sprime

sprime (S' or S prime) is a Python library for analyzing quantitative high-throughput screening (qHTS) data in preclinical drug discovery studies. The library provides tools for processing dose-response curves, fitting Hill equations, and calculating S' values--a single metric that summarizes a drug's dose-response profile relative to a cell line and assay.

Overview

sprime enables researchers to:

• Process qHTS data: Load and analyze dose-response data from screening campaigns
• Fit Hill curves: Automatically fit four-parameter logistic (4PL) models to dose-response data
• Calculate S' values: Compute S' (S prime) metrics that combine potency and efficacy into a single score
• Compare cell lines: Use delta S' (ΔS') to identify compounds with selective activity between reference and test cell lines
• Rank compounds: Systematically prioritize drug candidates based on their dose-response profiles

About S' (S Prime)

S' is a single value score that summarizes a drug's dose-response curve. The metric is calculated from Hill curve parameters using the formula:

S' = asinh((Zero_asymptote - Inf_asymptote) / EC50)

(If your CSV uses legacy headers Lower / Upper, map them to zero_asymptote / inf_asymptote respectively--the numerator is not (Inf - Zero).)

This is equivalent to (read as Asymptote with Zero / Inf subscript, and EC with 50 subscript):

$$S^{\prime} = \ln\left(\frac{\mathrm{Asymptote}_{\mathrm{Zero}} - \mathrm{Asymptote}_{\mathrm{Inf}}}{\mathrm{EC}_{50}} + \sqrt{\left(\frac{\mathrm{Asymptote}_{\mathrm{Zero}} - \mathrm{Asymptote}_{\mathrm{Inf}}}{\mathrm{EC}_{50}}\right)^2 + 1}\right)$$

In code and CSV those are Zero_asymptote, Inf_asymptote, and EC50 (same as the asinh line above). Legacy Lower / Upper map to the two asymptotes.

• Zero asymptote = Response as concentration -> 0 (baseline; left of curve)
• Inf asymptote = Response at saturating concentration (right of curve)
• EC50 = Half-maximal effect concentration

Sign is meaningful: for many inhibition / viability assays the fitted Zero asymptote is above Inf, so the numerator is positive and larger |S'| indicates a stronger combined potency/efficacy signal in that orientation. See Background and Concepts for detailed information.

Delta S' and Comparative Analysis

Delta S' enables quantitative comparison of drug responses between different cell lines within a single assay:

Delta S' = S'(reference cell line) - S'(test cell line)

This metric allows researchers to:

• Compare drug effects across cell lines
• Rank compounds by selectivity
• Prioritize drug candidates for further investigation

For detailed information and examples, see Delta S' for Comparative Analysis. For Path A / Path B, control ratio, and normalization (buried reference), see Background and Concepts — S' pipeline branches.

This library implements Generation 2 of the S' methodology, which evolved from the original S metric. See the Citation section below for references.

Installation

Requirements

• Python 3.8 or higher
• numpy >= 1.20.0, < 3.0
• scipy >= 1.7.0

Install from PyPI

pip install sprime

Install from Source

For development or to get the latest version:

# Clone the repository
git clone https://github.com/MoCoMakers/sprime.git
cd sprime

# Install in editable mode
pip install -e .

# Or install with development dependencies
pip install -e ".[dev]"

Development Setup

To set up a development environment:

git clone https://github.com/MoCoMakers/sprime.git
cd sprime
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -e ".[dev]"
pytest tests/
ruff check .              # lint from repo root (includes docs/); see Development guide
pre-commit install        # optional: rebuild + stage pdoc_html on commit when .py changes

See Development guide for full setup, pre-commit, API docs, and CI.

Quick Start

The basic workflow in sprime is: Load raw data from CSV -> Process (fit curves, calculate S') -> Analyze (e.g. delta S' for comparative analysis).

Jupyter / IPython: same workflow, runnable cells

Skip ahead to code if you like. In a clone of the repo, open demonstration.ipynb in Jupyter or VS Code (IPython kernel).

