calculate_tsne: Perform t-Distributed Stochastic Neighbor Embedding (t-SNE)

In immunaut: Machine Learning Immunogenicity and Vaccine Response Analysis

Perform t-Distributed Stochastic Neighbor Embedding (t-SNE)

Description

The calculate_tsne function reduces high-dimensional data into a 2-dimensional space using t-SNE for visualization and analysis. This function dynamically adjusts t-SNE parameters based on the characteristics of the dataset, ensuring robust handling of edge cases. It also performs data validation, such as checking for sufficient data, removing zero variance columns, and adjusting perplexity for optimal performance.

Usage

calculate_tsne(dataset, settings, removeGroups = TRUE)

Arguments

dataset	A data frame or matrix containing the dataset to be processed. Must contain numeric columns.
settings	A list of settings for t-SNE, which may include fileHeader, groupingVariables, perplexity, max_iter, eta, theta, exaggeration_factor, and preProcessDataset.
removeGroups	Logical, indicating whether to remove grouping variables before performing t-SNE. Default is TRUE.

Value

A list containing:

• info.norm: The dataset with the t-SNE coordinates (tsne1, tsne2) added.

• tsne.norm: The output from the Rtsne function.

• tsne_columns: The names of the t-SNE columns used.

• initial_dims: The number of dimensions used in the initial PCA step.

• perplexity: The perplexity parameter used.

• exaggeration_factor: The exaggeration factor used.

• max_iter: The number of iterations used.

• theta: The Barnes-Hut approximation parameter used.

• eta: The learning rate used.

immunaut documentation built on April 12, 2025, 1:22 a.m.