Difference between revisions of "Linear models for Microarray analysis"
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===== Brief about Limma (10-15 mins)===== | ===== Brief about Limma (10-15 mins)===== | ||
| + | *Fits a linear model for each spot (''gene'') | ||
| + | *Focus on normalization and statistical analysis of cDNA microarray gene expression data | ||
| + | *OOP environment for handling information in a microarray experiment | ||
| + | *Statistical analysis approach can be used for Affymetrix microarray experiments | ||
| + | ---- | ||
*Essentially t-statistics for each spot/gene | *Essentially t-statistics for each spot/gene | ||
*Uses between gene information in moderated t-statistics | *Uses between gene information in moderated t-statistics | ||
Revision as of 04:12, 14 March 2006
Brief about Limma (10-15 mins)
- Fits a linear model for each spot (gene)
- Focus on normalization and statistical analysis of cDNA microarray gene expression data
- OOP environment for handling information in a microarray experiment
- Statistical analysis approach can be used for Affymetrix microarray experiments
- 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
