example_positions

HI titers of Influenza antigens and antisera published in Smith et al., 2004 were
used to find the antigenic relationships and coordinates of the antigens.
It can be used for mapping. The data captures how different influenza virus strains
(antigens) react with antisera from infected individuals.

datasets

An implementation of the TopoLow algorithm, a novel, physics-inspired method for
antigenic cartography. TopoLow addresses significant challenges in mapping antigenic relationships,
especially from sparse and noisy experimental data. The package transforms cross-reactivity and
binding affinity measurements into accurate spatial representations in a phenotype space.
Key features include:
* Robust Mapping from Sparse Data: Effectively creates complete and consistent maps even with high
proportions of missing data (e.g., >95%).
* Physics-Inspired Optimization: Models antigens as particles connected by springs (for measured
interactions) and subject to repulsive forces (for missing interactions), reducing the need for
complex gradient computations.
* Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal
number of dimensions for the antigenic map, avoiding distortions common in methods with fixed low dimensions.
* Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based,
error-dampening mechanism.
* Antigenic Velocity Calculation: Introduces and quantifies "antigenic velocity," a vector that describes
the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster
transitions and potential lineage replacements.
* Broad Applicability: Analyzes data from various pathogens, including influenza, HIV, and Dengue viruses.
It can be applied to any continuous and relational phenotype under directional selection pressure.
Methods are described in Arhami and Rohani (2025) <doi:10.1093/bioinformatics/btaf372>.

Omid Arhami

topolow

Antigenic Mapping and Antigenic Velocity Algorithm

example_positions function

A data frame with 285 rows and 11 variables:<dl>
<dt>V1</dt>
<dd>First dimension coordinate from 5D mapping</dd><dt>V2</dt>
<dd>Second dimension coordinate from 5D mapping</dd><dt>V3</dt>
<dd>Third dimension coordinate from 5D mapping</dd><dt>V4</dt>
<dd>Fourth dimension coordinate from 5D mapping</dd><dt>V5</dt>
<dd>Fifth dimension coordinate from 5D mapping</dd><dt>name</dt>
<dd>Strain identifier</dd><dt>antigen</dt>
<dd>Logical; TRUE if point represents an antigen</dd><dt>antiserum</dt>
<dd>Logical; TRUE if point represents an antiserum</dd><dt>cluster</dt>
<dd>Factor indicating antigenic cluster assignment (A/H3N2 1968-2003)</dd><dt>color</dt>
<dd>Color assignment for visualization</dd><dt>year</dt>
<dd>Year of strain isolation</dd>
</dl>

Format

Example Antigenic Mapping Data — example_positions

A data frame with 285 rows and 11 variables:<dl>
<dt>V1</dt>
<dd>First dimension coordinate from 5D mapping</dd>

<dt>V2</dt>
<dd>Second dimension coordinate from 5D mapping</dd>

<dt>V3</dt>
<dd>Third dimension coordinate from 5D mapping</dd>

<dt>V4</dt>
<dd>Fourth dimension coordinate from 5D mapping</dd>

<dt>V5</dt>
<dd>Fifth dimension coordinate from 5D mapping</dd>

<dt>name</dt>
<dd>Strain identifier</dd>

<dt>antigen</dt>
<dd>Logical; TRUE if point represents an antigen</dd>

<dt>antiserum</dt>
<dd>Logical; TRUE if point represents an antiserum</dd>

<dt>cluster</dt>
<dd>Factor indicating antigenic cluster assignment (A/H3N2 1968-2003)</dd>

<dt>color</dt>
<dd>Color assignment for visualization</dd>

<dt>year</dt>
<dd>Year of strain isolation</dd>


</dl>

example_positions: Example Antigenic Mapping Data

Description

Usage

Arguments

Format