How to detect and deal with outliers?

Potential outlier samples can be identified from PCA plots. The potential outlier(s) will distinguish itself as the one located far away from the major clusters formed by the remaining samples. Hierarchical heatmap clustering can also be used to reveal outlier as a unique group with distinctive patterns of expression.

To deal with outliers, the first thing is to check if the sample was measured properly. In many cases, outliers are the result of operational errors during the analytical process. If those values cannot be corrected, the sample should be removed from the input data and the analysis re-started.