fastlisi
Fast Local Inverse Simpson Index (LISI) — the standard statistic for judging whether a single-cell batch-integration method actually mixed the batches.
This is the LISI implementation from harmonypy, rewritten for speed. It returns the same values (agreement to ~1e-15) and is typically 15–30× faster.
Unlike harmonypy it has no Armadillo, BLAS, or LAPACK dependency — just numpy at runtime — so wheels install cleanly everywhere.
Install
pip install fastlisi
Use
import fastlisi
# X: (n_cells, n_dims) embedding, e.g. PCA or harmonized PCA coordinates
# metadata: pandas DataFrame (or dict of arrays) with one row per cell
lisi = fastlisi.compute_lisi(X, metadata, ["batch", "cell_type"])
# -> (n_cells, 2) array
Interpretation, for a label with 3 categories:
- LISI ≈ 3 → the cell's neighborhood contains all 3 categories (well mixed)
- LISI ≈ 1 → the cell's neighborhood is a single category (not mixed)
Good integration means high LISI on the batch label and low LISI on the cell-type label.
Passing several label columns at once is much cheaper than several calls: the neighbor graph does not depend on the labels, so it is built once and reused.
# Limit threads (0, the default, uses one per core)
lisi = fastlisi.compute_lisi(X, metadata, ["batch"], perplexity=30, n_threads=4)
# Skip the label-encoding step if you already have integer codes
lisi = fastlisi.compute_lisi_from_codes(X, codes) # codes: (n_labels, n_cells)
fastlisi.max_threads() # threads used when n_threads=0
Speed
Measured on the datasets shipped with harmonypy, on a 2-core machine. old is
harmonypy 2.0.0, new is fastlisi. Outputs agree to 2.2e-15.
| dataset | cells | dims | old | new | speedup |
|---|---|---|---|---|---|
lisi_x fixture |
400 | 2 | 0.017 s | 0.008 s | 2.3× |
pbmc_3500 |
3,500 | 30 | 1.41 s | 0.17 s | 8.4× |
ircolitis_cd8 |
68,785 | 20 | 800 s | 29.6 s | 27.1× |
ircolitis_cd8 |
68,785 | 50 | 2,613 s | 94.5 s | 27.7× |
More cores helps roughly linearly beyond this.
Where the speed comes from
- Point-contiguous storage. The original stored the embedding column-major,
so reading one point's coordinates strided by
8 × n_cellsbytes — a cache miss per dimension. Storing points contiguously makes a distance evaluation one vectorizable pass over ~2 cache lines. - Leaf buckets. 16 points per kd-tree leaf, with points permuted into leaf order so a bucket scan is a sequential read.
- Tighter pruning. An incrementally maintained bounding-box lower bound instead of testing the splitting plane alone.
- One neighbor search for all label columns, instead of rebuilding the tree per column.
- Threaded over cells (plain
std::thread, no OpenMP runtime to install or bundle), with per-thread scratch buffers replacing roughly4 × n_cellsheap allocations.
Scaling
Be aware that LISI is close to quadratic in the number of cells on real
embeddings — kd-trees prune poorly at 20–50 dimensions, so the neighbor search
tends toward all-pairs. Measured exponent here is about N^1.95. This package is
a large constant-factor improvement, not an asymptotic one. Past a few hundred
thousand cells you want an approximate neighbor index instead.
Accuracy
tests/ checks against lisi_lisi.tsv.gz, the reference values produced by the
original R implementation, and the same fixture harmonypy tests against.
Results are bit-for-bit identical regardless of n_threads, since each cell is
computed independently.
Credit and license
The algorithm and the reference test data are from harmonypy by Ilya Korsunsky and Kamil Slowikowski. If you use LISI, cite the Harmony paper:
Korsunsky I, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods (2019). doi:10.1038/s41592-019-0619-0
GPL-3.0-or-later, inherited from harmonypy. See LICENSE.
1 maintainer
Metadata
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Release files for fastlisi 0.1.0
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