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

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

For ATAC-seq (optional):

# conda install -c bioconda macs2 homer

Quick Start

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])