compute_individual

Computes Integrated Covariance Mahalanobis (ICM) distances for
individuals, in PCA-reduced space, against either global or batch-wise
references.

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 function

<dl><dt>pc_data</dt>
<dd>PCA-reduced data frame.</dd>
<dt>ref</dt>
<dd>Reference type: "global" for global barycenter, "batch" for
batch-wise barycenters.</dd>
<dt>batch_col</dt>
<dd>Name of the column representing batch or group.</dd></dl>

Arguments

Computes Integrated Covariance Mahalanobis (ICM) distances for
individuals, in PCA-reduced space, against either global or batch-wise
references. — compute_individual

<dl>

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


<dt>ref</dt>
<dd>Reference type: "global" for global barycenter, "batch" for
batch-wise barycenters.</dd>


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

</dl>

compute_individual: Computes Integrated Covariance Mahalanobis (ICM) distances for individuals, in PCA-reduced space, against either global or batch-wise references.

Description

Usage

Value

Arguments