Difference between revisions of "Introduction to Microarray analysis"
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* Large amounts of data to analyse can be overwhelming and make interpretation subjective | * Large amounts of data to analyse can be overwhelming and make interpretation subjective | ||
* Independent reproducibility of results by another collegue | * Independent reproducibility of results by another collegue | ||
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Revision as of 21:22, 14 March 2006
Overview of experimental process
- Competitive hybridization to spotted oligo/cDNA transcripts
- Interested in genes that change between treatments
- → differential expression versus equivalent expression
Statistical analysis process
- Raw data (GPR file format)
- Each GPR intensity file is typically >8 megabytes
- Each TIFF image file is typically >30 megabytes
- A microarray experiment consists of several → many slides
Statistical issues
- In the past statistics was developed for n >>p
- n observations, p variables
- Gene expression data n<<p
- Thousands of measured genes (p)
- Small number of biological replicate slides (n)
- Gene expression data can be highly correlated
- groups of genes are regulated in the same way
- Data not normally distributed
- log transform highly skewed intensity data
Analysis wish list
- Ideally would like unambiguous interpretation of results
- Large amounts of data to analyse can be overwhelming and make interpretation subjective
- Independent reproducibility of results by another collegue
- →Keep a record (log) of what was done
Analysis aim
- Easier to rank genes in order of evidence of differential expression than it is to select a specific cutoff
- If we do select a cutoff, False Discovery Rate (FDR) cutoff is usually used
- FDR threhold is the expected proportion of genes in a list that are likely to be incorrect
TODO: Picie of a gene list
