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Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables.

ABSTRACT: A fundamental step in the analysis of gene expression and other high-dimensional genomic data is the calculation of the similarity or distance between pairs of individual samples in a study. If one has collected N total samples and assayed the expression level of G genes on those samples, then an N x N similarity matrix can be formed that reflects the correlation or similarity of the samples with respect to the expression values over the G genes. This matrix can then be examined for patterns via standard data reduction and cluster analysis techniques. We consider an alternative to conventional data reduction and cluster analyses of similarity matrices that is rooted in traditional linear models. This analysis method allows predictor variables collected on the samples to be related to variation in the pairwise similarity/distance values reflected in the matrix. The proposed multivariate method avoids the need for reducing the dimensions of a similarity matrix, can be used to assess relationships between the genes used to construct the matrix and additional information collected on the samples under study, and can be used to analyze individual genes or groups of genes identified in different ways. The technique can be used with any high-dimensional assay or data type and is ideally suited for testing subsets of genes defined by their participation in a biochemical pathway or other a priori grouping. We showcase the methodology using three published gene expression data sets.

SUBMITTER: Zapala MA

PROVIDER: S-EPMC1748243 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature

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Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables.

Zapala Matthew A MA Schork Nicholas J NJ

Proceedings of the National Academy of Sciences of the United States of America 20061204 51

A fundamental step in the analysis of gene expression and other high-dimensional genomic data is the calculation of the similarity or distance between pairs of individual samples in a study. If one has collected N total samples and assayed the expression level of G genes on those samples, then an N x N similarity matrix can be formed that reflects the correlation or similarity of the samples with respect to the expression values over the G genes. This matrix can then be examined for patterns via ...[more]

PMID: 17146048

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Project description:BACKGROUND: Coronary heart disease (CHD) is a common medical problem worldwide that demands shared care of general practitioners and cardiologists for concerned patients. In order to improve the cooperation between both medical specialists and to optimize evidence-based care, a treatment pathway for patients with CHD was developed and evaluated in a feasibility study according to the recommendation for the development and evaluation of complex interventions of the British Medical Research Council (MRC). In the context of this feasibility study the objective of the present research was to investigate the contributions of different disease related (e.g. prior myocardial infarction), pathway related (e.g. basic medication) and demographic variables on patients` perceived health related quality of life (HRQoL) as a relevant and widely used outcome measure in cardiac populations. METHODS: Data assessing demographic, disease and pathway related variables of CHD patients included in the study were collected in a quasi-experimental design with three study arms (pathway developers, users, control group) via case record forms and questionnaires at baseline and after 6 and 12 (intervention groups), and 9 months (control group), respectively after the initial implementation on GP level. Additionally, at the same measuring points the CHD patients participating in the study were interviewed by phone regarding their perceived HRQoL, measured with the EuroQol EQ-5D as an index-based health questionnaire. Due to the hierarchical structure of the data, we performed cross-sectional and longitudinal linear mixed models to investigate the impact of disease related, pathway related and demographic variables on patients` perceived HRQoL. RESULTS: Of 334 initially recruited patients with CHD, a total of 290 were included in our analysis. This was an average 13.2% dropout rate from baseline assessment to the 12-month follow-up. At all assessment points, patients` HRQoL was associated with a variety of sociodemographic variables (e.g. gender, employment, education) in each study group, but there was no association with pathway related variables. In both cross-sectional and longitudinal analyses highest HRQoL values in patients were reported in the physician group that had developed the pathway. In the longitudinal analyses there were no significant changes in the reported HRQoL values of the three groups over time. CONCLUSIONS: The found associations between sociodemographic variables and the perceived HRQoL of patients with CHD are in line with other research. As there are no associations of HRQoL with pathway related variables like the basic medication, possible weaknesses in the study design or the choice of outcome have to be considered before planning and conducting an evaluation study according to the MRC recommendations. Additionally, as patients in the developer group reported the highest HRQoL values over time, a higher commitment of the GPs in the developer group can be assumed and should be considered in further research.

Dataset Information

Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables.

Publications

Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables.

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