Three ways to load data:

Path	values_as	Your data	Required columns	What sprime does
A -- Raw (columns)	"columns"	DATA0..DATAN, CONC0..CONCN (one column per value)	Cell_Line, Compound_ID, Concentration_Units, Control_Response (unless skip_control_response_normalization=True)	Validates layout; in process, optionally ÷ Control_Response then response_normalization, then Hill fit → S'.
A -- Raw (list)	"list"	Responses, Concentrations (comma-separated in one cell each)	Same as columns path for raw	Same as columns path
B -- Pre-calculated	N/A	AC50, Upper, Lower (optional: Hill Slope, r2, S', etc.)	Cell_Line, Compound_ID	Uses imported Hill params; no vehicle ratio on precalc-only rows. Always recomputes S' from params (warns if S' was provided when raw refit overwrites).

Vehicle ratio and response_normalization (matches demonstration.ipynb)

Situation	÷ Control_Response at process?	response_normalization (after ratio, raw rows only)
Path A, skip_control_response_normalization=False (default)	Yes — test response ÷ vehicle readout, then pipeline step	Required on load: asymptote_normalized = ratio then max → 1 then ×100; response_scale = ratio then ×100 only.
Path A, skip_control_response_normalization=True	No — values already post-control	Still required on load; documents scale; no ratio re-applied.
Path B (rows with only precalc params)	N/A — no dose points to scale	Still required on load; unused unless the file also has raw curves.

Use Path A (columns) by default (values_as="columns"). Use Path A (list) with sp.load(..., values_as="list") when your data has Responses and Concentrations as comma-separated values. For list format, if the CSV is comma-delimited, quote those cells (e.g. "4000,300,2"). Any non-reserved columns in your CSV (e.g. MOA, assay_timescale) propagate forward by default into S' output and the master CSV export.

Path A -- Raw data

Templates: template_raw.csv (columns), template_raw_list.csv (list). See Required headings below.

The load call changes with format; process does not. You must pass values_as="columns" (default) or values_as="list" -- format is not auto-detected from headings.

Run from the directory containing the demo CSVs (e.g. docs/usage/), or use paths like docs/usage/demo_precontrol_normalized_s_prime.csv.

Path A import flags (see notebook Import-time choices):

• skip_control_response_normalization=False (default): each raw row needs a non-empty, non-zero Control_Response; process applies response ÷ Control_Response (then response_normalization).
• skip_control_response_normalization=True: responses are already on the post-control analysis scale (e.g. empty Control_Response on those rows); process does not re-divide; still pass response_normalization on load to document the scale.

Demo families:

• demo_precontrol_normalized_*: upstream already applied control + reference scaling. Use skip_control_response_normalization=True, response_normalization="asymptote_normalized" (or response_scale if that matches your sheet).
• demo_raw_vehicle_control_*: raw % nucleus + Control_Response = 35.3 (DMSO row in tests/fixtures/SPrime_variation_reference.csv). Use defaults for skip and set response_normalization to match the lab (Normalized vs Nonnormalized x100).

Option 1 -- Columns (DATA0..N, CONC0..N), pre-control-normalized demo

from sprime import SPrime as sp

# Download (columns), then save locally:
# https://raw.githubusercontent.com/MoCoMakers/sprime/refs/heads/main/docs/usage/demo_precontrol_normalized_s_prime.csv
# Demo files may include both raw DATA/CONC and pre-calc params; refit from raw with allow_overwrite_precalc_params=True.

raw_data, _ = sp.load(
    "demo_precontrol_normalized_s_prime.csv",
    values_as="columns",
    skip_control_response_normalization=True,
    response_normalization="asymptote_normalized",
)
screening_data, _ = sp.process(raw_data, allow_overwrite_precalc_params=True)
screening_data.export_to_csv("master_s_prime_table.csv")
results = screening_data.to_dict_list()
for profile in results:
    print(f"{profile['compound_name']} vs {profile['cell_line']}: S' = {profile['s_prime']:.2f}")

Raw + vehicle (DMSO ratio in process) — same idea as Route 1 in demonstration.ipynb; try demo_raw_vehicle_control_s_prime.csv (columns) or demo_raw_vehicle_control_raw_list.csv (list).

