A PCA plot compresses genetic variation into a small map. Samples that appear near each other often share more genetic similarity than samples that are far apart, but the plot is still a summary of a larger dataset.
This makes PCA excellent for orientation. It can show broad neighborhoods, outliers, and whether a selected sample sits where the submitted name suggests it should sit. It should not be treated as a standalone identity decision.
How to use the PCA view
- Start with the selected sample and look at its nearest visual neighbors.
- Check whether those neighbors match the expected naming or source context.
- Use similarity and branch views to inspect relationships that the map compresses.
- Treat unexpected distance as a reason to ask a better question, not as the final answer by itself.