Multilevel Component Analysis Research Articles

Statistical multi-level shape models for scalable modeling of multi-organ anatomies.

Statistical shape modeling is an indispensable tool in the quantitative analysis of anatomies. Particle-based shape modeling (PSM) is a state-of-the-art approach that enables the learning of population-level shape representation from medical imaging data (e.g., CT, MRI) and the associated 3D models of anatomy generated from them. PSM optimizes the placement of a dense set of landmarks (i.e., correspondence points) on a given shape cohort. PSM supports multi-organ modeling as a particular case of the conventional single-organ framework via a global statistical model, where multi-structure anatomy is considered as a single structure. However, global multi-organ models are not scalable for many organs, induce anatomical inconsistencies, and result in entangled shape statistics where modes of shape variation reflect both within- and between-organ variations. Hence, there is a need for an efficient modeling approach that can capture the inter-organ relations (i.e., pose variations) of the complex anatomy while simultaneously optimizing the morphological changes of each organ and capturing the population-level statistics. This paper leverages the PSM approach and proposes a new approach for correspondence-point optimization of multiple organs that overcomes these limitations. The central idea of multilevel component analysis, is that the shape statistics consists of two mutually orthogonal subspaces: the within-organ subspace and the between-organ subspace. We formulate the correspondence optimization objective using this generative model. We evaluate the proposed method using synthetic shape data and clinical data for articulated joint structures of the spine, foot and ankle, and hip joint.

Endovascular Biopsy: In Vivo Cerebral Aneurysm Endothelial Cell Sampling and Gene Expression Analysis.

There is limited data describing endothelial cell (EC) gene expression between aneurysms and arteries partly because of risks associated with surgical tissue collection. Endovascular biopsy (EB) is a lower risk alternative to conventional surgical methods, though no such efforts have been attempted for aneurysms. We sought (1) to establish the feasibility of EB to isolate viable ECs by fluorescence-activated cell sorting (FACS), (2) to characterize the differences in gene expression by anatomic location and rupture status using single-cell qPCR, and (3) to demonstrate the utility of unsupervised clustering algorithms to identify cell subpopulations. EB was performed in 10 patients (5 ruptured, 5 non-ruptured). FACS was used to isolate the ECs and single-cell qPCR was used to quantify the expression of 48 genes. Linear mixed models and exploratory multilevel component analysis (MCA) and self-organizing maps (SOMs) were performed to identify possible subpopulations of cells. ECs were collected from all aneurysms and there were no adverse events. A total of 437 ECs was collected, 94 (22%) of which were aneurysmal cells and 319 (73%) demonstrated EC-specific gene expression. Ruptured aneurysm cells, relative controls, yielded a median p value of 0.40 with five genes (10%) with p values <0.05. The five genes (TIE1, ENG, VEGFA, MMP2, and VWF) demonstrated uniformly reduced expression relative the remaining ECs. MCA and SOM analyses identified a population of outlying cells characterized by cell marker gene expression profiles different from endothelial cells. After removal of these cells, no cell clustering based on genetic co-expressivity was found to differentiate aneurysm cells from control cells. Endovascular sampling is a reliable method for cell collection for brain aneurysm gene analysis and may serve as a technique to further vascular molecular research. There is utility in combining mixed and clustering methods, despite no specific subpopulation identified in this trial.

Cross-platform analysis of longitudinal data in metabolomics

Metabolic profiling is considered to be a very promising tool for diagnostic purposes, for assessing nutritional status and response to drugs. However, it is also evident that human metabolic profiles have a complex nature, influenced by many external factors. This, together with the understanding of the difficulty to assign people to distinct groups and a general move in clinical science towards personalized medicine, raises the interest to explore individual and variable metabolic features for each individual separately in longitudinal study design. In the current paper we have analyzed a set of metabolic profiles of a selection of six urine samples per person from a set of healthy individuals by (1)H NMR and reversed-phase UPLC-MS. We have demonstrated that the method for recovery of individual metabolic phenotypes can give complementary information to another established method for analysis of longitudinal data--multilevel component analysis. We also show that individual metabolic signatures can be found not only in (1)H NMR data, as has been demonstrated before, but also even more strongly in LC-MS data.

