see [`scale_factors`]. +//! * Every block is **z-scored per column before scaling**, so λ mixes standardised blocks +//! rather than raw ones. Without that, a block with larger variance dominates regardless +//! of λ. +//! +//! The weight matrix is row-normalised, hence asymmetric, so it is a +//! [`NeighbourhoodOperator`] rather than a [`CSRNetwork`](crate::network::CSRNetwork). + +use rayon::prelude::*; + +use crate::error::{ClusteringError, Result}; +use crate::spatial::grid::{Grid, distance_sq}; + +/// How neighbour weights fall off with distance, before row normalisation. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)] +pub enum Decay { + /// Every neighbour counts the same. + Uniform, + /// `1 / r`. + Reciprocal, + /// `1 / r²`. + ReciprocalSquared, + /// `exp(-(r / median_r)²)`, where `median_r` is the median neighbour distance **for that + /// cell**. Scale-free per cell, which is why it is the reference default. + #[default] + ScaledGaussian, + /// `exp(-((rank + 1) · 1.5 / k)²)`, assigned in order of distance. Ignores the distances + /// themselves, so it is unaffected by local density. + Ranked, +} + +/// Row-normalised directed neighbour weights. +/// +/// Not a graph: row normalisation makes it asymmetric, so `w[i][j] != w[j][i]`. It is an +/// operator applied to a feature matrix. +pub struct NeighbourhoodOperator { + node_ptrs: Vec, + neighbours: Vec, + /// Row-normalised magnitudes, summing to 1 per cell. + weights: Vec, + /// Azimuth from each cell to each of its neighbours, `atan2(dy, dx)`. + theta: Vec, + n_cells: usize, + /// Harmonic order this operator was built for. + m: usize, +} + +impl NeighbourhoodOperator { + /// Builds the operator for harmonic `m` over `k` nearest neighbours. + /// + /// `k` is explicit per harmonic rather than derived from `m`, because the two references + /// disagree: R takes a `k_geom` vector and uses `k_geom[m]` (both 18 for DLPFC), while + /// Python silently multiplies, `num_neighbours * (m + 1)`, so the same nominal settings + /// give the gradient twice the neighbourhood. R produced the published DLPFC numbers, so + /// its convention is the one exposed here — pass `k * (m + 1)` yourself to reproduce + /// Python. + /// + /// Excludes the cell itself — its own expression enters the augmented matrix as its own + /// block, not through the neighbourhood. + /// + /// # Errors + /// + /// If `k` is zero or a coordinate is not finite. + pub fn for_harmonic(coords: &[[f64; 2]], k: usize, m: usize, decay: Decay) -> Result { + Self::knn(coords, k, decay, m) + } + + fn knn(coords: &[[f64; 2]], k: usize, decay: Decay, m: usize) -> Result { + if k == 0 { + return Err(ClusteringError::InvalidConfig( + "k must be at least 1".into(), + )); + } + for (i, p) in coords.iter().enumerate() { + if !p[0].is_finite() || !p[1].is_finite() { + return Err(ClusteringError::InvalidConfig(format!( + "cell {i} has a non-finite coordinate" + ))); + } + } + let n = coords.len(); + if n == 0 { + return Ok(Self { + node_ptrs: vec![0], + neighbours: Vec::new(), + weights: Vec::new(), + theta: Vec::new(), + n_cells: 0, + m, + }); + } + + let want = k.min(n.saturating_sub(1)); + let grid = Grid::new(coords, typical_spacing(coords, want.max(1))); + + // Nearest `want` neighbours per cell, with distances, sorted by (distance, id) so + // ties never depend on visit order. + let found: Vec> = coords + .par_iter() + .enumerate() + .map(|(i, p)| { + let centre = grid.cell_of(p); + let mut cand: Vec<(u32, f64)> = Vec::with_capacity(want * 2); + for ring in 0..=grid.max_ring(¢re) { + grid.for_each_in_ring(¢re, ring, |j| { + if j as usize != i { + cand.push((j, distance_sq(p, &coords[j as usize]))); + } + }); + if cand.len() >= want { + cand.sort_unstable_by(|a, b| { + a.1.partial_cmp(&b.1).unwrap().then(a.0.cmp(&b.0)) + }); + cand.truncate(want); + let safe = ring as f64 * grid.cell_size(); + if cand[cand.len() - 1].1 <= safe * safe { + break; + } + } + } + cand.sort_unstable_by(|a, b| a.1.partial_cmp(&b.1).unwrap().then(a.0.cmp(&b.0))); + cand.truncate(want); + cand + }) + .collect(); + + let mut node_ptrs = Vec::with_capacity(n + 1); + node_ptrs.push(0usize); + let mut total = 0; + for row in &found { + total += row.len(); + node_ptrs.push(total); + } + + let mut neighbours = Vec::with_capacity(total); + let mut weights = Vec::with_capacity(total); + let mut theta = Vec::with_capacity(total); + + for (i, row) in found.iter().enumerate() { + let distances: Vec = row.iter().map(|&(_, d2)| d2.sqrt()).collect(); + let mut w = decay_weights(&distances, decay); + + // Row-normalise. Done *before* any phase factor, matching the reference: the + // magnitudes sum to 1, the complex weights do not. + let sum: f64 = w.iter().sum(); + if sum > 0.0 { + for x in &mut w { + *x /= sum; + } + } + + for (slot, &(j, _)) in row.iter().enumerate() { + neighbours.push(j); + weights.push(w[slot]); + let (dx, dy) = ( + coords[j as usize][0] - coords[i][0], + coords[j as usize][1] - coords[i][1], + ); + theta.push(dy.atan2(dx)); + } + } + + Ok(Self { + node_ptrs, + neighbours, + weights, + theta, + n_cells: n, + m, + }) + } + + /// Number of cells. + pub fn n_cells(&self) -> usize { + self.n_cells + } + + /// Harmonic order this operator computes. + pub fn harmonic(&self) -> usize { + self.m + } + + /// Applies this operator's harmonic to a feature matrix. + /// + /// `m = 0` is the plain weighted neighbourhood mean. For `m >= 1` the weights carry a + /// phase `exp(i·m·θ)`, and the result is the **magnitude** of the complex sum — large + /// where a gene varies systematically with direction, near zero where the neighbourhood + /// looks the same all round. That is the "gradient" term. + /// + /// `features` is row-major, `n_cells × n_features`. + /// + /// # Errors + /// + /// If `features` does not match the cell count. + pub fn apply(&self, features: &[f64], n_features: usize) -> Result> { + let m = self.m; + if n_features == 0 { + return Ok(Vec::new()); + } + if features.len() != self.n_cells * n_features { + return Err(ClusteringError::InvalidConfig(format!( + "features has {} entries, expected {} cells x {n_features}", + features.len(), + self.n_cells + ))); + } + + let mut out = vec![0.0f64; self.n_cells * n_features]; + out.par_chunks_mut(n_features) + .enumerate() + .for_each(|(i, row)| { + let span = self.node_ptrs[i]..self.node_ptrs[i + 1]; + #[allow(clippy::reversed_empty_ranges)] + if m == 0 { + for e in span { + let (w, j) = (self.weights[e], self.neighbours[e] as usize); + let src = &features[j * n_features..