perform_mea_pca

Template function for performing PCA on MEA data

A comprehensive toolkit for analyzing and visualizing neural data
outputs, including Principal Component Analysis (PCA) trajectory plotting,
Multi-Electrode Array (MEA) heatmap generation, and variable importance
analysis. Provides publication-ready visualizations with flexible
customization options for neuroscience research applications.

Alex Tudoras

NOVA

Neural Output Visualization and Analysis

perform_mea_pca function

<dl><dt>data</dt>
<dd>Data frame or tibble with processed MEA data</dd>
<dt>variables</dt>
<dd>Character vector. Variables to include in PCA (if NULL, uses all numeric)</dd>
<dt>scale</dt>
<dd>Logical. Whether to scale variables before PCA (default: TRUE)</dd>
<dt>center</dt>
<dd>Logical. Whether to center variables before PCA (default: TRUE)</dd>
<dt>...</dt>
<dd>Additional PCA parameters</dd></dl>

Arguments

Perform MEA PCA Analysis — perform_mea_pca

<dl>

<dt>data</dt>
<dd>Data frame or tibble with processed MEA data</dd>


<dt>variables</dt>
<dd>Character vector. Variables to include in PCA (if NULL, uses all numeric)</dd>


<dt>scale</dt>
<dd>Logical. Whether to scale variables before PCA (default: TRUE)</dd>


<dt>center</dt>
<dd>Logical. Whether to center variables before PCA (default: TRUE)</dd>


<dt>...</dt>
<dd>Additional PCA parameters</dd>

</dl>

perform_mea_pca: Perform MEA PCA Analysis

Description

Usage

Value

Arguments