Universal Intracultural and Intercultural Dimensions of the Recalled Frequency of Emotional Experience

This study examined the relative contribution and the nature of dimensions underlying intracultural and intercultural differences in the recalled frequency of emotional experience. From 48 nations, 9,300 participants provided self-reports of the frequency of experienced emotions and several other variables relevant to emotional experience. The data were analyzed by means of multilevel component analysis, which decomposes the data into intracultural and intercultural components. The results showed that positive affect and negative affect emerged as universal dimensions underlying intracultural differences, accounting for the relatively largest part of variance in the data (40%). These dimensions were related to life satisfaction and other variables reflecting positive and negative affectivity. Two dimensions, reflecting positive emotions and inter-personal (negative) emotions, emerged as dimensions underlying nation-level differences, accounting for a smaller proportion of the variance (6%). Intercultural differences on these dimensions were related to nation-level life satisfaction, individualism, and the cultural appropriateness of experiencing corresponding emotions. Differences among individuals affect recalled emotional experience to a greater extent than differences among nations.

Multilevel component analysis and multilevel PLS of chemical process data

AbstractPrincipal component analysis (PCA) and partial least squares (PLS) are well‐established techniques for analyzing multivariate process data. However, chemical processes often vary at different levels, due to, for instance, catalyst deactivation or fouling. In such cases, data from a time period that comprises multiple catalyst or fouling runs contain both variation within runs at the lower level and variation between runs at the higher level. In ordinary PCA and PLS models, these sources of variation are confounded. Multiway PCA and PLS are usually not appropriate either, because the runs in the data set can be very different, which means that the overall data set does not have a proper multiway structure. Multilevel component analysis (MLCA) and multilevel partial least squares (MLPLS) are proposed as better options for analyzing such process data. The models obtained with these techniques contain submodels for the different levels in the data, and thereby separate the within‐run and between‐run variation in the process variables (X). In addition, MLPLS can use response variables (Y) to guide the projections into meaningful directions, and provide information on the sources of variation in Y and the relationship between X and Y. Extensions to more than two levels are straightforward, and can be used, for instance, for the comparison of runs from different plants. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Multilevel component analysis of time-resolved metabolic fingerprinting data

Genomics-based technologies in systems biology have gained a lot of popularity in recent years. These technologies generate large amounts of data. To obtain information from this data, multivariate data analysis methods are required. Many of the datasets generated in genomics are multilevel datasets, in which the variation occurs on different levels simultaneously (e.g. variation between organisms and variation in time). We introduce multilevel component analysis (MCA) into the field of metabolic fingerprinting to separate these different types of variation. This is in contrast to the commonly used principal component analysis (PCA) that is not capable of doing this: in a PCA model the different types of variation in a multilevel dataset are confounded. MCA generates different submodels for different types of variation. These submodels are lower-dimensional component models in which the variation is approximated. These models are easier to interpret than the original data. Multilevel simultaneous component analysis (MSCA) is a method within the class of MCA models with increased interpretability, due to the fact that the time-resolved variation of all individuals is expressed in the same subspace. MSCA is applied on a time-resolved metabolomics dataset. This dataset contains 1H NMR spectra of urine collected from 10 monkeys at 29 time-points during 2 months. The MSCA model contains a submodel describing the biorhythms in the urine composition and a submodel describing the variation between the animals. Using MSCA the largest biorhythms in the urine composition and the largest variation between the animals are identified. Comparison of the MSCA model to a PCA model of this data shows that the MSCA model is better interpretable: the MSCA model gives a better view on the different types of variation in the data since they are not confounded.