compute_individual_global

Computes Integrated Covariance Mahalanobis (ICM) distances of all
individuals in PCA-reduced space, against their global barycenter reference.

Provides methods for analyzing the dispersion of tabular datasets
with batched and ordered samples. Based on convex hull or integrated covariance
Mahalanobis, several indicators are implemented for inter and intra batch dispersion
analysis. It is designed to facilitate robust statistical assessment of data
variability, supporting applications in exploratory data analysis and quality
control, for such datasets as the one found in metabololomics studies.
For more details see Salanon (2024) <doi:10.1016/j.chemolab.2024.105148> and
Salanon (2025) <doi:10.1101/2025.08.01.668073>.

Etienne Jules

dispersionIndicators

Indicators for the Analysis of Dispersion of Datasets with
Batched and Ordered Samples

Brice Mulot

Elfried Salanon

INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) 

compute_individual_global function

<dl><dt>pc_data</dt>
<dd>PCA-reduced data frame.</dd>
<dt>batch_col</dt>
<dd>Name of the column representing batch or group.</dd></dl>

Arguments

Computes Integrated Covariance Mahalanobis (ICM) distances of all
individuals in PCA-reduced space, against their global barycenter reference. — compute_individual_global

<dl>

<dt>pc_data</dt>
<dd>PCA-reduced data frame.</dd>


<dt>batch_col</dt>
<dd>Name of the column representing batch or group.</dd>

</dl>

compute_individual_global: Computes Integrated Covariance Mahalanobis (ICM) distances of all individuals in PCA-reduced space, against their global barycenter reference.

Description

Usage

Value

Arguments