@freeman-lab Hi Jeremy, I'm trying to use the PCA code in the thunder project but I suspect the code isn't returning correct results or I might be doing something wrong. E.g. if you take the simple array: X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) the PCA code in thunder returns the following principal components: array([[-0.83849224, -0.54491354],
[ 0.54491354, -0.83849224]]) while the result from scikit-learn is array([[ 0.83849224, 0.54491354],
[ 0.54491354, -0.83849224]]). Also when I call transform on the same array and compute the variance along each PC I get ([ 0.16666667, 0.16666667]) which is wrong because the variance along the first PC should be larger than the second. I'm getting similar results for my actual example i.e. the variance alone each PC is the same. Do you know what might be going on here? Thanks a lot for putting together this project. It's awesome.