(j + 1) * n_features]; + for (o, &x) in row.iter_mut().zip(src) { + *o += w * x; + } + } + } else { + // Centre on this cell's neighbourhood before taking the phase sum. + // Without it the result measures the neighbourhood's overall level as + // well as its directional variation, because the phase sum + // `sum_j w_ij e^{i m phi}` is generally non-zero, so subtracting a + // constant is not a no-op. Easy to miss: it lives in the reference's + // matrix builder, not in its weight construction. + let mut centre = vec![0.0f64; n_features]; + for e in span.clone() { + let (w, j) = (self.weights[e], self.neighbours[e] as usize); + let src = &features[j * n_features..(j + 1) * n_features]; + for (c, &x) in centre.iter_mut().zip(src) { + *c += w * x; + } + } + + let mut im = vec![0.0f64; n_features]; + for e in span { + let (w, j) = (self.weights[e], self.neighbours[e] as usize); + let angle = m as f64 * self.theta[e]; + let (re_w, im_w) = (w * angle.cos(), w * angle.sin()); + let src = &features[j * n_features..(j + 1) * n_features]; + for (((o, i_acc), &x), &c) in row + .iter_mut() + .zip(im.iter_mut()) + .zip(src) + .zip(centre.iter()) + { + *o += re_w * (x - c); + *i_acc += im_w * (x - c); + } + } + for (o, &i_acc) in row.iter_mut().zip(im.iter()) { + *o = (*o * *o + i_acc * i_acc).sqrt(); + } + } + }); + Ok(out) + } +} + +/// Unnormalised weights for one cell's neighbour distances. +fn decay_weights(distances: &[f64], decay: Decay) -> Vec { + match decay { + Decay::Uniform => vec![1.0; distances.len()], + Decay::Reciprocal => distances.iter().map(|&r| 1.0 / r).collect(), + Decay::ReciprocalSquared => distances.iter().map(|&r| 1.0 / (r * r)).collect(), + Decay::ScaledGaussian => { + let mut sorted = distances.to_vec(); + sorted.sort_unstable_by(|a, b| a.partial_cmp(b).unwrap()); + // numpy's median: mean of the middle two on even counts. + let n = sorted.len(); + let median = if n == 0 { + 0.0 + } else if n % 2 == 1 { + sorted[n / 2] + } else { + 0.5 * (sorted[n / 2 - 1] + sorted[n / 2]) + }; + distances + .iter() + .map(|&r| (-(r / median).powi(2)).exp()) + .collect() + } + Decay::Ranked => { + let k = distances.len(); + // Rank by distance, then assign a fixed decreasing profile in that order. + let mut order: Vec = (0..k).collect(); + order.sort_unstable_by(|&a, &b| { + distances[a] + .partial_cmp(&distances[b]) + .unwrap() + .then(a.cmp(&b)) + }); + let mut w = vec![0.0; k]; + for (rank, &idx) in order.iter().enumerate() { + let t = (rank + 1) as f64 * 1.5 / k as f64; + w[idx] = (-t * t).exp(); + } + w + } + } +} + +/// Column z-scores, using the **population** variance `E[x²] − E[x]²`. +/// +/// Not the sample variance — the reference divides by `n`, not `n − 1`, and a column with +/// zero variance becomes zero rather than NaN. +fn zscore_columns(matrix: &mut [f64], n_rows: usize, n_cols: usize) { + if n_rows == 0 || n_cols == 0 { + return; + } + for c in 0..n_cols { + let (mut sum, mut sum_sq) = (0.0f64, 0.0f64); + for r in 0..n_rows { + let x = matrix[r * n_cols + c]; + sum += x; + sum_sq += x * x; + } + let n = n_rows as f64; + let mean = sum / n; + let variance = sum_sq / n - mean * mean; + let sd = variance.max(0.0).sqrt(); + for r in 0..n_rows { + let slot = &mut matrix[r * n_cols + c]; + *slot = if sd > 0.0 { (*slot - mean) / sd } else { 0.0 }; + } + } +} + +/// Block scale factors for `max_m` harmonics at neighbourhood contribution `lambda`. +/// +/// Returns `max_m + 2` factors: own expression first, then one per harmonic. +/// +/// **Each harmonic gets half the weight of the one before it**, not an equal share. With +/// `max_m = 1` that makes the mean ⅔ of λ and the gradient ⅓ — assuming an even split would +/// over-weight the gradient by a factor of two. +pub fn scale_factors(max_m: usize, lambda: f64) -> Vec { + let n_blocks = max_m + 1; + let mut squared = vec![0.0f64; n_blocks + 1]; + squared[0] = 1.0 - lambda; + + let denom: f64 = (0..n_blocks).map(|k| 1.0 / 2f64.powi(k as i32 + 1)).sum(); + for k in 0..n_blocks { + squared[k + 1] = 1.0 / 2f64.powi(k as i32 + 1) / denom * lambda; + } + squared.iter().map(|&s| s.max(0.0).sqrt()).collect() +} + +/// Assembles the augmented matrix from the own-expression block and the harmonic blocks. +/// +/// Each block is z-scored per column and then scaled, so λ mixes standardised blocks. The +/// result is `n_cells × (n_features · (max_m + 2))`, row-major, ready to PCA and cluster. +/// +/// # Errors +/// +/// If `lambda` is outside `[0, 1]` or a block has the wrong length. +pub fn banksy_matrix( + own: &[f64], + harmonics: &[Vec], + n_cells: usize, + n_features: usize, + lambda: f64, +) -> Result> { + if !(0.0..=1.0).contains(&lambda) || !lambda.is_finite() { + return Err(ClusteringError::InvalidConfig(format!( + "lambda must be in [0, 1], got {lambda}" + ))); + } + let expected = n_cells * n_features; + if own.len() != expected { + return Err(ClusteringError::InvalidConfig(format!( + "own block has {} entries, expected {expected}", + own.len() + ))); + } + for (m, h) in harmonics.iter().enumerate() { + if h.len() != expected { + return Err(ClusteringError::InvalidConfig(format!( + "harmonic {m} has {} entries, expected {expected}", + h.len() + ))); + } + } + + let factors = scale_factors(harmonics.len().saturating_sub(1), lambda); + let n_blocks = harmonics.len() + 1; + let width = n_features * n_blocks; + let mut out = vec![0.0f64; n_cells * width]; + + for (b, block) in std::iter::once(own) + .chain(harmonics.iter().map(|h| h.as_slice())) + .enumerate() + { + let mut scaled = block.to_vec(); + zscore_columns(&mut scaled, n_cells, n_features); + let f = factors[b]; + for r in 0..n_cells { + let dst = r * width + b * n_features; + for c in 0..n_features { + out[dst + c] = f * scaled[r * n_features + c]; + } + } + } + Ok(out) +} + +/// One-call BANKSY augmentation. +/// +/// Builds the neighbourhood operator, applies harmonics `0..=max_m`, and assembles the +/// augmented matrix. `lambda = 0.2` and `k = 18` are the reference's Visium settings for +/// domain segmentation; `max_m = 1` includes the gradient term. +/// +/// # Errors +/// +/// If any argument is out of range or the feature matrix does not match the cell count. +pub fn banksy_augment( + coords: &[[f64; 2]], + features: &[f64], + n_features: usize, + k: usize, + max_m: usize, + lambda: f64, + decay: Decay, +) -> Result> { + let harmonics: Vec> = (0..