Type I collagen is the principal functional ECM component of the menisci, which is organized into circumferential bands to convert weight bearing forces into tensile hoop strains [ 52, 55, 56].
Functional principal component analysis Functional principal component analysis (FPCA) can be viewed as rotating functional data to optimal empirical continuous basis functions, referred to as functional principal component (FPC) curves [ 17, 18].
This work aims at performing functional principal components analysis (FPCA) with Horvitz Thompson estimators when the observations are curves collected with survey sampling techniques.
Principal components and functional annotation analyses indicated that the tumor subgroups exhibited distinct transcriptional and biological features.
One approach is use of functional principal components analysis (PCA) as the basis for assessing measurement reproducibility.
Linear regression with scores on the functional principal components for the smooth curve fits for the mean as the dependent variable and basic pregnancy characteristics as explanatory variables.
We simultaneously performed missing imputation, fitting of smooth curves to each woman's FM chart, and estimation of general temporal patterns across women by calculation of functional principal components, using a Bayesian approach [ 29, 47].
Thus, for each pair of functional principal components estimated in two data sets, we can compute two coefficients, one measuring the co-occurence of the regions of variation, and the other measuring the correlation of scores corresponding to this pair of components.
Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies.
We combined raw coverage profiles for a chromatin mark, normalized signal values across the genome (factor 100×10), and applied functional principal component analysis in regions ± 5 kb around the TSSs to study the variation of deposition of one histone mark across the various genes in H1 cells, and to understand the correlation between different histone marks.
