diff --git a/.gitignore b/.gitignore index 014044b..8cf1011 100644 --- a/.gitignore +++ b/.gitignore @@ -54,6 +54,12 @@ data/ *.parquet *.lzo +# ...but keep bundled ship-in-wheel data (e.g. disease-effect catalog). +# Only re-include source files; pycache stays ignored via the global rule above. +!synthlab/data/ +!synthlab/data/*.py +!synthlab/data/*.csv + # OS .DS_Store Thumbs.db diff --git a/README.md b/README.md index c8a9a6d..2b29340 100644 --- a/README.md +++ b/README.md @@ -59,6 +59,79 @@ df = load_meds_events("~/.cache/synthlab/meds/synthea_100") Install the optional extra: `pip install synthlab[meds]`. +### Olink NPX simulator (NEW) + +Simulate case/control Olink proteomics data with LOD-driven missingness +and configurable group effects — the first greenfield open-source +simulator targeted at Olink's NPX / PEA readout (existing tools like +[MSstatsSampleSize](https://bioconductor.org/packages/MSstatsSampleSize/) +target LC-MS/MS, and [OlinkAnalyze](https://github.com/Olink-Proteomics/OlinkRPackage) +ships demo data but no simulator). Priors reflect UKB-PPP +([Sun et al. 2023](https://www.nature.com/articles/s41586-023-06592-6)) +and OlinkAnalyze `npx_data1` / `npx_data2` baseline distributions: + +```python +from synthlab import OlinkSimConfig, default_explore_3072_panel, simulate_olink_npx + +cfg = OlinkSimConfig( + n_samples=500, + panel=default_explore_3072_panel(), + group_effects={"CRP": {"case": 1.8}, "IL6": {"case": 1.2}}, + group_assignments=["case"] * 250 + ["control"] * 250, + seed=42, +) +df = simulate_olink_npx(cfg) +``` + +See [`synthlab/olink.py`](synthlab/olink.py) for the full API +(`OlinkPanelConfig`, `OlinkSimConfig`, `simulate_olink_npx`, +`default_explore_3072_panel`, `write_olink_parquet`, +`load_olink_parquet`). + +#### Disease-conditional effect catalog (NEW) + +Rather than hand-pick effect sizes, plug in a curated, source-cited +catalog of per-disease protein NPX shifts mined from published plasma +proteomics literature. Every row in +[`synthlab/data/olink_disease_effects.csv`](synthlab/data/olink_disease_effects.csv) +cites a real DOI — so a downstream user always knows *where* a given +effect-size estimate came from. See +[`docs/olink_disease_catalog.md`](docs/olink_disease_catalog.md) for the +schema and a "how to add a new disease" checklist. + +```python +from synthlab import ( + OlinkSimConfig, default_explore_3072_panel, simulate_olink_npx, + load_disease_effect_catalog, +) + +catalog = load_disease_effect_catalog() # bundled with package +print(catalog.diseases()) # ('Alzheimer', 'BRCA_hereditary', 'CAD', 'CKD', 'Cancer_broad', 'IBD', 'T2D') +effects = catalog.effects_for(["T2D", "CAD"]) # {protein: {disease: delta_npx}} +cfg = OlinkSimConfig( + n_samples=900, + panel=default_explore_3072_panel(), + group_effects=effects, + group_assignments=["T2D"]*300 + ["CAD"]*300 + ["baseline"]*300, + seed=42, +) +df = simulate_olink_npx(cfg) +``` + +Disease-group row ranges in the shipped CSV (see +[`synthlab/data/olink_disease_effects.csv`](synthlab/data/olink_disease_effects.csv)): + +- **T2D**: rows 2-9 ([Sun et al. 2023 UKB-PPP](https://doi.org/10.1038/s41586-023-06592-6), + [Sun et al. 2018 INTERVAL](https://doi.org/10.1038/s41586-018-0175-2)) +- **CAD**: rows 10-16 ([Williams et al. 2022 Sci Transl Med](https://doi.org/10.1126/scitranslmed.abj9625), + [Eldjarn et al. 2023 deCODE](https://doi.org/10.1038/s41586-023-06563-x)) +- **Cancer (broad)**: rows 17-22 ([Cohen et al. 2018 CancerSEEK Science](https://doi.org/10.1126/science.aar3247)) +- **BRCA hereditary**: rows 23-26 (null-hypothesis placeholders from + [Ahn et al. 2021 Cancers](https://doi.org/10.3390/cancers13102300)) +- **Alzheimer's**: rows 27-31 ([Guo et al. 2024 Nat Aging](https://doi.org/10.1038/s43587-023-00565-0)) +- **CKD**: rows 32-37 ([Dubin et al. 2023 Nat Comm CRIC](https://doi.org/10.1038/s41467-023-41642-7)) +- **IBD**: rows 38-44 ([Hu et al. 2025 Nat Comm UKB-PPP](https://doi.org/10.1038/s41467-025-57879-3)) + ## Installation ```bash diff --git a/docs/olink_disease_catalog.md b/docs/olink_disease_catalog.md new file mode 100644 index 0000000..0837042 --- /dev/null +++ b/docs/olink_disease_catalog.md @@ -0,0 +1,139 @@ +# Olink disease-effect catalog + +This document walks through the schema, sourcing rules, and contribution +checklist for +[`synthlab/data/olink_disease_effects.csv`](../synthlab/data/olink_disease_effects.csv) — +the curated per-disease protein NPX shift catalog that powers +[`synthlab.olink.load_disease_effect_catalog`](../synthlab/olink.py). + +The goal is to give users **realistic, source-cited** effect sizes for +simulating disease cohorts with +[`synthlab.simulate_olink_npx`](../synthlab/olink.py), without requiring +them to re-mine the literature themselves. + +## Schema + +| Column | Type | Meaning | +| ------------------- | -------- | ------- | +| `disease` | `str` | Canonical disease label (e.g. `T2D`, `CAD`, `Alzheimer`, `CKD`, `IBD`, `Cancer_broad`, `BRCA_hereditary`). Match these labels to the `group_assignments` you feed into `OlinkSimConfig`. | +| `protein_uniprot` | `str` | UniProt accession or gene symbol. Match these to the `proteins` field of your `OlinkPanelConfig`. | +| `delta_npx` | `f64` | **log2 NPX-unit mean shift** of cases vs a demographically-matched baseline. Negative values are allowed (e.g. adiponectin goes down in T2D). | +| `se_delta` | `f64` | Standard error of the delta, reflecting between-study / between-cohort variability. Used by `effects_for(noise_sd=...)` to draw Monte-Carlo perturbations. | +| `source` | `str` | Human-readable study tag, e.g. `"Sun et al. 2023 UKB-PPP (N=54,219) Nature"`. | +| `doi` | `str` | Real DOI (starts with `10.`). **Fabricated DOIs are not allowed** — if you cannot find one for a given protein/disease association, omit the row. | +| `evidence_strength` | `str` | One of `"strong"` / `"moderate"` / `"weak"`. See the rubric below. | +| `meta` (optional) | `str` | Free-form note — e.g. "largest-N meta-estimate", "MR-supported causal locus", or which of several papers the estimate was chosen from. | + +Every row **must** provide the first seven columns. `meta` is optional but +strongly encouraged. + +## Effect-size units + +Deltas are in **log2 NPX units** — the native scale used across all Olink +[Explore](https://olink.com/products/olink-explore) panels. A `delta_npx` +of `1.0` means cases sit at roughly `2x` the median baseline (log2(2) = +1). A `delta_npx` of `-0.5` means cases sit at roughly `0.7x` the baseline. + +### Converting from other scales + +- **Fold change** (e.g. microarray log2 fold change is already compatible): + `delta_npx = log2(fold_change)`. So a reported 2-fold change maps to + `delta_npx = 1.0`; a 4-fold change maps to `delta_npx = 2.0`. +- **Odds ratio** (from logistic GWAS / PRS work): approximately, + `delta_npx = log2(OR) * (protein_sd)` — but this is often misleading + because it doesn't translate a case/control association into a plasma + NPX shift. Prefer studies that directly report case-baseline NPX + differences. +- **Z-scored deltas** (reported as "effect in SD units"): multiply by + the protein's baseline NPX SD (typically ~0.5-0.8 NPX units on + Olink Explore). + +### Defensibility sanity check + +Keep magnitudes in a range the real assay has been observed to span: + +| Scenario | Plausible `delta_npx` | +| ------------------------------------------ | --------------------- | +| CRP in sepsis / active severe infection | +3.0 to +4.0 | +| CRP in subclinical CAD / metabolic disease | +0.3 to +0.5 | +| GDF15 in advanced CKD or heart failure | +0.7 to +1.2 | +| NEFL in mild cognitive impairment | +0.4 to +0.8 | +| NEFL in clinically diagnosed AD | +0.8 to +1.2 | +| Null hypothesis (no real effect) | 0.0 +/- 0.3 | + +If you're recording a delta > 3.0, double-check the source. + +### Standard-error guidance + +- If the source reports a 95% CI for the delta, set + `se_delta = (upper - lower) / (2 * 1.96)`. +- If the source gives only a point estimate and the study is large + (N >= 10,000), set `se_delta = 0.15` (2x the typical UKB-PPP per-protein + SE). +- If the study is small (N < 3,000) or the effect is drawn from a single + paper, set `se_delta = 0.3` (generic conservative default). + +## Evidence-strength rubric + +| Label | Criteria | +| ----------- | -------- | +| `strong` | Replicated in >= 2 large cohorts (N >= 5,000 each), OR supported by Mendelian randomization / pQTL causal inference in a large-N study, OR used clinically today (e.g. CRP for IBD activity). | +| `moderate` | Single large cohort (N >= 5,000) with clear effect, OR multiple smaller cohorts (N < 5,000) in agreement. | +| `weak` | Single small study, or mechanistic inference only, or null-hypothesis placeholder (e.g. pre-symptomatic hereditary cancer carriers where no consistent plasma signature exists). | + +Weak rows are fine to include, but their downstream use should assume the +noise term dominates — e.g. set `noise_sd=1.0` in +`DiseaseEffectCatalog.effects_for` so the delta is meaningfully perturbed +during Monte-Carlo sampling. + +## How to add a new disease / row + +Checklist for a PR touching +[`olink_disease_effects.csv`](../synthlab/data/olink_disease_effects.csv): + +- [ ] **Source**: I have a published paper (journal or preprint) with a + real DOI. I've verified the DOI resolves at `https://doi.org/`. + No synthesized DOIs. +- [ ] **Unit conversion**: I've converted the source's reported effect + into log2 NPX units per the table above, and the resulting magnitude + is within the defensibility range. +- [ ] **SE choice**: `se_delta` follows the standard-error guidance + (explicit 95% CI -> computed; large-N point estimate -> 0.15; otherwise + 0.3). +- [ ] **Evidence tag**: `evidence_strength` is set per the rubric. +- [ ] **No duplicates**: `(disease, protein_uniprot)` is unique within the + CSV. If multiple papers report an estimate for the same protein + + disease, take the largest-N meta-estimate and record which paper in the + `meta` column. Use multiple rows only when you truly want independent + draws from N(delta, se_delta^2) for a single protein + disease cell. +- [ ] **Disease label**: either matches an existing label in `diseases()`, + OR is a new canonical short label you've introduced consistently across + all rows for that disease. +- [ ] **Tests**: I've added a test that loads the new rows via + `load_disease_effect_catalog()` and asserts the expected disease + + protein coverage. +- [ ] **Docs**: I've updated the per-disease row range in + [`README.md`](../README.md) if a new disease was added. + +## Example: adding a hypothetical "post-COVID" disease + +```csv +post_covid,CRP,0.55,0.20,"Made-up et al. 2025 Made-up Journal",10.0/example.post-covid,moderate,placeholder only +post_covid,IL6,0.70,0.25,"Made-up et al. 2025 Made-up Journal",10.0/example.post-covid,moderate,placeholder only +post_covid,GDF15,0.45,0.25,"Made-up et al. 2025 Made-up Journal",10.0/example.post-covid,moderate,placeholder only +``` + +*(In a real PR, the DOI would be real — this is for illustration only.)* + +## Open questions / deferred work + +- **Covariate-adjusted effects**: Currently deltas are marginal (cases vs + baseline, aggregated across age / sex / ancestry). A future rev could + ship age- or BMI-conditional deltas, which would plug into an extended + simulator that accepts per-sample covariate effects. +- **Longitudinal effects**: For incidence-cohort simulations, the delta + grows over time pre-diagnosis. A future rev could add + `time_to_dx_years` -> `delta_npx` curves per (disease, protein). +- **Interaction terms**: Some effects depend on comorbidities (e.g. GDF15 + in T2D with CKD is larger than T2D alone). Future rev: an interactions + CSV that layers on top of the marginal catalog. diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 0000000..15296a6 --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,41 @@ +# SynthLab notebooks + +Demos and walkthroughs for the [SynthLab](https://github.com/bschilder/synthlab) +synthetic-healthcare-data toolkit. + +## Index + +| Notebook | Module | Description | +| -------- | ------ | ----------- | +| [`olink_demo.ipynb`](olink_demo.ipynb) | [`synthlab.olink`](../synthlab/olink.py) | Olink NPX proteomics simulator — generate, analyse, visualise case vs control with LOD missingness + plate batch effects. | +| [`Synthea.ipynb`](Synthea.ipynb) | [`synthlab.synthea`](../synthlab/synthea.py) | Run Synthea and convert CSV to OMOP. | +| [`Coherent_MultimodalDataset.ipynb`](Coherent_MultimodalDataset.ipynb) | [`synthlab.coherent`](../synthlab/coherent.py) | Load and explore the Synthea Coherent multimodal dataset. | +| [`Generate_MultimodalDataset.ipynb`](Generate_MultimodalDataset.ipynb) | [`synthlab.coherent`](../synthlab/coherent.py) | Generate multimodal synthetic cohorts. | +| [`MedGemma_SOAP_Notes.ipynb`](MedGemma_SOAP_Notes.ipynb) | [`synthlab.soap`](../synthlab/soap.py) | MedGemma-based SOAP note generation with causal graph analysis. | +| [`SNOMED_Entity_Linking.ipynb`](SNOMED_Entity_Linking.ipynb) | [`synthlab.snomed`](../synthlab/snomed.py) | SNOMED entity linking with SapBERT and FAISS. | +| [`UKBiobank_Synthetic.ipynb`](UKBiobank_Synthetic.ipynb) | [`synthlab.download_ukbiobank_synthetic`](../synthlab/download_ukbiobank_synthetic.py) | Download and explore the UK Biobank Synthetic Dataset. | + +## Running the Olink demo + +The Olink NPX demo notebook needs the `viz` optional dependency group +(`matplotlib`, `seaborn`, `scikit-learn`, `umap-learn`). Install alongside the +core SynthLab package: + +```bash +pip install 'synthlab[viz]' +``` + +Then open [`olink_demo.ipynb`](olink_demo.ipynb) in Jupyter — it runs +end-to-end in under a minute on CPU with no external data dependencies. + +## Reproducing `olink_demo.ipynb` + +The notebook is assembled from +[`_build_olink_demo.py`](_build_olink_demo.py) so the cell layout stays +reviewable under version control. After editing the builder: + +```bash +python notebooks/_build_olink_demo.py # rebuild cells +jupyter nbconvert --to notebook --execute --inplace \\ + notebooks/olink_demo.ipynb # embed outputs +``` diff --git a/notebooks/_build_olink_demo.py b/notebooks/_build_olink_demo.py new file mode 100644 index 0000000..58241e6 --- /dev/null +++ b/notebooks/_build_olink_demo.py @@ -0,0 +1,699 @@ +"""Build notebooks/olink_demo.ipynb as a hand-authored nbformat notebook. + +Run once with the SynthLab source on ``PYTHONPATH``. Emits a fresh +``.ipynb`` next to this script; outputs are embedded in-place by a +follow-up ``jupyter nbconvert --execute`` step from the shell. +""" + +from __future__ import annotations + +from pathlib import Path + +import nbformat as nbf + + +def _md(src: str) -> nbf.NotebookNode: + """Wrap ``src`` in a markdown cell.""" + return nbf.v4.new_markdown_cell(src.strip("\n")) + + +def _code(src: str) -> nbf.NotebookNode: + """Wrap ``src`` in a code cell.""" + return nbf.v4.new_code_cell(src.strip("\n")) + + +def build() -> nbf.NotebookNode: + """Assemble and return the Olink demo notebook.""" + cells: list[nbf.NotebookNode] = [] + + # === Section 1 — Title + motivation ================================== + cells.append(_md(""" +# Olink NPX simulator — end-to-end demo + +This notebook walks through [`synthlab.olink.simulate_olink_npx`](../synthlab/olink.py) +— a minimal, dependency-light simulator for [Olink](https://olink.com)-style NPX +(Normalized Protein eXpression) proteomics data. As of 2026-04 there is no +widely-used open-source Olink simulator; the closest analogues are +[MSstatsSampleSize](https://bioconductor.org/packages/MSstatsSampleSize/) +(LC-MS/MS, not NPX) and the [OlinkAnalyze R +package](https://github.com/Olink-Proteomics/OlinkRPackage), which ships demo +tables but no generative simulator. + +The model implemented in [`synthlab/olink.py`](../synthlab/olink.py) is deliberately +minimal and covers the three features needed to exercise downstream ML / +biomarker-discovery pipelines: + +- Per-protein NPX ~ N(mean, sd) draws with user-configurable **group-mean + shifts** (the "biomarker" signal). +- **Limit-of-detection (LOD)** driven missingness: 80% of sub-LOD values are + dropped (MNAR), 20% are retained and can be flagged with `qc_warning`. +- **Per-plate batch effects** — 96 samples / plate, plate intercepts drawn + from N(0, `plate_effect_sd`^2). + +Deferred to follow-up PRs: full MAR missingness, multi-factor batch effects, +panel-version LOD bridging, and a realistic PEA dilution noise model. See +[PR #2](https://github.com/bschilder/synthlab/pull/2) for the roadmap. + +**Install extras for this notebook** (`matplotlib`, `seaborn`, `scikit-learn`, +`umap-learn`): + +```bash +pip install synthlab[viz] +``` +""")) + + # === Section 2 — Minimal generate + peek ============================= + cells.append(_md(""" +## 2. Minimal generate + peek + +Draw 500 samples split 250/250 case/control, with three manually-injected +biomarkers (CRP, IL6, TNF). We use the built-in 50-protein +[`default_explore_3072_panel`](../synthlab/olink.py) — a UKB-PPP-informed subset +of the Olink Explore 3072 panel. +""")) + + cells.append(_code(""" +# NOTE: only the `viz` extra is required in addition to core deps. +import warnings + +import matplotlib.pyplot as plt +import numpy as np +import polars as pl +import seaborn as sns + +warnings.filterwarnings("ignore", category=UserWarning) +warnings.filterwarnings("ignore", category=FutureWarning) + +sns.set_theme(style="whitegrid", palette="colorblind") +plt.rcParams["figure.dpi"] = 100 +plt.rcParams["savefig.dpi"] = 100 + +RNG_SEED = 42 +""")) + + cells.append(_code(""" +from synthlab import ( + OlinkSimConfig, + default_explore_3072_panel, + simulate_olink_npx, +) + +panel = default_explore_3072_panel() +cfg = OlinkSimConfig( + n_samples=500, + panel=panel, + group_effects={ + "CRP": {"case": 1.8}, + "IL6": {"case": 1.2}, + "TNF": {"case": 0.9}, + }, + group_assignments=["case"] * 250 + ["control"] * 250, + seed=RNG_SEED, +) +df = simulate_olink_npx(cfg) +df.head(10) +""")) + + cells.append(_code(""" +df.describe() +""")) + + cells.append(_code(""" +# Frame schema + size + row-count + missingness summary. +n_samples = 500 +n_proteins = len(panel.proteins) +n_expected = n_samples * n_proteins +n_observed = df.height +drop_rate = 1.0 - n_observed / n_expected + +print(f"schema : {dict(df.schema)}") +print(f"rows : {n_observed:,} / {n_expected:,} possible") +print(f"overall drop rate : {drop_rate:.2%}") +print(f"unique samples : {df['sample_id'].n_unique()}") +print(f"unique proteins : {df['protein_id'].n_unique()}") +print(f"unique plates : {df['plate_id'].n_unique()}") +print(f"qc_warning rate : {df['qc_warning'].mean():.2%}") +""")) + + # === Section 3 — Per-protein NPX distributions ======================= + cells.append(_md(""" +## 3. Per-protein NPX distributions + +A 4x4 grid of NPX histograms for a representative slice of the panel. Case +(orange) vs control (blue) KDEs are overlaid; the red dashed line marks each +protein's LOD. Proteins with a non-zero `group_effects` entry are annotated +with their injected mean shift. +""")) + + cells.append(_code(""" +# Focus on a mix of injected biomarkers (first 3) plus 13 unrelated proteins. +display_proteins = ["CRP", "IL6", "TNF"] + [ + p for p in panel.proteins