Difference between revisions of "Rosaceae"
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=== Workflow === | === Workflow === | ||
====Introduction to microarray analysis (10mins) ==== | ====Introduction to microarray analysis (10mins) ==== | ||
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==== Normalization talk (10mins) ''Robert Schaffer''==== | ==== Normalization talk (10mins) ''Robert Schaffer''==== | ||
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==== Introduce Bioconductor/R framework (15+ mins do a tutorial 45mins→ 1hr)==== | ==== Introduce Bioconductor/R framework (15+ mins do a tutorial 45mins→ 1hr)==== | ||
=====Bioconductor===== | =====Bioconductor===== | ||
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*To develop a computing environment for both biologists and statisticians | *To develop a computing environment for both biologists and statisticians | ||
*Promote high-quality dynamic documentation and reproducible research | *Promote high-quality dynamic documentation and reproducible research | ||
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| + | =====Advantages===== | ||
| + | *Newest cutting edge statistical methods available | ||
| + | *Its free | ||
| + | =====Disadvantages===== | ||
| + | *Steep learning curve | ||
| + | *Need to have experience programming R | ||
| + | *Like all software there are bugs | ||
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<table class=document-code><tr><td> | <table class=document-code><tr><td> | ||
Revision as of 10:18, 13 March 2006
Contents
Microarray analysis workshop
Time schedule: 8:30 - 10:30am, 11-12:30am (3.5 hours)
Workflow
Introduction to microarray analysis (10mins)
Normalization talk (10mins) Robert Schaffer
Introduce Bioconductor/R framework (15+ mins do a tutorial 45mins→ 1hr)
Bioconductor
- Bioconductor is an open source development software project
- Provides tools for analysis and comprehension of genomic data
- Extensively for Affymetrix and cDNA microarray technologies
- The project started in the autumn of 2001
- Includes 23 core collaborating developers
Bioconductor Goals
The broad goals of the project are:
- To enable sound and powerful statistical analyses in genomics
- To provide a computing platform that allows the rapid design and deployment of high-quality software
- To develop a computing environment for both biologists and statisticians
- Promote high-quality dynamic documentation and reproducible research
Advantages
- Newest cutting edge statistical methods available
- Its free
Disadvantages
- Steep learning curve
- Need to have experience programming R
- Like all software there are bugs
- 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)
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)
