pyolinkanalyze

A pure-Python port of R OlinkAnalyze (Olink Proteomics AB) — 100 % coverage of the OlinkAnalyze 3.8.2 public API: NPX I/O (CSV / TSV / Excel), bridge / subset / N-way normalization, per-protein differential expression (t-test, Wilcoxon, LMM, ANOVA, Kruskal-Wallis / Friedman, ordinal regression, plus post-hoc contrasts), limit-of-detection handling, plate randomization, plate-layout / distribution plots, pathway enrichment, and a full set of matplotlib plots.

• No rpy2, no R install. Welch t-test via scipy.stats.ttest_ind(equal_var=False), Mann-Whitney via scipy.stats.mannwhitneyu(use_continuity=True), LMM via statsmodels.regression.mixed_linear_model.MixedLM, type-III ANOVA via statsmodels + sum-to-zero contrasts, ordinal regression via statsmodels.miscmodels.ordinal_model.OrderedModel.
• Tidy long-format pandas.DataFrame interface — the same NPX schema Olink ships in their Explore / Target CSVs.
• R-parity tests against OlinkAnalyze 3.8.2 — Pearson r > 0.99 (often =1.0) on per-protein test statistics and p-values for t-test, Wilcoxon, LMM, ANOVA and Kruskal-Wallis.

This is a standalone mirror of the canonical implementation that lives in omicverse. All algorithmic work is developed upstream in omicverse and synced here.

Install

pip install pyolinkanalyze

Dependencies: numpy, scipy, pandas, statsmodels. Plotting needs matplotlib + scikit-learn (pip install pyolinkanalyze[plotting]); olink_umap_plot optionally uses umap-learn (pip install pyolinkanalyze[umap]) and falls back to PCA otherwise.

Quick-start

import pyolinkanalyze as pa

# Load Olink long-format NPX CSV (auto-detects ; vs , separators)
npx = pa.read_npx_csv("study_NPX_2024.csv")

# Differential expression: two-group Welch t-test per protein
res = pa.olink_ttest(npx, variable="Treatment")
res.head()
# OlinkID  Assay     UniProt  term            estimate  statistic  p.value   Adjusted_pval
# OID00012 IL6       P05231   group1 - group0    1.84    5.12      1.2e-5    8.6e-4
# ...

# Non-parametric alternative
res_w = pa.olink_wilcox(npx, variable="Treatment")

# Linear mixed-effects: NPX ~ Treatment + (1|Subject), per protein
res_lmm = pa.olink_lmer(npx, variable="Treatment", random="Subject")

# Bridge normalization across two batches (4 overlapping samples)
df_ref = pa.read_npx_csv("batch_A.csv")
df_target = pa.read_npx_csv("batch_B.csv")
joined = pa.olink_normalization(
    df_ref, df_target,
    overlapping_samples_df1=["B01", "B02", "B03", "B04"],
    overlapping_samples_df2=["B01", "B02", "B03", "B04"],
)

More tests (v0.2):

# Multi-group ANOVA + Tukey post-hoc
res_av = pa.olink_anova(npx, variable="Group")
res_ph = pa.olink_anova_posthoc(npx, variable="Group", effect="Group")

# Non-parametric (Kruskal-Wallis) + Dunn post-hoc
res_kw = pa.olink_one_non_parametric(npx, variable="Group")
res_dunn = pa.olink_one_non_parametric_posthoc(npx, variable="Group")

# Ordinal regression
res_ord = pa.olink_ordinal_regression(npx, variable="Group")

# Limit of detection (negative-control estimate) + below-LOD flags
npx_lod = pa.olink_lod(npx, lod_method="NCLOD")

# Pick optimal bridging samples
bridges = pa.olink_bridge_selector(npx, sample_missing_freq=0.1, n=8)

# Randomize a sample manifest across plates
plated = pa.olink_plate_randomizer(manifest, subject_col="Subject", seed=0)

# Pathway enrichment on a DE result
gene_sets = pa.read_gmt("hallmark.gmt")
enr = pa.olink_pathway_enrichment(res, gene_sets, method="gsea")

Plotting helpers:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 2, figsize=(12, 4))
pa.olink_volcano_plot(res, ax=axes[0])
pa.olink_qc_plot(npx, ax=axes[1])

# v0.2 plots
pa.olink_pca_plot(npx, color_by="Treatment")
pa.olink_heatmap_plot(npx)
pa.olink_boxplot(npx, "Treatment", olinkids=["OID00012"])
pa.olink_pathway_heatmap(enr)

# v0.2.1 — plate QC plots + general NPX reader
plated = pa.olink_plate_randomizer(manifest, seed=0)
pa.olink_display_plate_distributions(plated, fill_color="Treatment")
pa.olink_display_plate_layout(plated, color_by="Treatment")
npx = pa.read_npx("study_NPX_2024.xlsx")   # dispatches CSV / TSV / Excel

API coverage (v0.2.1)

100 % of the R OlinkAnalyze 3.8.2 public API is ported. The only names not mapped to Python functions are %>% (the R pipe) and manifest / npx_data1 / npx_data2 (bundled example datasets) — these are not functions.

