In this thesis, we have used the kernel based orthogonal projection to latent structures (K-OPLS) method to discriminate between Alzheimer's Disease patients (AD) and healthy control subjects (CTL), and to predict conversion from mild cognitive impairment (MCI) to AD. In this regard three cohorts were used to create two different datasets; a small dataset including 63 subjects based on the Alzheimer’s Research Trust (ART) cohort and a large dataset including 1074 subjects combining the AddNeuroMed (ANM) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts. In the ART dataset, 34 regional cortical thickness measures and 21 volumetric measures from MRI in addition to 3 metabolite ratios from MRS, altogether 58 variables obtained for 28 AD and 35 CTL subjects. Three different K-OPLS models were created based on MRI and MRS measures and their combination. Combining the MRI and the MRS measures significantly improved the discriminant power resulting in a sensitivity of 96.4% and a specificity of 97.1%. In the combined dataset (ADNI and AddNeuroMed), the Freesurfer pipeline was utilized to extract 34 regional cortical thickness measures and 23 volumetric measures from MRI scans of 295 AD, 335 CTL and 444 MCI subjects. The classification of AD and CTL subjects using the K-OPLS model resulted in a high sensitivity of 85.8% and a specificity of 91.3%. Subsequently, the K-OPLS model was used to prospectively predict conversion from MCI to AD, according to the one year follow up diagnosis. As a result, 78.3% of the MCI converters were classified as AD-like and 57.5% of the MCI non-converters were classified as control-like. Furthermore, an age correction method was proposed to remove the effect of age as a confounding factor. The age correction method successfully removed the age-related changes of the data. Also, the age correction method slightly improved the performance regarding to classification and prediction. This resulted in that 82.1% of the MCI converters were correctly classified. All analyses were performed using 7-fold cross validation. The K-OPLS method shows strong potential for classification of AD and CTL, and for prediction of MCI conversion.

With the increasing availability of high-throughput systems for parallel monitoring of multiple variables, e.g. levels of large numbers of transcripts in functional genomics experiments, massive amounts of data are being collected even from single experiments. Extracting useful information from such systems is a non-trivial task that requires powerful computational methods to identify common trends and to help detect the underlying biological patterns. This thesis deals with the general computational problems of classifying and integrating high-dimensional empirical data using a latent variable based modeling approach. The underlying principle of this approach is that a complex system can be characterized by a few independent components that characterize the systematic properties of the system. Such a strategy is well suited for handling noisy, multivariate data sets with strong multicollinearity structures, such as those typically encountered in many biological and chemical applications. The main foci of the studies this thesis is based upon are applications and extensions of the orthogonal projections to latent structures (OPLS) method in life science contexts. OPLS is a latent variable based regression method that separately describes systematic sources of variation that are related and unrelated to the modeling aim (for instance, classifying two different categories of samples). This separation of sources of variation can be used to pre-process data, but also has distinct advantages for model interpretation, as exemplified throughout the work. For classification cases, a probabilistic framework for OPLS has been developed that allows the incorporation of both variance and covariance into classification decisions. This can be seen as a unification of two historical classification paradigms based on either variance or covariance. In addition, a non-linear reformulation of the OPLS algorithm is outlined, which is useful for particularly complex regression or classification tasks. The general trend in functional genomics studies in the post-genomics era is to perform increasingly comprehensive characterizations of organisms in order to study the associations between their molecular and cellular components in greater detail. Frequently, abundances of all transcripts, proteins and metabolites are measured simultaneously in an organism at a current state or over time. In this work, a generalization of OPLS is described for the analysis of multiple data sets. It is shown that this method can be used to integrate data in functional genomics experiments by separating the systematic variation that is common to all data sets considered from sources of variation that are specific to each data set. / Funktionsgenomik är ett forskningsområde med det slutgiltiga målet att karakterisera alla gener i ett genom hos en organism. Detta inkluderar studier av hur DNA transkriberas till mRNA, hur det sedan translateras till proteiner och hur dessa proteiner interagerar och påverkar organismens biokemiska processer. Den traditionella ansatsen har varit att studera funktionen, regleringen och translateringen av en gen i taget. Ny teknik inom fältet har dock möjliggjort studier av hur tusentals transkript, proteiner och små molekyler uppträder gemensamt i en organism vid ett givet tillfälle eller över tid. Konkret innebär detta även att stora mängder data genereras även från små, isolerade experiment. Att hitta globala trender och att utvinna användbar information från liknande data-mängder är ett icke-trivialt beräkningsmässigt problem som kräver avancerade och tolkningsbara matematiska modeller. Denna avhandling beskriver utvecklingen och tillämpningen av olika beräkningsmässiga metoder för att klassificera och integrera stora mängder empiriskt (uppmätt) data. Gemensamt för alla metoder är att de baseras på latenta variabler: variabler som inte uppmätts direkt utan som beräknats från andra, observerade variabler. Detta koncept är väl anpassat till studier av komplexa system som kan beskrivas av ett fåtal, oberoende faktorer som karakteriserar de huvudsakliga egenskaperna hos systemet, vilket är kännetecknande för många kemiska och biologiska system. Metoderna som beskrivs i avhandlingen är generella men i huvudsak utvecklade för och tillämpade på data från biologiska experiment. I avhandlingen demonstreras hur dessa metoder kan användas för att hitta komplexa samband mellan uppmätt data och andra faktorer av intresse, utan att förlora de egenskaper hos metoden som är kritiska för att tolka resultaten. Metoderna tillämpas för att hitta gemensamma och unika egenskaper hos regleringen av transkript och hur dessa påverkas av och påverkar små molekyler i trädet poppel. Utöver detta beskrivs ett större experiment i poppel där relationen mellan nivåer av transkript, proteiner och små molekyler undersöks med de utvecklade metoderna.