Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
population-genetics
demographic-inference
effective-population-size
coalescent
msprime
simulation
License:
| # DLCoalSim | |
| Coalescent simulation of demographic histories, for training and evaluating | |
| methods that infer effective population size through time, N<sub>e</sub>(t), | |
| from sequence data. | |
| A row is one simulated sample: bit-packed haplotypes, variant positions, the | |
| recombination and mutation maps *as a method would receive them* rather than as | |
| they truly were, and a 64-bin log-time target. Nothing is precomputed into | |
| summary statistics — the point is that a method computes its own. | |
| ```bash | |
| pip install -e . | |
| dlcoalsim --preset mini --out-dir runs/first | |
| ``` | |
| That writes 512 rows in a few minutes. `--preset core_v01` is 20,000 rows at | |
| 10 Mb; `--preset full_v01` is 100,000 rows at 20 Mb. | |
| ## Reading a dataset | |
| ``` | |
| runs/first/ | |
| samples/shard_00000.npz numerical arrays, 128 rows a shard | |
| samples/shard_00000.jsonl.gz one metadata object per row | |
| metadata/samples.csv the same metadata, whole dataset | |
| config.json manifest.json what produced it | |
| scripts/loader.py a minimal reader, written beside the data | |
| ``` | |
| ```python | |
| import numpy as np | |
| shard = np.load("runs/first/samples/shard_00000.npz") | |
| row = 0 | |
| lo, hi = shard["variant_offsets"][row], shard["variant_offsets"][row + 1] | |
| target = shard["target_log10_ne"][row] # (64,) log10 Ne per time bin | |
| positions = shard["variant_positions_bp"][lo:hi] # uint32 | |
| packed = shard["genotype_packed"][lo:hi] # (variants, 8) uint8 | |
| n_haplotypes = int(shard["n_haplotypes"][0]) | |
| haplotypes = np.unpackbits(packed, axis=1, bitorder="little")[:, :n_haplotypes] | |
| ``` | |
| Time bins are log-spaced from 50 to 500,000 generations; `time_edges` in | |
| `manifest.json` gives the boundaries. Genotypes are packed one bit per | |
| haplotype, `n_haplotypes` to a row. Missing calls are a sparse flat index (`missing_flat_idx`, | |
| `missing_offsets`) rather than a mask. | |
| A map stores one offsets array, not two. Its `*_pos` array holds the | |
| breakpoints and `*_rate` the rates *between* them, so a row holds one fewer | |
| rate than positions — none of either where a row has no map: | |
| ```python | |
| offsets = shard["obs_rec_offsets"] | |
| lengths = np.diff(offsets.astype(np.int64)) | |
| rate_at = np.concatenate(([0], np.cumsum(np.maximum(lengths - 1, 0)))) | |
| breakpoints = shard["obs_rec_pos"][offsets[row]:offsets[row + 1]] | |
| rates = shard["obs_rec_rate"][rate_at[row]:rate_at[row + 1]] | |
| ``` | |
| The maps come in two forms. `obs_rec_*` and `obs_mut_*` are what a method is | |
| allowed to see: a published map's resolution, its error, and its overall scale | |
| offset. `true_rec_*` and `true_mut_*` are what the sequence was actually | |
| simulated under, written for `--truth-fraction` of rows — 2% by default — so | |
| the gap between the two can be measured on a subsample. **Training on the true | |
| maps is cheating.** | |
| ## What a row can be | |
| Two mixtures are drawn independently, so any history can appear under any map. | |
| ### Demographic history | |
| | recipe | weight | what it is | | |
| | --- | --- | --- | | |
| | `spectral_walk` | 0.200 | Band-limited Fourier series on continuous log time. Spectral slope sets roughness, an explicit log<sub>10</sub> range sets amplitude, an envelope tilt sets the recent–ancient asymmetry. | | |
| | `empirical_human_ne_template` | 0.190 | 191 curves inferred from real human data by SMC++, phlash, MSMC2 and MSMC-IM, with time warp, level shift, contrast and smoothed noise. | | |
| | `size_event` | 0.100 | One size change: time, duration, signed depth, and the level it settles at. A duration running past the window is a permanent step. | | |
| | `bottleneck` | 0.100 | A drop of 0.4 to 3 decades that ends and recovers. | | |
| | `event_on_background` | 0.086 | A quieter spectral walk with one or two sharp events on it. | | |
| | `multi_event` | 0.080 | Two to four events with independent signs. | | |
| | `ooa_chain` | 0.080 | Ancestral size, ancient expansion, a bottleneck held open below the ancestral level, then recent exponential growth — ordered. | | |
| | `constant` | 0.040 | A single effective size. The control. | | |
| | `exponential` | 0.040 | A signed rate through msprime's own `growth_rate`, so the trajectory is exponential in linear time. | | |