if p not in {"CRP", "IL6", "TNF"} +][:13] +group_shifts = {"CRP": 1.8, "IL6": 1.2, "TNF": 0.9} + +fig, axes = plt.subplots(4, 4, figsize=(14, 12), sharex=False, sharey=False) +fig.suptitle( + "Per-protein NPX distributions (case vs control)", + fontsize=15, + fontweight="bold", +) + +palette = sns.color_palette("colorblind", 2) +for ax, prot in zip(axes.flat, display_proteins): + sub = df.filter(pl.col("protein_id") == prot) + for (grp, color) in zip(["control", "case"], palette): + vals = sub.filter(pl.col("group") == grp)["npx"].to_numpy() + if vals.size == 0: + continue + ax.hist(vals, bins=25, alpha=0.45, color=color, label=grp) + sns.kdeplot(vals, ax=ax, color=color, linewidth=1.5) + ax.axvline(panel.lod[prot], color="crimson", linestyle="--", linewidth=1.2, + label="LOD") + shift = group_shifts.get(prot) + title = f"{prot}" if shift is None else f"{prot} (case +{shift})" + ax.set_title(title, fontsize=10) + ax.set_xlabel("NPX") + ax.set_ylabel("count") +axes.flat[0].legend(loc="upper left", fontsize=8) +fig.tight_layout(rect=[0, 0, 1, 0.97]) +plt.show() +""")) + + # === Section 4 — LOD missingness ===================================== + cells.append(_md(""" +## 4. LOD-driven missingness + +The `mnar_lod` missingness model in +[`simulate_olink_npx`](../synthlab/olink.py) drops 80% of sub-LOD values and +retains the remaining 20% (which downstream pipelines would flag with +`qc_warning`). On top of LOD dropout, a small MCAR pass removes +`missing_rate` (default 5%) of the survivors. + +**Left:** per-protein mean NPX vs LOD, coloured by observed drop rate. The +closer a protein's mean is to its LOD, the larger its drop rate. +**Right:** top-20 most-dropped proteins, with rate bars. +""")) + + cells.append(_code(""" +per_protein = ( + df.group_by("protein_id") + .agg(observed_mean=pl.col("npx").mean(), + n_observed=pl.col("npx").len()) + .with_columns( + drop_rate=1.0 - pl.col("n_observed") / n_samples, + lod=pl.col("protein_id").map_elements( + lambda p: panel.lod[p], return_dtype=pl.Float64), + prior_mean=pl.col("protein_id").map_elements( + lambda p: panel.mean[p], return_dtype=pl.Float64), + ) + .sort("drop_rate", descending=True) +) +per_protein.head(5) +""")) + + cells.append(_code(""" +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) + +# --- (a) observed mean vs LOD, coloured by drop rate ------------------------ +pp = per_protein.to_pandas() +sc = ax1.scatter( + pp["lod"], pp["observed_mean"], + c=pp["drop_rate"], cmap="viridis", s=70, edgecolor="k", + linewidth=0.4, +) +lo = min(pp["lod"].min(), pp["observed_mean"].min()) - 0.2 +hi = max(pp["lod"].max(), pp["observed_mean"].max()) + 0.2 +ax1.plot([lo, hi], [lo, hi], "k--", linewidth=1, alpha=0.5, label="y = x") +cbar = plt.colorbar(sc, ax=ax1) +cbar.set_label("drop rate", rotation=270, labelpad=14) +ax1.set_xlabel("per-protein LOD") +ax1.set_ylabel("observed mean NPX") +ax1.set_title("(a) per-protein mean vs LOD") +ax1.legend(loc="lower right") + +# --- (b) top-20 most-dropped proteins --------------------------------------- +top20 = per_protein.head(20).to_pandas() +ax2.barh(top20["protein_id"][::-1], top20["drop_rate"][::-1], + color=sns.color_palette("colorblind")[2]) +ax2.set_xlabel("drop rate") +ax2.set_title("(b) top-20 most-dropped proteins") +ax2.set_xlim(0, max(top20["drop_rate"].max() * 1.1, 0.05)) + +fig.tight_layout() +plt.show() +""")) + + # === Section 5 — Plate batch effects ================================= + cells.append(_md(""" +## 5. Plate batch effects + +With 500 samples at 96 per plate we get 6 plates; each plate gets an +intercept drawn from N(0, `plate_effect_sd`^2). The violin plot shows that +CRP still separates case vs control *within* each plate, despite the +plate-level offset. +""")) + + cells.append(_code(""" +subset = ( + df.filter(pl.col("protein_id").is_in(["CRP", "IL6", "TNF"])) + .to_pandas() +) + +fig, axes = plt.subplots(1, 3, figsize=(15, 5.5), sharey=True) +for ax, prot in zip(axes, ["CRP", "IL6", "TNF"]): + sub = subset[subset["protein_id"] == prot] + sns.violinplot( + data=sub, x="plate_id", y="npx", hue="group", + split=True, inner="quartile", ax=ax, palette="colorblind", + density_norm="width", + ) + ax.set_title(f"{prot}: per-plate NPX (case vs control)") + ax.set_xlabel("plate") + ax.set_ylabel("NPX") + ax.tick_params(axis="x", rotation=30) + +fig.tight_layout() +plt.show() +""")) + + # === Section 6 — PCA + UMAP sample-level ============================= + cells.append(_md(""" +## 6. Sample-level dimensionality reduction (PCA + UMAP) + +Pivot long -> wide (`sample` x `protein`), impute missing NPX with the +per-protein median (the standard Olink convention), then run PCA and UMAP. +Cases are expected to separate along the three injected-biomarker axes. +""")) + + cells.append(_code(""" +wide = ( + df.pivot(values="npx", index="sample_id", on="protein_id", + aggregate_function="first") + .sort("sample_id") +) +# Per-protein median imputation. +imputed = wide.with_columns([ + pl.col(col).fill_null(pl.col(col).median()) + for col in wide.columns if col != "sample_id" +]) +# Sample-level group labels. +sample_groups = ( + df.group_by("sample_id").agg(group=pl.col("group").first()) + .sort("sample_id") +) +X = imputed.drop("sample_id").to_numpy() +y = sample_groups["group"].to_numpy() +print(f"sample x protein matrix: {X.shape}") +print(f"label counts : case={int((y == 'case').sum())}, " + f"control={int((y == 'control').sum())}") +""")) + + cells.append(_code(""" +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler +import umap + +Xz = StandardScaler().fit_transform(X) +pca = PCA(n_components=2, random_state=RNG_SEED) +pcs = pca.fit_transform(Xz) +umap_model = umap.UMAP(n_components=2, random_state=RNG_SEED, n_neighbors=15, + min_dist=0.1) +emb = umap_model.fit_transform(Xz) + +fig, (axL, axR) = plt.subplots(1, 2, figsize=(14, 6)) +palette = dict(zip(["control", "case"], sns.color_palette("colorblind", 2))) +for grp, color in palette.items(): + mask = y == grp + axL.scatter(pcs[mask, 0], pcs[mask, 1], c=[color], label=grp, alpha=0.75, + edgecolor="k", linewidth=0.3, s=35) + axR.scatter(emb[mask, 0], emb[mask, 1], c=[color], label=grp, alpha=0.75, + edgecolor="k", linewidth=0.3, s=35) +axL.set_xlabel(f"PC1 ({pca.explained_variance_ratio_[0]:.1%} var)") +axL.set_ylabel(f"PC2 ({pca.explained_variance_ratio_[1]:.1%} var)") +axL.set_title("PCA of sample x protein matrix") +axL.legend(title="group") +axR.set_xlabel("UMAP 1") +axR.set_ylabel("UMAP 2") +axR.set_title("UMAP of sample x protein matrix") +axR.legend(title="group") +fig.tight_layout() +plt.show() +""")) + + # === Section 7 — Correlation heatmap ================================= + cells.append(_md(""" +## 7. Protein-protein correlation + +Pairwise Pearson correlations for the 25 most-expressed proteins, ordered by +hierarchical clustering. Injected biomarkers (CRP, IL6, TNF) are expected to +cluster together because every case sample shifts all three simultaneously. +""")) + + cells.append(_code(""" +means = ( + df.group_by("protein_id").agg(mean_npx=pl.col("npx").mean()) + .sort("mean_npx", descending=True).head(25)["protein_id"].to_list() +) +wide_top = imputed.select(["sample_id", *means]).drop("sample_id").to_numpy() +corr = np.corrcoef(wide_top.T) + +g = sns.clustermap( + corr, cmap="vlag", center=0, xticklabels=means, yticklabels=means, + figsize=(12, 11), linewidths=0.1, cbar_kws={"label": "Pearson r"}, +) +g.fig.suptitle("Protein-protein NPX correlation (top 25 by mean)", + fontsize=14, y=1.02) +plt.show() +""")) + + # === Section 8 — Volcano ============================================= + cells.append(_md(""" +## 8. Case vs control differential expression + +Per-protein Welch t-test of case vs control NPX. The volcano plot shows the +mean NPX delta on the x axis and `-log10(p)` on the y axis. The three +manually-injected biomarkers (CRP, IL6, TNF) should sit in the upper-right +corner (positive delta, small p). +""")) + + cells.append(_code(""" +from scipy import stats as sstats + +records = [] +for prot in panel.proteins: + sub = df.filter(pl.col("protein_id") == prot) + case_vals = sub.filter(pl.col("group") == "case")["npx"].to_numpy() + ctrl_vals = sub.filter(pl.col("group") == "control")["npx"].to_numpy() + if case_vals.size < 3 or ctrl_vals.size < 3: + continue + delta = float(case_vals.mean() - ctrl_vals.mean()) + t, p = sstats.ttest_ind(case_vals, ctrl_vals, equal_var=False) + records.append({"protein": prot, "delta": delta, + "neg_log10_p": -np.log10(max(float(p), 1e-300))}) +volc = pl.DataFrame(records).sort("neg_log10_p", descending=True) +volc.head(10) +""")) + + cells.append(_code(""" +fig, ax = plt.subplots(figsize=(10, 7)) +vp = volc.to_pandas() +injected = {"CRP", "IL6", "TNF"} +is_inj = vp["protein"].isin(injected) + +ax.scatter(vp.loc[~is_inj, "delta"], vp.loc[~is_inj, "neg_log10_p"], + color="grey", alpha=0.6, s=40, edgecolor="k", linewidth=0.3, + label="other") +ax.scatter(vp.loc[is_inj, "delta"], vp.loc[is_inj, "neg_log10_p"], + color="crimson", s=110, edgecolor="k", linewidth=0.6, + label="injected biomarker") +for _, row in vp[is_inj].iterrows(): + ax.annotate(row["protein"], (row["delta"], row["neg_log10_p"]), + xytext=(7, 4), textcoords="offset points", fontsize=11, + fontweight="bold") +ax.axhline(-np.log10(0.05), color="steelblue", linestyle="--", linewidth=1, + label="p = 0.05") +ax.axvline(0, color="black", linewidth=0.5) +ax.set_xlabel("NPX delta (case − control)") +ax.set_ylabel("-log10(Welch t-test p)") +ax.set_title("Volcano plot — case vs control differential NPX") +ax.legend() +fig.tight_layout() +plt.show() +""")) + + # === Section 9 — Parquet round-trip ================================= + cells.append(_md(""" +## 9. Parquet round-trip + +[`write_olink_parquet`](../synthlab/olink.py) and +[`load_olink_parquet`](../synthlab/olink.py) are the canonical on-disk +serialisation entrypoints — snappy-compressed, schema-validated. +""")) + + cells.append(_code(""" +import pathlib +import tempfile + +from synthlab import load_olink_parquet, write_olink_parquet + +tmp = pathlib.Path(tempfile.mkdtemp()) / "olink_demo.parquet" +write_olink_parquet(df, tmp) +round_trip = load_olink_parquet(tmp) +assert df.equals(round_trip), "round-trip mismatch" +print(f"wrote {tmp}") +print(f"size : {tmp.stat().st_size // 1024} KB") +print(f"rows : {round_trip.height:,}") +""")) + + # === Section 10 — Disease-conditional generation ===================== + cells.append(_md(""" +## 10. Disease-conditional generation + +So far we've hand-picked the three biomarkers (CRP, IL6, TNF) and their +effect sizes. In practice users want to **reuse published, source-cited +effect sizes** per disease — exactly what +[`synthlab.load_disease_effect_catalog`](../synthlab/olink.py) exposes. + +The bundled CSV +[`synthlab/data/olink_disease_effects.csv`](../synthlab/data/olink_disease_effects.csv) +captures per-disease protein log2-NPX shifts from published plasma-proteomics +studies — each row cites a real DOI. Coverage at PR-time (see [PR +#TODO](https://github.com/bschilder/synthlab/pulls)): + +- **T2D**: 8 proteins — [Sun et al. 2023 UKB-PPP](https://doi.org/10.1038/s41586-023-06592-6) +- **CAD**: 7 proteins — [Williams et al. 2022 Sci Transl Med](https://doi.org/10.1126/scitranslmed.abj9625) + [Eldjarn et al. 2023 deCODE](https://doi.org/10.1038/s41586-023-06563-x) +- **Cancer (broad)**: 6 proteins — [Cohen et al. 2018 CancerSEEK](https://doi.org/10.1126/science.aar3247) +- **BRCA hereditary**: 4 proteins, null-hypothesis placeholders from [Ahn et al. 2021](https://doi.org/10.3390/cancers13102300) +- **Alzheimer's**: 5 proteins — [Guo et al. 2024 Nat Aging](https://doi.org/10.1038/s43587-023-00565-0) +- **CKD**: 6 proteins — [Dubin et al. 2023 Nat Comm](https://doi.org/10.1038/s41467-023-41642-7) +- **IBD**: 7 proteins — [Hu et al. 2025 Nat Comm UKB-PPP](https://doi.org/10.1038/s41467-025-57879-3) + +Load the catalog and inspect what's available: +""")) + + cells.append(_code(""" +from synthlab import load_disease_effect_catalog + +catalog = load_disease_effect_catalog() +print(f"registered diseases ({len(catalog.diseases())}):") +for d in catalog.diseases(): + n = len(catalog.proteins_for(d)) + print(f" {d:<18s} -> {n} proteins") +""")) + + cells.append(_code(""" +# Peek at the raw catalog rows for T2D. +catalog.effects.filter(pl.col("disease") == "T2D").select( + ["protein_uniprot", "delta_npx", "se_delta", "evidence_strength", "source"] +) +""")) + + cells.append(_md(""" +### 10.1 3-group cohort (T2D / CAD / baseline) using catalog effects + +We'll simulate 300 subjects per group. The panel is a uniform 5.0-NPX +baseline covering every catalog protein; LOD is set far below baseline so +we can cleanly recover the injected means. +""")) + + cells.append(_code(""" +from synthlab import OlinkPanelConfig +from synthlab import simulate_olink_npx + +# Build a panel from all proteins referenced in the catalog. +catalog_proteins = tuple(catalog.effects["protein_uniprot"].unique().to_list()) +disease_panel = OlinkPanelConfig( + name="disease_catalog_panel", + proteins=catalog_proteins, + mean={p: 5.0 for p in catalog_proteins}, + sd={p: 0.5 for p in catalog_proteins}, + lod={p: -1000.0 for p in catalog_proteins}, # disable LOD drop for clean recovery +) +effects = catalog.effects_for(["T2D", "CAD"]) + +N_PER = 300 +assignments = ["T2D"] * N_PER + ["CAD"] * N_PER + ["baseline"] * N_PER +cfg = OlinkSimConfig( + n_samples=len(assignments), + panel=disease_panel, + group_effects=effects, + group_assignments=assignments, + missingness="none", + qc_warn_rate=0.0, + plate_effect_sd=0.0, + seed=RNG_SEED, +) +df_catalog = simulate_olink_npx(cfg) +print(f"rows : {df_catalog.height:,}") +print(f"groups : {df_catalog['group'].value_counts().to_dict(as_series=False)}") +print(f"proteins : {len(catalog_proteins)}") +""")) + + cells.append(_md(""" +### 10.2 Top-5 per-disease delta NPX — catalog vs empirical + +Grouped bar chart: for each disease, show the top-5 absolute delta-NPX +proteins according to the catalog, side-by-side with the empirical case - +baseline mean shift from the simulation. +""")) + + cells.append(_code(""" +# Catalog top-5 deltas per disease (absolute magnitude). +top5_records = [] +for dname in ["T2D", "CAD"]: + rows = ( + catalog.effects.filter(pl.col("disease") == dname) + .sort(pl.col("delta_npx").abs(), descending=True) + .head(5) + ) + for r in rows.iter_rows(named=True): + prot = r["protein_uniprot"] + # empirical delta from simulation + case_mean = df_catalog.filter( + (pl.col("protein_id") == prot) & (pl.col("group") == dname) + )["npx"].mean() + base_mean = df_catalog.filter( + (pl.col("protein_id") == prot) & (pl.col("group") == "baseline") + )["npx"].mean() + top5_records.append({ + "disease": dname, + "protein": prot, + "catalog_delta": float(r["delta_npx"]), + "empirical_delta": float(case_mean - base_mean), + }) +top5_df = pl.DataFrame(top5_records) +top5_df +""")) + + cells.append(_code(""" +import pandas as pd + +fig, axes = plt.subplots(1, 2, figsize=(14, 5), sharey=True) +for ax, dname in zip(axes, ["T2D", "CAD"]): + sub = top5_df.filter(pl.col("disease") == dname).to_pandas() + x = np.arange(len(sub)) + width = 0.38 + ax.bar(x - width / 2, sub["catalog_delta"], width, + label="catalog", color=sns.color_palette("colorblind")[0]) + ax.bar(x + width / 2, sub["empirical_delta"], width, + label="empirical", color=sns.color_palette("colorblind")[1]) + ax.set_xticks(x) + ax.set_xticklabels(sub["protein"], rotation=30, ha="right") + ax.axhline(0, color="black", linewidth=0.5) + ax.set_title(f"{dname} — top-5 absolute catalog delta-NPX") + ax.set_ylabel("delta NPX (case - baseline)") + ax.legend() +fig.tight_layout() +plt.show() +""")) + + cells.append(_md(""" +### 10.3 Catalog-vs-empirical consistency check + +Scatter of catalog delta vs empirical simulation delta across every +(disease, protein) row used. Points should hug the identity line (y = x); +deviations reflect Monte-Carlo sampling noise at 300 samples per group. +""")) + + cells.append(_code(""" +records = [] +for r in catalog.effects.filter(pl.col("disease").is_in(["T2D", "CAD"])).iter_rows( + named=True +): + dname = r["disease"] + prot = r["protein_uniprot"] + expected = float(r["delta_npx"]) + if expected == 0.0: + continue + case_mean = df_catalog.filter( + (pl.col("protein_id") == prot) & (pl.col("group") == dname) + )["npx"].mean() + base_mean = df_catalog.filter( + (pl.col("protein_id") == prot) & (pl.col("group") == "baseline") + )["npx"].mean() + records.append({"disease": dname, "protein": prot, + "catalog_delta": expected, + "empirical_delta": float(case_mean - base_mean)}) +consistency = pl.DataFrame(records) + +fig, ax = plt.subplots(figsize=(7, 7)) +palette = dict(zip(["T2D", "CAD"], sns.color_palette("colorblind", 2))) +for dname, color in palette.items(): + sub = consistency.filter(pl.col("disease") == dname).to_pandas() + ax.scatter(sub["catalog_delta"], sub["empirical_delta"], + s=70, color=color, edgecolor="k", linewidth=0.4, + label=dname) + for _, row in sub.iterrows(): + ax.annotate(row["protein"], (row["catalog_delta"], row["empirical_delta"]), + xytext=(5, 4), textcoords="offset points", fontsize=8) +lo = float(consistency.select(pl.col("catalog_delta").min(), + pl.col("empirical_delta").min()) + .min_horizontal()[0]) - 0.15 +hi = float(consistency.select(pl.col("catalog_delta").max(), + pl.col("empirical_delta").max()) + .max_horizontal()[0]) + 0.15 +ax.plot([lo, hi], [lo, hi], "k--", linewidth=1, alpha=0.5, label="y = x") +ax.set_xlabel("catalog delta NPX") +ax.set_ylabel("empirical delta NPX (case - baseline)") +ax.set_title("Catalog-vs-empirical consistency (300 subjects / group)") +ax.legend() +fig.tight_layout() +plt.show() +""")) + + # === Section 11 — Where to next ====================================== + cells.append(_md(""" +## 11. Where to next + +The simulator is deliberately minimal — the scope of [PR +#2](https://github.com/bschilder/synthlab/pull/2) is "smallest viable NPX +simulator with LOD + plates + group effects" and the follow-up adds a +curated effect-size catalog. Remaining roadmap: + +- **Full MAR missingness**: model missingness as a function of sample-level + covariates (age, QC batch) rather than aliasing to MCAR. +- **Multi-factor batch effects**: plate x run x operator, with per-factor + variance components. +- **Panel-version LOD bridging**: Olink Explore HT vs Explore 3072 have + different LOD distributions; a bridging mode should allow cross-panel + simulation. +- **PEA dilution / matrix effects**: the real [PEA + assay](https://olink.com/technology/proximity-extension-assay) has a + noise model that scales with dilution; currently we use a flat per-protein + sigma. +- **Covariate-adjusted catalog effects**: age / sex / BMI-conditional + deltas instead of the current marginal means. +- **Longitudinal effects**: time-to-event modulation of the catalog deltas + for incidence-cohort simulations. + +Tracking discussion in [PR #2](https://github.com/bschilder/synthlab/pull/2); +please open issues for missing features. +""")) + + nb = nbf.v4.new_notebook() + nb["cells"] = cells + nb["metadata"] = { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3", + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3", + }, + } + return nb + + +def main() -> None: + """Entry-point — build and write the notebook.""" + nb = build() + out = Path(__file__).resolve().parent / "olink_demo.ipynb" + nbf.write(nb, out) + print(f"wrote {out} ({len(nb['cells'])} cells)") + + +if __name__ == "__main__": + main() diff --git a/notebooks/olink_demo.ipynb b/notebooks/olink_demo.ipynb new file mode 100644 index 0000000..49154ac --- /dev/null +++ b/notebooks/olink_demo.ipynb @@ -0,0 +1,1410 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cc30a7aa", + "metadata": {}, + "source": [ + "# Olink NPX simulator — end-to-end demo\n", + "\n", + "This notebook walks through [`synthlab.olink.simulate_olink_npx`](../synthlab/olink.py)\n", + "— a minimal, dependency-light simulator for [Olink](https://olink.com)-style NPX\n", + "(Normalized Protein eXpression) proteomics data. As of 2026-04 there is no\n", + "widely-used open-source Olink simulator; the closest analogues are\n", + "[MSstatsSampleSize](https://bioconductor.org/packages/MSstatsSampleSize/)\n", + "(LC-MS/MS, not NPX) and the [OlinkAnalyze R\n", + "package](https://github.com/Olink-Proteomics/OlinkRPackage), which ships demo\n", + "tables but no generative simulator.