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         Short Course in Microarray Data Analysis 
          May 25, 2002  
        
        
 
        
           
            | Talk slides available online at http://www.stat.berkeley.edu/users/terry/zarray/Course/ | 
           
           
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               Led by:  
                Terry Speed, University of California - Berkeley, 
                Department of Statistics and Program in Biostatistics, and The 
                Walter & Eliza Hall Institute of Medical Research, Division 
                of Genetics and Bioinformatics, Australia 
               
              Jean (Yee Hwa) Yang, University of California - Berkeley, 
                Department of Statistics  
               
              Ben Bolstad, University of California - Berkeley, 
                Department of Statistics 
              See their web site for microarray data analysis: http://www.stat.Berkeley.EDU/users/terry/zarray/Html/index.html 
               With assistance from: Iobian 
                Informatics, and The University Health Network Microarray 
                Centre, University of Toronto 
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               Overview 
                Microarray technology, which provides a way to globally measure 
                differential gene expression, promises to be extremely useful 
                for the diagnosis, treatment, and prevention of complex disease 
                as well as for the elucidation of biological mechanisms. These 
                studies yield tens of thousands of simultaneous gene measurements 
                from each biological sample. Issues in measurement and calibration 
                of the microarrays need to be addressed appropriately in order 
                to obtain valid datasets. To gain insight into genes and their 
                function, patterns of expression and expression changes must then 
                be discerned from high-dimensional data in which the number of 
                observations is small relative to the number of variables.  
              The purpose of the one-day Shortcourse in Statistics for Microarray 
                Data Analysis is to introduce statisticians and other researchers 
                to statistical issues in the design and analysis of microarray 
                studies of current interest to biologists and biomedical researchers. 
                Experience with statistical methods and in data analysis is a 
                pre-requisite, but no previous exposure to microarray data is 
                assumed. The course will include the opportunity for participants 
                to apply statistical methods to several datasets that will be 
                provided.  
                 
                Register early since space is limited to 90 participants (2 participants 
                for each computer terminal). 
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            | Schedule | 
     
     
      
     
           
            9:00 - 9:45 am 
               
               
               
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             Session 1. Biological and technical 
              background 
              Brief summary of issues relating to DNA, RNA, transcription,  
              cDNA, hybridization, cDNA microarray construction and use, 
              including imaging and image analysis. | 
     
           
            | 9:45 - 10:00 | 
             Break | 
     
           
            10:00 - 10:45 
               
               
               
               
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             Session 2. Design and preprocessing 
              Pros and cons of different designs including direct, reference, 
              loop,  
              factorial, and time series alternatives. Ways of looking at the 
              data, 
              and normalization to adjust for intensity-dependent and spatial 
               
              biases, and other systematic effects. | 
     
           
            | 10:45 - 11:00 | 
            Break | 
     
           
            | 11:00 - 12:30 | 
            Computer Lab (Room 208 and 210) | 
     
           
            12:30 - 2:00 
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             Lunch | 
     
           
            2:00 - 2:45 
               
               
               
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             Session 3. Basic analyses 
              Estimating and testing for differential expression.  
              Multiple testing adjustments. Empirical Bayes.  
              Linear models for designed experiments. | 
     
           
            | 2:45 - 3:00 | 
            Break | 
     
           
            3:00 - 3:45 
               
               
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             Session 4. Advanced analyses 
              Classification, clustering and other multivariate methods.  
              Ideas for addressing issues relating to pathways and networks. | 
     
           
            | 3:45 - 4:00 | 
             Break | 
     
           
            | 4:00 - 5:30 | 
             Computer Lab (Room 210) (Room 208 participants 
              begin at 4:30) | 
     
   
    
  
 
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