{"id":51299695,"url":"https://github.com/eneskemalergin/questvar","last_synced_at":"2026-06-30T18:01:29.205Z","repository":{"id":358640103,"uuid":"1181127394","full_name":"eneskemalergin/QuEStVar","owner":"eneskemalergin","description":"QuEStVar as python package providing direct API/CLI access to hypothesting framework to expand statistical explainability via added equivalence 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markdownlint-disable MD033 MD036 MD041 MD045 --\u003e\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/eneskemalergin/QuEStVar/main/assets/questvar.jpg\" alt=\"QuEStVar\" width=\"270\" /\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003cstrong\u003eQuantitative Exploration of Stability and Variability through statistical hypothesis testing\u003c/strong\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://pypi.org/project/questvar/\"\u003e\u003cimg src=\"https://img.shields.io/badge/version-v0.1.0-0f766e?style=for-the-badge\" alt=\"v0.1.0\" /\u003e\u003c/a\u003e\n    \u003ca href=\"ref/statistical-testing-for-protein-equivalence-identifies-core-functional-modules-conserved-across-360-cancer-cell-lines.pdf\"\u003e\u003cimg src=\"https://img.shields.io/badge/paper-linked-7c3aed?style=for-the-badge\" alt=\"Paper\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://eneskemalergin.github.io/QuEStVar/\"\u003e\u003cimg src=\"https://img.shields.io/badge/docs-site-0891b2?style=for-the-badge\" alt=\"Documentation\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://github.com/eneskemalergin/QuEStVar/actions\"\u003e\u003cimg src=\"https://img.shields.io/github/actions/workflow/status/eneskemalergin/QuEStVar/ci.yml?branch=main\u0026style=for-the-badge\u0026logo=github\u0026label=CI\" alt=\"CI\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://www.python.org/\"\u003e\u003cimg src=\"https://img.shields.io/badge/python-3.12%20|%203.13%20|%203.14-f59e0b?style=for-the-badge\" alt=\"Python 3.12+\" /\u003e\u003c/a\u003e\n    \u003ca href=\"#requirements\"\u003e\u003cimg src=\"https://img.shields.io/badge/numpy-2.0%2B-2563eb?style=for-the-badge\u0026logo=numpy\u0026logoColor=white\" alt=\"NumPy 2.0+\" /\u003e\u003c/a\u003e\n    \u003ca href=\"#requirements\"\u003e\u003cimg src=\"https://img.shields.io/badge/scipy-1.10%2B-8b5cf6?style=for-the-badge\u0026logo=scipy\u0026logoColor=white\" alt=\"SciPy 1.10+\" /\u003e\u003c/a\u003e\n    \u003ca href=\"#requirements\"\u003e\u003cimg src=\"https://img.shields.io/badge/polars-1.0%2B-cd7c2f?style=for-the-badge\u0026logo=polars\u0026logoColor=white\" alt=\"Polars 1.0+\" /\u003e\u003c/a\u003e\n    \u003ca href=\"#requirements\"\u003e\u003cimg src=\"https://img.shields.io/badge/matplotlib-3.10%2B-003049?style=for-the-badge\u0026logo=python\u0026logoColor=white\" alt=\"Matplotlib 3.10+\" /\u003e\u003c/a\u003e\n    \u003ca href=\"#requirements\"\u003e\u003cimg src=\"https://img.shields.io/badge/pyyaml-6.0%2B-6b2800?style=for-the-badge\u0026logo=python\u0026logoColor=white\" alt=\"PyYAML 6.0+\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\nQuEStVar is a testing utility for paired equivalence and difference testing. It is packaged and easy to access built around the logic we published with biological application: [Statistical testing for protein equivalence identifies core functional modules conserved across 360 cancer cell lines](https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00131).\n\nThe point is simple. A non-significant difference test does not mean two groups are equivalent. QuEStVar adds an equivalence test so that case is explicit instead of implied.\n\n**v0.1.0 is a stable minimum working version.