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Results for “Principal Component Analysis”

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At least 595 records · Page 33Linked to original sources
PubMed2006

Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. We have used a metabolite profiling approach to test the hypothesis that quantitative signatures of primary metabolites can be used to characterize molecular changes in ovarian tumor tissues. Sixty-six invasive ovarian carcinomas and nine borderline tumors of the ovary were analyzed by gas chromatography/time-of-flight mass spectrometry (GC-TOF MS) using a novel contamination-free injector system. After automated mass spectral deconvolution, 291 metabolites were detected, of which 114 (39.1%) were annotated as known compounds. By t test statistics with P < 0.01, 51 metabolites were significantly different between borderline tumors and carcinomas, with a false discovery rate of 7.8%, estimated with repeated permutation analysis. Principal component analysis (PCA) revealed four principal components that were significantly different between both groups, with the highest significance found for the second component (P = 0.00000009). PCA as well as additional supervised predictive models allowed a separation of 88% of the borderline tumors from the carcinomas. Our study shows for the first time that large-scale metabolic profiling using GC-TOF MS is suitable for analysis of fresh frozen human tumor samples, and that there is a consistent and significant change in primary metabolism of ovarian tumors, which can be detected using multivariate statistical approaches. We conclude that metabolomics is a promising high-throughput, automated approach in addition to functional genomics and proteomics for analyses of molecular changes in malignant tumors.

Cluster Analysis↗
PubMed1988

Are the classical markers sufficient to describe uremic solute accumulation in dialyzed patients? Hippurates reconsidered.

Interdependencies of accumulated solutes, analyzed by liquid chromatography in dialyzed and non-dialyzed patients, were studied by multivariate statistical analysis. In principal component analysis, three principal components (PC1-PC3) were retained from the data on 22 accumulated compounds in dialyzed patients, whereas only one principal component was retained from analogous data of a non-dialyzed patient group. PC1 in the dialyzed patient group comprises concentrations of hippuric acid, p-hydroxyhippuric acid, tryptophan, and five unidentified fluorescent solutes in serum. Concentrations of the classical markers urea, uric acid, creatinine, and phosphate were closely related to PC2 in these patients. Indoleacetic acid and two unidentified fluorescent compounds constitute PC3. The compounds associated with the groups found by principal component analysis may be characterized by chemical structure and by the mechanism of their excretion via the remaining nephrons of dialyzed patients. In the non-dialyzed group, most of the solutes could be described by a single PC. This PC and PC1 from the dialyzed group correlated significantly with residual renal function, and with total ultraviolet absorbance and total fluorescence emission. The data suggest that it is of value to introduce a marker of uremic solute retention in addition to urea, to account for renal-function-related "organic-acid-like" compounds that are excreted by renal tubular secretion in dialyzed patients. The hippurates may serve this purpose.

Chromatography, High Pressure Liquid↗
PubMed1996

Test of linear trend in eigenvalues of a covariance matrix with application to data analysis.

Principal component analysis and factor analysis are the most widely used tools for dimension reduction in data analysis. Both methods require some good criterion to judge the number of dimensions to be kept. The classical method focuses on testing the equality of eigenvalues. As real data hardly have this property, practitioners turn to some ad hoc criterion in judging the dimensionality of their data. One such popular method, the 'scree test' or 'scree plot' as described in many texts and statistical programs, is based on the trend in eigenvalues of sample covariance (correlation) matrix. The principal components or common factors corresponding to eigenvalues which exhibit a slow linear decrease are discarded in further data analysis. This paper develops a formal statistical test for the 'scree plot'. A special case of this test is the classical test for equality of eigenvalues which has been suggested in several texts as the criterion to decide the number of principal components to retain. Comparisons between equality of eigenvalues and the slow linear decrease in eigenvalues on some classical examples support the hypothesis of slow linear decrease. A physical background to such a phenomenon is also suggested.

Analysis of Variance↗
PubMed1976

[Demonstration by the analysis of principal components of the of the equivalence of two models of clonal survival].

The principal component analysis of 21 chlorella cell survival curves, adjusted by one-hit and two-hit target models, lead to quite similar projections on the principal plan: the homologous parameters of these models are linearly correlated; the reason for the statistical equivalence of these two models, in the present state of experimental inaccuracy, is revealed.

Chlorella↗
PubMed1985

Comparative study of vibration disease among operators of vibrating tools by factor analysis.

Principal component analysis was carried out to determine the interrelationships between subjective symptoms experienced by workers using various types of vibrating tools (brush saws (41), rock drills (39), chipping hammers (42) and chain saws (49)) and control groups (clerks (44), farmers (42)). The characteristics of the patterns of common factors extracted from these symptoms and the frequency distribution of the factor score of the main principal components were compared among the groups. From a wide variety of symptoms, a principal component related to peripheral nerves, muscles, and joints and a component related to peripheral circulation were extracted specifically and separately. The complaints of rock drillers reflected peripheral circulatory factor and those of chain saw users presented a complex factor of nerve muscle, and joints.

Adult↗