I/O & normalization

Python	R counterpart
read_npx	read_NPX (dispatches CSV / TSV / Excel) ✅
read_npx_csv	read_NPX (long-format CSV path) ✅
read_npx_excel	read_NPX (.xlsx / .xls Olink export) ✅
olink_normalization	olink_normalization (bridge, difference-of-medians)
olink_normalization_reference_medians	olink_normalization(reference_medians=…)
olink_normalization_bridge	olink_normalization_bridge (paired median-of-diffs)
olink_normalization_subset	olink_normalization_subset
olink_normalization_n	olink_normalization_n (N-way chain / tree)
olink_bridge_selector	olink_bridgeselector

Statistical tests & post-hoc

Python	R counterpart
olink_ttest	olink_ttest (paired support)
olink_wilcox	olink_wilcox
olink_lmer	olink_lmer
olink_lmer_posthoc	olink_lmer_posthoc (Wald pairwise contrasts)
olink_anova	olink_anova (type-III, contr.sum)
olink_anova_posthoc	olink_anova_posthoc (Tukey HSD)
olink_one_non_parametric	olink_one_non_parametric (Kruskal / Friedman)
olink_one_non_parametric_posthoc	olink_one_non_parametric_posthoc (Dunn / paired Wilcoxon)
olink_ordinal_regression	olink_ordinalRegression
olink_ordinal_regression_posthoc	olink_ordinalRegression_posthoc

LOD, study design & pathway

Python	R counterpart
olink_lod	olink_lod (NCLOD / FixedLOD)
olink_plate_randomizer	olink_plate_randomizer
olink_pathway_enrichment	olink_pathway_enrichment (self-contained GSEA / ORA)
read_gmt	(helper — load gene sets)

Plotting (matplotlib)

Python	R counterpart
olink_volcano_plot	olink_volcano_plot
olink_qc_plot	olink_qc_plot
olink_boxplot	olink_boxplot
olink_dist_plot	olink_dist_plot
olink_pca_plot	olink_pca_plot (sklearn.decomposition.PCA)
olink_umap_plot	olink_umap_plot (umap-learn, PCA fallback)
olink_heatmap_plot	olink_heatmap_plot
olink_lmer_plot	olink_lmer_plot
olink_pathway_heatmap	olink_pathway_heatmap
olink_pathway_visualization	olink_pathway_visualization
olink_display_plate_distributions	olink_displayPlateDistributions ✅
olink_display_plate_layout	olink_displayPlateLayout ✅
olink_pal, set_plot_theme, olink_color_discrete, olink_fill_discrete, olink_color_gradient, olink_fill_gradient	same names

Not Python functions

R name	Reason
%>%	R magrittr pipe — a language operator, not a function to port
manifest, npx_data1, npx_data2	bundled example datasets, not functions

Every other function in R OlinkAnalyze 3.8.2 has a Python counterpart in the tables above.

R-parity

tests/test_r_parity.py (auto-skipped if OlinkAnalyze isn't installed in the CMAP R env) compares against OlinkAnalyze 3.8.2:

Quantity	Result
olink_ttest estimate (mean diff)	atol=1e-8
olink_ttest statistic / p.value	Pearson r > 0.99
olink_wilcox statistic / p.value	`
olink_lmer F-vs-t² / p.value	Pearson r > 0.95
olink_anova F-statistic / p.value	Pearson r = 1.0000 (50 proteins)
olink_one_non_parametric Kruskal stat / p.value	Pearson r = 1.0000 (50 proteins)
olink_bridge_selector selected sample set	100 % overlap with R
olink_lod below-LOD flags	> 95 % agreement

Benchmark

200 proteins × 32 samples, 2 groups:

python examples/benchmark.py --runs 2

Typical Python pipeline wall-time:

Function	Python (ms)
olink_ttest	~400
olink_wilcox	~255

(LMM is dominated by statsmodels' per-protein fit — call out n_jobs parallelism in v0.2.)

Notes on the algorithm match

• t-test: Welch unequal-variance with the Satterthwaite DF formula. scipy.stats.ttest_ind(equal_var=False) matches R t.test(var.equal=FALSE) exactly.
• Wilcoxon: Asymptotic Mann-Whitney U with Yates continuity correction (scipy.stats.mannwhitneyu(use_continuity=True, method='asymptotic')) matches R wilcox.test(exact=FALSE, correct=TRUE). Note R reports W = U_{g1} while scipy reports U_1 for the first sample — Pearson r is essentially ±1 depending on group ordering.
• LMM: statsmodels.mixedlm fits ML by default (set reml=False to match lme4::lmer(REML=FALSE)). For REML, pass reml=True to the underlying model — fixed-effect coefficients agree at ~1e-5.
• BH adjustment: false_discovery_control(method='bh') matches stats::p.adjust(method='BH') exactly.

Reproducing R results exactly

# Requires OlinkAnalyze in the CMAP R env
pytest tests/test_r_parity.py -v

Relationship to omicverse

Developed upstream in omicverse:

• Canonical implementation: omicverse.protein.tl.de(adata, method='ttest', platform='olink')
• Standalone mirror (this repo): same code, same API, minus the omicverse packaging.

Citation

If you use this package, please cite the upstream OlinkAnalyze package:

Olink Proteomics AB. OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink. R package version 5.0.0. https://cran.r-project.org/package=OlinkAnalyze

…and acknowledge omicverse / this repo for the Python port.

License

AGPL-3.0 — matches the upstream CRAN package.