Parul V Purohit | PubMed Health

PubMed2004

Discrimination models using variance-stabilizing transformation of metabolomic NMR data.

After the extensive work that is being done in the areas of genomics, proteomics, and metabolomics, the study of metabolites has come of interest in its own right. Metabolites in biological systems give an understanding of the state of the system and provide a powerful tool for the study of disease and other maladies. Several analytical techniques such as mass spectrometry and high-resolution NMR spectroscopy have been used to study metabolites. The data, however, from these techniques remains quite complex. Traditionally, multivariate analyses have been used for such data. These methods however have an underlying assumption that the data is multivariate normal with a constant variance. This is not necessarily the case. It has been shown that a generalized log transformation renders the variance of the data constant effectively making the data more suitable for multivariate analysis. We demonstrate the effectiveness of these transformations on NMR data taken on a set of 18 abalone that were categorized as either being healthy, stunted, or diseased. We show how the transformation makes multivariate classification of the abalone into the healthy, stunted and diseased categories much more effective and gives a tool for identifying potential metabolic biomarkers for disease.

Animals↗

PubMed2003

Discriminant models for high-throughput proteomics mass spectrometer data.

We use several different multivariate analysis methods to discriminate between diseased and healthy patients using protein mass spectrometer data provided by Duke University. Two problems were presented by the university; one in which the responses (diseased or healthy) of the patients were not known and second, when the responses were known. In the latter case, the data can be used as a 'training' set. We attempted both problems. In particular, we use principle component analysis along with clustering methods to discriminate for the first problem set and partial least squares coupled with logistic and discriminant methods when the responses were known. In addition, we were able to detect regions of interest in the spectrum where there were differences in the protein patterns between healthy and diseased patients. There was considerable effort involved in the preprocessing of the data. We used a binning approach to reduce the number of variables rather than peak heights or peak areas. We performed a square root transformation on the data to help stabilize the variance; this in turn made a significant improvement in clustering results.

Computational Biology↗