Hi Rebecca,
The PCA scores in the table describe the position that each subject has along that mode of variation. The mean shape has scores 0 for all modes. Moving further from 0 in either direction corresponds to moving along the eigenvector for that mode. The PCA tab in the analysis module allows you to do this individually for each mode (e.g. +/- 2 standard deviations).
If a given PCA mode describes a given feature, such as a lengthening and shortening of the overall shape, then you will find the samples that are long or short will be on opposite sides of the PCA spectrum (e.g. plus and minus).
There are lots of resources on the web for PCA and PCA Component Scores that should be applicable to this data. The values being input to the PCA are the x,y and z of each correspondence point (a high dimensional space).
Thanks, Alan
From:
Beroukhim, Rebecca <Rebecca.Beroukhim@CARDIO.CHBOSTON.ORG> Thanks Shireen,
Forgive me if I sound colloquial (I’m copying my engineering team here). We performed a brief analysis of our data and 5 shape modes were generated by the software. However when we exported the PCA data, it sent us 6 columns of data (P0-P5). The table looked something like the one below. We are trying to understand how to interpret this output.
Thanks Rebecca
From: Shireen Elhabian <shireen@sci.utah.edu>
Hi Rebecca,
The question is a bit ambiguous. Are you referring to the corresponding particles, or the various analytical outcomes? This could include reconstructed shapes, PCA loadings, or DeepSSM results.
To best address this, I'd suggest checking out the analysis documentation page.
Please let us know if you have further questions.
Regards Fax (801) 585-6513
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