\n", + "\n", + "The model implemented in [`synthlab/olink.py`](../synthlab/olink.py) is deliberately\n", + "minimal and covers the three features needed to exercise downstream ML /\n", + "biomarker-discovery pipelines:\n", + "\n", + "- Per-protein NPX ~ N(mean, sd) draws with user-configurable **group-mean\n", + " shifts** (the \"biomarker\" signal).\n", + "- **Limit-of-detection (LOD)** driven missingness: 80% of sub-LOD values are\n", + " dropped (MNAR), 20% are retained and can be flagged with `qc_warning`.\n", + "- **Per-plate batch effects** — 96 samples / plate, plate intercepts drawn\n", + " from N(0, `plate_effect_sd`^2).\n", + "\n", + "Deferred to follow-up PRs: full MAR missingness, multi-factor batch effects,\n", + "panel-version LOD bridging, and a realistic PEA dilution noise model. See\n", + "[PR #2](https://github.com/bschilder/synthlab/pull/2) for the roadmap.\n", + "\n", + "**Install extras for this notebook** (`matplotlib`, `seaborn`, `scikit-learn`,\n", + "`umap-learn`):\n", + "\n", + "```bash\n", + "pip install synthlab[viz]\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e7f28af1", + "metadata": {}, + "source": [ + "## 2. Minimal generate + peek\n", + "\n", + "Draw 500 samples split 250/250 case/control, with three manually-injected\n", + "biomarkers (CRP, IL6, TNF). We use the built-in 50-protein\n", + "[`default_explore_3072_panel`](../synthlab/olink.py) — a UKB-PPP-informed subset\n", + "of the Olink Explore 3072 panel." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d27da3cd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:45.203849Z", + "iopub.status.busy": "2026-04-23T21:12:45.203536Z", + "iopub.status.idle": "2026-04-23T21:12:46.173975Z", + "shell.execute_reply": "2026-04-23T21:12:46.173521Z" + } + }, + "outputs": [], + "source": [ + "# NOTE: only the `viz` extra is required in addition to core deps.\n", + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import polars as pl\n", + "import seaborn as sns\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n", + "\n", + "sns.set_theme(style=\"whitegrid\", palette=\"colorblind\")\n", + "plt.rcParams[\"figure.dpi\"] = 100\n", + "plt.rcParams[\"savefig.dpi\"] = 100\n", + "\n", + "RNG_SEED = 42" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "33bcc061", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:46.175442Z", + "iopub.status.busy": "2026-04-23T21:12:46.175332Z", + "iopub.status.idle": "2026-04-23T21:12:48.417390Z", + "shell.execute_reply": "2026-04-23T21:12:48.416951Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "                             not load constructor refs for GPU indexes.                                            \n",
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sample_idprotein_idnpxqc_warninggroupplate_id
strstrf64boolstrstr
"S0000""CRP"6.922412false"case""plate_0"
"S0000""IL6"6.055962false"case""plate_0"
"S0000""TNF"5.935627false"case""plate_0"
"S0000""IL1B"4.533881false"case""plate_0"
"S0000""IL10"5.573346false"case""plate_0"
"S0000""IL8"5.512383false"case""plate_0"
"S0000""IFNG"5.085326false"case""plate_0"
"S0000""BNP"5.326213false"case""plate_0"
"S0000""NT-proBNP"4.530132false"case""plate_0"
"S0000""TROPT"5.266958false"case""plate_0"
" + ], + "text/plain": [ + "shape: (10, 6)\n", + "┌───────────┬────────────┬──────────┬────────────┬───────┬──────────┐\n", + "│ sample_id ┆ protein_id ┆ npx ┆ qc_warning ┆ group ┆ plate_id │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ str ┆ f64 ┆ bool ┆ str ┆ str │\n", + "╞═══════════╪════════════╪══════════╪════════════╪═══════╪══════════╡\n", + "│ S0000 ┆ CRP ┆ 6.922412 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ IL6 ┆ 6.055962 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ TNF ┆ 5.935627 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ IL1B ┆ 4.533881 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ IL10 ┆ 5.573346 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ IL8 ┆ 5.512383 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ IFNG ┆ 5.085326 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ BNP ┆ 5.326213 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ NT-proBNP ┆ 4.530132 ┆ false ┆ case ┆ plate_0 │\n", + "│ S0000 ┆ TROPT ┆ 5.266958 ┆ false ┆ case ┆ plate_0 │\n", + "└───────────┴────────────┴──────────┴────────────┴───────┴──────────┘" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from synthlab import (\n", + " OlinkSimConfig,\n", + " default_explore_3072_panel,\n", + " simulate_olink_npx,\n", + ")\n", + "\n", + "panel = default_explore_3072_panel()\n", + "cfg = OlinkSimConfig(\n", + " n_samples=500,\n", + " panel=panel,\n", + " group_effects={\n", + " \"CRP\": {\"case\": 1.8},\n", + " \"IL6\": {\"case\": 1.2},\n", + " \"TNF\": {\"case\": 0.9},\n", + " },\n", + " group_assignments=[\"case\"] * 250 + [\"control\"] * 250,\n", + " seed=RNG_SEED,\n", + ")\n", + "df = simulate_olink_npx(cfg)\n", + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fe439301", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:48.418606Z", + "iopub.status.busy": "2026-04-23T21:12:48.418520Z", + "iopub.status.idle": "2026-04-23T21:12:48.422842Z", + "shell.execute_reply": "2026-04-23T21:12:48.422407Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (9, 7)
statisticsample_idprotein_idnpxqc_warninggroupplate_id
strstrstrf64f64strstr
"count""23737""23737"23737.023737.0"23737""23737"
"null_count""0""0"0.00.0"0""0"
"mean"nullnull5.0096210.010995nullnull
"std"nullnull0.661795nullnullnull
"min""S0000""AB40"2.7264760.0"case""plate_0"
"25%"nullnull4.569688nullnullnull
"50%"nullnull4.992854nullnullnull
"75%"nullnull5.424174nullnullnull
"max""S0499""VWF"8.4487661.0"control""plate_5"
" + ], + "text/plain": [ + "shape: (9, 7)\n", + "┌────────────┬───────────┬────────────┬──────────┬────────────┬─────────┬──────────┐\n", + "│ statistic ┆ sample_id ┆ protein_id ┆ npx ┆ qc_warning ┆ group ┆ plate_id │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ str ┆ str ┆ f64 ┆ f64 ┆ str ┆ str │\n", + "╞════════════╪═══════════╪════════════╪══════════╪════════════╪═════════╪══════════╡\n", + "│ count ┆ 23737 ┆ 23737 ┆ 23737.0 ┆ 23737.0 ┆ 23737 ┆ 23737 │\n", + "│ null_count ┆ 0 ┆ 0 ┆ 0.0 ┆ 0.0 ┆ 0 ┆ 0 │\n", + "│ mean ┆ null ┆ null ┆ 5.009621 ┆ 0.010995 ┆ null ┆ null │\n", + "│ std ┆ null ┆ null ┆ 0.661795 ┆ null ┆ null ┆ null │\n", + "│ min ┆ S0000 ┆ AB40 ┆ 2.726476 ┆ 0.0 ┆ case ┆ plate_0 │\n", + "│ 25% ┆ null ┆ null ┆ 4.569688 ┆ null ┆ null ┆ null │\n", + "│ 50% ┆ null ┆ null ┆ 4.992854 ┆ null ┆ null ┆ null │\n", + "│ 75% ┆ null ┆ null ┆ 5.424174 ┆ null ┆ null ┆ null │\n", + "│ max ┆ S0499 ┆ VWF ┆ 8.448766 ┆ 1.0 ┆ control ┆ plate_5 │\n", + "└────────────┴───────────┴────────────┴──────────┴────────────┴─────────┴──────────┘" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0ce7ead9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:48.423783Z", + "iopub.status.busy": "2026-04-23T21:12:48.423723Z", + "iopub.status.idle": "2026-04-23T21:12:48.426327Z", + "shell.execute_reply": "2026-04-23T21:12:48.426025Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "schema : {'sample_id': String, 'protein_id': String, 'npx': Float64, 'qc_warning': Boolean, 'group': String, 'plate_id': String}\n", + "rows : 23,737 / 25,000 possible\n", + "overall drop rate : 5.05%\n", + "unique samples : 500\n", + "unique proteins : 50\n", + "unique plates : 6\n", + "qc_warning rate : 1.10%\n" + ] + } + ], + "source": [ + "# Frame schema + size + row-count + missingness summary.\n", + "n_samples = 500\n", + "n_proteins = len(panel.proteins)\n", + "n_expected = n_samples * n_proteins\n", + "n_observed = df.height\n", + "drop_rate = 1.0 - n_observed / n_expected\n", + "\n", + "print(f\"schema : {dict(df.schema)}\")\n", + "print(f\"rows : {n_observed:,} / {n_expected:,} possible\")\n", + "print(f\"overall drop rate : {drop_rate:.2%}\")\n", + "print(f\"unique samples : {df['sample_id'].n_unique()}\")\n", + "print(f\"unique proteins : {df['protein_id'].n_unique()}\")\n", + "print(f\"unique plates : {df['plate_id'].n_unique()}\")\n", + "print(f\"qc_warning rate : {df['qc_warning'].mean():.2%}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8623f4dc", + "metadata": {}, + "source": [ + "## 3. Per-protein NPX distributions\n", + "\n", + "A 4x4 grid of NPX histograms for a representative slice of the panel. Case\n", + "(orange) vs control (blue) KDEs are overlaid; the red dashed line marks each\n", + "protein's LOD. Proteins with a non-zero `group_effects` entry are annotated\n", + "with their injected mean shift." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "38abc42b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:48.427236Z", + "iopub.status.busy": "2026-04-23T21:12:48.427186Z", + "iopub.status.idle": "2026-04-23T21:12:49.444196Z", + "shell.execute_reply": "2026-04-23T21:12:49.443817Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Focus on a mix of injected biomarkers (first 3) plus 13 unrelated proteins.\n", + "display_proteins = [\"CRP\", \"IL6\", \"TNF\"] + [\n", + " p for p in panel.proteins if p not in {\"CRP\", \"IL6\", \"TNF\"}\n", + "][:13]\n", + "group_shifts = {\"CRP\": 1.8, \"IL6\": 1.2, \"TNF\": 0.9}\n", + "\n", + "fig, axes = plt.subplots(4, 4, figsize=(14, 12), sharex=False, sharey=False)\n", + "fig.suptitle(\n", + " \"Per-protein NPX distributions (case vs control)\",\n", + " fontsize=15,\n", + " fontweight=\"bold\",\n", + ")\n", + "\n", + "palette = sns.color_palette(\"colorblind\", 2)\n", + "for ax, prot in zip(axes.flat, display_proteins):\n", + " sub = df.filter(pl.col(\"protein_id\") == prot)\n", + " for (grp, color) in zip([\"control\", \"case\"], palette):\n", + " vals = sub.filter(pl.col(\"group\") == grp)[\"npx\"].to_numpy()\n", + " if vals.size == 0:\n", + " continue\n", + " ax.hist(vals, bins=25, alpha=0.45, color=color, label=grp)\n", + " sns.kdeplot(vals, ax=ax, color=color, linewidth=1.5)\n", + " ax.axvline(panel.lod[prot], color=\"crimson\", linestyle=\"--\", linewidth=1.2,\n", + " label=\"LOD\")\n", + " shift = group_shifts.get(prot)\n", + " title = f\"{prot}\" if shift is None else f\"{prot} (case +{shift})\"\n", + " ax.set_title(title, fontsize=10)\n", + " ax.set_xlabel(\"NPX\")\n", + " ax.set_ylabel(\"count\")\n", + "axes.flat[0].legend(loc=\"upper left\", fontsize=8)\n", + "fig.tight_layout(rect=[0, 0, 1, 0.97])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3fe454be", + "metadata": {}, + "source": [ + "## 4. LOD-driven missingness\n", + "\n", + "The `mnar_lod` missingness model in\n", + "[`simulate_olink_npx`](../synthlab/olink.py) drops 80% of sub-LOD values and\n", + "retains the remaining 20% (which downstream pipelines would flag with\n", + "`qc_warning`). On top of LOD dropout, a small MCAR pass removes\n", + "`missing_rate` (default 5%) of the survivors.\n", + "\n", + "**Left:** per-protein mean NPX vs LOD, coloured by observed drop rate. The\n", + "closer a protein's mean is to its LOD, the larger its drop rate.\n", + "**Right:** top-20 most-dropped proteins, with rate bars." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b36c9775", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:49.445405Z", + "iopub.status.busy": "2026-04-23T21:12:49.445334Z", + "iopub.status.idle": "2026-04-23T21:12:49.450988Z", + "shell.execute_reply": "2026-04-23T21:12:49.450655Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 6)
protein_idobserved_meann_observeddrop_ratelodprior_mean
strf64u32f64f64f64
"GHRL"4.9230214640.0723.05.0
"HGF"4.9339044670.0663.05.0
"FABP4"4.9776094680.0643.05.0
"IGF1"4.9809174680.0643.05.0
"TROPT"4.9485684680.0643.05.0
" + ], + "text/plain": [ + "shape: (5, 6)\n", + "┌────────────┬───────────────┬────────────┬───────────┬─────┬────────────┐\n", + "│ protein_id ┆ observed_mean ┆ n_observed ┆ drop_rate ┆ lod ┆ prior_mean │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ f64 ┆ u32 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞════════════╪═══════════════╪════════════╪═══════════╪═════╪════════════╡\n", + "│ GHRL ┆ 4.923021 ┆ 464 ┆ 0.072 ┆ 3.0 ┆ 5.0 │\n", + "│ HGF ┆ 4.933904 ┆ 467 ┆ 0.066 ┆ 3.0 ┆ 5.0 │\n", + "│ FABP4 ┆ 4.977609 ┆ 468 ┆ 0.064 ┆ 3.0 ┆ 5.0 │\n", + "│ IGF1 ┆ 4.980917 ┆ 468 ┆ 0.064 ┆ 3.0 ┆ 5.0 │\n", + "│ TROPT ┆ 4.948568 ┆ 468 ┆ 0.064 ┆ 3.0 ┆ 5.0 │\n", + "└────────────┴───────────────┴────────────┴───────────┴─────┴────────────┘" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "per_protein = (\n", + " df.group_by(\"protein_id\")\n", + " .agg(observed_mean=pl.col(\"npx\").mean(),\n", + " n_observed=pl.col(\"npx\").len())\n", + " .with_columns(\n", + " drop_rate=1.0 - pl.col(\"n_observed\") / n_samples,\n", + " lod=pl.col(\"protein_id\").map_elements(\n", + " lambda p: panel.lod[p], return_dtype=pl.Float64),\n", + " prior_mean=pl.col(\"protein_id\").map_elements(\n", + " lambda p: panel.mean[p], return_dtype=pl.Float64),\n", + " )\n", + " .sort(\"drop_rate\", descending=True)\n", + ")\n", + "per_protein.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3deff5f2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:49.452155Z", + "iopub.status.busy": "2026-04-23T21:12:49.452077Z", + "iopub.status.idle": "2026-04-23T21:12:49.639004Z", + "shell.execute_reply": "2026-04-23T21:12:49.638616Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# --- (a) observed mean vs LOD, coloured by drop rate ------------------------\n", + "pp = per_protein.to_pandas()\n", + "sc = ax1.scatter(\n", + " pp[\"lod\"], pp[\"observed_mean\"],\n", + " c=pp[\"drop_rate\"], cmap=\"viridis\", s=70, edgecolor=\"k\",\n", + " linewidth=0.4,\n", + ")\n", + "lo = min(pp[\"lod\"].min(), pp[\"observed_mean\"].min()) - 0.2\n", + "hi = max(pp[\"lod\"].max(), pp[\"observed_mean\"].max()) + 0.2\n", + "ax1.plot([lo, hi], [lo, hi], \"k--\", linewidth=1, alpha=0.5, label=\"y = x\")\n", + "cbar = plt.colorbar(sc, ax=ax1)\n", + "cbar.set_label(\"drop rate\", rotation=270, labelpad=14)\n", + "ax1.set_xlabel(\"per-protein LOD\")\n", + "ax1.set_ylabel(\"observed mean NPX\")\n", + "ax1.set_title(\"(a) per-protein mean vs LOD\")\n", + "ax1.legend(loc=\"lower right\")\n", + "\n", + "# --- (b) top-20 most-dropped proteins ---------------------------------------\n", + "top20 = per_protein.head(20).to_pandas()\n", + "ax2.barh(top20[\"protein_id\"][::-1], top20[\"drop_rate\"][::-1],\n", + " color=sns.color_palette(\"colorblind\")[2])\n", + "ax2.set_xlabel(\"drop rate\")\n", + "ax2.set_title(\"(b) top-20 most-dropped proteins\")\n", + "ax2.set_xlim(0, max(top20[\"drop_rate\"].max() * 1.1, 0.05))\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a8eb80a0", + "metadata": {}, + "source": [ + "## 5. Plate batch effects\n", + "\n", + "With 500 samples at 96 per plate we get 6 plates; each plate gets an\n", + "intercept drawn from N(0, `plate_effect_sd`^2). The violin plot shows that\n", + "CRP still separates case vs control *within* each plate, despite the\n", + "plate-level offset." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ff72bea7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:49.640117Z", + "iopub.status.busy": "2026-04-23T21:12:49.640058Z", + "iopub.status.idle": "2026-04-23T21:12:49.885697Z", + "shell.execute_reply": "2026-04-23T21:12:49.885114Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "subset = (\n", + " df.filter(pl.col(\"protein_id\").is_in([\"CRP\", \"IL6\", \"TNF\"]))\n", + " .to_pandas()\n", + ")\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(15, 5.5), sharey=True)\n", + "for ax, prot in zip(axes, [\"CRP\", \"IL6\", \"TNF\"]):\n", + " sub = subset[subset[\"protein_id\"] == prot]\n", + " sns.violinplot(\n", + " data=sub, x=\"plate_id\", y=\"npx\", hue=\"group\",\n", + " split=True, inner=\"quartile\", ax=ax, palette=\"colorblind\",\n", + " density_norm=\"width\",\n", + " )\n", + " ax.set_title(f\"{prot}: per-plate NPX (case vs control)\")\n", + " ax.set_xlabel(\"plate\")\n", + " ax.set_ylabel(\"NPX\")\n", + " ax.tick_params(axis=\"x\", rotation=30)\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "11ec740e", + "metadata": {}, + "source": [ + "## 6. Sample-level dimensionality reduction (PCA + UMAP)\n", + "\n", + "Pivot long -> wide (`sample` x `protein`), impute missing NPX with the\n", + "per-protein median (the standard Olink convention), then run PCA and UMAP.\n", + "Cases are expected to separate along the three injected-biomarker axes." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "550cc0d0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:49.887144Z", + "iopub.status.busy": "2026-04-23T21:12:49.887040Z", + "iopub.status.idle": "2026-04-23T21:12:49.893200Z", + "shell.execute_reply": "2026-04-23T21:12:49.892528Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sample x protein matrix: (500, 50)\n", + "label counts : case=250, control=250\n" + ] + } + ], + "source": [ + "wide = (\n", + " df.pivot(values=\"npx\", index=\"sample_id\", on=\"protein_id\",\n", + " aggregate_function=\"first\")\n", + " .sort(\"sample_id\")\n", + ")\n", + "# Per-protein median imputation.\n", + "imputed = wide.with_columns([\n", + " pl.col(col).fill_null(pl.col(col).median())\n", + " for col in wide.columns if col != \"sample_id\"\n", + "])\n", + "# Sample-level group labels.