=max_m) + .map(|m| { + NeighbourhoodOperator::for_harmonic(coords, k, m, decay)?.apply(features, n_features) + }) + .collect::>()?; + banksy_matrix(features, &harmonics, coords.len(), n_features, lambda) +} + +/// A cell size that puts roughly `k` points in each grid cell. +fn typical_spacing(points: &[[f64; 2]], k: usize) -> f64 { + let (mut min, mut max) = ([f64::INFINITY; 2], [f64::NEG_INFINITY; 2]); + for p in points { + for d in 0..2 { + min[d] = min[d].min(p[d]); + max[d] = max[d].max(p[d]); + } + } + let area = (max[0] - min[0]).max(f64::MIN_POSITIVE) * (max[1] - min[1]).max(f64::MIN_POSITIVE); + let cells = (points.len() as f64 / (k.max(1) as f64)).max(1.0); + (area / cells).sqrt().max(f64::MIN_POSITIVE) +} diff --git a/src/spatial/mod.rs b/src/spatial/mod.rs index 4440286..1af9f54 100644 --- a/src/spatial/mod.rs +++ b/src/spatial/mod.rs @@ -23,6 +23,7 @@ //! //! Neither affects `array_row` / `array_col`, which is what [`lattice_graph`] wants. +pub mod banksy; pub mod delaunay; pub(crate) mod grid; pub mod lattice; diff --git a/tests/banksy_reference.rs b/tests/banksy_reference.rs new file mode 100644 index 0000000..fd26e7c --- /dev/null +++ b/tests/banksy_reference.rs @@ -0,0 +1,291 @@ +//! Differential tests for the BANKSY feature augmentation. +//! +//! Fixtures come from `banksy-py` itself via `tools/gen_banksy_fixtures.py` — the reference's +//! own `generate_spatial_weights_fixed_nbrs` and `concatenate_all`, not a reimplementation of +//! them. So this checks against what BANKSY computes rather than against a reading of the +//! paper. Fixtures are committed; the suite needs no Python. +//! +//! Three things this pins that a formula transcribed from the paper would get wrong: the λ +//! budget halves with each harmonic rather than splitting evenly, every block is z-scored +//! before scaling, and the gradient term is centred on its own neighbourhood first. +//! +//! Tolerance is 1e-6, not tighter, because the reference stores azimuths as `float32` +//! (`theta_data = np.zeros_like(..., dtype=np.float32)`). We carry f64 throughout, so the +//! residual is the reference's precision, not ours. + +use serde_json::Value; +use single_clustering::spatial::banksy::{Decay, NeighbourhoodOperator, banksy_matrix}; + +fn fixtures() -> Value { + let raw = include_str!("fixtures/banksy_reference.json"); + serde_json::from_str(raw).expect("fixture JSON should parse") +} + +fn coords(v: &Value) -> Vec<[f64; 2]> { + v.as_array() + .unwrap() + .iter() + .map(|p| { + let a = p.as_array().unwrap(); + [a[0].as_f64().unwrap(), a[1].as_f64().unwrap()] + }) + .collect() +} + +/// Row-major flatten of a nested JSON matrix. +fn matrix(v: &Value) -> Vec { + v.as_array() + .unwrap() + .iter() + .flat_map(|row| { + row.as_array() + .unwrap() + .iter() + .map(|x| x.as_f64().unwrap()) + .collect::>() + }) + .collect() +} + +fn decay_of(name: &str) -> Decay { + match name { + "uniform" => Decay::Uniform, + "reciprocal" => Decay::Reciprocal, + "reciprocal_squared" => Decay::ReciprocalSquared, + "scaled_gaussian" => Decay::ScaledGaussian, + "ranked" => Decay::Ranked, + other => panic!("unknown decay {other}"), + } +} + +/// Largest absolute difference, and where. +fn max_abs_diff(a: &[f64], b: &[f64]) -> (f64, usize) { + assert_eq!(a.len(), b.len(), "matrix shapes differ"); + let mut worst = (0.0f64, 0usize); + for (i, (&x, &y)) in a.iter().zip(b).enumerate() { + let d = (x - y).abs(); + if d > worst.0 { + worst = (d, i); + } + } + worst +} + +/// Points on a regular grid have many exactly-equidistant neighbours, and nothing specifies +/// which of them a k-nearest search returns. Our tie-break is (distance, index); sklearn's is +/// unspecified, so that case is compared structurally elsewhere rather than value-for-value. +fn has_distance_ties(name: &str) -> bool { + name.starts_with("grid") +} + +/// Every harmonic must match the reference elementwise. +#[test] +fn harmonics_match_the_reference() { + let f = fixtures(); + let mut checked = 0; + + for c in f["cases"].as_array().unwrap() { + let name = c["name"].as_str().unwrap(); + if has_distance_ties(name) { + continue; + } + let pts = coords(&c["locations"]); + let features = matrix(&c["features"]); + let n_features = c["n_genes"].as_u64().unwrap() as usize; + let ks: Vec = c["k_per_harmonic"] + .as_array() + .unwrap() + .iter() + .map(|v| v.as_u64().unwrap() as usize) + .collect(); + let decay = decay_of(c["decay"].as_str().unwrap()); + + for (m, expected) in c["harmonics"].as_array().unwrap().iter().enumerate() { + let op = NeighbourhoodOperator::for_harmonic(&pts, ks[m], m, decay).unwrap(); + let got = op.apply(&features, n_features).unwrap(); + let want = matrix(expected); + + let (diff, at) = max_abs_diff(&got, &want); + assert!( + diff < 1e-6, + "{name} harmonic {m}: max |ours - reference| = {diff:e} at entry {at} \ + (ours {}, reference {})", + got[at], + want[at] + ); + checked += 1; + } + } + assert!(checked >= 12, "only {checked} harmonics compared"); +} + +/// The assembled matrix — scaling, z-scoring and block layout together. +#[test] +fn augmented_matrix_matches_the_reference() { + let f = fixtures(); + let mut checked = 0; + + for c in f["cases"].as_array().unwrap() { + let name = c["name"].as_str().unwrap(); + if has_distance_ties(name) { + continue; + } + let pts = coords(&c["locations"]); + let features = matrix(&c["features"]); + let n_cells = c["n_cells"].as_u64().unwrap() as usize; + let n_features = c["n_genes"].as_u64().unwrap() as usize; + let ks: Vec = c["k_per_harmonic"] + .as_array() + .unwrap() + .iter() + .map(|v| v.as_u64().unwrap() as usize) + .collect(); + let max_m = c["max_m"].as_u64().unwrap() as usize; + let decay = decay_of(c["decay"].as_str().unwrap()); + + let harmonics: Vec> = (0..=max_m) + .map(|m| { + NeighbourhoodOperator::for_harmonic(&pts, ks[m], m, decay) + .unwrap() + .apply(&features, n_features) + .unwrap() + }) + .collect(); + + for (lambda_str, expected) in c["banksy"].as_object().unwrap() { + let lambda: f64 = lambda_str.parse().unwrap(); + let got = banksy_matrix(&features, &harmonics, n_cells, n_features, lambda).unwrap(); + let want = matrix(expected); + + let (diff, at) = max_abs_diff(&got, &want); + assert!( + diff < 1e-6, + "{name} at lambda {lambda}: max |ours - reference| = {diff:e} at entry {at} \ + (ours {}, reference {})", + got[at], + want[at] + ); + checked += 1; + } + } + assert!(checked >= 10, "only {checked} matrices compared"); +} + +/// On a regular grid the neighbour *set* is ambiguous, but the neighbourhood mean over a +/// symmetric lattice should still land close — a large gap would mean something worse than a +/// tie-break difference. +#[test] +fn grid_case_agrees_up_to_tie_breaking() { + let f = fixtures(); + for c in f["cases"].as_array().unwrap() { + let name = c["name"].as_str().unwrap(); + if !has_distance_ties(name) { + continue; + } + let pts = coords(&c["locations"]); + let features = matrix(&c["features"]); + let n_features = c["n_genes"].as_u64().unwrap() as usize; + let k = c["k_per_harmonic"][0].as_u64().unwrap() as usize; + let decay = decay_of(c["decay"].as_str().unwrap()); + + let op = NeighbourhoodOperator::for_harmonic(&pts, k, 0, decay).unwrap(); + let got = op.apply(&features, n_features).unwrap(); + let want = matrix(&c["harmonics"][0]); + + // Features are standard normal and weights sum to 1, so a mean is O(1/sqrt(k)). + let (diff, _) = max_abs_diff(&got, &want); + assert!