The Use of Multiple Group Outlier Detection Methods to Identify Informative Brain Regions in Magnetic Resonance Images

The discovery of genetic markers that exhibit differential expression is of great interest in cancer studies. Researchers have now looked to find ways to identify genes with different expression patterns that exist only in a subset of the disease samples. Recently, a class of outlier detection methods has been developed to search for genes with outlier subsets. Using this approach, results in increased power to detect differences across groups relative to standard methods for group comparisons. Outlier detection has also been extended to handle multiple disease groups that are relevant to many more studies. The purpose of this research is to provide a comprehensive review of the class of two-group outlier detection methods which has been limited to date. From these results a modification is proposed to an existing method and the performance of this modification is examined via simulation studies. In addition, three extensions of two-group outlier detection methods are proposed to handle multiple group comparisons. Lastly, a novel application of these methods to structural magnetic resonance imaging data to identify informative brain regions related to cognitive decline in elderly adults is discussed. Public Health Significance: Outlier detection is a significant contribution to public health as a method that allows researchers to investigate high-dimensional data where issues such as heterogeneity and multiple comparisons are problematic. These methods allow for the identification of factors, such as genes or brain regions, that are related to group membership while identifying homogeneous subpopulations in the data

Multivariate classification of gene expression microarray data

L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombinacriterisbasats en els residuals x, el leverage ielsvalorspredits. A més,esdesenvolupa un mètode de selecció de variables per triarels gens mésrellevants, jaque la majoriadels gens analitzatsamb un microarraysónirrellevants per al propòsit particular de classificacióI podenconfondre el classificador. Finalment, el DPLSs'estenen a la classificació multi-classemitjançant la combinació de PLS ambl'anàlisidiscriminant lineal