\n", + "sample_groups = (\n", + " df.group_by(\"sample_id\").agg(group=pl.col(\"group\").first())\n", + " .sort(\"sample_id\")\n", + ")\n", + "X = imputed.drop(\"sample_id\").to_numpy()\n", + "y = sample_groups[\"group\"].to_numpy()\n", + "print(f\"sample x protein matrix: {X.shape}\")\n", + "print(f\"label counts : case={int((y == 'case').sum())}, \"\n", + " f\"control={int((y == 'control').sum())}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bc309ebc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:49.894759Z", + "iopub.status.busy": "2026-04-23T21:12:49.894611Z", + "iopub.status.idle": "2026-04-23T21:12:55.706478Z", + "shell.execute_reply": "2026-04-23T21:12:55.706036Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.preprocessing import StandardScaler\n", + "import umap\n", + "\n", + "Xz = StandardScaler().fit_transform(X)\n", + "pca = PCA(n_components=2, random_state=RNG_SEED)\n", + "pcs = pca.fit_transform(Xz)\n", + "umap_model = umap.UMAP(n_components=2, random_state=RNG_SEED, n_neighbors=15,\n", + " min_dist=0.1)\n", + "emb = umap_model.fit_transform(Xz)\n", + "\n", + "fig, (axL, axR) = plt.subplots(1, 2, figsize=(14, 6))\n", + "palette = dict(zip([\"control\", \"case\"], sns.color_palette(\"colorblind\", 2)))\n", + "for grp, color in palette.items():\n", + " mask = y == grp\n", + " axL.scatter(pcs[mask, 0], pcs[mask, 1], c=[color], label=grp, alpha=0.75,\n", + " edgecolor=\"k\", linewidth=0.3, s=35)\n", + " axR.scatter(emb[mask, 0], emb[mask, 1], c=[color], label=grp, alpha=0.75,\n", + " edgecolor=\"k\", linewidth=0.3, s=35)\n", + "axL.set_xlabel(f\"PC1 ({pca.explained_variance_ratio_[0]:.1%} var)\")\n", + "axL.set_ylabel(f\"PC2 ({pca.explained_variance_ratio_[1]:.1%} var)\")\n", + "axL.set_title(\"PCA of sample x protein matrix\")\n", + "axL.legend(title=\"group\")\n", + "axR.set_xlabel(\"UMAP 1\")\n", + "axR.set_ylabel(\"UMAP 2\")\n", + "axR.set_title(\"UMAP of sample x protein matrix\")\n", + "axR.legend(title=\"group\")\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "901543d4", + "metadata": {}, + "source": [ + "## 7. Protein-protein correlation\n", + "\n", + "Pairwise Pearson correlations for the 25 most-expressed proteins, ordered by\n", + "hierarchical clustering. Injected biomarkers (CRP, IL6, TNF) are expected to\n", + "cluster together because every case sample shifts all three simultaneously." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a4066069", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.707671Z", + "iopub.status.busy": "2026-04-23T21:12:55.707588Z", + "iopub.status.idle": "2026-04-23T21:12:55.815335Z", + "shell.execute_reply": "2026-04-23T21:12:55.814853Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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7b+HXX38d/3n79u1uG6xdu7YwTCeddFLhtGnT4j/rONEx/OSTT8afW7hwoTu2gnTQQQe5zyfRv/71L3fs/vnPf3Y/69oYxn7y8ssvF7Zv377wP//5T2GU9DmUdX6PvccHOpcE3ZZu3boVzpo1K/6zzmvaBq+++mqRa8ERRxwReDtmz54d//m7774r7N27t7sf+vTTT0PdX3v16lX49NNPF3nuzTffLOzZs2f859tuu82dhzNhm+j3J96DqF0fffRRkfd8/vnnhQceeGCg7dB+2aNHj8J58+YVe61du3aFn3zySWFY5s6dW3jRRRe5e0f1Ka666qrCOXPmFBYUFITeFgSLDCggTWikJnnEOcwR6FSj74k0PSOKbBuNJMamPohGoJOLoSorizpI4dPImjIDNJKnWgZRUTZJYnF2jejpZ00lqlevXqhtOeSQQ1y9Gk1f1Wiw0utVuytG0zKaNWsWeDt0/A4bNsyNOD/88MOu5lFUVHNC+4h06NDBqlWrVuxz0XQ4ZUUFRVPsNF1V0zGUIafpFzGxrCtlNYRxztXUB7VBC08oq1SZWFHR1LfEaY/6XtkKYWd2qtahanQl1h3SZ6FpO4nbTXVUgqRp3BqR19+K0fGrzFtlKjRo0MDtR2E47rjjXLacsns1BTxKymL0ZYEUHyiDI3Haks5rujf5zW9+E39O+5DOfUFat25dkWyimjVruhIKuh5rP9W0Y01fDcPHH39crKj2YYcd5jJvNOVNx44K2gddysGXbaL7ZV3/VQuqdevWbmqZMsIS68c9+eSTtvfeewfajksuucRNbda92l577WU9evRw55UoyjaoDcrq1HbRNOPEkhaoWAhAAWlCFyt1FhXoSVz1rE+fPsUulmHUfIgq+JVMHUV1pJXurk7rqaee6lKsR40a5baLAk9KcVcR3UzYJqmmQmhFleR9JIypEOowKrihaWcKMARZ0yidpieqE6upPLrpUxDq7LPPdgXSw6bArY4dFcWNkm56deMdmyL5zjvvFCk0re91TAe50qYCf0r3V40fTd1NpiCUglOaKhgGdUhUD0X/7ygDUL7Q+SuxZoymq2iqijorMQoMBVnnT4455hg39U8PdWJjwTkFgzQVT7Wxvv76awuLpohqKlPUFIRTHR/8TPuqArWJtK8kBnN1v5B4nguCrrtvvfVWkWCCOvUKuKgulWrtqZxCGNQWTVnVlLvEaXm6p43Vbfvss88CD2T6sk10vVNQTov5qIapphVrn9A9gbbH8ccf7wKZGowIkqZUq9bhP//5T3cd1j2b7pk1HVH3SEEO/CRT4Ev9Fy3IMnv2bLcNdG5ldeeKhwAUkCaCrsOyq3SjrUyFxBuuWBHFREEWYhWNFl1wwQXuInX44Ye7mwstHaufVahWBT91g6O6C5mwTZQ1kUg3EApShl07JjEIpZoCGqVX0U9fsgd9oJFebR9l3+jGVzegYTvwwAPdI0q6+VWwRQsK6MZTx2ss0K56UIMHD7bvvvvOjQYHHZDTOSRVUFc1oBTEDiOQnTgyrUeUfMm8VbBHxXkVcFJAXQVzf//73xd5jzpPiTVcgqAabeqw/eEPf3AdVgUlY5RBof1WdVzCooBb0BkS6aQ8xdkzadW1888/32Xb/vvf/3bF82OZewreKritwSFlHYVBgR2dy1WXU9c9BWp1X6ZVlJUdphpqWkgg6HOeL9tEgaYYDZQq+KaAVCwYp+CLBh+CrIcVo2NG5zI9lHU7Y8aMeM0yndfUFn0Nuhi5Bua0iqj+/rRp09zAjwK5apfuXalpWnFU0jy8qBsBIL0kBjTKMw0raLp4a2UkFVX+/PPP3QVMNzTKalDWjTJM6tatW+G3iaallJdS38OiwIqmuynVPQrqoCnIkjj6rA7sfvvtVyRgKCqAGjbd5Km4pm74tOKMCoFmGt14q0OiDJNECpRqyuI555wT2UqOyu7I1JUkdewkd+hTTcGWIIPaGp1Xh037h/6Osp10vChTTIMMKlirYsLqxCbvQ0H44osv3DUl1TlN1yAVVA66I63A6Msvv+wCp/o/63oXVdBH2YNBX2PLQ6vdljdAGuSgno4bBTcSMzcUsNS9SCzDR9dETZ0MejBI508dG+rMJw82KHtOg3i6HoYxKKX7NAV5Vq5c6YL6Kkiu40T3a1pkQudYZfoHzadt4jOdWzVwqM9Ngamwt8cnn3zizvMKSGmapmY5aLBOAwCNGjUKtS34dRGAAoAKYtmyZYFOU0pXiTUVyhJ0xl6MRoHVcVSWXhSUcaRpBolTlhYsWOACtrGgnFaTVCahpsRVdKWNeCcHK8MIUiqAoZtu7SOqyaHPIQoahS6v5IykX5sGGBRoUpaRshRiHUdlnqqDpOykM88804Kmqe/KstVno+BkFHVKtE8qoySWzaM2qA5TYk0s3yiDUZ9bJlBQrrzmzZtnUVNdJAWEfKBs1+Q6npm+TYKeCqjVpEubvqxs/ldffdW6detmUdAgpv6+At0ajIjd7yJ9EYAC0ngkOhW958MPP7So6SZdSwyrLkbUdMFS3YGKPpqlfUTp25pGpI7IEUccEWk2jQpJapSZQpL/n7J8FPxRxo+oAKnqLYSdXaO/q4BTYt2W5EwfjThqP9IoaNAZjNpXFHBRltoJJ5zglplPrqESJGUrKMBxwAEHFAsKql3qUMam0gYdpJw8ebLdeOONbmqIMgP++9//upF6LRuO1Nca7SthTA3UcavPQceGaBReNVPCmCaTnHGrDqNqUak+lmq5aQqPAnFRUIF+1UfT8aupxcpYTMxiUK2qpUuXBn4NLk9nOtP4Esy+6aab3H5aVmBV5zoFG4LCPlL2vcDJJ5/sak+Fff9YnntGnXu1P6v0BtIXNaCANKG6EiXdYKsDpxtATQsIIwNGo666UCTecGr0OVa3RRczzbFXZ9uHAFRY1FHUFAB9Vkq91wh9Yr0JBYbGjRsX2N9/+umnXRFl3cSpk6zika1atXIrz+ih+jVhBoM0vUAd+8S/qUCgMhYyYWQxFR032idUG0zHi6be6cY87FWsUs2+T/VcGJ16BaqV5q/pGNom+l7TU7SfhEXnzyeeeMIFE3TzrbplMS+++KLrFIUVJFSGlaabxYqyqyOg80YUASjtlwomJE5V1f6bGBzcsmWL2z6a/haFMAvUKmtQdQZ1nlfgR8exgkAqJBwmDTIpGyx2HtV19thjj3XTz8MOviiArvOYri/aT3T903Gs6WY6rjS1SteAMLJLFWzR+TRxG0TVmfZBcjD7hRdeiCyYrewV7R/XX399ymvPQw895PajoD8nX/aR8tQrC2NhoVTXfU1D1AqoYSvPPaNWSyT4lP4IQAFp4rTTTkv5/AcffOAKOyrYo6wBpeUHTTfduuFWh1Gjalq2VhdzzeXXhUI/a7nhoIsG+0TBPxXT3HPPPV0nINYpUmBOI0tazUSdFq04FlSNEhX21EMXbN1UaLRZq0UpKKURaM3hV4aHglGJBTDDvLFRe8Jewt2n7EEV1dZ+EFtSXtk2mjakILKOpUyklZHUaVXmk6gjrULPt956a6gFr1WcVkHigQMH2muvveY6zLrZDduqVauK1EFRMEyrempKpGpghB0w1Xk+MQClzMrETDkdz0GvpFjezpre88orrwTWjvfee891kmIZT9pHtd8qCyvMQJgCtLFixbFMLA0G6RwfdgBKA1F//OMf3RLq8vzzz7ti0uvXr3dBBRUwVoc/jH3Xp860D3wKZuu+UQtuaD/V1xgtKqBzru4NNI03VYCqIu4jul/X/1ULK3Tt2jXUv+0rX+4ZETwCUECa0sXywQcfdAUd1cFWrQ4FP8KgmgW6cKoQYKzDqLR3BcGUvaCbG93wZFKHWgVGFVRIzgLo3r17vKP26aefulHAMIrkqiOmFaH0UGBMdXzUUdAIqAJSYQSgfOFT9qA6iImZNLEVrFRUORNH6GO1NjT9L0YBUmXZqAMbdqZc27Zt3TGqwItGxbUqX9irvun/nhjM0NQ//aygQ9gBqPJmygVNHdPSPgcVFdYx3KxZs0DbocGFxH1SGabK9tEKjmEGoFIVgldGlhY0CJsCCJraFaOAnOruaQBqxIgRduqpp1qmUae5vFR3LxOC2bEBBgWfdG+o1U91LxnLktfAjFZ9yxQnnXSSyyTVYIsGO1jNEpmEABSQhpSloawnrbaj+dIXXXSRu/kMS6w2TMxRRx1lX375pVuRR6PDYS5V7os33njDdVpLo0yXQYMGhdIeLTGs2leaDqnvlX6vAJkCgz4Xqq3o2YMqppl4rKoTqRHhTB2hF/3fE1co1Pe6MY9q1FOdI2UMaulndY6U3eKDTF60+Kqrrkr5vI5dfVaxDNTEzIogKMATm2qeuL/quM5UOk4Ti0br+NHxq3NqJgafRP/3WJ2w0o5bnf+DrIvlUzA7FoTS8aOMp1i5AN0/apAoimzTqKke14knnmh33nln6NPwE/fBsAdZAAJQQJp11FSjRGnUe+21lxup19ewaUWMxDna6lDH5vaHHXwqzwpnGqENmm42k2vEKPCROB1Co+XffvttYG3Q1AcFnRQM04h8mzZtXLDpiiuucKOsUWSk+XhjE2X2oE/efffd+JLgsY6SCgVrefdYtlYmUwBKmTXz588vUqA1aHQIyl/fRkH/mjVrumlFmhqYSfR/Tgwu6LymKVeJx7RokCoKUXwevhw7Om+olo2CLaqB5UObfAlma0qmtofKE2gf0f1sJu4jMQqgawpglPuC7hETB4EUVFaQMHHqdVgrwPr02SA4BKCANKFRMmVrqNC1Lhaqt5Q8Ehu1Dh06hP43y3vhPuSQQwJth276FVxq3Lhx/DkV/0ykaVZBjjpq/2jatKnLFtDImg+FvlUTLPEmRsFL1YKIrSgWE0aBWh+yB8sT+AljaoZoP0nuiKhTEPbNYKoOgS83odo3lC2gIKWmh4RxTPnWIfCNBhQ02KHszlhWaZh1j2bPnl3k7ykrStm/yef2ILN/dJ5XOxLl5eUVK1as4yiqAFTY51Wfjp26deu6gtpaoOXNN990+2kUfAu2JJYm0P2rMsWU+RNmUWlf9pEYnUuinH6n6c3Jgp7KnE73jAgGASggTegGRiOcCnDohkaPkkQ1ShHFjc748ePNB6q1pOK3pd1IqNjy/vvvH1gbVIdD2U+6MGvlLmU/Kb1dy+xGQQEU1fFJpJUBlZ2lRyZmD+5K4CfIqRlBrqqzq7QtVPcp+blu3boVe29Q20Q3ucpUeO6559zPvXr1ckvdn3HGGfHA4D333ONWygs60O5Th8C34KCOW53flPWkDMYophOnWp0xefq1tlGQASjVzvGJL9lY2ubJ+2dUx44KwyuoofuCqAJQPgVbzjvvvJTBFx072kaJAcsg2+LL+VWrFuv6kvw5hM2nQI5P94wIVqXCTC4qAKSR8kw1C+uCoiCL5vInXjhnzJjhVioKe5Qi1U1NKropDXJVPt1A6aZOnVgVZU+m0XqtEqRi5VppKyg6pavmk6YNxeo/qROgVHd11tTR10h5pmYP6jOIMntQtdLKK8pRyDApu+h/6Tz8GhRcUjt0PtF57dlnn3WZLcp4GjlypDuulHWj/UYrfGWKVCtIpiqALUEGTJX1pIxSTTFWUWVdD5OvNYiGrvvloX3Gp8B3JtB1r7wB46Dv1Xy6h/WBBgZ1j5Y4vVuLXmhFySjqcyXSoEvigga6h+R8i18TASggjS9WUVFmgC8ZSj7d1GhkfMKECdapUycX8FEavuroLF682H12ffv2Da0IeYz+vrLlFi5c6NqhVXFUG0rLqGcKrW4Xyx78zW9+U+p7M2VKk+qFlVcmFBJWJ/rmm2+Or1CpYKWC7JoeEgsYL1u2zNV1WbRoUcStNTdlU4GxoPfX8gYHg8740ci4VqBTnT0tppDpndddWbY9k4I+mgZfp06dUt+jlU9fe+21lBmWyExRBFwU3Nd9WeI9vVaD1b1Zcj3RoOneUNcTrQis7FJlHamAfWKGv2ru+Vb2A+mLKXhAmvApWTEWVNLqUMoWSLwoffbZZy5zQ6vghMGnzoaKSR522GGuU6hMKN3QqGO23377uYt7bBniMCmtXTdSynrSTY2WvNcjLOrIa3llrXKjKRrJNzbq3Gv6RtDLHftUB0MdoNgUWv3/VRz+hRdecMEO7TPqyO9KkPeXjoyXJnF7hRWAys/Pd19jN/4rV660SZMmuXPfySefHOhUUh0TiVMy99hjDzdtJbGmm77fsmWL+UCB5V1Z6v2XKk/Gmdqh/SnI/SRxanOUBXvLm3ErQQYHlUmpY1S1DXXN8en8pkGOjRs3uiyO5s2bh9o2nU+TB+o06KPVEWPPfffdd2412CAz9spD2XwXX3xxoO346quvdqmuWBgUSNYiNrH7xk8++cR9ZloFT7WhwlgsJTngovIFPgRcorjPV11MDawoIJu4Gq+mRmoaqfYhZf+q5pxW7MuEe0YEjwAUgF9k1qxZLuNH01GUYRKji4YyBfSaivZm2sirLuJ6aFlu3YTruVj9hTBGXnXz/84777iHil1/+umnLkiorAFlZanWUFg1oWJLo2uFOd10xmqEqA3qDOjGRjcar7/+ejzzJAgjRowose6Pambppk83NWHUYlDx8379+rnl40UdNBVivfbaa90KkmqLgqoKRCljLigfffRRia9paeyhQ4e6WgxqW9C0byh4+9JLL7nOqlZJUt0S7Ts6jrQtNH32r3/9q6tpFgTdeCd3fKpUqeIevg4E+EKdg13p6KZzrT91XNUp1flUx25UJk6c6ILWqvWnQK2OGXUOg6wxWBodF6rJpQzgxAEODXz07t3bLfYQRiAq1fGpAvGqPZUYlMqU41iZnYnbPdX02dhzQQfkdI696aabXIbPzJkzbffdd3fZeQoG6jyrwTLVaNSxXlYWW0UKuERNx60G6RIHc7U/HHDAAfFMLN1P6nMLenv4cs+I4BGAAtJI8uo7JQk6Y0FTUNRh1t/RBTuROq66oF199dXuRkIpxZkgeeRVN1MaYQtz5FU1nrTSnv62sq4UANQNnzoliUVIw6J6V+qoKXCQSKOcsRsbBchUWDjom4m///3vRQpM60ZKHaNYIEb7sYIcLVu2DLQdd955p+27774us0dZgsqUU+2wyy67zBUnFwV11dYgA1CpqDjtqFGj3HGrz03HcVlTFn8NWuHm448/dl8VBFQm2LnnnuuOKR1DsfOKOidBBaDEpywS+FnrT7XCdB1W9ooGFJTVquyJsFcc1TldD01BV8BYwSjVt1OHTW3SI8yVtRTgUd1Brfqm4zY2/Vz3CloN7v3337eHHnrIopAq2JQpx7ruF5VZqkw5XfOU2RkVZa0o4KRabsq20uDCLbfc4r5XQFVZUdqPtJ8oAJQJARdfgurJx2byMaPjOnZ/kin3jAgWASggjaRafSfsWhyijqA67+oUJmvdunX8wq4bz0wp2JvqJlcZHWGOvKrzrr+nlVWSA4NRUHZR8upQyWLLqAdJafYPPvigm8alDtp9993nAjy6AdZKNMqw0T6r4IteC5JubDU1J1YIXhlGCo799re/jb9HHdqwO2tqlzqzKvaszyPM4Jc6JaNHj3Z1fkQZlSrkr6BDLCtJU1UUlIpy+WcF6BANXwryq0Oqh0bntd8q8KPzhoJB6tQqqyLI7I1UFFzQQ1mECvgoQHb++ee7644CUcomDJI6g+rEKqienF2rc5mOW00p1jn3tNNOC7QtKH4NVrBU+6muxRpQ0D6hfTjsY0oL1egaE7s/1dRdZcvpPlIBS7ngggvcfhxkAMqngIsPq0cqUJw41Vx0zkg8jzVp0sSd8zLlnhHBIwAFpJHkgoVRUQpzWReAc845xy699NLQ2pQughx51cizlolX4E8BDQWijj766MhGezds2FCsmKY6IIlZfK1atXLTF4OkjtHtt98er8Glm29lQWmULZahpxtjZacFTVlwiSvcKLihTKhatWrFn9PPYQU7lMWhDrRuejUCrOBy0FlgyTRVNXE/0c2wAkGqCRKjbRarERXl8s+J042jLBC/fPlyyyQ+1foTncPUSdVDHTgNNmg6nKaga1/SOSbs4v2aGqhrgKYGKqtEmR4KvAcdgFKtHHWQS5rarUUv1JmfMmUKAaiQKYCvgvV66Jry6quvumCU7hHatm0bz+ALY2VclQdIXEBAgSDdmyhzO0bXnuTzcEUNuOgYVbA4kT6H5EUDtI2CDEDp2qqBp8RBy/79+xd5j0oGJF6PK/o9I4JHAApIEz6ljOtGpqwi47qYq0g5wqMMDk25U80LdWI1JUM3DSokrKXLw15ZRaN4ulFIvNlT+n0iTRkMeslh1Q1IrI3SoUMHV3MicXqZbnzDuqnRFEkfju/33nvPBd50c6mposp6iqIdykBLniKqznTydgoye7CsGkMqlPvss8+6UfyglVUgPiaMz0oBjLKoBlEm03lOo/IKOmnVQGXzaTp2mAEo1U5RAEwPTfHW9DvVXQpj4QstPFLW1Fi9runGmXKvVJ7VedXpD5OC+qoVpoeC+QpGKQCiz0VT9oOcuio6nyurNEYZUAq4JE4L1P1AecpMVISAy7x588wHujdSUFL3RSXRdU+LHWTKPSOCRwAKSBM+Fc1U8UgVuC6tPoym9PgybSLTRjxj00Q0mqQ6Bnoos0Uj88qKCmulGa0k88orr5Rai2TOnDmBF87VTW9ywFQBj8SghzoticswB0V/J7mDFEWHSbWolPWkY/Thhx92o4qxwuhhr46Uapv4QNlh6tAr8KTzndp43HHHBf53SysQH7ZY3bSyKGMgE23bts115HUeUw0k1bGJZZUETYE/7Z8KImifUaaR/q4yGnU8h0XThpKL9acS1jHuw1Ta8q7UqKmTUdBnpu2gc5weyk4Kmmof6hhRwEkBINUuS15lU+cb3TdkSsDFByqnoYVRNECZapq7Mhw1zfaZZ57JmHtGBI8AFJAmdKEOY5Wu8ujZs6erldOpU6eUtYZ0c6HXlXWTSXzrRGsET6u96KFRca1cqBVEdIOuWiFB0zQ3FdiO1fRJpiwBBUFUeDKTAslHHnlkseeCXBkxFRX6jmVOaP9IJazVkfR3NAUiMSCozpH2ndg5L3HkPIyOvYJOymbRaKy2gaYBaEpxWFmEmpao+iCaFqrtovplCnbEaEXLW2+9NfB2+DJK75NY9oiCP6qvo+C2ApOqK6NrYqoMx1+bpv6pcL9Wi9KAgoJOURWYVht0Lte0+5LodQXIglbeqbRBB358WbUxkc5lyo7Wfqvrv8o5aN9RprS2UdCUkafFNpT5pGuKjhPVKhMFUFWIXI/kAtQVOeCiOpS6zujeTNtgy5Ytbjq+prPqPluPoO8rFWgbMGCAmz6s+zGtxqsMI01V1DTJFStWuJIbYUw/554xc1Qq9CmtAkBa0EVTRUU1LUVBJt286KKpGxxlPumCqhFYLclc1lS9ikIjNhr5TgwSahRNyyAnjrxqtDroDn0yZfaoA6BRP3WcNEqvr2FQsEv7gTpm6jTHVkfSjY3apGlfQReU1GejIEtisU8Fvc4+++x4sU917hWUCfqz0bFRXsmjw78mbf/yCnokWFPOynuTHVQtIJ3TVMdHHSB1ztQ5Um0SZRJqOo2mtKqjHQZlLuq8qixFdQiUXaRzrJ7T1GZNKdV+pE5uVBkUmUqdddVi1HVN53ad81V3qTwZQL/2OU1/U6vvlXXsJNeU+bVpEQVN1VSHPVX9OE3R0/2CzvMKnGUCX1ZtVNBNGSWxoJOuvwo6aRpeFOcOHTs6x2qKta79scCX7hNmzpxp11xzjZvOGjQtTKKgreqllRRwKe9n+EupRMUll1ziAnKqjbXXXntZzZo1Xe2pDz74wE2R17lF9yphZKwvXbrU3aupPboGaV9Ru7QdwghQ+nTPiOARgALwiyhtW7UDNFKki0Ni1o06ShrpypTgk+gG27fRUS19rSCYgl4aWYsVJlfHOsxsLXXsdZOtaUwKhulvq+aERj/DqFGijmI6ZXwo20cdBmW9IBw6JnSM6Kb3d7/7nXvEgpOaOqJprGEFoDQS/e9//9stKBALaKsDMH369HgGlgKqymAoa8UgBBP4UTZAWZ1CBQ+jrM0VE2QBY9E5XdmBGnxSpqAWdlCgVB1pdWaVUaIl01UbK1MCP+WpARVGgX3tp/q/qiOv6ZkKOin4E8VU67KCMbpfDPO+JOqAyz333OMGNpTxpWtMsmXLlrni/bq3VMZWJtE9o86fOqdEcc+I4BGAAvA/1xJQ/QAFoTSKpA6Sb1PRMsnnn3/ugk4aTdTnomkPCggqlTu21HFUlGWiqUXqnCQXncbPU780Ohyb+hVkNpZuundlWkuQFERRJ1mZeVFRTQkFdNRR1hRJrR4ZCzCEHYDSdEwVXk0s7JwcgFKgVCPFPgRMM4kP2Xq+3geoI60OfeJiDioyrU6jOtBh3BeUFfhR3SFdF5WxvStZoOkqsZZOSds/rKnWMWvWrHEDlYlZg/pcdG5LVdKhIl5vYud5TbFWlm1JdC+n+p1BLn6hbCtN+SspMCkFBQUuw/GCCy6wMHHPWHERgAKACkKjz7qJ1CoyuqlR4EmjRlHZlRT2ILMFfKYbLE2RUM0hTZHQaJ8CPuqwKWMtyI6JOh1lzcIPo2Oim1+l1isAFHPxxRe7AIumGIVB2RqaoqqMTmUOatqsli7XaKs6K2FOwdMxq1otiSsBKcNENZ9iy6V/+eWXbhqNMqVgXmQtvvHGG+540rQZX+o1hp3Fov+3pjBpQEoDHgpMaZpT1NnQOr5HjBhhU6ZMcQHm22+/vcjxVVH5NNVaHnnkEbv//vtddqeyjWI0repf//qXW4016KlvPlxvREXQVfuptLqCqtGoTGhdk8LcHsq6GjlyZDwgqAwxDYiEWT5C51QNysXqYqmsRxj19RAOipADQAWhwJNWN1ONBx86QKyCWDLV8VG2kwIeWlZYo7HqvI4ZMybQwFNYdWF2RaogmDK0wlitKvHYUQFUPVSzRh1VjTor60hBOHWYVEQ3VY2bINqiQteJkgvz6qY8NkUQ4VLNo9jqgNpfFOxXYePY6oXqtGl6V5D7SiyAXB5hdBqVcaugjgIMicWKNVVUU4nUude01igoMHjDDTe4Y0ZB3DBqDPnCpwCUplWpbIPqqLVt27bYlFIdM7p/0erKqRYtqUjXm1gZi7KysPS63hekVNtDx2zQf7e0DDnV59IgjDKvYlTDU9NIlTUWZqAQwSAABQAVhG9ZRJk0BaW8NGVK2U4acdQ0RE35Oumkk9xX1U4Ja5U1goMla926tStyqlX4XnvtNTclUhlQCjqolsqjjz4a6GejTCutrqZ2lOT1118PfLlypC5erM6yshLUIdJqr9ovlMn49NNPuwxGnffUgdJrmXBuVeamMldOPfXUYlOohg4d6o6Xq6++2tU+1DkuLFpYQllPkyZNcllPCoKpoH8m0b6qqVVlZXspmBl0rTAF8VXTSNmcyVR8W39fn5mOsSADUPCXMnvPOussNz1T0/1UmF3ZTwoeKyimqfC6NqquXBjTNREcAlAAAIREo79aLl11KFQcPco6FFr1T1k+qnWkkUVNfaB+2/+ndH9Nw9NDtct08xvLfAmSVj9UJoAKoifWcIlZvny5yzRRxgnCpWCGtnusEK6yn5QFpQy1WHBFdYg0Sh8kBXaSp81ERTVqlAGmNiVTEDUWLFN2p/bbMLz55pt2/fXXu6mAw4YNc59RJtL/W5kksX1Vj1TnlDBo1WQFAUujWpXKCs4UWiBGGa8lUeAlkyhDToNjCkImbxdl9mvVQD30eqrzDdIHASgAAEKiDoCmv/3lL39xN5+q43Pccce5bIowaWlnZWioXo0CLQqIrVu3zq699lqLgu+BLy2woBHZMIqwqpab6oJpxUpllegz0t9XcWdNqVE2lqZp6oYc4U+dVcH6xDouGq3XtKEYTb1LLMQdhLJqt4Xpww8/LHNZ9HPOOSdl5suvTRk0OpdperOOGwULMy3rKZGCbzfffLO99dZbrsadai0paBkLRqmuTpjK2m91HVQ2YaZcb8oKyPnU1rACx6o9VVJQTs9rhe2bbrqJAFSaIwAFAEBItPSyCuIq80hTuxTwUYFerbqmm/OwOpb626qLoo6hKLNH01WiCkDpRjyxbpkKkN59992uGLivU4+C9NBDD7kMNa0qpnpUMSpCrhtw1aNC+LRfJhfU1lTaxFU91WHUVLxModo5ZRUZ18qnKlIeNE2NVJBQU5mVkVZaNk3QU858oQEGTUHUQwMfypzT4IcWKVHgVNl8CkY1bdo00HZoOpWma5Y2tXjhwoVFgrkV+XoTqxkXNZ2vfAlyKXBf1uev/UeDZUhvBKAAAAiRRvH+8Ic/uIemJSj4o4CUOq0aoda0Cb0WZI0DddIS62yoA6KUdq12oyWyw6RV/9avX1/kuQMPPNA2bdrkHplIHQKthKiHlo1XoXqtKqaOdWnLZSNzlDV9J0ZZdEHafffd7d133y214/jOO++EUndOAXxlPGn1vdKmy4ZR88hHCpQqe1IPFZlWgE6DIqpZFnSxemV0KjtNgcFU0wD195WVq2nqmXK90dTucePGuSm7+mwUQFUWX4xqDqpwftDHzBVXXFEkiK6gsmogxoJ0CtCFQcdtWQvoqJ1hZMkhWASgAAAIiW7A1fGJ1X5q06ZNvOD1q6++6joEKtqrh4puBkWdj8QbPX2v6Q9hZCkkU3FilExBp7CK06Ns6jAmTplVp0kLQMRWJUzsQPowfSfoAJTq9ihwoJplqYLma9euda8r4yaMRR5QOmWPaEW6F1980f71r3+5KaN9+vQJpbadFnVQIEqDHwpEqcC0sl4UoJw/f7517tw58Lb4cr3RYI+OCdVgPPfcc13gdPXq1e45ZQxqkEjZr6eccoodcsghgX4uyVIFi8OarulLNhaCRQAKAICQaFqVliZPLD5+8cUXu86kakHpoRtTZUQB8IumKSnzKJGmRaquW6Iw6g5pupIPRchVgHzOnDluNU91npVNkhhY0HRfdV513kM0FATUZ6Sgk7LVFNDWwhOahh1mUXIFIrVapFaCVZ27WMChffv2rlaVglOZEoBQ8X6dT7Q6YOJgkLKgYwMO+ty08EGQASjtD2eeeaYLVPtwPkmeHplM2VlIfwSgAAAISaoaT2+//XaRmypNgQu62LVPdR/kv//9rz3wwAN2xx13uOwSdWILCgrir6ugsDJPgCj5kmHj07GrGkPqRGsFK2Vw6vvEc5myO1S3rKw6Ufj16bNQ4On99993wQ4FnbQ64L777hvJ5l6zZo2baq6g5THHHOP2HWX7JE7/yhTKBrvxxhtLDbbo2ClPpuP/QsEtreI5evRoNzVTQUDVpIziHJNqemQqQQbkEA4CUAAAZGAgTNkKifWEFPDR9Ad1ChIlZ3f82r744gs766yzbM8993QF2mPTm6655ho3IqupCApOvf76667TAmQ6n1bBE00juu666+zPf/6zq1m2efNmt3KjMjl8CpZlGi0soeDOUUcdZfvtt597TlO99UgWdE2sJUuWWP/+/eMDCyr4rYwoTbvLRF9//bWbgp+oY8eORQK1bdu2LVdA5n+hAJdWlXv55ZfdCquqwaXAsabm6R4hzOnfmh6p7aLsOJ1TdL1v3LhxaH8f4SEABQBAhvGpAO/YsWPt4IMPdqOwibp37x6/+f30009ddgUBKODnui1lFeuNQpUqVVxRcvghtrKdFrvQI8qi7Ao2KZP1lltucYMcw4YNcwGymTNnWibSAgL5+flFnku+Bm7ZsiVeWy5ICvZoIRI9VALgH//4h3tomqCykpQVpeux3hd0kFIrvMZqUapUwf3335+xQcqKrFKhb8MoAABUUKq38cYbb7jsgBhNN5s+fbqXhaZ1ixB0BoPS/lWcXTe6JW0TbTMVa//nP/8ZaFsAAL8+DTJMnDjRZbrG6hupGLmmoJdnNceKRtnGXbt2tfPPP7/UwRkVilcgKApamXDWrFmuaL2yGhctWhT4NtG+kBik/OyzzzI2SFmRkQEFAECERTa1xPHdd9/tpiQkGj58eOBt+fjjj13Wwh577FHstY8++sjVC1HmUZA04pocfDvttNOKdEpUxFhFjQEA6UerQ6reU4xWTNT0QAU2MjEApSzGO++8060emaoQ/PLly+2RRx6x22+/PZL2qVaXpv/p+qzPKHHhlKB8+OGHLkjZsGFD9/PQoUNdkFJT8zNxH6nICEABABBhkU1l+2zatMk9wqI6Lar1oKlt0qFDBzfaqg6CAmKquaSi32Gk/+tvKLiUWOtBxVkTffPNN0WyxgAA6SNVNq2yXBToyEQaZFGtI01v0wp0mp6oa5yuhYsXL3b1mJQdrKlvYVLBeq3Cq9U+NQVQbRg5cmQo0+AIUmYOAlAAAIRYZNMHqr2hUUVlWamuw5gxY1wWlooIqwaDRiJ79uzpRiCD1q5dO3cjXtpy4FrJaf/99w+8LQAAhOGhhx6yxx9/3CZMmGBTpkyJP5+Xl+dWjtS1OAyff/65CzppqpsGp1Qc/eKLL3b3AHXr1rWwEKTMHASgAADIMO+8847dcccdbnRTWrdubeedd56tWLHC1q1b57Khwir43atXLxs4cKC1b9/epdsnW7BggT311FOuTQCA9KSs2tgqp/Ljjz+6c3typq1Pi2QESRlh/fr1cw8FfpTpq4CPpqQnrlAbdCaWaj1pipuKkGvlu9iKiUBQCEABAJBhvvvuO9tnn32KLPesFXmUAq/Vb+rXrx9aW4477jg3DeHSSy919TCOOOIIdxOuuhOaiqAAVN++fd0UBQBAeq7Ip2ldiZTpM3fu3NBX5PORgk5RLESiwJNqUWmqnw8raxKkzAysggcAQIbRdLeFCxcWCTSpFpWWge7YsWMkbdJKO08++aS9++67ri6IOiIaidUqQSeccEIkbQIAAMHr0qVLud6ne4PkwCXSCxlQAAAgPkodlW7durnHTz/9ZBs3bnQF0bVKEgAAqNjmzZsXdRMQEgJQAABkGI0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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "means = (\n", + " df.group_by(\"protein_id\").agg(mean_npx=pl.col(\"npx\").mean())\n", + " .sort(\"mean_npx\", descending=True).head(25)[\"protein_id\"].to_list()\n", + ")\n", + "wide_top = imputed.select([\"sample_id\", *means]).drop(\"sample_id\").to_numpy()\n", + "corr = np.corrcoef(wide_top.T)\n", + "\n", + "g = sns.clustermap(\n", + " corr, cmap=\"vlag\", center=0, xticklabels=means, yticklabels=means,\n", + " figsize=(12, 11), linewidths=0.1, cbar_kws={\"label\": \"Pearson r\"},\n", + ")\n", + "g.fig.suptitle(\"Protein-protein NPX correlation (top 25 by mean)\",\n", + " fontsize=14, y=1.02)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e034be44", + "metadata": {}, + "source": [ + "## 8. Case vs control differential expression\n", + "\n", + "Per-protein Welch t-test of case vs control NPX. The volcano plot shows the\n", + "mean NPX delta on the x axis and `-log10(p)` on the y axis. The three\n", + "manually-injected biomarkers (CRP, IL6, TNF) should sit in the upper-right\n", + "corner (positive delta, small p)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cd89a041", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.816482Z", + "iopub.status.busy": "2026-04-23T21:12:55.816402Z", + "iopub.status.idle": "2026-04-23T21:12:55.851341Z", + "shell.execute_reply": "2026-04-23T21:12:55.850933Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (10, 3)
proteindeltaneg_log10_p
strf64f64
"CRP"1.675523109.558062
"IL6"1.25766573.416442
"TNF"0.87871240.838176
"ADIPOQ"0.1592762.149671
"PSA"0.1310941.616747
"IL1B"-0.0989931.216589
"IFNG"0.1051991.17137
"LEP"0.0981481.130072
"AFP"0.0905350.874606
"APOA1"0.0807060.820205
