Application of linear mixed models in microarray
Abstract
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.
Citation
Master of science in biometrySponsorhip
University of NairobiPublisher
College of biological and physical sciences school of mathematics