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AntibodyMethodsDoseResponse

About

AntibodyMethodsDoseResponse.jl, AntibodyMethodsDoseResponseConvenience.jl and AntibodyMethodsDoseResponseRecipes.jl are Julia packages for the analysis of (antibody-binding) dose-response curves.

In essence, the dose-response data is modelled by the following accumulation model (see https://arxiv.org/abs/2407.06052 for further details and applications):

\[r(a) = \int_0^\infty g(K_\tau) \left(1- e^{-\frac{a}{K_\tau}} \right) \ dK_\tau\]

where $K_\tau \sim \frac{1}{k_{\text{on}}}$ is proportionally inverse to the binding rate $k_{\text{on}}$ and $g(K_\tau)$ is the $K_\tau$-density of epitopes. The packages also implement the Langmuir isotherm (equilibrium model). In either case, the analysis consists of a model fit, estimating the density $g(K_\tau)$ from measurement data.

Example block output

In this documentation, both the terms density plot and histogram will be used interchangeably to refer to the plot of the estimated density $g(K_\tau)$.

The different packages
PackageShort Description
AntibodyMethodsDoseResponseConvenience.jlConvenience package for the analysis of dose-response curves. Contains predefined analysis and plotting methods. Recommended for the analysis of dose-response data.
AntibodyMethodsDoseResponse.jlMinimal package defining the underlying models and methods for the analysis of dose-response curves. Intended for the development of analysis methods from scratch. Requires fewer dependencies.
AntibodyMethodsDoseResponseRecipes.jlPlots.jl recipes for AntibodyMethodsDoseResponse objects.

About the tutorials

  • The quick start guide

    • A minimal tutorial, covering only the necessary steps to analyze dose-response data without any in-depth explanations.
    • From here, the AnitbodyMethodsDoseResponseConvenience API is highly recommended to explore the additional options of the convenience functions that are not covered in the minimal tutorial.
    • The recommendation for most users.

  • The Detailed explanations

    • Covers the background and the internals of the packages.
    • Assumes (at some points) to be read in order from top to bottom.
    • The convenience functions (from the quick start guide) use the same data types and methods internally.
    • Intended for developers that need access to the internal methods and that want to take full control over the analysis.

Installation

First, add the registry Translational-Pain-ResearchRegistry:

using Pkg
Pkg.Registry.add()
Pkg.Registry.add(RegistrySpec(url = "https://github.com/Translational-Pain-Research/Translational-Pain-ResearchRegistry"))

Then, the packages can be installed as usual. E.g. AntibodyMethodsDoseResponseConvenience.jl, which installs all necessary dependencies for the analysis of dose-response curves:

using Pkg
Pkg.add("AntibodyMethodsDoseResponseConvenience")

How to cite the package

If you would like to cite this package for scientific purposes, you might also want to cite the corresponding paper https://arxiv.org/abs/2407.06052.