Refine
Has Fulltext
- no (2)
Document Type
- Article (2)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Keywords
- principal component analysis (2) (remove)
Institute
- Institut für Biochemie und Biologie (2) (remove)
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coil, Saccharomycies cerevisiae, and Arabidopsis thaliana.
The human face shows individual features and features that are characteristic for sex and age (the loss of childlike characteristics during maturation). The analysis of facial dimensions is essential for identifying individual features also for forensic issues.
The analysis of facial proportions was performed on photogrammetric data from front views of 125 children. The data were pooled from 2 different studies. The children's data were obtained from a longitudinal study and reduced by random generator to ensure the data of adults from a separate cross-sectional study.
We applied principal component analysis on photogrammetric facial proportions of 169 individuals: 125 children (63 boys and 62 girls) aged 2-7 years and 44 adults (18 men and 26 women) aged 18-65 years.
Facial proportions depend on age and sex. Three components described age: (1) proportions of facial height to head height, (2) proportions that involve endocanthal breadth, and (3) bigonial to bizygonial proportions. Proportions that associate with sex are connected with nasal distances and nasal to bizygonial distances.
Twenty-three percent of the variance, particularly variance that are connected with proportions of lower and middle face heights to head height, do neither depend on sex nor on age and thus appear useful for screening purposes, eg, for dysmorphic genetic syndromes.