Application of linear mixed models in microarray.
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Mwero, D. K. 2007. Application of linear mixed models in microarray. MSc thesis in biometry. University of Nairobi.
Permanent link to cite or share this item: https://hdl.handle.net/10568/79638
External link to download this item: http://erepository.uonbi.ac.ke/handle/11295/19724
This project captures the problem of large microarray datasets and seeks to identify a statistical model of microarray hybridization intensity data that describes;differential regulation, sample variability and measurement noise. It also shows how one can use the data model to analyze the microarray data and develop optimal methods for detecting differentially regulated peripheral blood leukocyte mRNA from cattle infected with Trypanosoma congolense using microarray in order to assay components of the immune and inflammatory responses and identify potential correlates of the pathology. We conclude by giving an insight into linear mixed effects models by analysing a data set from a cattle experiment that seeks to compare 'genome-wide' transcriptional responses in blood leukocytes following infection with species of Trypanosoma that differ in the severity of pathogenicity.