Multivariate Analysis

Introduction

Multivariate analysis is an analysis of more than one statistical outcome variable at a time. In meta-omics studies, the data are high dimensional, i.e. each feature (protein, gene, metabolite) is a dimension across the samples.

This app is a wrapper of several methods of multivariate analysis for your high dimensional data. The following functions are included:

  • Unsupervised approaches, including clustering, PCA and t-SNE
    • Hierarchical clustering in combination with heatmap helps visualize the high dimensional data using heat colors.
    • PCA and t-SNE are both for dimensionality reduction and visualization. PCA is a linear dimension reduction method, while t-SNE is non-linear.
  • Supervised approach, PLS-DA.
    • PLS-DA uses your meta table as grouping information to supervise the study. A valid PLS-DA model finds out the features that are responsible for the between-group differences.

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