Morphology of the normal visual field in a population-based random sample: Principal components analysis

1992 ◽  
Vol 11 (9) ◽  
pp. 1131-1150 ◽  
Author(s):  
Neal Oden
Keyword(s):  
Visual Field ◽  
Principal Components Analysis ◽  
Random Sample ◽  
Principal Components ◽  
Population Based ◽  
Normal Visual Field ◽  
Components Analysis

scholarly journals Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS)

2020 ◽  
Author(s):  
Ruey Leng Loo ◽  
Queenie Chan ◽  
Henrik Antti ◽  
Jia V Li ◽  
H Ashrafian ◽  
...  
Keyword(s):  
Principal Components Analysis ◽  
Spectral Data ◽  
Principal Components ◽  
Large Scale ◽  
Principal Component ◽  
Population Based ◽  
Correlation Spectroscopy ◽  
Supplementary Information ◽  
Components Analysis ◽  
Statistical Spectroscopy

Abstract Motivation Large-scale population omics data can provide insight into associations between gene–environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets. Results Here, we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is subsequently achieved by implementing Statistical TOtal Correlation SpectroscopY on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset are used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS method thus provides an efficient semi-automated approach for screening population datasets. Availability and implementation Source code is available at https://github.com/cheminfo/COMPASS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Principal Components Analysis of Atopy-Related Traits in a Random Sample of Children

ISRN Allergy ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-4
Author(s):  
Simon Francis Thomsen ◽  
Vibeke Backer
Keyword(s):  
Atopic Dermatitis ◽  
Principal Components Analysis ◽  
Random Sample ◽  
Skin Test ◽  
Principal Components ◽  
Skin Test Reactivity ◽  
Total Ige ◽  
Components Analysis ◽  
Serum Total Ige ◽  
The Relationship

Aim. To study the relationship between atopy-related traits in a random sample of children. Methods. A total of 1007 randomly selected children, 7–17 years of age, from Copenhagen, Denmark were studied. The children were interviewed about symptoms of atopic diseases, and skin test reactivity, serum total IgE, lung function, and airway responsiveness were measured. Principal components analysis was performed in order to examine the relationship between the different traits. Results. Most of the studied traits were significantly correlated. A three-component solution explained about 55% of the variation in the observed traits. The first component loaded most strongly on hay fever, serum total IgE, skin test reactivity and sensitisation to grass, cat and house dust mite allergen; the second factor was most associated with asthma, airway obstruction, and airway hyperresponsiveness, whereas the third factor corresponded most strongly to atopic dermatitis. There was some indication of cross-relations between the three components with respect to serum total IgE. Conclusion. Asthma, hay fever, and atopic dermatitis are characterised by different sets of biomarkers suggestive of a high degree of heterogeneity within the atopic syndrome.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

Sign in / Sign up

Export Citation Format

Share Document