( + diff < 1.0, + "{name}: means differ by {diff:e}, too much for tie-breaking alone" + ); + } +} + +/// λ = 0 must leave only the own-expression block carrying signal, λ = 1 only the +/// neighbourhood blocks. Checked against the reference, so the convention is theirs. +#[test] +fn lambda_endpoints_behave() { + let f = fixtures(); + let c = f["cases"] + .as_array() + .unwrap() + .iter() + .find(|c| c["name"] == "uniform_small") + .expect("uniform_small carries lambda 0 and 1"); + + let pts = coords(&c["locations"]); + let features = matrix(&c["features"]); + let n_cells = c["n_cells"].as_u64().unwrap() as usize; + let n_features = c["n_genes"].as_u64().unwrap() as usize; + let ks: Vec = c["k_per_harmonic"] + .as_array() + .unwrap() + .iter() + .map(|v| v.as_u64().unwrap() as usize) + .collect(); + let max_m = c["max_m"].as_u64().unwrap() as usize; + let decay = decay_of(c["decay"].as_str().unwrap()); + + let harmonics: Vec> = (0..=max_m) + .map(|m| { + NeighbourhoodOperator::for_harmonic(&pts, ks[m], m, decay) + .unwrap() + .apply(&features, n_features) + .unwrap() + }) + .collect(); + + let width = n_features * (max_m + 2); + + let at_zero = banksy_matrix(&features, &harmonics, n_cells, n_features, 0.0).unwrap(); + let nbr_energy: f64 = (0..n_cells) + .flat_map(|r| (n_features..width).map(move |c| (r, c))) + .map(|(r, c)| at_zero[r * width + c].abs()) + .sum(); + assert!( + nbr_energy < 1e-9, + "lambda 0 left {nbr_energy:e} in the neighbour blocks" + ); + + let at_one = banksy_matrix(&features, &harmonics, n_cells, n_features, 1.0).unwrap(); + let own_energy: f64 = (0..n_cells) + .flat_map(|r| (0..n_features).map(move |c| (r, c))) + .map(|(r, c)| at_one[r * width + c].abs()) + .sum(); + assert!( + own_energy < 1e-9, + "lambda 1 left {own_energy:e} in the own block" + ); +} + +/// The halving rule, stated explicitly so it cannot drift. +#[test] +fn scale_factors_halve_with_each_harmonic() { + use single_clustering::spatial::banksy::scale_factors; + + // max_m = 0: one neighbour block takes all of lambda. + let f = scale_factors(0, 0.2); + assert!((f[0] - 0.8f64.sqrt()).abs() < 1e-12); + assert!((f[1] - 0.2f64.sqrt()).abs() < 1e-12); + + // max_m = 1: two-thirds to the mean, one-third to the gradient — not half each. + let f = scale_factors(1, 0.2); + assert!((f[0] - 0.8f64.sqrt()).abs() < 1e-12); + assert!((f[1] - (2.0 / 3.0 * 0.2f64).sqrt()).abs() < 1e-12); + assert!((f[2] - (1.0 / 3.0 * 0.2f64).sqrt()).abs() < 1e-12); + assert!( + (f[1] / f[2] - 2f64.sqrt()).abs() < 1e-12, + "the mean block should carry twice the gradient's variance" + ); + + // Squared factors always partition 1. + for max_m in 0..4 { + for lambda in [0.0, 0.2, 0.5, 0.8, 1.0] { + let total: f64 = scale_factors(max_m, lambda).iter().map(|f| f * f).sum(); + assert!( + (total - 1.0).abs() < 1e-12, + "max_m {max_m}, lambda {lambda}" + ); + } + } +} diff --git a/tests/fixtures/banksy_reference.json b/tests/fixtures/banksy_reference.json new file mode 100644 index 0000000..7c84002 --- /dev/null +++ b/tests/fixtures/banksy_reference.json @@ -0,0 +1 @@ +{"cases": [{"name": "uniform_small", "n_cells": 60, "n_genes": 4, "k": 6, "max_m": 1, "decay": "scaled_gaussian", "k_per_harmonic": [6, 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itself. + +Calls the reference implementation's own functions rather than reimplementing them, so the +Rust side is checked against what BANKSY actually computes and not against a reading of it. + + uv venv && uv pip install banksy-py + uv run tools/gen_banksy_fixtures.py tests/fixtures/banksy_reference.json + +Fixtures are committed, so the test suite needs no Python. +""" + +import json +import sys + +import numpy as np +import anndata +from banksy.embed_banksy import create_nbr_matrix +from banksy.main import concatenate_all, generate_spatial_weights_fixed_nbrs + +DECAYS = ["uniform", "reciprocal", "reciprocal_squared", "scaled_gaussian", "ranked"] + + +def case(name, n_cells, n_genes, k, max_m, lambdas, decay, seed): + """One synthetic dataset put through the reference, with every intermediate captured.""" + rng = np.random.default_rng(seed) + + if name.startswith("grid"): + side = int(np.sqrt(n_cells)) + xs, ys = np.meshgrid(np.arange(side), np.arange(side)) + locations = np.column_stack([xs.ravel(), ys.ravel()]).astype(float) + n_cells = locations.shape[0] + else: + locations = rng.uniform(0, 50, size=(n_cells, 2)) + + features = rng.normal(size=(n_cells, n_genes)) + + # Go through the reference's own matrix builder rather than a raw matmul: the AGF + # centring lives in create_nbr_matrix, not in the weight construction, so a matmul + # against the weights silently omits it. + weights = {} + for m in range(max_m + 1): + w, _dist, _theta = generate_spatial_weights_fixed_nbrs( + locations, + m=m, + num_neighbours=k, + decay_type=decay, + verbose=False, + ) + weights[m] = w + + adata = anndata.AnnData(features.copy()) + banksy_dict = {decay: {"weights": weights}} + nbr_matrices = create_nbr_matrix(adata, banksy_dict, decay, max_m, verbose=False) + harmonics = [nbr_matrices[m] for m in range(max_m + 1)] + + # Python derives the harmonic-m neighbourhood as k*(m+1); record what it actually used + # so the Rust side can be given the same k explicitly. + k_per_harmonic = [int(np.diff(weights[m].indptr).max()) for m in range(max_m + 1)] + + out = { + "name": name, + "n_cells": int(n_cells), + "n_genes": int(n_genes), + "k": int(k), + "max_m": int(max_m), + "decay": decay, + "k_per_harmonic": k_per_harmonic, + "locations": locations.tolist(), + "features": features.tolist(), + # As create_nbr_matrix returns them: H_0 is real and signed, and the m >= 1 + # harmonics have already had their magnitude taken inside it. Applying abs() here + # would flip the sign of every negative H_0 entry. + "harmonics": [np.asarray(h).tolist() for h in harmonics], + "banksy": {}, + } + + for lam in lambdas: + mat_list = [features] + harmonics + combined = concatenate_all(mat_list, lam, adata=None) + out["banksy"][str(lam)] = np.asarray(combined).tolist() + + return out + + +def main(): + cases = [ + case("uniform_small", 60, 4, 6, 1, [0.0, 0.2, 0.5, 