2019
DOI: 10.1080/23737484.2019.1656117
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A comparison of two estimation methods for common principal components

Abstract: Common principal components (CPCs) are often estimated using maximum likelihood estimation through an algorithm called the Flury-Gautschi (FG) Algorithm. Krzanowski proposed a simpler estimation method via a principal component analysis of a weighted sum of the sample covariance matrices. These methods are compared for real-world datasets and in a Monte Carlo simulation. The real-world data is used to compare the selection of a common eigenvector model and the estimated coefficients. The simulation study inves… Show more

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Cited by 4 publications
(6 citation statements)
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“…To further confirm the coherence of these clusters, we displayed the RNA and protein levels of their genes as heatmaps in Fig. 6 c using common PCA to order the cells [ 44 ]. Ordering the single cells and the cluster 1 genes based on the first common principal component (CPC 1) of the RNA and protein datasets confirms that the majority of genes exhibit qualitatively similar RNA and protein profiles.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…To further confirm the coherence of these clusters, we displayed the RNA and protein levels of their genes as heatmaps in Fig. 6 c using common PCA to order the cells [ 44 ]. Ordering the single cells and the cluster 1 genes based on the first common principal component (CPC 1) of the RNA and protein datasets confirms that the majority of genes exhibit qualitatively similar RNA and protein profiles.…”
Section: Resultsmentioning
confidence: 99%
“…To jointly analyze the protein and RNA data, we performed common principal component analysis (CPCA) in the space of genes from cluster 1 using the Krzanowski method [ 44 ]. Specifically, we computed the correlation matrices of RNA correlations R r and protein correlations R p for all genes from cluster 1 and determined the eigenvector with the largest eigenvalue of the matrix R r + R p .…”
Section: Methodsmentioning
confidence: 99%
“…To further confirm the coherence of these clusters, we displayed the RNA and protein levels of their genes as heatmaps in Fig. 5c using common PCA to order the cells [44]. Ordering the single cells and the Cluster 1 genes based on the first common principal component (CPC 1) of the RNA and protein datasets confirms that the majority of genes exhibit qualitatively similar RNA and protein profiles.…”
Section: Resultsmentioning
confidence: 99%
“…Common Principal Component AnalysisTo jointly analyze the protein and RNA data, we performed Common Principal Component Analysis (CPCA) in the space of genes from Cluster 1 using the Krzanowski method[44]. Specifically, we computed the correlation matrices of RNA correlations…”
mentioning
confidence: 99%
“…To jointly analyze the cell cycle protein from HeLa and U-937 cells, we performed Common Principal Component Analysis (CPCA) in the space of 20 cell cycle dependant(CDC) proteins using the Krzanowski method 49 . Specifically, we computed the correlation matrices of CDC proteins in the U-937, R u , and in the HeLa cells, R h , and determined the eigenvector with the largest and second largest eigenvalue of the matrix R u + R h .…”
Section: Common Principal Component Analysismentioning
confidence: 99%