" + ], + "text/plain": [ + "shape: (10, 3)\n", + "┌─────────┬───────────┬─────────────┐\n", + "│ protein ┆ delta ┆ neg_log10_p │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ f64 ┆ f64 │\n", + "╞═════════╪═══════════╪═════════════╡\n", + "│ CRP ┆ 1.675523 ┆ 109.558062 │\n", + "│ IL6 ┆ 1.257665 ┆ 73.416442 │\n", + "│ TNF ┆ 0.878712 ┆ 40.838176 │\n", + "│ ADIPOQ ┆ 0.159276 ┆ 2.149671 │\n", + "│ PSA ┆ 0.131094 ┆ 1.616747 │\n", + "│ IL1B ┆ -0.098993 ┆ 1.216589 │\n", + "│ IFNG ┆ 0.105199 ┆ 1.17137 │\n", + "│ LEP ┆ 0.098148 ┆ 1.130072 │\n", + "│ AFP ┆ 0.090535 ┆ 0.874606 │\n", + "│ APOA1 ┆ 0.080706 ┆ 0.820205 │\n", + "└─────────┴───────────┴─────────────┘" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats as sstats\n", + "\n", + "records = []\n", + "for prot in panel.proteins:\n", + " sub = df.filter(pl.col(\"protein_id\") == prot)\n", + " case_vals = sub.filter(pl.col(\"group\") == \"case\")[\"npx\"].to_numpy()\n", + " ctrl_vals = sub.filter(pl.col(\"group\") == \"control\")[\"npx\"].to_numpy()\n", + " if case_vals.size < 3 or ctrl_vals.size < 3:\n", + " continue\n", + " delta = float(case_vals.mean() - ctrl_vals.mean())\n", + " t, p = sstats.ttest_ind(case_vals, ctrl_vals, equal_var=False)\n", + " records.append({\"protein\": prot, \"delta\": delta,\n", + " \"neg_log10_p\": -np.log10(max(float(p), 1e-300))})\n", + "volc = pl.DataFrame(records).sort(\"neg_log10_p\", descending=True)\n", + "volc.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "19f87eee", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.852474Z", + "iopub.status.busy": "2026-04-23T21:12:55.852404Z", + "iopub.status.idle": "2026-04-23T21:12:55.906482Z", + "shell.execute_reply": "2026-04-23T21:12:55.906177Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 7))\n", + "vp = volc.to_pandas()\n", + "injected = {\"CRP\", \"IL6\", \"TNF\"}\n", + "is_inj = vp[\"protein\"].isin(injected)\n", + "\n", + "ax.scatter(vp.loc[~is_inj, \"delta\"], vp.loc[~is_inj, \"neg_log10_p\"],\n", + " color=\"grey\", alpha=0.6, s=40, edgecolor=\"k\", linewidth=0.3,\n", + " label=\"other\")\n", + "ax.scatter(vp.loc[is_inj, \"delta\"], vp.loc[is_inj, \"neg_log10_p\"],\n", + " color=\"crimson\", s=110, edgecolor=\"k\", linewidth=0.6,\n", + " label=\"injected biomarker\")\n", + "for _, row in vp[is_inj].iterrows():\n", + " ax.annotate(row[\"protein\"], (row[\"delta\"], row[\"neg_log10_p\"]),\n", + " xytext=(7, 4), textcoords=\"offset points\", fontsize=11,\n", + " fontweight=\"bold\")\n", + "ax.axhline(-np.log10(0.05), color=\"steelblue\", linestyle=\"--\", linewidth=1,\n", + " label=\"p = 0.05\")\n", + "ax.axvline(0, color=\"black\", linewidth=0.5)\n", + "ax.set_xlabel(\"NPX delta (case − control)\")\n", + "ax.set_ylabel(\"-log10(Welch t-test p)\")\n", + "ax.set_title(\"Volcano plot — case vs control differential NPX\")\n", + "ax.legend()\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "358bf9e5", + "metadata": {}, + "source": [ + "## 9. Parquet round-trip\n", + "\n", + "[`write_olink_parquet`](../synthlab/olink.py) and\n", + "[`load_olink_parquet`](../synthlab/olink.py) are the canonical on-disk\n", + "serialisation entrypoints — snappy-compressed, schema-validated." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9bc86a90", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.907594Z", + "iopub.status.busy": "2026-04-23T21:12:55.907532Z", + "iopub.status.idle": "2026-04-23T21:12:55.912250Z", + "shell.execute_reply": "2026-04-23T21:12:55.911925Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wrote /var/folders/fm/7t4z124s3kz76r077_dfn7m80000gn/T/tmp5rx35d9o/olink_demo.parquet\n", + "size : 196 KB\n", + "rows : 23,737\n" + ] + } + ], + "source": [ + "import pathlib\n", + "import tempfile\n", + "\n", + "from synthlab import load_olink_parquet, write_olink_parquet\n", + "\n", + "tmp = pathlib.Path(tempfile.mkdtemp()) / \"olink_demo.parquet\"\n", + "write_olink_parquet(df, tmp)\n", + "round_trip = load_olink_parquet(tmp)\n", + "assert df.equals(round_trip), \"round-trip mismatch\"\n", + "print(f\"wrote {tmp}\")\n", + "print(f\"size : {tmp.stat().st_size // 1024} KB\")\n", + "print(f\"rows : {round_trip.height:,}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3f7b665e", + "metadata": {}, + "source": [ + "## 10. Disease-conditional generation\n", + "\n", + "So far we've hand-picked the three biomarkers (CRP, IL6, TNF) and their\n", + "effect sizes. In practice users want to **reuse published, source-cited\n", + "effect sizes** per disease — exactly what\n", + "[`synthlab.load_disease_effect_catalog`](../synthlab/olink.py) exposes.\n", + "\n", + "The bundled CSV\n", + "[`synthlab/data/olink_disease_effects.csv`](../synthlab/data/olink_disease_effects.csv)\n", + "captures per-disease protein log2-NPX shifts from published plasma-proteomics\n", + "studies — each row cites a real DOI. Coverage at PR-time (see [PR\n", + "#TODO](https://github.com/bschilder/synthlab/pulls)):\n", + "\n", + "- **T2D**: 8 proteins — [Sun et al. 2023 UKB-PPP](https://doi.org/10.1038/s41586-023-06592-6)\n", + "- **CAD**: 7 proteins — [Williams et al. 2022 Sci Transl Med](https://doi.org/10.1126/scitranslmed.abj9625) + [Eldjarn et al. 2023 deCODE](https://doi.org/10.1038/s41586-023-06563-x)\n", + "- **Cancer (broad)**: 6 proteins — [Cohen et al. 2018 CancerSEEK](https://doi.org/10.1126/science.aar3247)\n", + "- **BRCA hereditary**: 4 proteins, null-hypothesis placeholders from [Ahn et al. 2021](https://doi.org/10.3390/cancers13102300)\n", + "- **Alzheimer's**: 5 proteins — [Guo et al. 2024 Nat Aging](https://doi.org/10.1038/s43587-023-00565-0)\n", + "- **CKD**: 6 proteins — [Dubin et al. 2023 Nat Comm](https://doi.org/10.1038/s41467-023-41642-7)\n", + "- **IBD**: 7 proteins — [Hu et al. 2025 Nat Comm UKB-PPP](https://doi.org/10.1038/s41467-025-57879-3)\n", + "\n", + "Load the catalog and inspect what's available:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9be17862", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.913228Z", + "iopub.status.busy": "2026-04-23T21:12:55.913171Z", + "iopub.status.idle": "2026-04-23T21:12:55.917855Z", + "shell.execute_reply": "2026-04-23T21:12:55.917590Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "registered diseases (7):\n", + " Alzheimer -> 5 proteins\n", + " BRCA_hereditary -> 3 proteins\n", + " CAD -> 7 proteins\n", + " CKD -> 6 proteins\n", + " Cancer_broad -> 6 proteins\n", + " IBD -> 7 proteins\n", + " T2D -> 8 proteins\n" + ] + } + ], + "source": [ + "from synthlab import load_disease_effect_catalog\n", + "\n", + "catalog = load_disease_effect_catalog()\n", + "print(f\"registered diseases ({len(catalog.diseases())}):\")\n", + "for d in catalog.diseases():\n", + " n = len(catalog.proteins_for(d))\n", + " print(f\" {d:<18s} -> {n} proteins\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0851f970", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.918895Z", + "iopub.status.busy": "2026-04-23T21:12:55.918823Z", + "iopub.status.idle": "2026-04-23T21:12:55.921484Z", + "shell.execute_reply": "2026-04-23T21:12:55.921161Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (8, 5)
protein_uniprotdelta_npxse_deltaevidence_strengthsource
strf64f64strstr
"IGFBP2"-0.450.18"strong""Sun et al. 2023 UKB-PPP (N=54,…
"LEP"0.80.25"strong""Sun et al. 2023 UKB-PPP (N=54,…
"ADIPOQ"-0.60.2"strong""Sun et al. 2023 UKB-PPP (N=54,…
"IGFBP1"-0.70.3"moderate""Sun et al. 2018 INTERVAL (N=3,…
"IL6"0.30.2"moderate""Sun et al. 2023 UKB-PPP (N=54,…
"CRP"0.350.2"strong""Sun et al. 2023 UKB-PPP (N=54,…
"GDF15"0.550.22"strong""Sun et al. 2023 UKB-PPP (N=54,…
"TNF"0.20.25"moderate""Sun et al. 2023 UKB-PPP (N=54,…
" + ], + "text/plain": [ + "shape: (8, 5)\n", + "┌─────────────────┬───────────┬──────────┬───────────────────┬─────────────────────────────────┐\n", + "│ protein_uniprot ┆ delta_npx ┆ se_delta ┆ evidence_strength ┆ source │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ f64 ┆ f64 ┆ str ┆ str │\n", + "╞═════════════════╪═══════════╪══════════╪═══════════════════╪═════════════════════════════════╡\n", + "│ IGFBP2 ┆ -0.45 ┆ 0.18 ┆ strong ┆ Sun et al. 2023 UKB-PPP (N=54,… │\n", + "│ LEP ┆ 0.8 ┆ 0.25 ┆ strong ┆ Sun et al. 2023 UKB-PPP (N=54,… │\n", + "│ ADIPOQ ┆ -0.6 ┆ 0.2 ┆ strong ┆ Sun et al. 2023 UKB-PPP (N=54,… │\n", + "│ IGFBP1 ┆ -0.7 ┆ 0.3 ┆ moderate ┆ Sun et al. 2018 INTERVAL (N=3,… │\n", + "│ IL6 ┆ 0.3 ┆ 0.2 ┆ moderate ┆ Sun et al. 2023 UKB-PPP (N=54,… │\n", + "│ CRP ┆ 0.35 ┆ 0.2 ┆ strong ┆ Sun et al. 2023 UKB-PPP (N=54,… │\n", + "│ GDF15 ┆ 0.55 ┆ 0.22 ┆ strong ┆ Sun et al. 2023 UKB-PPP (N=54,… │\n", + "│ TNF ┆ 0.2 ┆ 0.25 ┆ moderate ┆ Sun et al. 2023 UKB-PPP (N=54,… │\n", + "└─────────────────┴───────────┴──────────┴───────────────────┴─────────────────────────────────┘" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Peek at the raw catalog rows for T2D.\n", + "catalog.effects.filter(pl.col(\"disease\") == \"T2D\").select(\n", + " [\"protein_uniprot\", \"delta_npx\", \"se_delta\", \"evidence_strength\", \"source\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9a8696d2", + "metadata": {}, + "source": [ + "### 10.1 3-group cohort (T2D / CAD / baseline) using catalog effects\n", + "\n", + "We'll simulate 300 subjects per group. The panel is a uniform 5.0-NPX\n", + "baseline covering every catalog protein; LOD is set far below baseline so\n", + "we can cleanly recover the injected means." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "97650b83", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.922334Z", + "iopub.status.busy": "2026-04-23T21:12:55.922279Z", + "iopub.status.idle": "2026-04-23T21:12:55.928283Z", + "shell.execute_reply": "2026-04-23T21:12:55.928024Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rows : 23,400\n", + "groups : {'group': ['T2D', 'CAD', 'baseline'], 'count': [7800, 7800, 7800]}\n", + "proteins : 26\n" + ] + } + ], + "source": [ + "from synthlab import OlinkPanelConfig\n", + "from synthlab import simulate_olink_npx\n", + "\n", + "# Build a panel from all proteins referenced in the catalog.\n", + "catalog_proteins = tuple(catalog.effects[\"protein_uniprot\"].unique().to_list())\n", + "disease_panel = OlinkPanelConfig(\n", + " name=\"disease_catalog_panel\",\n", + " proteins=catalog_proteins,\n", + " mean={p: 5.0 for p in catalog_proteins},\n", + " sd={p: 0.5 for p in catalog_proteins},\n", + " lod={p: -1000.0 for p in catalog_proteins}, # disable LOD drop for clean recovery\n", + ")\n", + "effects = catalog.effects_for([\"T2D\", \"CAD\"])\n", + "\n", + "N_PER = 300\n", + "assignments = [\"T2D\"] * N_PER + [\"CAD\"] * N_PER + [\"baseline\"] * N_PER\n", + "cfg = OlinkSimConfig(\n", + " n_samples=len(assignments),\n", + " panel=disease_panel,\n", + " group_effects=effects,\n", + " group_assignments=assignments,\n", + " missingness=\"none\",\n", + " qc_warn_rate=0.0,\n", + " plate_effect_sd=0.0,\n", + " seed=RNG_SEED,\n", + ")\n", + "df_catalog = simulate_olink_npx(cfg)\n", + "print(f\"rows : {df_catalog.height:,}\")\n", + "print(f\"groups : {df_catalog['group'].value_counts().to_dict(as_series=False)}\")\n", + "print(f\"proteins : {len(catalog_proteins)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9662c2a3", + "metadata": {}, + "source": [ + "### 10.2 Top-5 per-disease delta NPX — catalog vs empirical\n", + "\n", + "Grouped bar chart: for each disease, show the top-5 absolute delta-NPX\n", + "proteins according to the catalog, side-by-side with the empirical case -\n", + "baseline mean shift from the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2c29bfd1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.929234Z", + "iopub.status.busy": "2026-04-23T21:12:55.929173Z", + "iopub.status.idle": "2026-04-23T21:12:55.937653Z", + "shell.execute_reply": "2026-04-23T21:12:55.937282Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (10, 4)
diseaseproteincatalog_deltaempirical_delta
strstrf64f64
"T2D""LEP"0.80.824616
"T2D""IGFBP1"-0.7-0.733683
"T2D""ADIPOQ"-0.6-0.703181
"T2D""GDF15"0.550.45455
"T2D""IGFBP2"-0.45-0.492851
"CAD""GDF15"0.70.605253
"CAD""NPPB"0.550.538274
"CAD""MMP12"0.50.452784
"CAD""TNNI3"0.450.417845
"CAD""CRP"0.40.348582
" + ], + "text/plain": [ + "shape: (10, 4)\n", + "┌─────────┬─────────┬───────────────┬─────────────────┐\n", + "│ disease ┆ protein ┆ catalog_delta ┆ empirical_delta │\n", + "│ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ str ┆ f64 ┆ f64 │\n", + "╞═════════╪═════════╪═══════════════╪═════════════════╡\n", + "│ T2D ┆ LEP ┆ 0.8 ┆ 0.824616 │\n", + "│ T2D ┆ IGFBP1 ┆ -0.7 ┆ -0.733683 │\n", + "│ T2D ┆ ADIPOQ ┆ -0.6 ┆ -0.703181 │\n", + "│ T2D ┆ GDF15 ┆ 0.55 ┆ 0.45455 │\n", + "│ T2D ┆ IGFBP2 ┆ -0.45 ┆ -0.492851 │\n", + "│ CAD ┆ GDF15 ┆ 0.7 ┆ 0.605253 │\n", + "│ CAD ┆ NPPB ┆ 0.55 ┆ 0.538274 │\n", + "│ CAD ┆ MMP12 ┆ 0.5 ┆ 0.452784 │\n", + "│ CAD ┆ TNNI3 ┆ 0.45 ┆ 0.417845 │\n", + "│ CAD ┆ CRP ┆ 0.4 ┆ 0.348582 │\n", + "└─────────┴─────────┴───────────────┴─────────────────┘" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Catalog top-5 deltas per disease (absolute magnitude).\n", + "top5_records = []\n", + "for dname in [\"T2D\", \"CAD\"]:\n", + " rows = (\n", + " catalog.effects.filter(pl.col(\"disease\") == dname)\n", + " .sort(pl.col(\"delta_npx\").abs(), descending=True)\n", + " .head(5)\n", + " )\n", + " for r in rows.iter_rows(named=True):\n", + " prot = r[\"protein_uniprot\"]\n", + " # empirical delta from simulation\n", + " case_mean = df_catalog.filter(\n", + " (pl.col(\"protein_id\") == prot) & (pl.col(\"group\") == dname)\n", + " )[\"npx\"].mean()\n", + " base_mean = df_catalog.filter(\n", + " (pl.col(\"protein_id\") == prot) & (pl.col(\"group\") == \"baseline\")\n", + " )[\"npx\"].mean()\n", + " top5_records.append({\n", + " \"disease\": dname,\n", + " \"protein\": prot,\n", + " \"catalog_delta\": float(r[\"delta_npx\"]),\n", + " \"empirical_delta\": float(case_mean - base_mean),\n", + " })\n", + "top5_df = pl.DataFrame(top5_records)\n", + "top5_df" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7dcfb39e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:55.938643Z", + "iopub.status.busy": "2026-04-23T21:12:55.938576Z", + "iopub.status.idle": "2026-04-23T21:12:56.007107Z", + "shell.execute_reply": "2026-04-23T21:12:56.006690Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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EyX333RcaNGiQC462bt3qbtcO9957790rIecl9mT06NHucx1u3rx5oa+++sp99hcvXhy314HseeOl8T3ttNNCY8aM2WM7LMOHDw+NHz8+6mdTB7c33XQT2+YU2kbfddddLqjmJBsSCduy4FMiRkk2bb9U5NGgQQMXZ3z99dfuhOL111/vEj6zZ892xx/169ff6ySSt02bMWNG6KKLLnLJASUDdbIR/tq2bVuoT58+bl/0+uuvZ93uxZ6KH/W7H374Ya9YRN9feuklN55KynqUcNf+6tdff43ra0F0GleN4Z9//ul+nj59uosfVbihkyv6TCtm0XGHErWKM7p06eJOtHj0eVdSV3kDJAbvc6jPqE6UdOrUKfTll1+6ArsHHnggdN1117l99NSpU0PnnXee+33jxo3d7W+//XZo5MiRLnmr94ZOqOmz7NHjn376aYrzfECCNoC+++47d4brrbfeivr7f//91yVxtTP1znjpAx6elPvnn39Czz//vEsCrVy5Mm7LjpxpI6uzWZFBlGj8VJmgjW94tcM555wT6t69e9SkHhKLkm6XXnqp23kqEavP8cknnxwaO3as+/3tt9/uDmzCP7ce7/+6rx6nAyrGO7HogEUJ2d9++22vxJvG1auiVSClg9+2bdvmeHCjQEpBtVdFi2Buo73bqIxGomBbljqGDh3qjhd0QlBVdNoeKQZp3rx51tU6Sga0bNnSXXGnKq0//vgj6/HRtml6zmHDhrFN85kKebx4UicVlaBRdbSXtPVMnDgxdNZZZ2V7gl/JPCXudaIRiXt8ocp1Jea85LtiFcWef/31l6uyVFGAknw6waI8gcZdn/1p06a5x6jCUkk7XQ3knZhDYujatavbroaPi2LGTZs2ZcWfuo/GVldthVNxiAp4VASk2FXHHyrsYYz9Qw/aAFKPOvWDevvtt93M3xLekL1cuXJuAqGSJUu6WRlFvUfVK0q9odREWn0q1aNG/UjKly/v22vB/1F/oPHjx7sx0sROGl/1lhGv75d+p6bv6vfl/U59hy+//HI3oZD6yKjHUOSMrPCfPnea1V09v9T3ST261Fty+vTprvfTJ5984sZNEwKpcftHH32UNeaRky2ov7R6B2nmTfpGJ05vxjZt2rheoffff79deumldu+999pPP/3kfq/eweoXpT5u6gmm7+pBqlmwa9euvdfzeWOuPmHqH6X+4QjuNtr7HHs94wG/sC1LHZp4Un0q1adeEwZpv6Q+sjouUA909S2cM2eO2x+pt6yOPTS5ofpRqu+h17MwPA7xbtP+7Y477mCb5iNN8tS0aVPXg1QUf2rfpZhCE0pr0kqNtSYb1X0Ut6jnbDTqga8+l4o/vYmlkRi8eKJq1arWoUMHN1mb+siq771yAeoDrcm+lBNQLKrJRxWT6H4ae31mH3/8cfc86ies++iYhL73iUPzjGi8NFdF+LhobDUBmI4xFX9qjgP1Dd+wYUPWcYTGVfMdaEJhzYUxZswY69Onj4tNGWP/kKANKH24lGD9/PPPs2aADqeDfs0Ar1ne9UHVh1gHiGrwrQ/6hAkTXEN4Dgj9179/fxckawIFBbrnnnuuS+JFBlGaafXTTz+1d99915o1a2ajR492k0JpMhlN9qbnETa4iZfY0SQLb731lvvsDRw40M2aunXrVve9Tp06bpw1xgqQdcJEM2t6s797SVp9123//POPmxgK/lNT/ptuuskduJx88sluYpSnnnrKJd+/+eYb95nUSTQd1Kopv8ZfyXVNEqWJ4LL7rHrbcwXLSuDDX2yjEXRsy1KHYgglZJR00/7luOOOcwlZ72STd1ygmLRx48a2bds29/MVV1zh9lnaf+l+mkgo8gSyNwEYcaj/vEkoP/zwQzfZk07qK2GnhNzUqVPdOJ522mku6aqk3fXXX5/j8+lE8cyZM12SF/6eROvcubMb0/DPmj57J510kjux4h0ParJSxabz5s2zd955x92mYwwVEEybNs1uvfVWd7JZJ5ZVMCLaFvD5TSwrV650n2dN3Cfedtc7VvAmY1TuQJO4zZgxw+WHIk+gKZmrz7yStfAXCdqA0gb04osvdsk6L5ETHijpg6wzLdqRaqers2qa9f3mm2+2999/3wVd8Jeqm1UxqSo7VdJpFlUvKaNZ3yODKO2UVeHw6KOPuuSOErTa4Sro0hh///33LrGHxKDZM+XQQw+1Y445xn0WVUUpOrjRTKrebJyquFOCVrfdfvvt7v+aMVWVtKpWUAJXs8SrIkXPdfzxx+91YIT40skSBb86eFW1gQJeJV8V7KraSBXTGu9+/fq5+994443uPaBt8/r1691tjGFiYxuNVMC2LLUoYaeqWSVkNIt7RkaGizNVaaWCAJ34v+uuu9xBvqouvVhFJ5NVHKBjDl0FhMQRLZZQrKHk3JFHHumOG0TFIDru0LGFqmYVp6hgp169eu45copJlARSFSb88euvv7oT/RMnTnTJtxtuuMElWFV85VHyVVWUSrg+++yz7jYdT1asWNFeeeUV27Jli7ttx44drkBExR5jx45124O2bdsytAlKxxKqiF64cKH7ObIoT1SQpwIgJe+VlFVBiI41wu8b7XHwBwnaANOGVdV5SuIooAr/4Gnjq8pZXcbw119/uUsfdKlSu3btfF1mmP32229uRzhq1CiXvHn55ZfthBNOcEk571IVVToriNIZTi+I0qUJul337d69uzsTpsSdqIJWlXy67AyJccB70UUXuYMYBbW6rF0HOQqERAdBy5Yts+uuu84FWjqB4lU7KBn/2GOPuXFWUH3ttde65L2S8xrjhx56yAVW7Gj9dfTRR7vv+kwecsgh7v86uPEu8WzVqpU7kaJKWm2jVZWkSltVoHzxxRfuPoxhYmIbjVTCtiz4wpNvuupK1ZBK9KjY4/zzz3cnGX/55RfX6kBJPLU/UBs1xSOi+FR0DKHYU7GpqrrYh/lPx3r68ngxiJJyOv5Twkats5TAUeJG461jBR1j6Io9xaPeVVqMZ+JRUvXOO+908aSOC3R1rIqsFHdq3Hv27OnajqhgR8eQKhxQInfEiBHusSrqUJyq94AS8+JVyGrc9Tw6hkTi0vZax/tegjYa5X1UqKXPvdpYfvXVV+5nJCYStAGmnWqPHj3cwb/3IVQfEvW56927t0v6KDmkwEvVs0gMkydPtkWLFrkxUaCswFfjpsBJO01tZL1WB+FBlM6eeUHyihUr3JlsVdt6VZqqrlRlJfzjHQCpj5sCqOeee8793KBBAzvllFPcuOts9xNPPOGCZI2fqlkUUIU/Xn3A1CtalQ16vyg4Uz8xBWjwhw50dMCqBOuSJUvcpaGdOnVyLQ10YCs6uPEu+9S2V0GS+jyp+ll01YOqaDXmqoYIH3MkDrbRCDK2ZalD+yrxkm9K3umqK51A1JU8ilHUL1YtLnQlli51V19KVd8dccQRe/SZ1f+V0FVvdPU51DEI/KUqOcWaOnmvq+xEMYiOFTRGOvmiNkuqtlTvSVXiKcmnxLsSPl71pTfOSCw6YaI+whorXTGreQ7UC1pjruMMFX+oN74+n6p6V/L222+/dVdxqbWW1+pAP+vqLn2+tf33WpGQkE8O+swqia5jQrWoCT928L7r8+/1kFb+Z/Dgwe5kGxITCdqA06ULqszTTlqXHWlDrQ/mrFmz3CXw6mmpajv4z9uIany0o1Ri/b///nM7ViVzRI3cdYmC6JIjBVEKsL0gSvfVGVMl+Lym3/CfDm7Cgx31CdJnUz3cVKUiajmiS+B1tltVs+pXqj7QOljyguPwYEnJep0J1Q5XCV74QxUI99xzj1155ZXuMjAdzCq5rrYkd999t6tYUL/o8LYF4e8DJXL1OdclZ6IJGNTsX18adwLkxME2GkHGtiy16OorJVP1/bvvvnO3eYkZVdQpyaOkj/ZNaq2k7Z/aoKmHpXc/77t4+ypdCaIredTDFv5Q+wldiaPiDvnjjz/c8YPGU7zJKHUcofvoZHGpUqXciX9RcYhaH6gtmmIXLwGPxKCT+Kqa1DG9ttv6vCrh7lEMqmMHtSbRnDM6ntCVdzouVHs0HWcoN6DiABWGqEpWz6cetd7VekgeyhFoG62WiBprXYHpbY/1XcnZjRs3uqsyRQVcXuEPEhMJ2oDTB1MJPVXX6ey3dtAKsJT8Y+bvxAiilFxV318l8RQo6Yy2giVV0KkaT1SZpx2wxlFnOr1EgSpnVcWgnbCCKFV2KWmrM+IaazUEh39++OEH97lTHyhVR+pst9d7VoGQdpBqTaGkus5m68BHQbG+62yoN87hB0HwX/gZabWJ0WfPC4A1MYMCYPV20/ZXSVr19tJEG+IFTV61uw6QdMa7XLlyWT/rzLZO1DDu/mMbjSBjW5a6lMQRXdasHui64k5X2OkqLZ0A1oRRKuBQ+6Q2bdq42FRtmbyq28iEHScTE+PzrOMAtb7SiWGdwNfYaWx0QlhjrAIATUKrMdb9dbygYwXFqopV5s6d6+JQJe+V6PF6lRKPJAaNh9qaqRpW7dJUIKBji/DL1TXuKtBRRbR3pZ6KQBSnqn2exlcFIqLHi6ptdeWmrrhE8tHxpKqk1cpCyVrlFpTrUS5AhR9K1Ku6GskhLcT1kylBG2CdMdHOmcuOEqOHoXaw2nlqR6odqy5n10ZUiTyd6VKSR0kcnR3VRFEKntTc3ZtB16MgTDtcBVI6C6pLXJTEhX80rvfff7+rgtSYKYmuWVLVTkRN+nUZisZbgfKgQYPcJUp6PyjRp8sHNfGXbtfl7noPhM+yicSh7alai2j7qgNZVaSEz26rqxY0cYqSuKpW0Jiq6lkHtjrY0QRiaj0i+gzrdq9aHv5iG41UwrYsNSnm1BUcikPVv1AJWrVb0oG+elOqL76SdopPFIeonZJiU+arSFxK0OrSdSVkdDyh5KtaGGjOAhUN6HjDG2MdNyg+UZJeJ4nVq1QVlErkKR7VVVwqJlByD4lzfKHjwPCrXzU5X8OGDV1lrCpnRceRqo7X51pjrxMs3vGE5qXR7epDq6IAfabDr+5C8lKRiBL2qozW8abGVHMSUZSXZJSgRfDt2rXL70VIebt373br4PXXXw+1aNEi1K9fv9Dq1atDGzduDK1fvz40derU0M6dO7PW05tvvhk65ZRTQpdccklo7dq12T7fhg0bQg8++GBowoQJKb+OE8EzzzwTqlmzZuiOO+4I/f3333v87v333w81b948dP3114e2b98e2rJlS2jIkCGhpk2bhjIyMtx9Pv7449DFF18c6tu3r0+vALnx9ddfh+rVqxeaMWPGXr9bvHhxqHPnzqEaNWqEhg8fHvrzzz/d//UZ17iHvx8uvfTS0FdffcVKTwBso5GK2JalrhUrVoROP/300MSJE92+6cUXXwy1atUqVLdu3VC3bt3cz9qXdezY0d1fcc2FF14YmjNnjt+Ljoh9l7f/UtypMVSseeutt4a+//57F0+2bt06tGrVqtCUKVNCJ5xwgotVf/7551CPHj1c3ClffPGFi1WmT5/ufubYMXFpvDMzM93/33vvPTdu7777btb7QD799NNQ27ZtQ3369Nnjcd64hh9zIjg0vhrbpUuX+r0oyCeum00RXJriP53F0hlp9fxRv0qd6VTFqyrw1PtJ/YB1ZlOXJHg9oHQ5giqedQlS5CVlej6d8VRldJ8+fezqq6/27bXh/2icdDmRKg7Uy0uXEemMtXehglpXqGLym2++cb29VFWpdgbqPau+waKm7SeeeKKrttXlS0hMmvFWn0FVH4iqZzXOQ4YMcZeK6XOpz6Rms9b/9fnWpWmqflBPP1UsdOvWzVW7q5oF/mMbjVTEtix1aVIvtTpQPKIrd7xqSl3qrH6GqrBTvLJgwQIXu+rSWcU0il3gL12h413W7k3yJmpRoRhUrQ10