Difference between revisions of "Rosaceae"

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;# Experimental analysis ("45mins")  
 
;# Experimental analysis ("45mins")  
 
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====Bioconductor/R framework====
 
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=====What is Bioconductor? (''http://www.bioconductor.org'')=====
 
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*Bioconductor is an open source development software project
 
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*Provides tools for analysis and comprehension of genomic data
 
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*Extensively for Affymetrix and cDNA microarray technologies
 
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*The project started in the autumn of 2001
 
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*Includes 23 core collaborating developers
 
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=====Bioconductor Goals=====
 
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The broad goals of the project are:
 
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*To enable sound and powerful statistical analyses in genomics
 
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*To provide a computing platform that allows the rapid design and deployment of high-quality software
 
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*To develop a computing environment for both biologists and statisticians
 
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*Promote high-quality dynamic documentation and reproducible research
 
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*Using [http://en.wikipedia.org/wiki/Latex L<sup>A</sup>T<sub><big>E</big></sub>X], the [http://www.ci.tuwien.ac.at/~leisch/Sweave/FAQ.html Sweave] system and [http://en.wikipedia.org/wiki/Tcl/Tk tcl/tk] to deliver interactive step by step pdf tutorials, e.g.
 
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library(tkWidgets)
 
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vExplorer()
 
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=====Object oriented class method design=====
 
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*Organized approach to handling large amounts of experimental data
 
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*Class structure encapsulates the data required for microarray analysis &rarr; ''object''
 
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*Allows efficient representation and manipulation (''including subsetting'') of data from many microarray slides in an experiment
 
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*A method is a function that performs an action on data (''objects'') throughout analysis
 
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=====Advantages=====
 
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*Newest cutting edge statistical methods available
 
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*Modern programming language
 
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*Powerful graphical tools available
 
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*Its freely available
 
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=====Disadvantages=====
 
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*Steep learning curve
 
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*Need to have experience programming in the R programming environment (''http://www.r-project.org'')
 
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*Like all software there are bugs
 
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=====Accessing Bioconductor=====
 
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*Bioconductor tools are accessed using the R programming language
 
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*R is a programming environment for statistical computing and graphics
 
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*Initially written by Robert Gentleman and Ross Ihaka (''[http://en.wikipedia.org/wiki/University_of_Auckland Auckland University]'')
 
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*Download R from a Comprehensive R archive network (''CRAN'') mirror (''http://cran.stat.auckland.ac.nz'')
 
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*Install R (''available for Unix, Windows, and Mac OS X'')
 
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*R version 2.2.1 has been released on 2005-12-20
 
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*R is the environment used to design and distribute software:
 
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**Locally downloaded files
 
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**Via the internet e.g. Commands in R
 
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getBioC()                                                  <font color="red">''# Running script''</font>
 
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=====Installing vs loading packages=====
 
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*Packages only need to be installed once onto a computer
 
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*Packages must be loaded with each new R session
 
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*The R function ''library'' is used to load packages e.g.
 
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library(limma)    <font color="red"> #Installs the limma package </font>
 
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=====Documentation and Help=====
 
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*R [http://cran.stat.auckland.ac.nz/manuals.html manuals] and [http://cran.stat.auckland.ac.nz/other-docs.html tutorials] are available from the R website or on-line in an R session
 
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* R on-line help system, detailed on-line documentation, available in text, HTML, PDF, and [http://en.wikipedia.org/wiki/Latex L<sup>A</sup>T<sub><big>E</big></sub>X] formats.
 
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help.start()          <font color="red"># Browser based help documentation</font>
 
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help()                <font color="red"># Help on a topic</font>
 
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? ls                  <font color="red"># alternative help method on ''ls'' function</font>
 
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apropos(mean)          <font color="red"># Find Objects by (Partial) Name</font>
 
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example(mean)          <font color="red"># Run an Examples Section from the Online Help</font>
 
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demo()                <font color="red"># Demonstrations of R Functionality</font>
 
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demo(graphics)        <font color="red"># Demonstration or graphics Functionality</font>
 
  
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<table class=document-code><tr><td>
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[[+Bioconductor/R framework]]
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</td></tr></table>
  
 
<table class=document-code><tr><td>
 
<table class=document-code><tr><td>

Revision as of 20:27, 13 March 2006

Microarray analysis workshop

Time schedule: 8:30 - 10:30am, 11-12:30am (3.5 hours)

TODO - Split and linkify article

Workflow

  1. Introduction to Microarray analysis (10mins - Marcus Davy)
  2. Normalization (10mins - Robert Schaffer)
  3. Bioconductor/R framework (15 mins - Marcus Davy
  4. R Lab Tutorial (45mins)
  5. Linear models for Microarray analysis (limma) (15 mins - Marcus Davy)
  6. Experimental analysis ("45mins")

+Bioconductor/R framework

+R tasklist

    • tasks available from web which utilze example data available in R - object assignment, subsetting, plotting, mathematical functions, sorting etc (20 tasks?)
  • Usage/interaction within environment
  • Bioconductor resources/vignettes(including downloading)
  • Bioconductor basics (any resources for limma out there?)
Brief about Limma (10-15 mins)
  • Essentially t-statistics for each spot/gene
  • Uses between gene information in moderated t-statistics
  • Computationally fast/robust
  • Handles missing information/use defined flag information
  • benefits/limitations?
  • FDR control? → ranking better than selecting cutoff
Analysis script (1+hours)

+Workshop.R

Scratch pad

  • A flow diagram for analysis
  • Recap of cDNA microarrays (slide 3)
  • Microarray data issues (slide 4)
  • Microarray data issues (continued)
    • Large amount of data (GPR/JPEG file size)
    • Subjective
    • Need a log of what was done so someone else can quickly reroduce the results
    • → Reproducible research (someone else can understand/reproduce the results) (McGintys talk)
  • Analysis process
  • R resources/contributed guides (including downloading)
  • R tutorial of basics (objects/indexing/functions)
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