1.0], "scaled_gaussian", 1), + case("uniform_mid", 200, 8, 12, 1, [0.2, 0.8], "scaled_gaussian", 2), + case("grid_regular", 100, 5, 8, 1, [0.2], "scaled_gaussian", 3), + case("mean_only", 120, 6, 10, 0, [0.2, 0.5], "scaled_gaussian", 4), + case("harmonic_two", 120, 4, 10, 2, [0.2], "scaled_gaussian", 5), + ] + # Every decay type, so none of the kernels can drift unnoticed. + # + # `ranked` is capped at max_m = 0: the reference crashes for m > 0, because it builds a + # weight profile of length `num_neighbours` while the row holds `num_neighbours * (m+1)` + # entries. An upstream bug, not a usage error -- so there is no reference behaviour to + # match above m = 0. + for decay in DECAYS: + max_m = 0 if decay == "ranked" else 1 + cases.append(case(f"decay_{decay}", 80, 3, 7, max_m, [0.2], decay, 11)) + + # To a file, not stdout: the reference print()s unconditionally, so stdout is not clean. + out = sys.argv[1] if len(sys.argv) > 1 else "banksy_reference.json" + with open(out, "w") as fh: + json.dump({"cases": cases}, fh) + print(f"\nwrote {out}", file=sys.stderr) + for c in cases: + print( + f" {c['name']:22} n={c['n_cells']:4} genes={c['n_genes']} " + f"k={c['k']} m={c['max_m']} decay={c['decay']}", + file=sys.stderr, + ) + + +if __name__ == "__main__": + main() From f44a5292cc9a19019434048d564ffce6edb9f045 Mon Sep 17 00:00:00 2001 From: Ian Date: Sun, 2 Aug 2026 12:15:23 +0200 Subject: [PATCH 09/13] feat(spatial): graph fusion and per-sample construction MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Completes the spatial layer. fuse() blends an expression graph with a spatial one over the same cells, alpha*a + (1-alpha)*b, as the union of both edge sets. Normalisation::TotalWeight scales each to unit total weight first, and is the default because without it the graph with heavier weights wins at every alpha and the parameter stops meaning anything — the same failure the BANKSY blocks avoid by z-scoring. There is a test that fusing a graph with a 1000x-scaled copy of itself gives back uniform weights when normalised, and is dominated when not. per_sample() runs a builder separately within each slice. Building globally and deleting cross-sample edges afterwards is not equivalent for kNN: a cell at a section's edge loses neighbours instead of taking k from its own section. There is a test asserting both halves of that — every cell keeps full degree under per_sample, and the filter-afterwards approach demonstrably starves some. It matters because sections are routinely stored overlapping in one coordinate frame, so cells from different slices can sit arbitrarily close. 8 tests including endpoint recovery, edge-set union, symmetry, sample labels being arbitrary, and single-cell samples. --- src/spatial/combine.rs | 417 +++++++++++++++++++++++++++++++++++++++++ src/spatial/mod.rs | 2 + 2 files changed, 419 insertions(+) create mode 100644 src/spatial/combine.rs diff --git a/src/spatial/combine.rs b/src/spatial/combine.rs new file mode 100644 index 0000000..a4ee9c7 --- /dev/null +++ b/src/spatial/combine.rs @@ -0,0 +1,417 @@ +//! Combining graphs, and building them per sample. +//! +//! Two operations that both exist because real experiments are not one clean tissue section: +//! you usually have an expression graph *and* a spatial graph over the same cells, and you +//! usually have several slices rather than one. + +use crate::error::{ClusteringError, Result}; +use crate::network::CSRNetwork; + +/// How to put two graphs on comparable footing before mixing them. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)] +pub enum Normalization { + /// Mix the weights as they are. + /// + /// Almost never what you want across an expression and a spatial graph: their weights + /// live on unrelated scales, so whichever is larger dominates at every `alpha` and the + /// parameter stops meaning anything. + None, + /// Scale each graph so its total edge weight is 1, so `alpha` divides a fixed budget. + #[default] + TotalWeight, +} + +/// Blends two graphs over the same nodes: `alpha · a + (1 − alpha) · b`. +/// +/// An edge present in only one contributes only its own side, so the result is the union of +/// the two edge sets. Both graphs must cover the same nodes. +/// +/// With [`Normalization::TotalWeight`] each graph is first scaled to unit total weight, which +/// is what makes `alpha` behave like a proportion rather than an arbitrary knob. It is the +/// same problem the BANKSY blocks solve by z-scoring, and the same fix. +/// +/// # Errors +/// +/// If the graphs have different node counts, or `alpha` is outside `[0, 1]`. +pub fn fuse( + a: &CSRNetwork, + b: &CSRNetwork, + alpha: f64, + normalization: Normalization, +) -> Result { + if !(0.0..=1.0).contains(&alpha) || !alpha.is_finite() { + return Err(ClusteringError::InvalidConfig(format!( + "alpha must be in [0, 1], got {alpha}" + ))); + } + if a.node_count() != b.node_count() { + return Err(ClusteringError::InvalidConfig(format!( + "graphs cover different node sets: {} vs {} nodes", + a.node_count(), + b.node_count() + ))); + } + let n = a.node_count(); + if n == 0 { + return CSRNetwork::from_csr_parts(vec![0], Vec::new(), Vec::new(), None); + } + + let (sa, sb) = match normalization { + Normalization::None => (1.0, 1.0), + Normalization::TotalWeight => ( + scale_to_unit(a.total_weight()), + scale_to_unit(b.total_weight()), + ), + }; + let (wa, wb) = (alpha * sa, (1.0 - alpha) * sb); + + // Merge each node's two neighbour lists. Both are sorted ascending, so this is a linear + // two-pointer walk and the output stays sorted. + let mut node_ptrs = Vec::with_capacity(n + 1); + node_ptrs.push(0usize); + let mut neighbors: Vec = Vec::new(); + let mut weights: Vec = Vec::new(); + + let (mut ra, mut rb) = (Vec::new(), Vec::new()); + for v in 0..n { + ra.clear(); + rb.clear(); + ra.extend(a.neighbors(v)); + rb.extend(b.neighbors(v)); + + let (mut i, mut j) = (0usize, 0usize); + while i < ra.len() || j < rb.len() { + let take_a = j >= rb.len() || (i < ra.len() && ra[i].0 <= rb[j].0); + let take_b = i >= ra.len() || (j < rb.len() && rb[j].0 <= ra[i].0); + let u = if take_a { ra[i].0 } else { rb[j].0 }; + + let mut w = 0.0; + if take_a { + w += wa * ra[i].1; + i += 1; + } + if take_b { + w += wb * rb[j].1; + j += 1; + } + if w > 0.0 { + neighbors.push(u as u32); + weights.push(w as f32); + } + } + node_ptrs.push(neighbors.len()); + } + + CSRNetwork::from_csr_parts(node_ptrs, neighbors, weights, None) +} + +/// Reciprocal of a total weight, or 1.0 when there is nothing to scale. +fn scale_to_unit(total: f64) -> f64 { + if total > 0.0 && total.is_finite() { + 