The interactive PCA visualization summarizes all the data into the the first 2 or 3 principal components (PCs). Each data point in the **Scores Plot** represents a sample. Samples that are close together are more similar to each other. The colors of these data points are based on the factor labels. Users can change the colors according to any of the two factor labels.

Each data point in the **Loadings Plot** represents a feature. When Scores plot and Loadings plot are viewed from the identical perspective, the direction of separation on the scores plot can be explained by the corresponding features on the same directions - i.e. features on the two ends of the direction contribute more to the pattern of separation.