# Immunology Assays

Computational analysis of immunology experimental data.

- Repository: https://github.com/synthetic-sciences/openscience (4321 stars)
- Category: Design
- License: MIT license
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add synthetic-sciences/openscience --skill immunology-assays -g -a claude-code -y`
Codex: `npx skills add synthetic-sciences/openscience --skill immunology-assays -g -a codex -y`
Cursor: `npx skills add synthetic-sciences/openscience --skill immunology-assays -g -a cursor -y`
Gemini CLI: `npx skills add synthetic-sciences/openscience --skill immunology-assays -g -a gemini-cli -y`

## Description

Computational analysis of immunology experimental data. ATAC-seq differential accessibility, immune cell tracking from microscopy, ELISA data processing with 4-parameter logistic fitting, immunohistochemistry quantification, antibody titer analysis, and cell cycle phase duration estimation. For flow cytometry use flow-cytometry-analysis; for scRNA-seq use scanpy.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Immunology Assays: Experimental Data Analysis

## Overview

Immunology Assays provides computational tools for analyzing data from common immunology experiments. This skill covers ATAC-seq differential accessibility analysis (MACS2 peak calling, motif enrichment), ELISA data processing with 4-parameter logistic standard curve fitting, immune cell tracking from time-lapse microscopy, immunohistochemistry (IHC) quantification with H-score calculation, antibody titer determination from serial dilution ELISA, cell cycle phase duration estimation from dual-nucleoside labeling, and multiplex cytokine assay data processing.

## When to Use This Skill

- Processing ELISA plate data with standard curve fitting (4PL)
- Quantifying IHC staining intensity (H-score, positive pixel percentage)
- Analyzing ATAC-seq peaks and differential chromatin accessibility
- Tracking immune cell migration from microscopy time-lapse data
- Determining antibody titers from serial dilution experiments
- Estimating cell cycle phase durations from pulse-labeling data
- Processing multiplex cytokine/chemokine assay data (Luminex, MSD)

**Related Skills:** For flow cytometry analysis use `flow-cytometry-analysis`. For single-cell RNA-seq use `scanpy`. For bioimage analysis use `bioimage-analysis`.

## Installation

```bash
uv pip install scipy scikit-image opencv-python numpy pandas matplotlib
```

For ATAC-seq (optional):
```bash
# conda install -c bioconda macs2 homer
```

## Quick Start

```python
import numpy as np
from scipy.optimize import curve_fit

# 4-Parameter Logistic (4PL) for ELISA standard curve
def four_pl(x, a, b, c, d):
    """a=min, b=Hill slope, c=EC50, d=max"""
    return d + (a - d) / (1 + (x / c) ** b)

# Standard curve data
concentrations = np.array([0, 15.6, 31.25, 62.5, 125, 250, 500, 1000])
od_values = np.array([0.05, 0.12, 0.22, 0.45, 0.82, 1.35, 1.85, 2.15])

The rest: https://github.com/synthetic-sciences/openscience/blob/HEAD/backend/cli/skills/biology/immunology-assays/SKILL.md