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PCA analyzes the variance of variables and reorganizes it into a new set of uncorrelated independent components (principal components) equal to the number of original variables as linear combinations of the measured variables (Swan and Sandilands 1995).
On the other hand, the dimension reduction technique used in the first algorithm step, PCA, analyzes covariation across the entire data matrix.
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Secondly, to compare results, we compute kernel PCA analyzing only FA levels.
In this study, we utilized Raman microspectroscopy in combination with principal component analysis (PCA) to analyze the molecular consequences of fibrillin-1 deficiency in skin of a mouse model (GT8) of Marfan syndrome.
We used principal component analysis (PCA) to analyze variation among the samples for DNA methylation and genotype data.
First, we performed principal component analysis (PCA) and analyzed associations between principal components and a variety of sample annotations (Figure S1 in File S2).
Firstly, we compute kernel PCA by analyzing only gene expression level data.
Given a list of such vectors, one can use Principal Component Analysis (PCA) to analyze clustering of genotypes.
Subsequently, we used principal component analysis (PCA) to analyze the orthogonal relationship between gene response and recovery, and between roots and shoots.
GC-MS was used with pattern recognition technology and principal component analysis (PCA) to analyze the plasma samples at 4, 21 and 45 day after operation.
Principle component analysis (PCA) was analyzed by using R. Differentially expressed genes between two consecutive developmental stages were identified using default parameters in DESeq [ 52].
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