from sprime import SPrime as sp

raw_data, _ = sp.load(
    "demo_raw_vehicle_control_s_prime.csv",
    values_as="columns",
    # skip_control_response_normalization=False is default
    response_normalization="asymptote_normalized",
)
screening_data, _ = sp.process(raw_data)

Option 2 -- List (quoted comma-separated Responses / Concentrations)

from sprime import SPrime as sp

# https://raw.githubusercontent.com/MoCoMakers/sprime/refs/heads/main/docs/usage/demo_precontrol_normalized_raw_list.csv

raw_data, _ = sp.load(
    "demo_precontrol_normalized_raw_list.csv",
    values_as="list",
    skip_control_response_normalization=True,
    response_normalization="asymptote_normalized",
)
screening_data, _ = sp.process(raw_data, allow_overwrite_precalc_params=True)
screening_data.export_to_csv("master_s_prime_table.csv")
results = screening_data.to_dict_list()
for profile in results:
    print(f"{profile['compound_name']} vs {profile['cell_line']}: S' = {profile['s_prime']:.2f}")

Path B -- Pre-calculated

Sample file: demo_precontrol_normalized_precalc.csv
Template: template_precalc.csv

from sprime import SPrime as sp

# Download, then save locally:
# https://raw.githubusercontent.com/MoCoMakers/sprime/refs/heads/main/docs/usage/demo_precontrol_normalized_precalc.csv

raw_data, _ = sp.load(
    "demo_precontrol_normalized_precalc.csv",
    response_normalization="asymptote_normalized",  # required on every load; no effect if no raw rows
)
screening_data, _ = sp.process(raw_data)
screening_data.export_to_csv("master_s_prime_table.csv")
results = screening_data.to_dict_list()
for profile in results:
    print(f"{profile['compound_name']} vs {profile['cell_line']}: S' = {profile['s_prime']:.2f}")

Delta S' example

Compare drug responses between reference and test cell lines (e.g. non-tumor vs tumor). Delta S' = S'(reference) - S'(test); more negative = more effective in test tissue.

Demo CSV (two cell lines, pre-control-normalized): demo_precontrol_normalized_delta.csv
Demo CSV (raw % nucleus + DMSO 35.3): demo_raw_vehicle_control_delta.csv

from sprime import SPrime as sp, ScreeningDataset

# Download, then save locally:
# https://raw.githubusercontent.com/MoCoMakers/sprime/refs/heads/main/docs/usage/demo_precontrol_normalized_delta.csv

raw_data, _ = sp.load(
    "demo_precontrol_normalized_delta.csv",
    values_as="columns",
    skip_control_response_normalization=True,  # pre-control-normalized rows; empty Control_Response cells
    response_normalization="asymptote_normalized",
)
screening_data, _ = sp.process(raw_data, allow_overwrite_precalc_params=True)
screening_data.export_to_csv("master_s_prime_table.csv")

delta_results = screening_data.calculate_delta_s_prime(
    reference_cell_lines=["ipnf05.5 mc"],   # e.g. non-tumor; list supports multiple
    test_cell_lines=["ipNF96.11C"]          # e.g. tumor; list supports multiple
)
# Opt-in: add compound-level columns (1:1 in ref & test) to delta output and export:
#   headings_one_to_one_in_ref_and_test=["assay_timescale"],  # etc.
#   source_profile="test",   # "ref" or "test" -- which profile to use for those values
# Pass the same list to export_delta_s_prime_to_csv(..., headings_one_to_one_in_ref_and_test=[...])
ScreeningDataset.export_delta_s_prime_to_csv(delta_results, "delta_s_prime_table.csv")

for ref_cl, comparisons in delta_results.items():
    for c in comparisons:
        print(f"{c['compound_name']}: Delta S' = {c['delta_s_prime']:.2f}")

Required headings

• All paths: Cell_Line; and Compound_ID. (NCGCID is optional pass-through per compound.)
• Path A (raw): Concentration_Units (required). Either (columns) DATA0..DATA N, CONC0..CONC N (same N), or (list) Responses and Concentrations (comma-separated values in one cell each). Use values_as="columns" (default) or values_as="list". See Supported concentration units below.
• Path B (pre-calc): AC50 (or ec50), Upper (or Infinity), Lower (or Zero). Optional: Hill_Slope, r2, S'.

