Prediction of metabolomic and transcriptomic patient profiles

Genetic and environmental conditions lead to global changes in the chemical composition of biological systems. The extended analysis of cellular metabolic pathway products gives insights into the functionality of enzymes in normal and pathological conditions. In the last years, metabolomics acquired an important role in detecting prognostic factors for various diseases including polycystic kidney disease. Effective methods employed for metabolomic studies are nuclear magnetic resonance (NMR) spectroscopy and coupling two-dimensional gas chromatography with time-of-flight mass spectrometry (GCxGC-TOF-MS). For both technologies, software exists that detects signals within the raw data as candidates for metabolites. Some of these candidates are verified as metabolites by comparison to a metabolite library, many remain unknown. The metabolite candidates and the number of metabolite candidates vary across different measurements. While NMR covers up to 150 metabolites, GCxGC-TOF-MS detects thousands of metabolites in one measurement. Hence, combining different measurements by GCxGC-TOF-MS is ambitious and required to apply statistical methods and machine learning techniques. Thus, we developed the integrative normalization and comparative analysis software tool INCA. INCA automatically identifies equal metabolite candidates among different measurements and combines them into one data matrix. The alignment algorithm is validated by an spike-in experiment and successfully applied to various metabolomic datasets. A commercial software tool was provided one year after completion of INCA. It is based on similar parameters and performs comparable. Not only the detection of new prognostic factors is of interest but also the prediction of treatment response or disease status. Autosomal polycystic kidney disease (ADPKD) is a frequent cause of kidney failure. It is usually diagnosed at a progressed stage of renal cystic transformation due to a lack of reliable laboratory tests early in the disease. Hence, the prognosis of patients to develop ADPKD is challenging. In terms of classification, prognosis is associated with the probability for developing the disease. Healthy patients and patients where the disease is reliably diagnosed are used to learn a classifier. This classifier consists of a set of features that defines a decision rule separating the healthy from the diseased, and assigning a new patient to the correct class with high probability. In microarray based classification, the performance of classification algorithms has been analyzed in great detail. However, little attention has been given to the usefulness of probability estimates and this is even more true for metabolomic analyses. Thus, I developed probability estimation methods based on local errors and compared them to existing methods from gene expression profiling, text categorization and digit recognition. I show that the local error based methods perform superior to more widely used methods, the PAM program, binary regression, and Compound Bayes classifiers. Especially the PAM approach performs poorly because its probability estimates depend on the number of selected features. I recommend not to make use of these estimates in the context of clinical diagnosis of patients. Although the estimators are evaluated on metabolomics data, I believe that similar results are obtained for different forms of clinical diagnosis based on high dimensional readouts, e.g. proteomic or transcriptomic profiling data. From the perspective of probability estimation the effective dimensionality is that of the feature signature and not that of the original data set. The dimensionality of gene expression based signatures described in the literature is well comparable to the metabolomics dataset.