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projetos:microarray [2008/05/07 14:43] – silviaprojetos:microarray [2008/11/07 12:05] (atual) – silvia
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 ===== Equipe ===== ===== Equipe =====
-[[pessoais:silvia|Silvia]], [[pessoais:paulojus|Paulo Justiniano]], Christian Probst (IBMP)+[[pessoais:silvia|Silvia]], [[pessoais:paulojus|Paulo Justiniano]], Christian Probst (IBMP), [[pessoais:joel|Joel]]
  
  
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 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. 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 (//we need to describe precisely which//) and also we wish to obtain normalization of results across multiple chips (//is it right?//). The key observation here is to recognize that the paired comparisons in two-dye systems imposes an incomplete block structure on the experiment.+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.
  
 === Analysis of variance for gene expression microarray data === === Analysis of variance for gene expression microarray data ===
  
-"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.  +"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/) 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 study we investigate how the ANOVA methods, 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. Is this so?+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? 
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