1.0 / total + } else { + 1.0 + } +} + +/// Runs a graph builder separately within each sample and reassembles one graph. +/// +/// Spatial neighbours are only meaningful within a slice: two cells on different sections are +/// not adjacent however close their stored coordinates happen to be, and sections are often +/// laid out overlapping in the same coordinate space. Building globally and deleting +/// cross-sample edges afterwards is **not** equivalent for k-nearest builders — a cell at a +/// section's edge would lose neighbours rather than take k from its own section. So the +/// builder runs per sample. +/// +/// `samples` labels each point. Node indices in the result are the caller's original ones. +/// +/// # Errors +/// +/// If `samples` does not match the point count, or the builder fails on a sample. +pub fn per_sample(points: &[[f64; 2]], samples: &[u32], build: F) -> Result +where + F: Fn(&[[f64; 2]]) -> Result, +{ + if samples.len() != points.len() { + return Err(ClusteringError::CoordinateLengthMismatch { + got: samples.len(), + expected: points.len(), + }); + } + let n = points.len(); + if n == 0 { + return CSRNetwork::from_csr_parts(vec![0], Vec::new(), Vec::new(), None); + } + + // Group point indices by sample, keeping each group in ascending original order so the + // result never depends on how the samples were labelled. + let mut order: Vec = (0..n as u32).collect(); + order.sort_by_key(|&v| (samples[v as usize], v)); + + let mut rows: Vec> = vec![Vec::new(); n]; + let mut start = 0usize; + while start < n { + let sample = samples[order[start] as usize]; + let mut end = start; + while end < n && samples[order[end] as usize] == sample { + end += 1; + } + + let members = &order[start..end]; + let subset: Vec<[f64; 2]> = members.iter().map(|&v| points[v as usize]).collect(); + let sub = build(&subset)?; + if sub.node_count() != subset.len() { + return Err(ClusteringError::InvalidConfig(format!( + "builder returned {} nodes for a sample of {}", + sub.node_count(), + subset.len() + ))); + } + + for local in 0..sub.node_count() { + let global = members[local] as usize; + for (u, w) in sub.neighbors(local) { + rows[global].push((members[u], w as f32)); + } + } + start = end; + } + + let mut node_ptrs = Vec::with_capacity(n + 1); + node_ptrs.push(0usize); + let mut neighbors = Vec::new(); + let mut weights = Vec::new(); + for row in &mut rows { + row.sort_unstable_by_key(|&(u, _)| u); + for &(u, w) in row.iter() { + neighbors.push(u); + weights.push(w); + } + node_ptrs.push(neighbors.len()); + } + + CSRNetwork::from_csr_parts(node_ptrs, neighbors, weights, None) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::spatial::{ + HullPruning, SpatialWeight, Symmetry, delaunay_graph, knn_graph, radius_graph, + }; + use rand::{Rng, SeedableRng}; + use std::collections::HashSet; + + fn edge_set(g: &CSRNetwork) -> HashSet<(usize, usize)> { + let mut out = HashSet::new(); + for v in 0..g.node_count() { + for (u, _) in g.neighbors(v) { + out.insert((v.min(u), v.max(u))); + } + } + out + } + + fn two_slices(seed: u64) -> (Vec<[f64; 2]>, Vec) { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(seed); + let mut pts = Vec::new(); + let mut samples = Vec::new(); + // Deliberately overlapping coordinates: two sections stored in the same frame. + for s in 0..2u32 { + for _ in 0..300 { + pts.push([rng.random::() * 50.0, rng.random::() * 50.0]); + samples.push(s); + } + } + (pts, samples) + } + + #[test] + fn fusion_endpoints_recover_each_input() { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(1); + let pts: Vec<[f64; 2]> = (0..200) + .map(|_| [rng.random::() * 30.0, rng.random::() * 30.0]) + .collect(); + let a = radius_graph(&pts, 4.0, SpatialWeight::Uniform, None).unwrap(); + let b = knn_graph(&pts, 5, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + + let at_one = fuse(&a, &b, 1.0, Normalization::TotalWeight).unwrap(); + assert_eq!(edge_set(&at_one), edge_set(&a)); + let at_zero = fuse(&a, &b, 0.0, Normalization::TotalWeight).unwrap(); + assert_eq!(edge_set(&at_zero), edge_set(&b)); + } + + #[test] + fn fusion_is_the_union_of_both_edge_sets() { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(2); + let pts: Vec<[f64; 2]> = (0..150) + .map(|_| [rng.random::() * 30.0, rng.random::() * 30.0]) + .collect(); + let a = radius_graph(&pts, 3.0, SpatialWeight::Uniform, None).unwrap(); + let b = delaunay_graph(&pts, HullPruning::default(), SpatialWeight::Uniform).unwrap(); + + let f = fuse(&a, &b, 0.5, Normalization::TotalWeight).unwrap(); + let union: HashSet<_> = edge_set(&a).union(&edge_set(&b)).copied().collect(); + assert_eq!(edge_set(&f), union); + f.validate_symmetry().unwrap(); + } + + /// The point of normalising: without it, the graph with heavier weights wins whatever + /// alpha says. + #[test] + fn normalisation_stops_one_graph_dominating() { + let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(3); + let pts: Vec<[f64; 2]> = (0..200) + .map(|_| [rng.random::() * 30.0, rng.random::() * 30.0]) + .collect(); + let light = radius_graph(&pts, 3.0, SpatialWeight::Uniform, None).unwrap(); + // Same shape, weights a thousand times larger. + let heavy = { + let (ptrs, nbrs, w) = light.to_csr_parts(); + CSRNetwork::from_csr_parts(ptrs, nbrs, w.iter().map(|x| x * 1000.0).collect(), None) + .unwrap() + }; + + // Normalised, an even blend of a graph with itself is that graph, whatever the scale. + let fused = fuse(&light, &heavy, 0.5, Normalization::TotalWeight).unwrap(); + let first = fused.neighbors(0).next().unwrap().1; + for v in 0..fused.node_count() { + for (_, w) in fused.neighbors(v) { + assert!( + (w / first - 1.0).abs() < 1e-4, + "normalised weights should be uniform here, saw {w} vs {first}" + ); + } + } + + // Unnormalised, the heavy graph contributes ~1000x as much. + let raw = fuse(&light, &heavy, 0.5, Normalization::None).unwrap(); + let w = raw.neighbors(0).next().unwrap().1; + assert!(w > 400.0, "expected the heavy graph to dominate, got {w}"); + } + + #[test] + fn fusion_rejects_mismatched_inputs() { + let a = radius_graph(&[[0.0, 0.0], [1.0, 0.0]], 2.0, SpatialWeight::Uniform, None).unwrap(); + let b = radius_graph( + &[[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]], + 2.0, + SpatialWeight::Uniform, + None, + ) + .unwrap(); + assert!(fuse(&a, &b, 0.5, Normalization::TotalWeight).is_err()); + assert!(fuse(&a, &a, 1.5, Normalization::TotalWeight).is_err()); + assert!