** The core workflow is complete and tested. Future releases will add multi-comparison analysis, expanded visualization, and deeper validation tools.\n\n## Features\n\n- Vectorized difference and equivalence testing across entire feature tables\n- CV quality filtering before statistical testing\n- Seven multiple testing correction methods: BH-FDR, Bonferroni, Holm, Hochberg, BY, q-value, or none\n- Antler plot combining TOST and t-test results\n- Eight-panel summary figure\n- Power analysis for experiment planning\n- CLI, Python API, and YAML config with identical results\n- CI-tested on Linux, macOS, and Windows\n\n## Installation\n\n```bash\npip install questvar[plot,yaml]\n```\n\nFor the core engine only (no plotting, no YAML config):\n\n```bash\npip install questvar\n```\n\n## Quick start\n\n```python\nimport polars as pl\nfrom questvar import QuestVar\n\ndf = pl.read_csv(\"data/demo_realistic.tsv\", separator=\"\\t\")\nqv = QuestVar(cv_thr=1.0, eq_thr=0.5, df_thr=1.0, p_thr=0.05, correction=\"fdr\")\nresults = qv.test(df, cond_1=[\"c1_0\",\"c1_1\",\"c1_2\"], cond_2=[\"c2_0\",\"c2_1\",\"c2_2\"])\nprint(results.summary())\n\n# Eight-panel summary figure\nresults.plot(cond_1_label=\"Control\", cond_2_label=\"Treatment\")\n```\n\n![Summary plot](https://raw.githubusercontent.com/eneskemalergin/QuEStVar/main/assets/summary_plot.png)\n\nThree statuses appear. Equivalent (+1) means the TOST test is significant and |log2FC| is below the equivalence boundary. Differential (-1) means the t-test is significant and |log2FC| is above the difference boundary. Not significant (0) means neither test was decisive. Excluded features failed the CV quality filter, some were testable but very high CV, and some were untestable due to missing values or zero variance (shows the stats in panel G.)\n\n## Documentation\n\nFull documentation, tutorials, and API reference are at the [documentation site](https://eneskemalergin.github.io/QuEStVar/).\n\n- [Quick start tutorial](https://eneskemalergin.github.io/QuEStVar/notebooks/quick_start/)\n- [Power analysis tutorial](https://eneskemalergin.github.io/QuEStVar/notebooks/power_analysis/)\n- [Configuration guide](https://eneskemalergin.github.io/QuEStVar/guides/configuration/)\n- [Plotting guide](https://eneskemalergin.github.io/QuEStVar/guides/plotting/)\n- [CLI reference](https://eneskemalergin.github.io/QuEStVar/guides/cli/)\n- [API reference](https://eneskemalergin.github.io/QuEStVar/api/questvar/)\n\n## CLI\n\n```bash\nquestvar test --data input.parquet --cond-1 A1,A2,A3 --cond-2 B1,B2,B3 --output results.parquet\nquestvar power --eq-boundaries 0.1,0.3,0.5 --n-reps-list 3,5,10 --output power_results.parquet\nquestvar plot --type antlers --results results.parquet --output antlers.png\n```\n\n## Roadmap\n\n- **Multi-comparison analysis**: metadata-driven pair generation, batch execution, per-pair and per-feature summaries.\n- **Visualization expansion**: stacked bar plots, RSM scatter, status heatmaps, agreement matrix for sophisticated summaries across multiple comparisons.\n- **Validation tools**: stability analysis, regression golden files, expanded hypothesis tests.\n- **Documentation and publishing**: mkdocstrings site, tutorial notebooks, PyPI release.\n\n## License\n\nMIT License. See [LICENSE](LICENSE) for details.\n\n---\n\n\u003cp align=\"center\"\u003e\n    \u003cem\u003eStatic cells in ice,\u003c/em\u003e\u003cbr /\u003e\n    \u003cem\u003eStrict bounds trace the quiet line,\u003c/em\u003e\u003cbr /\u003e\n    \u003cem\u003eWhere twin shapes agree.\u003c/em\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feneskemalergin%2Fquestvar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feneskemalergin%2Fquestvar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feneskemalergin%2Fquestvar/lists"}