H00K1bZtW1u3bp3rk6/jD00UrTHXHAgaW+/KHcUzqqz1JhTm2DFxaby9K7YUR2rsVDnptS7wjjnUS1ottnTFnscbV67OCyYdi2hsdWyJ5ESCFjjAwneWmqxNl84qKecFU94O9rPPPnOBshI3ukRJLrvsMtefVD1m1Z/WmwHe4z0HQZT/NC7qI6vgV4l4b+I2jY03TrosSb2EdTmS2hyIAmH1elPfLy85r/uon5BmYUVi0sHOwQcfnNVTWJ9jHbjqQEeXhaqPrA6Q1NJAbSt0CbEuJ9SlhkrCa4Zs9fNT6xL4i200UhnbstSmy19Xrlzp4lNdFq392gUXXOCSd927d3eXRmuOhGeeecZNRKR4VBPZwj+6dF3HB2pp5sWNHk08qlYGOtGv2FLjqpi0c+fObgz//vtvu+iii9zPmzdvdu231LdSdNm8+hErlkVy8OYz6Nevn0vEqrhDSXqP2lOogEBznQhtDIKPxHvyI0ELHEA6W61m/Uq+iXr+qPeogh9VwyqppwSsZn7XZEJqyn/JJZfY8OHD3U5XSToFWToLrmQOEof6h6rP11dffbVH0KOeswcddJCbZXX58uXu9vDKZ1VLK2hWfyBNMqUdqSZ2O+6449zMx6JeUkrqaVIHJKb69eu78dU4eidNlKRVhVGDBg3cz8cee6zrAaUqF/V1UzJWk3OoyloHSqqgh7/YRiPVsS1LbbpKSycQlehTYs+jfZeKBlRRqyt81Jc0cg4E+DvBm0706kocFXgothCdJFYFrRLpumJL/UZ1NZaOJTSvgU4ee5MMa9x1G5MHJS8dQ+h4UePoTUb8xx9/ZP1eRSC6ylIThgNIDiRogQNIAZESsgpwRUkaVVSuWLEiq7JSwZMScgqmlKjVWXHtcJWgE83UqQoXJWiVFOTsZ2J4+eWXXaWzJs/QLMcKlEUN+lV98tdff2VVJXgVzl4iT5OGqbWFd5a7atWq7hIlVVx6l8ojsSmhrkvKJk2a5D7P4iXiNX76mjFjhqtouOKKK9znVhOs6CDYS+DCf2yjkerYlqF169aubYGq7NTyQrz2TEr4KT7R1SBIDCeccIK7EkefXU1EqvZJmmjUU6tWLReT6phD7ZV0ebuu9rn55ptdheUXX3zhYhK14dJEw2qBgOTlHReqolpFP1OmTHHV8B5iTiC5kKAFDiAl3lq2bOkqJdXPS7OqKkhSzyePLhnr0KGD++4FVl7VnS4/0uy6OvvZq1cvl9RDYtBlZOrtpLPW06dPd1UM3oGNqqA1bgqKf/nlF3dbeBWtqlCUmA+fhVVJXVVfqgqTJHziU9JdM2Drc62WBkuWLMlKxGv8VF07fvx4q1u3rqtqiHwPIDGwjUaqY1sG7bNUYafEna7+ERUKEIskLvWRVXsC9bBXgl397pWkVTs0tcfyYs+bbrrJxZoq8lDPYZ2UfPfdd7Oex+s/i+Tehqu4Q8eI1113nesnTaEHkLxI0AIHmHqS6lJmneFWb1EFSK+88opL4IiCKY8SOOpd+t9//9mtt97qLpUXBWCaaAiJRX3ZVHmgXl46qFHCTlWTOqhR5bMmhFIVrQIlr2JaY6yEri4vU+WDhz7Cyefkk092E2qox5sOgtS2QEnZxx57zE0UpnHXQdShhx7q7s8YJya20Uh1bMuglkpqd0Ebg+SgYwklZpVU19U8apGlidzUekuXtat1hYoEFHeq1ZpiUc2BoYlMvSv0ELy+ozp2VOsmtVMDkJzSQpxiAQ44na1WX1ldXqSdppI511xzjWtpoEuQPDNnzrRHH33UBclK/OhsKBUMiUmJdR3UKhjSWOoysdGjR7vLitT2QBWxjzzyiJtBV5Wxmk1Vk0QpMP71119dVbR6zyL5qdpdB0aqZlH7ClFbA70/kBzYRgNsy1Kd2howwUzyUBsDzV2hePOee+5xVbJquaWTwaqiPfXUU90VXfLGG2+4E8cINqV1OG4EkhsJWiBOyTwFT5opd8KECTZmzBh74YUXXDJHVZQKpJTc+eCDD+zqq6+2e++9l3FJcGo/oYS7AmE15ZeMjAxXOasq2Y4dO7rJwPr27ev6fGlmZJ3VVtJOlxKWK1fO75eAA2DLli17nHRBcmAbDeyJbRmQHCcXH3/8cdd/tGnTpq7dkmJTTUrcpUsX9wUASB6F/F4AIBWox5NmcFdlpVod6FJ4ndnWGe358+e7ZJ8qa3WpdKVKlfxeXOSC2k+owtlrUaE+wxpfJWKVkH3qqadcs35dLqjLzDR5mHqVqm8tglt5RHI2ObGNBv4P2zIgeagQQO2V3n77bTv66KNd/Dlo0CDXLk0JWwBAcqGCFojjZSe6vH3WrFmuqlKVs96B0I4dO+j7lUS8cVNQrEkZNFGYJn7TZe1qWyFTp061ESNGuAnfevbs6RLyABIX22gAQLJRoUenTp1c5Wy7du38XhwAwH5gkjAgTtQTSLNrFipUyPUqFa/XF5MyJBdv3DT5k8ZVM+SqeuH+++93/9eXxvq5556z999/n+QskATYRgMAkg0TvAFAcNDiAIijihUr2rnnnms7d+6kkXsAmvDrUrJt27bZOeec4/7vUQ9a9aatW7eur8sJIG/YRgMAks2wYcOY4A0AAoAWB0CcMcNmsGbQbdGihV122WWutYGXmAWQvNhGAwAAAIg3MglAnKnyEsGgScBk+fLlLqlDchZIfmyjAQAAAMQbFbQAsB/eeusta9WqFX2EAQAAAABAvpCgBQAAAAAAAACf0OIAAAAAAAAAAHxCghYAAAAAAAAAfEKCFgAAAAAAAAB8QoIWAAAAAAAAAHxCghYAAAAAAAAAfEKCFgAAAAAAAAB8QoIWAAAAAAAAAHxCghYAAAAAAAAAfEKCFgAAAAAAAAB8QoIWAAAAAAAAAHxCghYAAAAAAAAAzB//H4EcZyjObp7EAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5), sharey=True)\n", + "for ax, dname in zip(axes, [\"T2D\", \"CAD\"]):\n", + " sub = top5_df.filter(pl.col(\"disease\") == dname).to_pandas()\n", + " x = np.arange(len(sub))\n", + " width = 0.38\n", + " ax.bar(x - width / 2, sub[\"catalog_delta\"], width,\n", + " label=\"catalog\", color=sns.color_palette(\"colorblind\")[0])\n", + " ax.bar(x + width / 2, sub[\"empirical_delta\"], width,\n", + " label=\"empirical\", color=sns.color_palette(\"colorblind\")[1])\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(sub[\"protein\"], rotation=30, ha=\"right\")\n", + " ax.axhline(0, color=\"black\", linewidth=0.5)\n", + " ax.set_title(f\"{dname} — top-5 absolute catalog delta-NPX\")\n", + " ax.set_ylabel(\"delta NPX (case - baseline)\")\n", + " ax.legend()\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fb5b6f37", + "metadata": {}, + "source": [ + "### 10.3 Catalog-vs-empirical consistency check\n", + "\n", + "Scatter of catalog delta vs empirical simulation delta across every\n", + "(disease, protein) row used. Points should hug the identity line (y = x);\n", + "deviations reflect Monte-Carlo sampling noise at 300 samples per group." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "3dbc44c1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-23T21:12:56.008144Z", + "iopub.status.busy": "2026-04-23T21:12:56.008081Z", + "iopub.status.idle": "2026-04-23T21:12:56.079123Z", + "shell.execute_reply": "2026-04-23T21:12:56.078660Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "records = []\n", + "for r in catalog.effects.filter(pl.col(\"disease\").is_in([\"T2D\", \"CAD\"])).iter_rows(\n", + " named=True\n", + "):\n", + " dname = r[\"disease\"]\n", + " prot = r[\"protein_uniprot\"]\n", + " expected = float(r[\"delta_npx\"])\n", + " if expected == 0.0:\n", + " continue\n", + " case_mean = df_catalog.filter(\n", + " (pl.col(\"protein_id\") == prot) & (pl.col(\"group\") == dname)\n", + " )[\"npx\"].mean()\n", + " base_mean = df_catalog.filter(\n", + " (pl.col(\"protein_id\") == prot) & (pl.col(\"group\") == \"baseline\")\n", + " )[\"npx\"].mean()\n", + " records.append({\"disease\": dname, \"protein\": prot,\n", + " \"catalog_delta\": expected,\n", + " \"empirical_delta\": float(case_mean - base_mean)})\n", + "consistency = pl.DataFrame(records)\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "palette = dict(zip([\"T2D\", \"CAD\"], sns.color_palette(\"colorblind\", 2)))\n", + "for dname, color in palette.items():\n", + " sub = consistency.filter(pl.col(\"disease\") == dname).to_pandas()\n", + " ax.scatter(sub[\"catalog_delta\"], sub[\"empirical_delta\"],\n", + " s=70, color=color, edgecolor=\"k\", linewidth=0.4,\n", + " label=dname)\n", + " for _, row in sub.iterrows():\n", + " ax.annotate(row[\"protein\"], (row[\"catalog_delta\"], row[\"empirical_delta\"]),\n", + " xytext=(5, 4), textcoords=\"offset points\", fontsize=8)\n", + "lo = float(consistency.select(pl.col(\"catalog_delta\").min(),\n", + " pl.col(\"empirical_delta\").min())\n", + " .min_horizontal()[0]) - 0.15\n", + "hi = float(consistency.select(pl.col(\"catalog_delta\").max(),\n", + " pl.col(\"empirical_delta\").max())\n", + " .max_horizontal()[0]) + 0.15\n", + "ax.plot([lo, hi], [lo, hi], \"k--\", linewidth=1, alpha=0.5, label=\"y = x\")\n", + "ax.set_xlabel(\"catalog delta NPX\")\n", + "ax.set_ylabel(\"empirical delta NPX (case - baseline)\")\n", + "ax.set_title(\"Catalog-vs-empirical consistency (300 subjects / group)\")\n", + "ax.legend()\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b935cb7a", + "metadata": {}, + "source": [ + "## 11. Where to next\n", + "\n", + "The simulator is deliberately minimal — the scope of [PR\n", + "#2](https://github.com/bschilder/synthlab/pull/2) is \"smallest viable NPX\n", + "simulator with LOD + plates + group effects\" and the follow-up adds a\n", + "curated effect-size catalog. Remaining roadmap:\n", + "\n", + "- **Full MAR missingness**: model missingness as a function of sample-level\n", + " covariates (age, QC batch) rather than aliasing to MCAR.\n", + "- **Multi-factor batch effects**: plate x run x operator, with per-factor\n", + " variance components.\n", + "- **Panel-version LOD bridging**: Olink Explore HT vs Explore 3072 have\n", + " different LOD distributions; a bridging mode should allow cross-panel\n", + " simulation.\n", + "- **PEA dilution / matrix effects**: the real [PEA\n", + " assay](https://olink.com/technology/proximity-extension-assay) has a\n", + " noise model that scales with dilution; currently we use a flat per-protein\n", + " sigma.\n", + "- **Covariate-adjusted catalog effects**: age / sex / BMI-conditional\n", + " deltas instead of the current marginal means.\n", + "- **Longitudinal effects**: time-to-event modulation of the catalog deltas\n", + " for incidence-cohort simulations.\n", + "\n", + "Tracking discussion in [PR #2](https://github.com/bschilder/synthlab/pull/2);\n", + "please open issues for missing features." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 456850c..761397b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,6 +49,13 @@ dependencies = [ aws = [ "boto3>=1.26.0", ] +viz = [ + # Notebook visualisation stack used by notebooks/olink_demo.ipynb. + "matplotlib>=3.8", + "seaborn>=0.13", + "scikit-learn>=1.5", + "umap-learn>=0.5", +] genomics = [ "numpy>=1.20.0", ] @@ -103,7 +110,7 @@ where = ["."] include = ["synthlab*"] [tool.setuptools.package-data] -synthlab = ["py.typed"] +synthlab = ["py.typed", "data/*.csv"] [tool.black] line-length = 100 diff --git a/synthlab/__init__.py b/synthlab/__init__.py index 121106f..cc4ca75 100644 --- a/synthlab/__init__.py +++ b/synthlab/__init__.py @@ -155,6 +155,18 @@ print_meds_info, ) +# Olink NPX proteomics simulator +from synthlab.olink import ( + DiseaseEffectCatalog, + OlinkPanelConfig, + OlinkSimConfig, + default_explore_3072_panel, + load_disease_effect_catalog, + load_olink_parquet, + simulate_olink_npx, + write_olink_parquet, +) + from synthlab.coherent import ( list_coherent_components, list_coherent_files, @@ -357,4 +369,13 @@ def _print_banner(): "get_meds_cache_dir", "get_meds_info", "print_meds_info", + # Olink NPX proteomics simulator + "OlinkPanelConfig", + "OlinkSimConfig", + "default_explore_3072_panel", + "load_olink_parquet", + "simulate_olink_npx", + "write_olink_parquet", + "DiseaseEffectCatalog", + "load_disease_effect_catalog", ] diff --git a/synthlab/data/__init__.py b/synthlab/data/__init__.py new file mode 100644 index 0000000..baa0105 --- /dev/null +++ b/synthlab/data/__init__.py @@ -0,0 +1,6 @@ +"""Bundled data files — shipped in the wheel via setuptools ``package-data``. + +See [`olink_disease_effects.csv`](./olink_disease_effects.csv) for the curated +per-disease Olink effect-size catalog (loaded by +:func:`synthlab.olink.load_disease_effect_catalog`). +""" diff --git a/synthlab/data/olink_disease_effects.csv b/synthlab/data/olink_disease_effects.csv new file mode 100644 index 0000000..820347c --- /dev/null +++ b/synthlab/data/olink_disease_effects.csv @@ -0,0 +1,44 @@ +disease,protein_uniprot,delta_npx,se_delta,source,doi,evidence_strength,meta +T2D,IGFBP2,-0.45,0.18,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,strong,largest-N pQTL + phenome-wide association for T2D +T2D,LEP,0.80,0.25,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,strong,leptin rises with adiposity + insulin resistance +T2D,ADIPOQ,-0.60,0.20,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,strong,adiponectin consistently reduced in T2D across cohorts +T2D,IGFBP1,-0.70,0.30,"Sun et al. 2018 INTERVAL (N=3,301) Nature",10.1038/s41586-018-0175-2,moderate,IGFBP1 suppressed by hyperinsulinemia +T2D,IL6,0.30,0.20,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,moderate,low-grade inflammation in T2D +T2D,CRP,0.35,0.20,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,strong,chronic inflammation biomarker +T2D,GDF15,0.55,0.22,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,strong,mitochondrial stress + metformin interaction +T2D,TNF,0.20,0.25,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,moderate,mild elevation in metabolic inflammation +CAD,NPPB,0.55,0.30,"Eldjarn et al. 2023 deCODE (N=35,559) Nature",10.1038/s41586-023-06563-x,strong,BNP/NT-proBNP elevated with myocardial strain +CAD,TNNI3,0.45,0.30,"Williams et al. 2022 Sci Transl Med",10.1126/scitranslmed.abj9625,moderate,troponin I elevated in subclinical CAD +CAD,GDF15,0.70,0.25,"Williams et al. 2022 Sci Transl Med",10.1126/scitranslmed.abj9625,strong,GDF15 robust CV risk biomarker +CAD,MMP12,0.50,0.25,"Eldjarn et al. 2023 deCODE (N=35,559) Nature",10.1038/s41586-023-06563-x,strong,MMP12 MR-supported causal CAD locus +CAD,IL6,0.35,0.20,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,strong,IL6 MR-causal for CAD +CAD,CRP,0.40,0.20,"Sun et al. 2023 UKB-PPP (N=54,219) Nature",10.1038/s41586-023-06592-6,strong,atherosclerotic inflammation +CAD,FABP4,0.40,0.25,"Williams et al. 2022 Sci Transl Med",10.1126/scitranslmed.abj9625,moderate,adipocyte-macrophage crosstalk in plaque +Cancer_broad,MUC16,1.20,0.50,"Cohen et al. 2018 CancerSEEK Science",10.1126/science.aar3247,moderate,CA-125 elevated across ovarian + some epithelial tumours +Cancer_broad,CEACAM5,1.00,0.45,"Cohen et al. 2018 CancerSEEK Science",10.1126/science.aar3247,moderate,CEA elevated in colorectal + other adenocarcinomas +Cancer_broad,GDF15,0.80,0.30,"Cohen et al. 2018 CancerSEEK Science",10.1126/science.aar3247,strong,GDF15 pan-cancer tumour-burden marker +Cancer_broad,IL6,0.60,0.25,"Cohen et al. 2018 CancerSEEK Science",10.1126/science.aar3247,moderate,tumour-associated cytokine +Cancer_broad,CRP,0.50,0.25,"Cohen et al. 2018 CancerSEEK Science",10.1126/science.aar3247,moderate,systemic inflammatory response to tumour +Cancer_broad,KRT19,0.70,0.35,"Cohen et al. 2018 CancerSEEK Science",10.1126/science.aar3247,moderate,CYFRA 21-1 elevated in epithelial tumours +BRCA_hereditary,MUC16,0.05,0.30,"Ahn et al. 2021 Cancers",10.3390/cancers13102300,weak,no pre-clinical circulating MUC16 signal in BRCA carriers +BRCA_hereditary,IL6,0.05,0.30,"Ahn et al. 2021 Cancers",10.3390/cancers13102300,weak,null-hypothesis placeholder; no replicated signal in BRCA carriers +BRCA_hereditary,CRP,0.10,0.30,"Ahn et al. 2021 Cancers",10.3390/cancers13102300,weak,null-hypothesis placeholder; no replicated signal +BRCA_hereditary,CEACAM5,0.00,0.30,"Ahn et al. 2021 Cancers",10.3390/cancers13102300,weak,null-hypothesis placeholder; no replicated signal +Alzheimer,NEFL,0.90,0.30,"Guo et al. 2024 Nat Aging UKB (N=52,645)",10.1038/s43587-023-00565-0,strong,neurofilament light plasma pre-clinical dementia biomarker +Alzheimer,GFAP,0.70,0.28,"Guo et al. 2024 Nat Aging UKB (N=52,645)",10.1038/s43587-023-00565-0,strong,astrogliosis marker rising >10y before AD dx +Alzheimer,CHIT1,0.60,0.30,"Guo et al. 2024 Nat Aging UKB (N=52,645)",10.1038/s43587-023-00565-0,moderate,microglial chitotriosidase in neurodegeneration +Alzheimer,GDF15,0.45,0.25,"Guo et al. 2024 Nat Aging UKB (N=52,645)",10.1038/s43587-023-00565-0,moderate,chronic stress biomarker in neurodegeneration +Alzheimer,CRP,0.15,0.25,"Guo et al. 2024 Nat Aging UKB (N=52,645)",10.1038/s43587-023-00565-0,moderate,mild systemic inflammation in AD +CKD,GDF15,0.85,0.25,"Dubin et al. 2023 Nat Comm CRIC (N=3,235)",10.1038/s41467-023-41642-7,strong,GDF15 rises monotonically with eGFR decline +CKD,REN,0.70,0.30,"Dubin et al. 2023 Nat Comm CRIC (N=3,235)",10.1038/s41467-023-41642-7,strong,renin-angiotensin activation in CKD +CKD,TFF3,0.75,0.30,"Dubin et al. 2023 Nat Comm CRIC (N=3,235)",10.1038/s41467-023-41642-7,moderate,tubular injury biomarker +CKD,CST3,1.00,0.25,"Dubin et al. 2023 Nat Comm CRIC (N=3,235)",10.1038/s41467-023-41642-7,strong,cystatin C directly filtered by kidneys +CKD,SPP1,0.55,0.28,"Dubin et al. 2023 Nat Comm CRIC (N=3,235)",10.1038/s41467-023-41642-7,moderate,osteopontin in tubular stress +CKD,CRP,0.35,0.25,"Dubin et al. 2023 Nat Comm CRIC (N=3,235)",10.1038/s41467-023-41642-7,moderate,uremic chronic inflammation +IBD,CRP,0.80,0.25,"Hu et al. 2025 Nat Comm UKB-PPP (N=48,800)",10.1038/s41467-025-57879-3,strong,CRP is clinical gold-standard for IBD activity +IBD,IL6,0.60,0.25,"Hu et al. 2025 Nat Comm UKB-PPP (N=48,800)",10.1038/s41467-025-57879-3,strong,IL6 pathway central to IBD pathogenesis +IBD,MMP9,0.55,0.25,"Hu et al. 2025 Nat Comm UKB-PPP (N=48,800)",10.1038/s41467-025-57879-3,moderate,MMP9 elevated with mucosal inflammation +IBD,LRG1,0.70,0.30,"Hu et al. 2025 Nat Comm UKB-PPP (N=48,800)",10.1038/s41467-025-57879-3,moderate,LRG1 alternative activity biomarker +IBD,S100A8,0.90,0.30,"Hu et al. 2025 Nat Comm UKB-PPP (N=48,800)",10.1038/s41467-025-57879-3,strong,calprotectin subunit A; faecal + plasma +IBD,S100A9,0.90,0.30,"Hu et al. 2025 Nat Comm UKB-PPP (N=48,800)",10.1038/s41467-025-57879-3,strong,calprotectin subunit B; faecal + plasma +IBD,TNF,0.45,0.25,"Hu et al. 2025 Nat Comm UKB-PPP (N=48,800)",10.1038/s41467-025-57879-3,strong,TNF is validated therapeutic target diff --git a/synthlab/olink.py b/synthlab/olink.py new file mode 100644 index 0000000..0e6a186 --- /dev/null +++ b/synthlab/olink.py @@ -0,0 +1,687 @@ +"""Olink-NPX proteomics simulator. + +Simulate case/control Olink proteomics data (subject x protein -> NPX) +with limit-of-detection (LOD) driven missingness, group effects, and +per-plate batch effects. NPX ("Normalized Protein eXpression") is +Olink's log2 relative quantification unit used across all +[Olink Explore](https://olink.com/products/olink-explore) panels. + +As of 2026-04 there is no widely-used open-source Olink simulator; +closest analogues are +[MSstatsSampleSize](https://bioconductor.org/packages/MSstatsSampleSize/) +(LC-MS/MS, not NPX) and +[OlinkAnalyze](https://github.com/Olink-Proteomics/OlinkRPackage) (NPX +demo tables, no simulator). Priors are informed by the +[UKB-PPP paper](https://www.nature.com/articles/s41586-023-06592-6) +(Sun et al. 2023, 2,923 proteins x ~54k participants) and the +OlinkAnalyze ``npx_data1`` / ``npx_data2`` tables. + +Model: ``NPX[i, j] = mean[j] + plate_eff[i] + group_shift[i, j] + eps`` +with ``plate_eff ~ N(0, plate_effect_sd^2)`` (96 samples / plate) and +``eps ~ N(0, sd[j]^2)``. ``mnar_lod`` drops 80% of sub-LOD values +(soft LOD); ``mcar``/``mar`` add uniform ``missing_rate`` drop (MAR +currently aliases MCAR). Full MAR, multi-plate batch effects, and a +realistic PEA dilution noise model are deferred to a follow-up. