ArrayTrack™ HCA-PCA Standalone Package – powerful data-exploring tools
Gene Expression Database and Interpretation Tool
Metadata Updated: April 24, 2018

The Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) are powerful data exploring tools extracted from ArrayTrack™ – a microarray database, data analysis, and interpretation tool developed by NCTR. ArrayTrack™ is MIAME (Minimum Information About A Microarray Experiment)-supportive for storing both microarray data and experiment parameters associated with a pharmacogenomics or toxicogenomics study. The primary emphasis of ArrayTrack™ is the direct linking of analysis results with functional information to facilitate the interaction between the choice of analysis methods and the biological relevance of analysis results. Hierarchical Cluster Analysis (HCA) – two-way HCA allows you to investigate the grouping of samples by their similarities in gene expression (or any data elements) profiles and by their similarity of samples. The primary purpose of the two-way HCA analysis is to present data so that genes (or any data elements) with a similar expression level across the samples are clustered along one axis while the samples with similar gene expression patterns are grouped together along another axis. Since the genes in the same cluster are likely to share similar functions, this analysis could reveal the relationships of molecular functions ( genes) and phenotypes (samples). Principal Component Analysis (PCA) PCA generates the linear combination of the genes (or any data elements) , namely principal components, using a mathematical transformation. The algorithm ensures that the first principal component explains the maximal amount of variance of the data. The second principal component explains the maximal remaining variance in the data subject to being orthogonal to the first principal component, and so on, such that all principal components taken together explain all the variance of the original data. The PCA plot of the first three principal components, which usually explains the majority of variance in the data, is a powerful data-exploring tool. PCA standalone tool offers both 2D and 3D views of the PCA results, along with the loading tables.