| | `sawtooth` | 0.030 | Periodic growth and decline, with strength, placement, extent, regularity and amplitude drift all drawn. | | |
| | `literature_sawtooth` | 0.030 | The Zigzag_1S14 character: a drawn number of half cycles, spaced about fourfold, each about a decade deep. | | |
| | `stdpopsim_single_pop` | 0.024 | All 18 single-population stdpopsim models across 8 species, verbatim. Rows carry `held_out_evaluation`. | | |
| Sizes span 10<sup>2</sup> to 10<sup>6</sup>, and the level prior is near-uniform | |
| across that range — the bottom decade is real (a vaquita's effective size is | |
| under three thousand) and a method that has never seen it cannot be trusted | |
| there. | |
| ### Maps | |
| | source | weight | what it is | | |
| | --- | --- | --- | | |
| | `custom_heterogeneous` | 0.55 | Synthetic: hotspots at 60 per Mb, median width 498 bp, carrying 2.0% of sequence above ten times the mean, on an Ornstein–Uhlenbeck background. Calibrated against deCODE 2019 measured on chr20. | | |
| | `empirical_map_slice` | 0.20 | A random interval of the real deCODE and Roulette maps, taken at the same chromosomal coordinates so the two stay in register. | | |
| | `random_global_mu_r` | 0.15 | Flat maps, recombination drawn 0.3–3× baseline and mutation 0.5–2×. | | |
| | `clean_constant_map` | 0.10 | Flat at baseline, no observation noise. The control. | | |
| Observation error is a *fraction of each map's own spread*, split between a | |
| regional component correlated over 80 kb–1.5 Mb and a per-cell one. The | |
| regional part is what makes the error survive averaging, which is what a real | |
| map's disagreement does: two published maps of one region correlate 0.687 at a | |
| kilobase and 0.890 at a hundred. | |
| ## Configuration | |
| Every field of `simulator.config.Config` is a command-line flag with the | |
| underscores turned into dashes. `--preset` sets a coherent group of them: | |
| | preset | rows | length | haplotypes | | |
| | --- | --- | --- | --- | | |
| | `mini` | 512 | 1 Mb | 24 | | |
| | `core_v01` | 20,000 | 10 Mb | 64 | | |
| | `full_v01` | 100,000 | 20 Mb | 64 | | |
| Useful ones beyond the preset: | |
| - `--truth-fraction` — what share of rows carry the true maps | |
| - `--arg-detail` — `none`, `summary` (default: pair coalescences and branch | |
| length per time bin, 512 bytes a row) or `full` (the tree sequence itself, | |
| which runs 0.8–28 MB a row) | |
| - `--workers`, `--shard-size`, `--compute-chunk-size` — parallelism and layout | |
| Generation is resumable and content-addressed: an interrupted run picks up from | |
| its receipts rather than starting over, and a completed dataset is verified | |
| against them before it is trusted. | |
| ## Data that ships with the package | |
| Everything the simulator needs is inside `simulator/assets`, resolved against | |
| the package rather than the working directory, so an installed copy works from | |
| anywhere. | |
| | | source | | |
| | --- | --- | | |
| | `maps/decode2019_avg_hg38.bed` | deCODE 2019, Halldórsson et al., *Science* 363:eaau1043. 4,531,535 localised crossovers, 682 bp average resolution, native GRCh38. A pedigree map, so it carries no demographic assumption — the LD-based alternatives are inferred *under* one, and this project infers demography from linkage. | | |
| | `maps/roulette_2023_...bed` | Roulette, Seplyarskiy et al., *Nature Genetics* 2023. 1 kb bins. | | |
| | `templates/human_ne/` | 191 inferred human curves: SMC++ on 1000 Genomes, phlash, MSMC2 and MSMC-IM. | | |
| | `templates/stdpopsim_single_pop/` | The 18 single-population models in the stdpopsim catalogue, across 8 species. | | |
| `simulator/assets/SOURCES.md` carries the full provenance. The build scripts | |
| that produced each frozen file sit beside it; rebuilding needs | |
| `pip install -e ".[build-assets]"`. | |
| ## Tests | |
| ```bash | |
| pip install -e ".[dev]" | |
| pytest | |
| ``` | |
| The suite is calibration as much as correctness. Where a number in the | |
| generator was measured from real data, a test pins it to that measurement and | |
| says which one — the synthetic map's hotspot width against deCODE chr20, the | |
| demography mixture's turning points against the 191 inferred curves, the | |
| observed map's correlation with the truth against two published maps of one | |
| region. | |
| ## License | |
| MIT. The maps and templates under `simulator/assets` are third-party data | |
| redistributed under their own terms; see `SOURCES.md`. | |