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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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On pca dataset prepared by using dudi.pca from {ade4}. … For pca calculation I selected 40 components while specifying parameters for dudi.pca: pca = dudi.pca(counts(sce_normalized), scannf=FALSE, nf=40) {Rtsne} gives me an error: “perplexity is too large …
asked Jan 21 '18 by Nikita Vlasenko
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I am new to bioinformatics and PCA. … After running PCA: fontsize <- theme(axis.text=element_text(size=12), axis.title=element_text(size=16)) plotPCA(sce, pca_data_input="pdata") + fontsize I see the following chart: I suppose that …
asked Dec 17 '17 by Nikita Vlasenko
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I know that we can not directly state that if there is a large difference on PCA there will be present a plenty of differentially expressed genes, but why is it not the case? … Simple logic tells me that pca shows the difference between the samples in their gene expression, so I would expect seeing a plenty of differentially expressed genes. …
asked Dec 18 '18 by Nikita Vlasenko