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Principal Components Analysis. A statistical method used to reduce the dimensionality of a dataset while keeping as much variance in the first principal components as possible. It can be used to visualise samples with many variables in 2-D or 3-D, thus allowing for a visual non-supervised grouping of points.

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A PCA is a very very generic method that uses the same input allelic information, but simply finds orthogonal projections of that onto lower dimensions that maximize the variance on the first principal …
answered Apr 19 '18 by Devon Ryan
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The general goal of PCA in RNA-seq can be stated as, "I'd like a low-dimension representation of my data to allow easy assessment of the gross structure of my samples, specifically for assessing missing … So you really don't have to use all of the data in the PCA, just the bits that will allow you to see any issues. …
answered Nov 30 '20 by Devon Ryan
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Since you've asked in the comments for a brief "results and description" section style summary of such a PCA I'll include something brief below. Note that one should not over-interpret PCA plots. … We performed a PCA on the variance-stabilized counts to check for batch effects and overall clustering of the data. …
answered Jul 22 '18 by Devon Ryan
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Yes, you can safely concatenate the technical replicates. Odds are good that these are even the same libraries just sequenced twice, so even labeling them as replicates is a bit of a stretch. As an as …
answered Sep 15 '19 by Devon Ryan