Heterogeneous samples collected from numerous continents present an additional challenge in the successful construction of an eigenmap. For instance, analysis of the four core HapMap samples [International-HapMap-Consortium, 2005] using the classical PC map produced by the software smartpca [Patterson et al., 2006] does not reveal substructure within the Asian sample; however, an eigenmap constructed using only the Asian samples discovers an additional substructure [Patterson et al., 2006]. Another feature of PCA is its sensitivity to outliers [Luca et al., 2008]. Due to outliers, numerous dimensions of ancestry appear to model a statistically significant amount of variation in the data, but in actuality they function to separate a single observation from the bulk of the data. This feature can be viewed as a drawback of the PCA method.
