Methods to improve gene signal : Application to cDNA microarrays

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http://urn.fi/URN:ISBN:978-952-10-5438-9
Title: Methods to improve gene signal : Application to cDNA microarrays
Author: Gupta, Rashi
Other contributor: Helsingin yliopisto, matemaattis-luonnontieteellinen tiedekunta, matematiikan ja tilastotieteen laitos
Helsingfors universitet, matematisk-naturvetenskapliga fakulteten, matematiska och statistiska institutionen
University of Helsinki, Faculty of Science, Department of Mathematics and Statistics
DNA sequencing and genomics laboratory, Institute of Biotechnology, University of Helsinki
Publisher: Helsingin yliopisto
Date: 2009-04-17
Language: en
URI: http://urn.fi/URN:ISBN:978-952-10-5438-9
http://hdl.handle.net/10138/21267
Thesis level: Doctoral dissertation (article-based)
Abstract: Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.Tarkastellaan menetelmiä, joilla voidaan parantaa geneetisiä signaaleja ja hyödyntää vahvistetun signaalin käyttöä myöhemmissä analyyseissä.
Subject: tilastotiede
Rights: Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.


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