Template files list the exact headers (required columns first); your CSV should match those. Column order in the file does not matter--matching is by header name only.

Supported concentration units

For Path A (raw), Concentration_Units must be present. All values are converted to microM internally. Supported units (case-insensitive), smallest to largest: fM (fm, femtom); pM (pm, picom); nM (nm, nanom); microM (uM, um, microm, micro); mM (mm, millim); M (m, mol).

Next steps

• Terms and pipeline names: Terminology reference
• Format details: Basic Usage Guide
• Hands-on examples: clone the repo (or copy docs/usage/ from GitHub) and open demonstration.ipynb beside the demo_*.csv files (demos are not shipped inside the PyPI wheel)
• Individual profiles & pre-calculated parameters: Basic Usage Guide (Processing Individual Profiles, Creating dose-response profiles from scratch, Working with Pre-calculated Hill Parameters)

Key Features

• Automatic Curve Fitting: Fits four-parameter logistic (Hill) curves to dose-response data
• Flexible Input: Supports raw dose-response data or pre-calculated Hill parameters. When both exist, use allow_overwrite_precalc_params=True to refit from raw; overwrites are logged as warnings.
• CSV Loading: Handles common screening data formats; rows are literal (empty = null, no forward-filling)
• Delta S' Analysis: Compare drug responses across cell lines within a single assay
• CSV Export: Built-in methods to export results and delta S' comparisons to CSV
• In-Memory Processing: Process data directly from list of dictionaries without CSV files
• Metadata Support: Extracts and preserves metadata (MOA, drug targets) from CSV files
• Type-Safe: Uses Python dataclasses and type hints throughout
• Comprehensive Documentation: Includes guides for users and detailed technical documentation

Documentation

Getting Started

• API Reference - API documentation (generated from docstrings)
• Basic Usage Guide - Step-by-step guide: formats, load/process, delta S', exports, testing
• Terminology reference - Plain-language definitions for screening jargon and sprime-specific pipeline names (Control_Response, asymptote-normalized vs response-scale, etc.)

Core Concepts

• Background and Concepts - Introduction to qHTS, assays, S' metric, and key terminology (see also the terminology reference for a compact glossary)
• Understanding 4PL Dose-Response Curves - Detailed explanation of the Hill equation model

Technical Documentation

• Hill Curve Fitting Configuration - Technical details on curve fitting parameters and configuration options
• Development guide - Dev setup, tests, pre-commit, API docs, CI (for contributors)

Examples

• demonstration.ipynb -- Jupyter walkthrough (load/process, ΔS′, optional CSV export); run from a clone with docs/usage and demo CSVs available (not inside pip install alone).

Requirements

Runtime Dependencies

• numpy >= 1.20.0, < 3.0 - Numerical computing (compatible with numpy 1.x and 2.x)
• scipy >= 1.7.0 - Scientific computing and curve fitting

Development Dependencies

Development dependencies are optional and only needed if you plan to contribute to sprime or run the test suite:

• pytest >= 7.0.0 - Testing framework for running the comprehensive test suite

  • Used to execute unit tests, integration tests, and edge case tests
  • Provides test discovery, fixtures, and assertion utilities
  • Required for: pytest tests/ commands

• pytest-cov >= 4.0.0 - Coverage reporting plugin for pytest

  • Generates code coverage reports to identify untested code
  • Used with: pytest --cov=src/sprime --cov-report=html
  • Helps ensure comprehensive test coverage during development

• pdoc3 - Generates API docs from docstrings into pdoc_html/

• ruff >= 0.3.0 - Lint and format (ruff check ., ruff format . from repo root—includes docs/ e.g. notebooks); configuration in pyproject.toml

• pre-commit - Optional Git hooks: runs ruff and rebuilds pdoc_html when Python files change (see .pre-commit-config.yaml)

The full [dev] set is defined in pyproject.toml ([project.optional-dependencies]).

Install development dependencies:

pip install -e ".[dev]"

These dependencies are not required for normal usage of sprime - only for development and testing.