(fuse(&a, &a, f64::NAN, Normalization::TotalWeight).is_err()); + } + + /// Cells in different sections must never be joined, even where their coordinates + /// coincide — which they do here by construction. + #[test] + fn per_sample_never_joins_two_slices() { + let (pts, samples) = two_slices(5); + + for name in ["knn", "radius", "delaunay"] { + let g = per_sample(&pts, &samples, |sub| match name { + "knn" => knn_graph(sub, 6, Symmetry::Union, SpatialWeight::Uniform), + "radius" => radius_graph(sub, 4.0, SpatialWeight::Uniform, None), + _ => delaunay_graph(sub, HullPruning::default(), SpatialWeight::Uniform), + }) + .unwrap(); + + g.validate_symmetry() + .unwrap_or_else(|e| panic!("{name}: {e}")); + for (a, b) in edge_set(&g) { + assert_eq!( + samples[a], samples[b], + "{name}: joined sample {} to {}", + samples[a], samples[b] + ); + } + assert!(g.edge_count() > 100, "{name} produced almost nothing"); + } + } + + /// The reason it builds per sample rather than filtering afterwards: with kNN, a cell at + /// a section's edge must take k neighbours from its own section, not lose them. + #[test] + fn per_sample_knn_keeps_every_cell_at_full_degree() { + let (pts, samples) = two_slices(6); + let k = 6; + let g = per_sample(&pts, &samples, |sub| { + knn_graph(sub, k, Symmetry::Union, SpatialWeight::Uniform) + }) + .unwrap(); + for v in 0..g.node_count() { + assert!(g.degree(v) >= k, "node {v} has degree {}", g.degree(v)); + } + + // Build globally and delete cross-sample edges, and cells lose neighbours instead. + let global = knn_graph(&pts, k, Symmetry::Union, SpatialWeight::Uniform).unwrap(); + let starved = (0..global.node_count()) + .filter(|&v| { + global + .neighbors(v) + .filter(|&(u, _)| samples[u] == samples[v]) + .count() + < k + }) + .count(); + assert!( + starved > 0, + "the filter-afterwards approach should starve some cells; if it no longer does, \ + this test's premise needs revisiting" + ); + } + + #[test] + fn per_sample_handles_edge_cases() { + let pts = vec![[0.0, 0.0], [1.0, 0.0], [50.0, 50.0]]; + // A sample of one has no neighbours, and must not break the reassembly. + let samples = vec![0u32, 0, 1]; + let g = per_sample(&pts, &samples, |sub| { + radius_graph(sub, 5.0, SpatialWeight::Uniform, None) + }) + .unwrap(); + assert_eq!(g.node_count(), 3); + assert_eq!(g.edge_count(), 1); + assert_eq!(g.degree(2), 0); + + let none: Vec<[f64; 2]> = Vec::new(); + assert_eq!( + per_sample(&none, &[], |sub| radius_graph( + sub, + 1.0, + SpatialWeight::Uniform, + None + )) + .unwrap() + .node_count(), + 0 + ); + assert!( + per_sample(&pts, &[0, 1], |sub| radius_graph( + sub, + 1.0, + SpatialWeight::Uniform, + None + )) + .is_err() + ); + } + + /// Sample labels are arbitrary; only the grouping matters. + #[test] + fn per_sample_is_independent_of_label_values() { + let (pts, samples) = two_slices(7); + let build = |sub: &[[f64; 2]]| radius_graph(sub, 4.0, SpatialWeight::Uniform, None); + + let a = per_sample(&pts, &samples, build).unwrap(); + let relabelled: Vec = samples + .iter() + .map(|&s| if s == 0 { 77 } else { 3 }) + .collect(); + let b = per_sample(&pts, &relabelled, build).unwrap(); + assert_eq!(edge_set(&a), edge_set(&b)); + } +} diff --git a/src/spatial/mod.rs b/src/spatial/mod.rs index 1af9f54..6c284e9 100644 --- a/src/spatial/mod.rs +++ b/src/spatial/mod.rs @@ -24,11 +24,13 @@ //! Neither affects `array_row` / `array_col`, which is what [`lattice_graph`] wants. pub mod banksy; +pub mod combine; pub mod delaunay; pub(crate) mod grid; pub mod lattice; pub mod points; +pub use combine::{Normalization, fuse, per_sample}; pub use delaunay::{HullPruning, delaunay_graph}; pub use lattice::{Lattice, lattice_graph, visium_isometric_coords}; pub use points::{SpatialWeight, Symmetry, knn_graph, radius_graph}; From c682ed348391f9b88d0a740021029c4d78ee7416 Mon Sep 17 00:00:00 2001 From: Ian Date: Sun, 2 Aug 2026 12:31:40 +0200 Subject: [PATCH 10/13] docs: report measured accuracy and performance in the README MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds what the crate has actually been measured against, with the comparison named in each case rather than left implicit. Accuracy: 160 committed leidenalg fixtures (-0.09% mean modularity single-seed, +0.42% best-of-2), igraph (+0.03%), exhaustive brute force (93.5% exact optimum against igraph's 94.4-96.3%), banksy-py (elementwise to 1e-6), and PBMC3k where cluster counts track scanpy within one at every resolution and agreement with the authors' cell types is 0.8599 against scanpy's 0.8609. States plainly that the leidenalg mean is noise — both are stochastic heuristics and leidenalg's own two fixture seeds differ by up to 3.5% on hard instances — and that what the fixtures pin exactly is the quality function's definition, not the optimiser's luck. Performance is framed against scanpy/igraph as a C implementation called from Python, not as an interpreter-overhead comparison: 2-3x on PBMC3k, 1.33x geometric mean on synthetic graphs. Also records what is *not* measured: spatial domain detection has no comparative result yet, so nothing in the README should be read as a claim about it, and points at the pre-registered benchmark target. --- README.md | 97 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 95 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c0ddc23..8c8e259 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,85 @@ reproducible, and fast enough for single-cell-scale graphs. you already hold - **k-NN graph construction** from high-dimensional data (optional `knn` feature) +## Accuracy + +Correctness is checked against the implementations people actually use, not against itself. +Fixtures generated from `leidenalg` and `banksy-py` are committed, so the test suite needs no +Python. + +**Against `leidenalg`** — 160 committed fixtures spanning karate, LFR at several mixing +parameters, SBM and a real k-NN graph, across resolutions and seeds, for both RB and CPM: + +| | modularity vs `leidenalg` | +|---|---| +| single seed | −0.09% mean (70 better, 70 equal, 20 worse) | +| best of 2 seeds | +0.42% mean | + +Both libraries are stochastic heuristics over the same landscape — `leidenalg`'s own two +fixture seeds differ by up to 3.5% on the harder instances — so a single-run comparison is +mostly noise, and the sign of the mean is not meaningful. What the fixtures do pin exactly is +the *definition*: on the same membership our quality function reproduces `leidenalg`'s value +to floating point, which is what caught the 0.6.x factor-of-two resolution bug. + +**Against `igraph`**: +0.03% mean modularity. + +**Against brute force** — every set partition enumerated on graphs small enough to allow it, +so this compares to the true optimum rather than to another heuristic: + +| | reaches the exact optimum | +|---|---| +| this crate | 93.5% | +| `igraph` | 94.4–96.3% | + +**Against `banksy-py`**: the feature augmentation reproduces the reference elementwise to +**1e-6** across five decay kernels, harmonics 0–2, and λ from 0 to 1. The residual is the +reference's own precision — it stores azimuths as `float32`. + +### On real data + +PBMC3k (2,638 cells, 15-NN), clustering the *same* connectivity matrix `scanpy` did, so +every difference is the algorithm rather than graph construction: + +| resolution | clusters (ours / scanpy) | modularity (ours / scanpy) | +|---|---|---| +| 0.1 | 3 / 3 | 0.9511 / 0.9511 | +| 0.5 | 6 / 5 | 0.8063 / 0.8048 | +| 1.0 | 8 / 8 | 0.6715 / 0.6696 | +| 2.0 | 20 / 19 | 0.5304 / 0.5315 | +| 4.0 | 50 / 49 | 0.4217 / 0.4240 | + +Cluster counts track `scanpy` within one at every resolution, so resolution means the same +thing in both. Against the authors' cell-type annotations — the only measure here about +biology rather than about matching another implementation — **ours scores ARI 0.8599 and +`scanpy` 0.8609**, both peaking at resolution 0.75. + +## Performance + +Timings are against `scanpy`/`igraph`, which is a C implementation called from Python, not +interpreted code — so these are not interpreter-overhead numbers. + +| workload | this crate | `scanpy` / `igraph` | +|---|---|---| +| PBMC3k, per resolution | 7–11 ms | 22–24 ms | +| synthetic k-NN graphs | — | **1.33× slower** (geometric mean) | + +Single-threaded scaling on synthetic k-NN graphs: + +| nodes | edges | time | +|---|---|---| +| 20k | 170k | 18 ms | +| 100k | 840k | 111 ms | +| 3M | 25M | 6.8 s | +| 8M | 57M | 20.1 s | + +Spatial graph construction: + +| | | +|---|---| +| Visium HD lattice, 10.5M bins / 21.1M edges | 954 ms, 591 MB | +| 500k cells, k-NN (k=6) | 299 ms | +| 500k cells, radius | 80 ms | + ## Usage ```rust @@ -112,13 +191,27 @@ single-clustering = { version = "0.7", default-features = false } - ✅ **CSR network representation** - ✅ **Quality functions**: RB configuration model and CPM - ✅ **Reproducibility**: deterministic under a fixed seed +- ✅ **Spatial neighbour graphs**: Visium/HD lattice, radius, k-NN, Delaunay with adaptive + pruning, graph fusion, and per-sample construction for multi-slice experiments +- ✅ **BANKSY feature augmentation**: neighbourhood mean and azimuthal gradient, so ordinary + Leiden finds spatial domains - 🚧 **Louvain**: available as `LeidenConfig { refine: false, .. }`; no separate entry point - 🚧 **Benchmarks**: `cargo run --release --example scaling` - ❌ **Parallel local moving**: planned, deliberately deferred until the sequential path is - measured -- ❌ **DBSCAN / HDBSCAN / spatial-aware clustering**: planned + measured. Profiling puts 94% of a pass in level 0, of which local moving is 65% and + refinement 29%, so that is where it would go +- ❌ **DBSCAN / HDBSCAN**: planned - ❌ **Python bindings**: PyO3 integration (planned) +### Not yet measured + +Spatial *domain detection* has no comparative result. The pieces exist and each is verified +against its reference, but the end-to-end number — ARI against manual annotations on the +DLPFC benchmark — has not been produced. Until it has, nothing here should be read as a claim +about spatial domain accuracy. The target is pre-registered in +[`docs/spatial-benchmark.md`](docs/spatial-benchmark.md), along with what the established +methods score and which of their published numbers reproduce independently. + ## Contributing This project is in active development. Contributions, bug reports, and feature requests are From de0e52a9b84227ec7674be34e40c3fc059460359 Mon Sep 17 00:00:00 2001 From: Ian Date: Sun, 2 Aug 2026 13:01:00 +0200 Subject: [PATCH 11/13] docs: clarify the speedup column in the performance table --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 8c8e259..b7942d4 100644 --- a/README.md +++ b/README.md @@ -77,10 +77,10 @@ biology rather than about matching another implementation — **ours scores ARI Timings are against `scanpy`/`igraph`, which is a C implementation called from Python, not interpreted code — so these are not interpreter-overhead numbers. -| workload | this crate | `scanpy` / `igraph` | -|---|---|---| -| PBMC3k, per resolution | 7–11 ms | 22–24 ms | -| synthetic k-NN graphs | — | **1.33× slower** (geometric mean) | +| workload | this crate | `scanpy` / `igraph` | speedup | +|---|---|---|---| +| PBMC3k, per resolution | 7–11 ms | 22–24 ms | 2–3× | +| synthetic k-NN graphs, geometric mean | — | — | 1.33× | Single-threaded scaling on synthetic k-NN graphs: From 1128f2c9b890887a53f534e3cf86591a5aa72783 Mon Sep 17 00:00:00 2001 From: Ian Date: Sun, 2 Aug 2026 22:06:28 +0200 Subject: [PATCH 12/13] version update --- Cargo.lock | 2 +- Cargo.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 6c06d49..848b2ee 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -1248,7 +1248,7 @@ dependencies = [ [[package]] name = "single-clustering" -version = "0.7.0" +version = "1.0.0" dependencies = [ "anyhow", "criterion", diff --git a/Cargo.toml b/Cargo.toml index e4e9a5b..d72b331 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -1,6 +1,6 @@ [package] name = "single-clustering" -version = "0.7.0" +version = "1.0.0" edition = "2024" authors = ["Ian F. Diks"] homepage = "https://singlerust.com" From df0750b324352be3f6f7ca71d0f40401a00379a4 Mon Sep 17 00:00:00 2001 From: Ian Date: Sun, 2 Aug 2026 22:23:59 +0200 Subject: [PATCH 13/13] ci: run the ignored tests too The #[ignore]d tests were running nowhere. Three of the four are real coverage, not diagnostics: the Visium HD scale run is the only thing exercising the 10.5M-bin path, the Xenium run the only 500k-cell one, and the exhaustive optimality sweep over Bell(12) is the strongest evidence the optimiser actually reaches optima. They cost 11 seconds together, so there was no reason for the gap beyond nobody having added the job. --- .github/workflows/ci.yml | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 9eef049..a726131 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -36,6 +36,18 @@ jobs: - name: Test (release) run: cargo test --no-default-features --release + # the #[ignore]d tests: scale runs and the exhaustive optimality sweep. ~11s total, and + # without this the HD-scale path and the Bell(12) optimality check never execute anywhere. + scale: + name: Scale and exhaustive + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: dtolnay/rust-toolchain@stable + - uses: Swatinem/rust-cache@v2 + - name: Ignored tests + run: cargo test --no-default-features --release -- --ignored --nocapture + # separate so an hnsw breakage doesn't take the core red knn: name: With kNN feature