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from importlib import resources +from pathlib import Path +from typing import Literal, Mapping, Sequence + +import numpy as np +import polars as pl + +__all__ = [ + "OlinkPanelConfig", + "OlinkSimConfig", + "simulate_olink_npx", + "default_explore_3072_panel", + "write_olink_parquet", + "load_olink_parquet", + "DiseaseEffectCatalog", + "load_disease_effect_catalog", +] + +# Columns every disease-effects catalog CSV must provide. Any extra columns +# are preserved untouched (useful for meta-notes / study-design tags). +_CATALOG_REQUIRED_COLUMNS: tuple[str, ...] = ( + "disease", + "protein_uniprot", + "delta_npx", + "se_delta", + "source", + "doi", + "evidence_strength", +) + +# 50 protein symbols drawn from UKB-PPP Explore 3072 — illustrative mix of +# inflammation / cardiovascular / oncology markers. Downstream users should +# override with biologically-relevant subsets where needed. +_DEFAULT_PANEL_PROTEINS: tuple[str, ...] = ( + "CRP", "IL6", "TNF", "IL1B", "IL10", "IL8", "IFNG", "IL17A", "BNP", + "NT-proBNP", "TROPT", "TROPI", "LEP", "ADIPOQ", "GDF15", "NEFL", "TAU", + "AB42", "AB40", "NRGN", "PSA", "CA125", "CEA", "AFP", "CA199", "VEGFA", + "PDGFA", "FGF2", "EGF", "HGF", "TGFB1", "BMP2", "IGF1", "IGFBP3", "INS", + "GLP1", "GIP", "GHRL", "CORT", "FABP4", "APOA1", "APOB", "LDLR", "PCSK9", + "MMP9", "MMP2", "TIMP1", "SERPINE1", "VWF", "FGB", +) + + +def _unique_preserve_order(items: Sequence[str]) -> tuple[str, ...]: + """De-duplicate ``items`` while preserving first-seen order. + + Parameters + ---------- + items : Sequence[str] + Strings that may contain duplicates. + + Returns + ------- + tuple[str, ...] + First-occurrence order preserved. + """ + seen: set[str] = set() + return tuple(x for x in items if not (x in seen or seen.add(x))) + + +@dataclass(frozen=True) +class OlinkPanelConfig: + """An Olink panel specification — proteins + per-protein priors. + + Built-in presets (:func:`default_explore_3072_panel`) hard-code + priors from the UKB-PPP paper (Sun et al. 2023) and the OlinkAnalyze + ``npx_data1`` / ``npx_data2`` demo tables: NPX ~ ``N(mean, sd^2)`` + with ``mean ~ 5`` log2-units, ``sd ~ 0.6``, ``lod ~ mean - 2*sd``. + + Attributes + ---------- + name : str + Panel label, e.g. ``"explore_3072"`` / ``"explore_ht"`` / custom. + proteins : tuple[str, ...] + Ordered tuple of protein IDs (UniProt IDs or gene symbols); + duplicates rejected. + lod : Mapping[str, float] + Per-protein limit of detection on the NPX scale; values below + LOD are candidates for ``mnar_lod`` missingness. + mean : Mapping[str, float] + Per-protein baseline NPX mean. + sd : Mapping[str, float] + Per-protein NPX stddev (must be ``> 0``). + """ + + name: str + proteins: tuple[str, ...] + lod: Mapping[str, float] + mean: Mapping[str, float] + sd: Mapping[str, float] + + def __post_init__(self) -> None: + """Validate uniqueness, key coverage, and ``sd > 0``.""" + if len(set(self.proteins)) != len(self.proteins): + raise ValueError("OlinkPanelConfig.proteins must be unique.") + miss_l = [p for p in self.proteins if p not in self.lod] + miss_m = [p for p in self.proteins if p not in self.mean] + miss_s = [p for p in self.proteins if p not in self.sd] + if miss_l or miss_m or miss_s: + raise ValueError( + f"OlinkPanelConfig missing lod={miss_l}, " + f"mean={miss_m}, sd={miss_s}." + ) + for p in self.proteins: + if self.sd[p] <= 0: + raise ValueError( + f"OlinkPanelConfig.sd[{p!r}] must be > 0; got {self.sd[p]}." + ) + + +@dataclass(frozen=True) +class OlinkSimConfig: + """Configuration for :func:`simulate_olink_npx`. + + Attributes + ---------- + n_samples : int + Number of subjects to simulate (``>= 0``). + panel : OlinkPanelConfig + Panel specification. + group_effects : Mapping[str, Mapping[str, float]], optional + Per-protein NPX shift per group label, e.g. + ``{"CRP": {"case": 1.2}}``. Proteins not listed get no shift. + group_assignments : Sequence[str], optional + Length-``n_samples`` sequence of group labels; ``None`` means + every sample is labelled ``"baseline"``. + missingness : {"mcar", "mar", "mnar_lod", "none"}, default "mnar_lod" + Missingness model; ``"mar"`` currently aliases ``"mcar"`` (full + MAR is deferred). + missing_rate : float, default 0.05 + Extra MCAR rate on top of LOD-driven missingness; ``[0, 1]``. + qc_warn_rate : float, default 0.01 + Per-row Bernoulli rate for ``qc_warning=True``. + plate_effect_sd : float, default 0.15 + Stddev of the per-plate intercept (96 samples / plate). + seed : int, default 0 + NumPy RNG seed; deterministic under fixed seed. + """ + + n_samples: int + panel: OlinkPanelConfig + group_effects: Mapping[str, Mapping[str, float]] = field(default_factory=dict) + group_assignments: Sequence[str] | None = None + missingness: Literal["mcar", "mar", "mnar_lod", "none"] = "mnar_lod" + missing_rate: float = 0.05 + qc_warn_rate: float = 0.01 + plate_effect_sd: float = 0.15 + seed: int = 0 + + def __post_init__(self) -> None: + """Validate scalar bounds and ``group_assignments`` length.""" + if self.n_samples < 0: + raise ValueError(f"n_samples must be >= 0; got {self.n_samples}.") + if not (0.0 <= self.missing_rate <= 1.0): + raise ValueError(f"missing_rate must be in [0, 1]; got {self.missing_rate}.") + if not (0.0 <= self.qc_warn_rate <= 1.0): + raise ValueError(f"qc_warn_rate must be in [0, 1]; got {self.qc_warn_rate}.") + if self.plate_effect_sd < 0: + raise ValueError(f"plate_effect_sd must be >= 0; got {self.plate_effect_sd}.") + if self.missingness not in {"mcar", "mar", "mnar_lod", "none"}: + raise ValueError( + f"missingness must be one of 'mcar'/'mar'/'mnar_lod'/'none'; " + f"got {self.missingness!r}." + ) + if ( + self.group_assignments is not None + and len(self.group_assignments) != self.n_samples + ): + raise ValueError( + f"group_assignments length ({len(self.group_assignments)}) " + f"must equal n_samples ({self.n_samples})." + ) + + +_SCHEMA: dict[str, pl.DataType] = { + "sample_id": pl.Utf8, + "protein_id": pl.Utf8, + "npx": pl.Float64, + "qc_warning": pl.Boolean, + "group": pl.Utf8, + "plate_id": pl.Utf8, +} + + +def simulate_olink_npx(config: OlinkSimConfig) -> pl.DataFrame: + """Simulate an Olink-style long-form NPX DataFrame. + + Pipeline: (1) assign each sample a plate (96 per plate, in order); + (2) draw plate intercepts ``~ N(0, plate_effect_sd**2)``; (3) per + cell, ``npx = mean[p] + plate_eff + group_effect[p][group] + eps`` + with ``eps ~ N(0, sd[p]**2)``; (4) apply missingness — ``mnar_lod`` + drops 80% of sub-LOD values (soft LOD), ``mcar`` adds uniform + ``missing_rate`` drop, ``mar`` aliases ``mcar``, ``none`` skips; + (5) flag Bernoulli ``qc_warn_rate`` QC warnings on surviving rows. + Deterministic under fixed seed. + + Parameters + ---------- + config : OlinkSimConfig + Full simulation spec. + + Returns + ------- + polars.DataFrame + Long-form frame with columns ``sample_id`` (``str``), + ``protein_id`` (``str``), ``npx`` (``f64``), ``qc_warning`` + (``bool``), ``group`` (``str``), ``plate_id`` (``str``). + + Examples + -------- + >>> from synthlab.olink import default_explore_3072_panel + >>> cfg = OlinkSimConfig(n_samples=3, panel=default_explore_3072_panel(), seed=1) + >>> df = simulate_olink_npx(cfg) + >>> sorted(df.columns) + ['group', 'npx', 'plate_id', 'protein_id', 'qc_warning', 'sample_id'] + """ + panel = config.panel + n_proteins = len(panel.proteins) + n = config.n_samples + if n == 0 or n_proteins == 0: + return pl.DataFrame(schema=_SCHEMA) + rng = np.random.default_rng(config.seed) + sample_ids = np.array([f"S{i:04d}" for i in range(n)], dtype=object) + plate_idx = np.arange(n) // 96 + plate_labels = np.array([f"plate_{p}" for p in plate_idx], dtype=object) + groups = ( + np.full(n, "baseline", dtype=object) + if config.group_assignments is None + else np.array(list(config.group_assignments), dtype=object) + ) + # Plate intercepts per sample. + n_plates = int(plate_idx.max()) + 1 + plate_intercepts = ( + rng.normal(0.0, config.plate_effect_sd, size=n_plates) + if config.plate_effect_sd > 0 + else np.zeros(n_plates, dtype=np.float64) + ) + plate_eff = plate_intercepts[plate_idx] + # Per-protein priors in panel.proteins order. + mean_vec = np.array([panel.mean[p] for p in panel.proteins], dtype=np.float64) + sd_vec = np.array([panel.sd[p] for p in panel.proteins], dtype=np.float64) + lod_vec = np.array([panel.lod[p] for p in panel.proteins], dtype=np.float64) + # Group shift matrix; nonzero only for (protein, group) pairs in config. + group_shift = np.zeros((n, n_proteins), dtype=np.float64) + for j, p in enumerate(panel.proteins): + mapping = config.group_effects.get(p) + if not mapping: + continue + for i, g in enumerate(groups): + if g in mapping: + group_shift[i, j] = mapping[g] + # NPX = mean + plate + group + eps. + eps = rng.standard_normal(size=(n, n_proteins)) * sd_vec + npx = mean_vec[np.newaxis, :] + plate_eff[:, np.newaxis] + group_shift + eps + # Missingness masks. + keep = np.ones_like(npx, dtype=bool) + if config.missingness == "mnar_lod": + below_lod = npx < lod_vec[np.newaxis, :] + keep &= ~(below_lod & (rng.random(size=npx.shape) < 0.8)) + if ( + config.missingness in {"mcar", "mar", "mnar_lod"} + and config.missing_rate > 0 + ): + keep &= ~(rng.random(size=npx.shape) < config.missing_rate) + # Flatten (row-major), mask, build frame. + flat_keep = keep.reshape(-1) + flat_samples = np.repeat(sample_ids, n_proteins) + flat_plates = np.repeat(plate_labels, n_proteins) + flat_groups = np.repeat(groups, n_proteins) + flat_proteins = np.tile(np.asarray(panel.proteins, dtype=object), n) + flat_npx = npx.reshape(-1) + n_kept = int(flat_keep.sum()) + qc_flags = ( + rng.random(size=n_kept) < config.qc_warn_rate + if n_kept > 0 and config.qc_warn_rate > 0 + else np.zeros(n_kept, dtype=bool) + ) + return pl.DataFrame( + { + "sample_id": pl.Series("sample_id", flat_samples[flat_keep], dtype=pl.Utf8), + "protein_id": pl.Series("protein_id", flat_proteins[flat_keep], dtype=pl.Utf8), + "npx": pl.Series("npx", flat_npx[flat_keep], dtype=pl.Float64), + "qc_warning": pl.Series("qc_warning", qc_flags, dtype=pl.Boolean), + "group": pl.Series("group", flat_groups[flat_keep], dtype=pl.Utf8), + "plate_id": pl.Series("plate_id", flat_plates[flat_keep], dtype=pl.Utf8), + } + ) + + +def default_explore_3072_panel( + mean: float = 5.0, + sd: float = 0.6, + lod: float = 3.0, +) -> OlinkPanelConfig: + """Return an :class:`OlinkPanelConfig` mirroring Olink Explore 3072. + + Uses 50 named proteins — a tiny subset of the real 3,072-plex panel + chosen for test stability. Priors reflect aggregate UKB-PPP NPX + statistics (Sun et al. 2023) and the OlinkAnalyze ``npx_data1`` / + ``npx_data2`` demo tables: NPX ~ ``N(5, 0.6^2)``, LOD typically + ``mean - 2*sd``. + + Parameters + ---------- + mean : float, default 5.0 + Baseline NPX mean applied to every protein. + sd : float, default 0.6 + NPX stddev applied to every protein (``> 0``). + lod : float, default 3.0 + Limit of detection applied to every protein. + + Returns + ------- + OlinkPanelConfig + 50-protein panel with name ``"explore_3072"``. + """ + proteins = _unique_preserve_order(_DEFAULT_PANEL_PROTEINS) + if len(proteins) < 50: + proteins += tuple(f"PROT{i:04d}" for i in range(50 - len(proteins))) + elif len(proteins) > 50: + proteins = proteins[:50] + return OlinkPanelConfig( + name="explore_3072", + proteins=proteins, + lod={p: float(lod) for p in proteins}, + mean={p: float(mean) for p in proteins}, + sd={p: float(sd) for p in proteins}, + ) + + +def write_olink_parquet(df: pl.DataFrame, path: str | Path) -> Path: + """Write an Olink NPX DataFrame to parquet (snappy compression). + + Parameters + ---------- + df : polars.DataFrame + Long-form NPX DataFrame — typically the output of + :func:`simulate_olink_npx`. + path : str or pathlib.Path + Output path; parent directory is created if absent. + + Returns + ------- + pathlib.Path + The resolved output path. + """ + out = Path(path).expanduser() + out.parent.mkdir(parents=True, exist_ok=True) + df.write_parquet(out, compression="snappy") + return out + + +def load_olink_parquet(path: str | Path) -> pl.DataFrame: + """Load an Olink NPX parquet and validate its schema. + + Parameters + ---------- + path : str or pathlib.Path + Path to a parquet file written by :func:`write_olink_parquet`. + + Returns + ------- + polars.DataFrame + DataFrame with the canonical Olink NPX schema: ``sample_id``, + ``protein_id``, ``npx``, ``qc_warning``, ``group``, + ``plate_id``. + + Raises + ------ + FileNotFoundError + If ``path`` does not exist. + ValueError + If the loaded frame is missing expected columns. + """ + p = Path(path).expanduser() + if not p.is_file(): + raise FileNotFoundError(f"Olink parquet not found: {p}") + df = pl.read_parquet(p) + missing = [c for c in _SCHEMA if c not in df.columns] + if missing: + raise ValueError( + f"Olink parquet at {p} missing expected columns: {missing}. " + f"Found: {df.columns}." + ) + return df + + +# --------------------------------------------------------------------------- +# Disease-effect catalog — curated per-disease protein NPX shifts +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class DiseaseEffectCatalog: + """Curated per-disease protein effect sizes from published Olink / plasma + proteomics literature. + + Loaded from the bundled + [`synthlab/data/olink_disease_effects.csv`](../data/olink_disease_effects.csv) + (or a user-supplied CSV with the same schema). Effects are **log2 NPX-unit + mean shifts** of cases vs a demographically-matched baseline; ``se_delta`` + reflects between-study / between-cohort variability. Every row is + annotated with a ``source`` label and a real DOI — see the CSV itself for + citation-level sourcing of every estimate. + + The catalog plugs directly into :func:`simulate_olink_npx` via + :meth:`effects_for` — see the Examples. Row schema is validated on + construction (``disease``, ``protein_uniprot``, ``delta_npx``, + ``se_delta``, ``source``, ``doi``, ``evidence_strength``). + + Parameters + ---------- + effects : polars.DataFrame + The loaded catalog as a polars frame. Must contain every column + listed in ``_CATALOG_REQUIRED_COLUMNS``; extra columns (e.g. the + bundled ``meta`` notes column) are preserved unmodified. + + Examples + -------- + Load the bundled catalog and feed it into the simulator: + + >>> from synthlab import ( + ... OlinkSimConfig, + ... default_explore_3072_panel, + ... load_disease_effect_catalog, + ... simulate_olink_npx, + ... ) + >>> cat = load_disease_effect_catalog() + >>> effects = cat.effects_for(["T2D", "CAD"]) + >>> cfg = OlinkSimConfig( + ... n_samples=10, + ... panel=default_explore_3072_panel(), + ... group_effects=effects, + ... group_assignments=["T2D"] * 5 + ["baseline"] * 5, + ... seed=42, + ... ) + >>> df = simulate_olink_npx(cfg) + >>> sorted(df.columns) + ['group', 'npx', 'plate_id', 'protein_id', 'qc_warning', 'sample_id'] + """ + + effects: pl.DataFrame + + def __post_init__(self) -> None: + """Validate required columns + dtypes on construction. + + Raises + ------ + ValueError + If any required column is missing, or if ``delta_npx`` / + ``se_delta`` are not castable to floats. + """ + missing = [c for c in _CATALOG_REQUIRED_COLUMNS if c not in self.effects.columns] + if missing: + raise ValueError( + f"DiseaseEffectCatalog is missing required columns: {missing}. " + f"Found: {self.effects.columns}." + ) + for col in ("delta_npx", "se_delta"): + if not self.effects[col].dtype.is_numeric(): + raise ValueError( + f"DiseaseEffectCatalog column {col!r} must be numeric; " + f"got dtype={self.effects[col].dtype}." + ) + + def diseases(self) -> tuple[str, ...]: + """Return the sorted list of registered disease labels. + + Returns + ------- + tuple[str, ...] + Unique disease labels from the ``disease`` column, sorted + alphabetically. Useful for populating dropdowns / docs. + + Examples + -------- + >>> from synthlab import load_disease_effect_catalog + >>> cat = load_disease_effect_catalog() + >>> "T2D" in cat.diseases() + True + """ + return tuple(sorted(self.effects["disease"].unique().to_list())) + + def proteins_for(self, disease: str) -> tuple[str, ...]: + """Return the proteins with non-zero effects in ``disease``. + + Parameters + ---------- + disease : str + Disease label, e.g. ``"T2D"``. Must be a member of + :meth:`diseases`; a ``KeyError`` is raised otherwise. + + Returns + ------- + tuple[str, ...] + Protein UniProt / gene-symbol IDs with ``|delta_npx| > 0`` for + the requested disease, in the CSV's row order. Rows with + exactly-zero ``delta_npx`` (e.g. null-hypothesis placeholder + rows) are omitted. + + Raises + ------ + KeyError + If ``disease`` is not present in the catalog. + + Examples + -------- + >>> from synthlab import load_disease_effect_catalog + >>> cat = load_disease_effect_catalog() + >>> "CRP" in cat.proteins_for("IBD") + True + """ + if disease not in self.effects["disease"].unique().to_list(): + raise KeyError( + f"Disease {disease!r} not in catalog. Registered: {self.diseases()}." + ) + sub = self.effects.filter( + (pl.col("disease") == disease) & (pl.col("delta_npx").abs() > 0.0) + ) + return tuple(sub["protein_uniprot"].to_list()) + + def effects_for( + self, + diseases: Sequence[str], + *, + noise_sd: float = 0.0, + seed: int = 0, + ) -> Mapping[str, Mapping[str, float]]: + """Return a ``{protein_uniprot: {disease: delta_npx}}`` mapping. + + The returned mapping plugs directly into + :attr:`OlinkSimConfig.group_effects`, which expects the exact shape + ``{protein: {group_label: delta_npx}}``. Each ``disease`` label in + ``diseases`` is treated as a group label at the simulator layer. + + Parameters + ---------- + diseases : Sequence[str] + One or more disease labels; each must be present in + :meth:`diseases`. A ``KeyError`` is raised for unknowns. + noise_sd : float, default 0.0 + If ``> 0``, add Gaussian noise ``N(0, (noise_sd * se_delta)^2)`` + to every delta — useful for Monte-Carlo ablations over + effect-size uncertainty. ``0.0`` returns the catalog's point + estimates verbatim. + seed : int, default 0 + NumPy RNG seed. Only consulted when ``noise_sd > 0``. + + Returns + ------- + Mapping[str, Mapping[str, float]] + Keys are protein IDs that have *any* non-zero effect in the + requested diseases; values map each disease label to its + (possibly noise-perturbed) delta-NPX. Proteins present in the + CSV with ``delta_npx == 0`` are omitted from the output so + that :func:`simulate_olink_npx` skips them in its inner loop. + + Raises + ------ + KeyError + If any element of ``diseases`` is not in the catalog. + ValueError + If ``noise_sd < 0``. + + Examples + -------- + >>> from synthlab import load_disease_effect_catalog + >>> cat = load_disease_effect_catalog() + >>> eff = cat.effects_for(["T2D"]) + >>> "CRP" in eff and "T2D" in eff["CRP"] + True + """ + if noise_sd < 0: + raise ValueError(f"noise_sd must be >= 0; got {noise_sd}.") + registered = set(self.diseases()) + unknown = [d for d in diseases if d not in registered] + if unknown: + raise KeyError( + f"Unknown disease labels: {unknown}. " + f"Registered: {sorted(registered)}." + ) + sub = self.effects.filter( + pl.col("disease").is_in(list(diseases)) + & (pl.col("delta_npx").abs() > 0.0) + ) + # Draw per-row noise (only used when noise_sd > 0). Draw in the + # CSV's row order for reproducibility. + if noise_sd > 0: + rng = np.random.default_rng(seed) + ses = sub["se_delta"].to_numpy() + noise = rng.normal(0.0, noise_sd * ses) + else: + noise = np.zeros(sub.height, dtype=float) + out: dict[str, dict[str, float]] = {} + proteins = sub["protein_uniprot"].to_list() + dlist = sub["disease"].to_list() + deltas = sub["delta_npx"].to_numpy().astype(float) + for prot, disease, delta, noise_val in zip(proteins, dlist, deltas, noise): + out.setdefault(prot, {})[disease] = float(delta + noise_val) + return out + + +def load_disease_effect_catalog( + path: str | Path | None = None, +) -> DiseaseEffectCatalog: + """Load the curated per-disease Olink effect-size catalog. + + Reads the bundled + [`synthlab/data/olink_disease_effects.csv`](../data/olink_disease_effects.csv) + (or a user-supplied CSV with the same schema) and wraps it in a + :class:`DiseaseEffectCatalog`. Every bundled row cites a real DOI — + see the CSV for sourcing. + + Parameters + ---------- + path : str or pathlib.Path, optional + Override CSV path. If ``None`` (the default), the bundled catalog + is loaded via :mod:`importlib.resources` — this works inside + installed wheels without requiring any download. + + Returns + ------- + DiseaseEffectCatalog + Frozen dataclass wrapping the loaded polars frame. + + Raises + ------ + FileNotFoundError + If ``path`` is supplied but does not exist. + ValueError + Propagated from :class:`DiseaseEffectCatalog` if the CSV schema is + invalid. + + Examples + -------- + Default load (bundled CSV): + + >>> from synthlab import load_disease_effect_catalog + >>> cat = load_disease_effect_catalog() + >>> len(cat.diseases()) >= 6 + True + + User-supplied override: + + >>> # cat = load_disease_effect_catalog("my_custom_catalog.csv") + """ + if path is None: + # Ship-in-wheel path via importlib.resources — works regardless of + # install location (editable, wheel, zip-app). + src = resources.files("synthlab.data").joinpath("olink_disease_effects.csv") + with resources.as_file(src) as csv_path: + df = pl.read_csv(csv_path) + else: + p = Path(path).expanduser() + if not p.is_file(): + raise FileNotFoundError(f"Disease-effect catalog not found: {p}") + df = pl.read_csv(p) + return DiseaseEffectCatalog(effects=df) diff --git a/tests/test_olink.py b/tests/test_olink.py new file mode 100644 index 0000000..94e3025 --- /dev/null +++ b/tests/test_olink.py @@ -0,0 +1,301 @@ +"""Unit tests for :mod:`synthlab.olink`. + +Covers determinism, the group-effects model, LOD-driven missingness, +MCAR overlay, plate intercepts, QC warning rate, parquet round-trip, +and empty-frame edge cases. No network / filesystem heavy dependencies +— everything runs on in-memory polars DataFrames. +""" + +from __future__ import annotations + +import pytest + +pl = pytest.importorskip("polars") +np = pytest.importorskip("numpy") + +from synthlab.olink import ( + OlinkPanelConfig, + OlinkSimConfig, + default_explore_3072_panel, + load_olink_parquet, + simulate_olink_npx, + write_olink_parquet, +) + + +# --------------------------------------------------------------------------- +# Panel helpers +# --------------------------------------------------------------------------- + + +def _uniform_panel( + proteins: tuple[str, ...], + mean: float = 5.0, + sd: float = 0.6, + lod: float = 3.0, +) -> OlinkPanelConfig: + """Build a small OlinkPanelConfig with uniform priors across proteins.""" + return OlinkPanelConfig( + name="test", + proteins=proteins, + lod={p: lod for p in proteins}, + mean={p: mean for p in proteins}, + sd={p: sd for p in proteins}, + ) + + +# --------------------------------------------------------------------------- +# Core simulator tests +# --------------------------------------------------------------------------- + + +def test_simulate_is_deterministic_under_seed() -> None: + """Two calls with the same seed return bit-identical DataFrames.""" + panel = default_explore_3072_panel() + cfg = OlinkSimConfig(n_samples=20, panel=panel, seed=42) + df1 = simulate_olink_npx(cfg) + df2 = simulate_olink_npx(cfg) + assert df1.equals(df2) + + +def test_group_assignments_mean_shift() -> None: + """A +2.0 delta on CRP for "case" group moves the case NPX mean by ~2.0.""" + # Zero plate SD and no LOD loss so the mean is clean to compare. + panel = _uniform_panel(("CRP", "IL6"), mean=5.0, sd=0.5, lod=-1000.0) + assignments = ["case"] * 500 + ["control"] * 500 + cfg = OlinkSimConfig( + n_samples=1000, + panel=panel, + group_effects={"CRP": {"case": 2.0}}, + group_assignments=assignments, + missingness="none", + qc_warn_rate=0.0, + plate_effect_sd=0.0, + seed=1, + ) + df = simulate_olink_npx(cfg) + crp = df.filter(pl.col("protein_id") == "CRP") + case_mean = crp.filter(pl.col("group") == "case")["npx"].mean() + ctrl_mean = crp.filter(pl.col("group") == "control")["npx"].mean() + delta = case_mean - ctrl_mean + # With sd=0.5, n=500 per arm, SE of the difference ~ 0.5 * sqrt(2/500) ~ 0.032. + # Shift should be ~ 2.0 within 2 standard errors. + assert abs(delta - 2.0) < 0.1, f"expected ~2.0 shift; got {delta:.3f}" + + +def test_lod_missingness_kicks_in() -> None: + """With mean << lod, >60% of rows are dropped under mnar_lod.""" + # mean=1.0, lod=2.0, sd=0.5 → most values fall below LOD. + panel = _uniform_panel(("CRP", "IL6"), mean=1.0, sd=0.5, lod=2.0) + cfg = OlinkSimConfig( + n_samples=200, + panel=panel, + missingness="mnar_lod", + missing_rate=0.0, # isolate LOD effect + qc_warn_rate=0.0, + plate_effect_sd=0.0, + seed=7, + ) + df = simulate_olink_npx(cfg) + kept = df.height + total = 200 * 2 + drop_rate = 1 - kept / total + assert drop_rate > 0.6, ( + f"expected >60% LOD drop with mean << lod; got {drop_rate:.2f}" + ) + + +def test_mcar_missingness_adds_rate() -> None: + """With missing_rate=0.3 on top of LOD, observed drop exceeds 0.3.""" + panel = _uniform_panel(("CRP", "IL6"), mean=5.0, sd=0.3, lod=3.0) + cfg = OlinkSimConfig( + n_samples=300, + panel=panel, + missingness="mnar_lod", + missing_rate=0.3, + qc_warn_rate=0.0, + plate_effect_sd=0.0, + seed=11, + ) + df = simulate_olink_npx(cfg) + drop_rate = 1 - df.height / (300 * 2) + # LOD contributes a tiny bit since mean=5, lod=3 → ~0 sub-LOD; MCAR + # contributes ~0.3. Combined rate should exceed 0.3. + assert drop_rate > 0.3, f"expected drop > 0.3; got {drop_rate:.3f}" + + +def test_plate_effects_vary_by_plate() -> None: + """2 plates (192 samples) get different means at non-zero plate_effect_sd.""" + panel = _uniform_panel(("CRP",), mean=5.0, sd=0.2, lod=-1000.0) + cfg_off = OlinkSimConfig( + n_samples=192, + panel=panel, + missingness="none", + qc_warn_rate=0.0, + plate_effect_sd=0.0, + seed=3, + ) + df_off = simulate_olink_npx(cfg_off) + p0_off = df_off.filter(pl.col("plate_id") == "plate_0")["npx"].mean() + p1_off = df_off.filter(pl.col("plate_id") == "plate_1")["npx"].mean() + # With plate_effect_sd=0 the two plate means are independent samples + # from the same protein noise — their difference is small but may be + # nonzero from the Gaussian noise. We check that plates EXIST. + assert p0_off is not None and p1_off is not None + + # With plate_effect_sd=1.0 the plates typically differ by > 0.2 (most + # draws from |N(0,1) - N(0,1)| exceed 0.2 by a wide margin). + cfg_on = OlinkSimConfig( + n_samples=192, + panel=panel, + missingness="none", + qc_warn_rate=0.0, + plate_effect_sd=1.0, + seed=3, + ) + df_on = simulate_olink_npx(cfg_on) + p0_on = df_on.filter(pl.col("plate_id") == "plate_0")["npx"].mean() + p1_on = df_on.filter(pl.col("plate_id") == "plate_1")["npx"].mean() + assert abs(p0_on - p1_on) > abs(p0_off - p1_off), ( + "plate_effect_sd=1.0 should produce larger inter-plate spread " + f"than 0.0; got |{p0_on - p1_on}| vs |{p0_off - p1_off}|" + ) + + +def test_qc_warning_bernoulli() -> None: + """qc_warn_rate=0.1 yields ~10% qc_warning=True rows (within tolerance).""" + panel = _uniform_panel(("CRP", "IL6", "TNF"), mean=5.0, sd=0.2, lod=-1000.0) + cfg = OlinkSimConfig( + n_samples=500, + panel=panel, + missingness="none", + qc_warn_rate=0.1, + plate_effect_sd=0.0, + seed=13, + ) + df = simulate_olink_npx(cfg) + observed = df["qc_warning"].sum() / df.height + # With 1500 rows, expected SE ~ sqrt(0.1 * 0.9 / 1500) ~ 0.008. Allow 3 SE. + assert abs(observed - 0.1) < 0.03, ( + f"qc_warning rate out of tolerance: {observed:.4f}" + ) + + +def test_parquet_roundtrip(tmp_path) -> None: + """write then load returns a frame bit-equivalent to the source.""" + panel = default_explore_3072_panel() + cfg = OlinkSimConfig(n_samples=12, panel=panel, seed=5) + df = simulate_olink_npx(cfg) + out = write_olink_parquet(df, tmp_path / "npx.parquet") + assert out.is_file() + loaded = load_olink_parquet(out) + assert loaded.equals(df) + + +def test_schema_types() -> None: + """Column dtypes are (str, str, f64, bool, str, str) in that order.""" + panel = default_explore_3072_panel() + cfg = OlinkSimConfig(n_samples=3, panel=panel, seed=1) + df = simulate_olink_npx(cfg) + assert df.columns == [ + "sample_id", + "protein_id", + "npx", + "qc_warning", + "group", + "plate_id", + ] + assert df.dtypes == [pl.Utf8, pl.Utf8, pl.Float64, pl.Boolean, pl.Utf8, pl.Utf8] + + +def test_default_panel_sizes() -> None: + """Default panel has exactly 50 proteins and complete lod/mean/sd maps.""" + panel = default_explore_3072_panel() + assert panel.name == "explore_3072" + assert len(panel.proteins) == 50 + assert len(set(panel.proteins)) == 50 # unique + for p in panel.proteins: + assert p in panel.lod + assert p in panel.mean + assert p in panel.sd + + +def test_empty_samples() -> None: + """n_samples=0 returns an empty frame with the canonical schema.""" + panel = default_explore_3072_panel() + cfg = OlinkSimConfig(n_samples=0, panel=panel, seed=1) + df = simulate_olink_npx(cfg) + assert df.shape == (0, 6) + assert df.columns == [ + "sample_id", + "protein_id", + "npx", + "qc_warning", + "group", + "plate_id", + ] + + +# --------------------------------------------------------------------------- +# Config validation +# --------------------------------------------------------------------------- + + +def test_panel_rejects_duplicates() -> None: + """Duplicate protein IDs are rejected in __post_init__.""" + with pytest.raises(ValueError, match="unique"): + OlinkPanelConfig( + name="bad", + proteins=("CRP", "CRP"), + lod={"CRP": 3.0}, + mean={"CRP": 5.0}, + sd={"CRP": 0.6}, + ) + + +def test_panel_rejects_nonpositive_sd() -> None: + """sd <= 0 raises.""" + with pytest.raises(ValueError, match="sd"): + OlinkPanelConfig( + name="bad", + proteins=("CRP",), + lod={"CRP": 3.0}, + mean={"CRP": 5.0}, + sd={"CRP": 0.0}, + ) + + +def test_sim_config_rejects_bad_missingness() -> None: + """Unknown missingness mode is rejected.""" + panel = default_explore_3072_panel() + with pytest.raises(ValueError, match="missingness"): + OlinkSimConfig( + n_samples=10, + panel=panel, + missingness="bogus", # type: ignore[arg-type] + ) + + +def test_sim_config_rejects_mismatched_assignments() -> None: + """group_assignments length must match n_samples.""" + panel = default_explore_3072_panel() + with pytest.raises(ValueError, match="group_assignments"): + OlinkSimConfig( + n_samples=5, + panel=panel, + group_assignments=["case", "control"], + ) + + +def test_load_parquet_missing_file(tmp_path) -> None: + """load_olink_parquet raises FileNotFoundError on a missing path.""" + with pytest.raises(FileNotFoundError): + load_olink_parquet(tmp_path / "nope.parquet") + + +def test_load_parquet_bad_schema(tmp_path) -> None: + """A parquet without the expected columns triggers ValueError.""" + bad = tmp_path / "bad.parquet" + pl.DataFrame({"foo": [1, 2, 3]}).write_parquet(bad) + with pytest.raises(ValueError, match="missing expected columns"): + load_olink_parquet(bad) diff --git a/tests/test_olink_disease_catalog.py b/tests/test_olink_disease_catalog.py new file mode 100644 index 0000000..49e2a70 --- /dev/null +++ b/tests/test_olink_disease_catalog.py @@ -0,0 +1,279 @@ +"""Unit tests for the disease-effect catalog (:class:`synthlab.olink.DiseaseEffectCatalog`). + +Covers: + +- bundled CSV loads with the expected schema; +- minimum catalog coverage (>= 6 diseases); +- ``effects_for`` returns the protein -> disease -> delta mapping that + :func:`simulate_olink_npx` expects; +- unknown diseases raise ``KeyError``; +- ``noise_sd=0`` is deterministic; ``noise_sd>0`` perturbs the deltas; +- round-trip: loading the catalog, plugging it into + :func:`simulate_olink_npx`, and recovering the catalog's expected + mean NPX shift per disease within 2 SE of the empirical estimate. +""" + +from __future__ import annotations + +import pytest + +pl = pytest.importorskip("polars") +np = pytest.importorskip("numpy") + +from synthlab.olink import ( + DiseaseEffectCatalog, + OlinkPanelConfig, + OlinkSimConfig, + default_explore_3072_panel, + load_disease_effect_catalog, + simulate_olink_npx, +) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _panel_from_catalog(cat: DiseaseEffectCatalog) -> OlinkPanelConfig: + """Build a uniform panel that covers every protein in the catalog. + + Parameters + ---------- + cat : DiseaseEffectCatalog + Catalog to derive the protein set from. + + Returns + ------- + OlinkPanelConfig + Panel with ``mean=5.0``, ``sd=0.5``, ``lod=-1000`` (so that no + row is dropped by LOD missingness — we want clean mean-shift + recovery in the round-trip test). + """ + proteins = tuple(cat.effects["protein_uniprot"].unique().to_list()) + return OlinkPanelConfig( + name="test_catalog", + proteins=proteins, + mean={p: 5.0 for p in proteins}, + sd={p: 0.5 for p in proteins}, + lod={p: -1000.0 for p in proteins}, + ) + + +# --------------------------------------------------------------------------- +# Catalog shape + schema +# --------------------------------------------------------------------------- + + +def test_catalog_loads_with_expected_columns() -> None: + """The bundled catalog exposes the 7 required columns plus ``meta``.""" + cat = load_disease_effect_catalog() + required = { + "disease", + "protein_uniprot", + "delta_npx", + "se_delta", + "source", + "doi", + "evidence_strength", + } + assert required.issubset(set(cat.effects.columns)) + assert cat.effects.height >= 40, ( + f"catalog is unexpectedly small: {cat.effects.height} rows; " + "expected >= 40 per spec" + ) + + +def test_catalog_has_at_least_6_diseases() -> None: + """Minimum coverage spec: >= 6 distinct disease labels.""" + cat = load_disease_effect_catalog() + assert len(cat.diseases()) >= 6 + + +def test_catalog_dois_nonempty_and_real_shape() -> None: + """Every row must cite a DOI (no fabricated placeholders).""" + cat = load_disease_effect_catalog() + dois = cat.effects["doi"].to_list() + # Basic shape: "10./". This is the real ISO 26324 + # pattern — every legitimate DOI starts with "10.". + assert all(isinstance(d, str) and d.startswith("10.") and "/" in d for d in dois), ( + "found a row with a malformed / missing DOI" + ) + + +def test_catalog_delta_magnitudes_defensible() -> None: + """No row exceeds |delta_npx| = 3.0 — guards against typos / rogue units.""" + cat = load_disease_effect_catalog() + max_abs = float(cat.effects["delta_npx"].abs().max()) + assert max_abs <= 3.0, ( + f"at least one row has |delta_npx|={max_abs:.2f}, " + "outside the defensible NPX range (sepsis CRP peaks at +3 to +4)" + ) + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + + +def test_effects_for_returns_valid_dict_shape() -> None: + """``effects_for`` returns ``{protein: {disease: delta_npx}}``.""" + cat = load_disease_effect_catalog() + eff = cat.effects_for(["T2D", "CAD"]) + assert isinstance(eff, dict) + for prot, by_disease in eff.items(): + assert isinstance(prot, str) + assert isinstance(by_disease, dict) + for disease, delta in by_disease.items(): + assert disease in {"T2D", "CAD"} + assert isinstance(delta, float) + # Sanity check: CRP is in both T2D and CAD rows, so it should appear + # with BOTH disease keys in the merged mapping. + assert "T2D" in eff["CRP"] + assert "CAD" in eff["CRP"] + + +def test_effects_for_unknown_disease_raises_keyerror() -> None: + """Passing an unregistered disease label raises ``KeyError``.""" + cat = load_disease_effect_catalog() + with pytest.raises(KeyError, match="Unknown disease"): + cat.effects_for(["fake_disease_xyz"]) + + +def test_proteins_for_unknown_disease_raises_keyerror() -> None: + """``proteins_for`` also raises on unknown diseases.""" + cat = load_disease_effect_catalog() + with pytest.raises(KeyError, match="not in catalog"): + cat.proteins_for("not_a_real_disease") + + +def test_noise_sd_zero_is_deterministic() -> None: + """With ``noise_sd=0`` two calls return identical deltas.""" + cat = load_disease_effect_catalog() + a = cat.effects_for(["T2D"], noise_sd=0.0, seed=1) + b = cat.effects_for(["T2D"], noise_sd=0.0, seed=999) + assert a == b, "noise_sd=0 should be independent of seed" + + +def test_noise_sd_positive_changes_deltas() -> None: + """With ``noise_sd>0`` deltas are perturbed but preserve overall structure.""" + cat = load_disease_effect_catalog() + clean = cat.effects_for(["CAD"], noise_sd=0.0) + noisy = cat.effects_for(["CAD"], noise_sd=1.0, seed=42) + # Every protein in the clean mapping should also appear in the noisy one. + assert set(clean.keys()) == set(noisy.keys()) + # But at least one delta should differ (probability of all matching is + # vanishingly small when noise_sd=1.0 * se_delta > 0). + differences = [ + abs(clean[p]["CAD"] - noisy[p]["CAD"]) + for p in clean + ] + assert max(differences) > 1e-6, "noise_sd=1.0 should perturb at least one delta" + + +def test_noise_sd_reproducible_across_seeds() -> None: + """Same seed + same ``noise_sd`` -> identical outputs.""" + cat = load_disease_effect_catalog() + a = cat.effects_for(["T2D"], noise_sd=0.7, seed=3) + b = cat.effects_for(["T2D"], noise_sd=0.7, seed=3) + assert a == b + + +def test_noise_sd_negative_raises() -> None: + """Negative ``noise_sd`` is a type error.""" + cat = load_disease_effect_catalog() + with pytest.raises(ValueError, match="noise_sd"): + cat.effects_for(["T2D"], noise_sd=-0.1) + + +# --------------------------------------------------------------------------- +# End-to-end round-trip with simulate_olink_npx +# --------------------------------------------------------------------------- + + +def test_catalog_integrates_with_simulate_olink_npx() -> None: + """Load -> effects_for -> simulate -> recover expected per-disease shift. + + Draws 500 samples per group (T2D / CAD / baseline), runs the + simulator with ``missingness="none"`` and ``plate_effect_sd=0``, + then checks that the empirical case vs baseline mean shift matches + the catalog's ``delta_npx`` within 2 SE for each catalog row. + """ + cat = load_disease_effect_catalog() + panel = _panel_from_catalog(cat) + effects = cat.effects_for(["T2D", "CAD"]) + + n_per_group = 500 + assignments = ( + ["T2D"] * n_per_group + ["CAD"] * n_per_group + ["baseline"] * n_per_group + ) + cfg = OlinkSimConfig( + n_samples=len(assignments), + panel=panel, + group_effects=effects, + group_assignments=assignments, + missingness="none", + qc_warn_rate=0.0, + plate_effect_sd=0.0, + seed=123, + ) + df = simulate_olink_npx(cfg) + + # SE of the difference: sqrt(sd^2/n + sd^2/n) with sd=0.5, n=500 -> ~0.032. + se_per_arm = 0.5 / (n_per_group ** 0.5) + se_diff = se_per_arm * (2 ** 0.5) + tolerance = 3.0 * se_diff # ~0.095 + + # Loop over every catalog row present in the used-in-sim effects. + catalog_sub = cat.effects.filter(pl.col("disease").is_in(["T2D", "CAD"])) + for row in catalog_sub.iter_rows(named=True): + prot, disease, expected = ( + row["protein_uniprot"], + row["disease"], + float(row["delta_npx"]), + ) + if abs(expected) == 0.0: # skip null-hypothesis rows + continue + case_mean = ( + df.filter( + (pl.col("protein_id") == prot) & (pl.col("group") == disease) + )["npx"] + .mean() + ) + base_mean = ( + df.filter( + (pl.col("protein_id") == prot) & (pl.col("group") == "baseline") + )["npx"] + .mean() + ) + observed = float(case_mean - base_mean) + assert abs(observed - expected) < tolerance, ( + f"{disease}/{prot}: expected delta={expected:.3f} " + f"observed={observed:.3f} tol={tolerance:.3f}" + ) + + +def test_user_supplied_csv_override(tmp_path) -> None: + """A user-supplied CSV at a custom path is honoured over the bundle.""" + custom = tmp_path / "mini.csv" + custom.write_text( + "disease,protein_uniprot,delta_npx,se_delta,source,doi,evidence_strength\n" + "minimal_disease,CRP,1.23,0.1,unit_test,10.0/unit.test,weak\n" + ) + cat = load_disease_effect_catalog(custom) + assert cat.diseases() == ("minimal_disease",) + assert cat.effects_for(["minimal_disease"]) == {"CRP": {"minimal_disease": 1.23}} + + +def test_load_disease_effect_catalog_missing_file(tmp_path) -> None: + """A non-existent ``path`` raises ``FileNotFoundError``.""" + with pytest.raises(FileNotFoundError): + load_disease_effect_catalog(tmp_path / "does_not_exist.csv") + + +def test_catalog_rejects_bad_schema(tmp_path) -> None: + """CSV missing a required column raises ``ValueError``.""" + bad = tmp_path / "bad.csv" + bad.write_text("foo,bar\n1,2\n") + with pytest.raises(ValueError, match="missing required columns"): + load_disease_effect_catalog(bad)