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Despite the usefulness of those other techniques, I still prefer to start with PCA. Its direct interpretability in terms of well understood statistical attribut
by zmmmmm 6y ago
Despite the usefulness of those other techniques, I still prefer to start with PCA. Its direct interpretability in terms of well understood statistical attributes of the data mean that for first line data interpretation it's better (in my mind) for the "QC" portion of analysis. The other methods are great to do after your PCA is looking sane.
- Der_Einzige 6y agoI don't think even starting with PCA is the right approach anymore. Ivis has better interpretability than PCA (e.g. built in features to watch it as it separates the data points out, feature importance explanations just like PCA, and it also supports inverse transforms (so does UMAP) just like PCA. The only thing that PCA has on top of UMAP or Ivis is estimated explained variance calculations which has almost no value. There will be many problems where you can't easily get a "PCA that looks sane", but you can definitely get a UMAP/Ivis that looks sane. I guess I see where you're coming from in regards to like massive datasets and making sure you're not doing silly things (e.g. misreading an ordinal as continuous) - but outside of this I think PCA is antiquated. The antiquation of PCA yet it's still ubiquitous usage is most likely one of the many factors holding back fields like bioinformatics dramatically. Please switch over to modern techniques. Your data will thank you!
- zmmmmm 6y agoInteresting - will admit I use UMAP but have not tried Ivis. Thanks for the suggestion!