Análise de microarrays

Silvia, Paulo Justiniano, Christian Probst (IBMP), Joel

Gene expression technology has seduced the genomics community with its power and promise to unravel the genetic program. However, the experimental paradigms for this technology are not yet fully developed. For example, the response function (signal as a function of RNA concentration) has not been carefully studied for most technologies. There are many levels at which experimental error and noise can enter into the system. In this project, we aim to estimate the baseline error variance in an array experiment and also we wish to obtain normalization of results across multiple chips. The main issue being to recognize that paired comparisons in two-dye systems imposes an incomplete block structure on the experiment.

“Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. As the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent “noise” in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars?” (Extracted from http://research.jax.org/faculty/churchill/research/expression/)

In this project we investigate how ANOVA methods, such as those proposed by Kerr et al (2000), can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. The authors claim, this approach establishes a framework for the general analysis and interpretation of microarray data. Our question: Is this so?

• Leitura de artigos listados abaixo
• Análises preliminares dos dados usando o programa SAMR (feito em 06.05.2008)
• Estudar como avaliar mudanças nas variâncias dos componentes genéticos após tratamento com carcerígeno.

• Manual do pacote 'R/maanova'
• Arquivo de instalação do pacote 'R/maanova'
• arquivo de dados do IBMP
• Experimental Design for Gene Expression
• Statistical Design and the Analysis of Gene Expression
• Analysis of Variance for Gene Expression Microarray Data
• Statistical Issues in cDNA Microarray Data Analysis
• How Many Mices and How Many Arrays?
• Replicated Microarray Data
• Significance Analysis of Microarrays
• Statistical Significance for Genome-Wide Experiments
• Statistical Analysis of a Gene Expression Microarray Experiment
• factDesign

• IBMP
• The Jackson Laboratory: desenvolvedores do 'R/maanova'

• MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments, Chapter of The analysis of gene expression data: methods and software. Wu, Kerr, Cui and Churchill(2002), Springer (two color figures are here: Color figure 4 and Color figure 6).
• Analysis of variance for expression data. M.K.Kerr, M.Martin, and G.Churchill (2000), Journal of Computational Biology, 7:819-837.
• Experimental design for gene expression microarrays. M.K.Kerr and G.Churchill (2001),Biostatistics, 2:183-201.
• Statistical Design and the Analysis of Gene Expression Microarray Data. M.K.Kerr and G.Churchill (2001), Genetical Research, 77:123-128.
• Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments, Kerr and Churchill(2000), Proceedings of the National Academy of Sciences, 98:8961-8965.
• Statistical analysis of a gene expression microarray experiment with replication. M.K.Kerr, C.A.Afshari, L.Bennett, P.Bushel, J.Martinez, N.Walker and G.Churchill (2001), Statistica Sinica.