Testing

sprime includes a comprehensive test suite. To run tests:

# Install development dependencies
pip install -e ".[dev]"

# Run all tests
pytest tests/

# Run with coverage
pytest tests/ --cov=src/sprime --cov-report=html

# Run specific test files
pytest tests/test_sprime.py
pytest tests/test_hill_fitting.py
pytest tests/test_integration.py

See the Basic Usage Guide for more testing information.

API docs (pdoc_html)

API docs live in pdoc_html/ in the repo and are built from docstrings. The API Reference on GitHub Pages is deployed from CI. Build locally: python -m pdoc --html --output-dir pdoc_html --force sprime. Optional: pre-commit install rebuilds and stages pdoc_html on commit when .py files change. See Development guide for details.

Project Status

This project is currently in active development (Alpha status). Features and API may change.

Current Version

The package uses semantic versioning. Version is derived from Git tags (via hatch-vcs); src/sprime/_version.py is generated at build/install time and is not committed. Check the installed version:

import sprime
print(sprime.__version__)

Tagging releases: Create a Git tag (e.g. v0.1.0) and push it. The version used in built packages and on PyPI comes from that tag.

git tag v0.1.0
git push origin v0.1.0

PyPI project description is built from README-PyPI.md (generated from this file so math displays as plain text on PyPI). After changing README.md, run python scripts/sync_readme_pypi.py and commit README-PyPI.md. See Development guide.

Contributing

Contributions are welcome! We appreciate your help in making sprime better.

How to Contribute

1. Fork the repository on GitHub
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Set up dev environment -- venv, pip install -e ".[dev]", pytest tests/, optionally pre-commit install. See Development guide.
4. Make your changes and add tests if applicable
5. Run the test suite -- pytest tests/
6. Commit your changes (git commit -m 'Add amazing feature'). If you use pre-commit, hooks may rebuild pdoc_html (when .py changes) and enforce README-PyPI.md sync when README.md changes.
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Development Guidelines

• Use Ruff as in pyproject.toml -- see Development guide; run ruff check . before commit so it matches what pre-commit can flag (including under docs/)
• Add tests for new features
• Update documentation as needed
• Ensure all tests pass before submitting

Reporting Issues

If you find a bug or have a feature request, please open an issue on GitHub:

• Issue Tracker

Questions

For questions or support, please reach out via:

• MoCo Makers Contact
• Open a GitHub Discussion

Repository

• GitHub: https://github.com/MoCoMakers/sprime
• Issues: https://github.com/MoCoMakers/sprime/issues

Citation

If you use sprime in your research, please cite:

Generation 2 (S') Methodology

New citations introducing Generation 2 (S') are forthcoming. Please check back for updated citation information.

Original S Metric

This library implements Generation 2 of the S' methodology, which evolved from the original S metric described in:

Zamora PO, Altay G, Santamaria U, Dwarshuis N, Donthi H, Moon CI, Bakalar D, Zamora M. Drug Responses in Plexiform Neurofibroma Type I (PNF1) Cell Lines Using High-Throughput Data and Combined Effectiveness and Potency. Cancers (Basel). 2023 Dec 12;15(24):5811. doi: 10.3390/cancers15245811. PMID: 38136356; PMCID: PMC10742026.

Hill Curve Fitting Implementation

The Hill curve fitting implementation in sprime is inspired by the four parameter logistic regression work by Giuseppe Cardillo:

Giuseppe Cardillo (2025). Four parameters logistic regression - There and back again (https://github.com/dnafinder/logistic4), GitHub. Retrieved December 24, 2025

Acknowledgments

This library was developed by the Computational Biology Working Group at DMV Petri Dish with support from the nonprofit DMV Petri Dish.

We're also part of the DIY tech/science community at MoCo Makers.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (C) 2026 MoCo Maker Labs LLC

Support

• Documentation: Usage docs (guides, demos) * Background / theory (derivation, 4PL, development). For vocabulary, see Terminology reference. For S' pipeline branches (Path A/B, controls, normalization), see Background and Concepts and S' derivation pipeline Sec.3.4.
• Issues: Report bugs or request features on GitHub Issues
• Contact: Reach out via MoCo Makers Contact

Related Projects

• DMV Petri Dish Computational Biology - Computational biology research group
• MoCo Makers - DIY tech/science community