Principal Component Analysis applied directly to Sequence Matrix
نویسندگان
چکیده
منابع مشابه
Principal Component Analysis applied to digital image compression.
OBJECTIVE To describe the use of a statistical tool (Principal Component Analysis - PCA) for the recognition of patterns and compression, applying these concepts to digital images used in Medicine. METHODS The description of Principal Component Analysis is made by means of the explanation of eigenvalues and eigenvectors of a matrix. This concept is presented on a digital image collected in th...
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Principal component analysis (PCA) was applied to test differences among metal concentrations in organisms collected in five coastal sites in a reference ecosystem that is Linosa island (Sicily, Italy). Thus, Monodonta turbinata B. and Patella caerulea L. were tested as biomonitors of trace metal contamination in marine coastal areas. The aim of this survey was to evaluate the concentrations of...
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We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. By avoiding explicit principal component analysis (PCA), our algorithm is the first with no runtime dependen...
متن کاملSubject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data.
BACKGROUND Polychromatic flow cytometry (PFC) allows the simultaneous determination of multiple antigens in the same cell, resulting in the generation of a high number of subsets. As a consequence, data analysis is the main difficulty with this technology. Here we show the use of cluster analysis (CA) and principal component analyses (PCA) to simplify multicolor data visualization and to allow ...
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Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout science and engineering. However, the principal components (PCs) can sometimes be difficult to interpret, because they are linear combinations of all the original variables. To facilitate interpretation, sparse PCA produces modified PCs with sparse loadings, i.e. loading...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2019
ISSN: 2045-2322
DOI: 10.1038/s41598-019-55253-0