adaptive_MC_sampling

Main function implementing adaptive Monte Carlo sampling to explore parameter space.
Updates sampling distribution based on evaluated likelihoods. This is an internal
function called by <code>run_adaptive_sampling</code>.

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

adaptive_MC_sampling function

<dl><dt>samples_file</dt>
<dd>Path to CSV with initial samples for this job.</dd>
<dt>distance_matrix</dt>
<dd>Distance matrix to fit</dd>
<dt>iterations</dt>
<dd>Number of sampling iterations per job</dd>
<dt>batch_size</dt>
<dd>Samples per iteration (fixed to 1)</dd>
<dt>mapping_max_iter</dt>
<dd>Maximum map optimization iterations</dd>
<dt>relative_epsilon</dt>
<dd>Convergence threshold</dd>
<dt>folds</dt>
<dd>Number of CV folds</dd>
<dt>num_cores</dt>
<dd>Number of cores for parallel processing</dd>
<dt>scenario_name</dt>
<dd>Name for output files</dd>
<dt>verbose</dt>
<dd>Logical. Whether to print progress messages. Default: FALSE</dd></dl>

Arguments

Perform Adaptive Monte Carlo Sampling — adaptive_MC_sampling

<dl>

<dt>samples_file</dt>
<dd>Path to CSV with initial samples for this job.</dd>


<dt>distance_matrix</dt>
<dd>Distance matrix to fit</dd>


<dt>iterations</dt>
<dd>Number of sampling iterations per job</dd>


<dt>batch_size</dt>
<dd>Samples per iteration (fixed to 1)</dd>


<dt>mapping_max_iter</dt>
<dd>Maximum map optimization iterations</dd>


<dt>relative_epsilon</dt>
<dd>Convergence threshold</dd>


<dt>folds</dt>
<dd>Number of CV folds</dd>


<dt>num_cores</dt>
<dd>Number of cores for parallel processing</dd>


<dt>scenario_name</dt>
<dd>Name for output files</dd>


<dt>verbose</dt>
<dd>Logical. Whether to print progress messages. Default: FALSE</dd>

</dl>

adaptive_MC_sampling: Perform Adaptive Monte Carlo Sampling

Description

Usage

Value

Arguments