This file contains functions for performing Latin Hypercube and adaptive Monte Carlo sampling in parameter space. The AMC sampling adapts based on previous evaluations to focus sampling in high-likelihood regions. The functions run locally using parallel processing.
Functions handle:
A suite of functions to get an initial estimate of the likelihood space through LHS
Core adaptive sampling algorithm
Likelihood calculations with cross-validation
Distribution updating and resampling
Safe evaluation wrappers
Helper functions for running RACMACS and Topolow during algorithm comparisons.
Core implementations of the TopoLow algorithm for mapping distances in high dimensions. This file contains the main optimization functions and their variants.
Functions for standardizing and preprocessing antigenic assay data from various sources into consistent formats. Handles titer and IC50 measurements, threshold values, and produces both long and matrix formats suitable for mapping.
This file contains functions for assessing model convergence, analyzing chains, and performing diagnostic tests. Functions are designed to be general-purpose and usable with any iterative optimization or sampling procedure.
Functions handle:
Convergence testing
Chain analysis
Statistical diagnostics
Parameter distribution analysis
This file contains functions for calculating error metrics and validation statistics between predicted and true distance matrices. Functions handle missing values and special cases like threshold measurements.
Functions for running parameter optimization, comparison experiments, and other computational experiments.
Functions for comparing different map configurations using Procrustes analysis. These functions help assess:
Statistical significance of differences between maps
Quantitative measures of map differences
Stability of mapping solutions
An implementation of the TopoLow algorithm for antigenic cartography mapping and analysis. The package provides tools for optimizing point configurations in high-dimensional spaces, handling missing and thresholded measurements, processing antigenic assay data, and visualizing antigenic maps.
This file contains functions for transforming data between different formats used in antigenic cartography. Functions handle conversion between:
Long and matrix formats
Distance and titer measurements
Handling of threshold measurements (< and >)
This file contains utility functions used throughout the topolow package. Functions include data manipulation, and format conversion.
This file contains functions for visualizing topolow results including dimension reduction plots and cluster visualizations. Supports multiple plotting methods and customization options.
Functions handle:
Temporal mapping visualizations
Cluster mapping visualizations
2D and 3D projections
Multiple dimension reduction methods
Interactive and static plots
Diagnostic visualizations
Monte Carlo analysis visualizations
create_topolow_map: Core optimization algorithm
process_antigenic_data: Process raw antigenic data
initial_parameter_optimization: Optimize algorithm parameters
plot_temporal_mapping: Create temporal visualizations
plot_cluster_mapping: Create cluster-based visualizations
Functions that generate output files (like parameter optimization results) will create subdirectories in either:
The current working directory (if output_dir = NULL)
A user-specified directory (via output_dir parameter)
The following subdirectories may be created:
model_parameters/: Contains optimization results and parameter evaluations
init_param_optimization/: Contains files and outputs when using initial_parameter_optimization
If you use this package, please cite: Omid Arhami, Pejman Rohani, Topolow: A mapping algorithm for antigenic cross-reactivity and binding affinity assays, Bioinformatics, 2025;, btaf372, https://doi.org/10.1093/bioinformatics/btaf372 tools:::Rd_expr_doi("10.1093/bioinformatics/btaf372").
Maintainer: Omid Arhami omid.arhami@uga.edu (ORCID) [copyright holder]
The package implements a physics-inspired approach combining spring forces and repulsive interactions to find optimal point configurations. Key features include:
Optimization of point configurations in high-dimensional spaces
Handling of missing and thresholded measurements
Processing of antigenic assay data
Interactive visualization of antigenic maps
Cross-validation and error analysis
Network structure analysis
Support for parallel processing
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