Difference between revisions of "Linear models for Microarray analysis"
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| − | ===== | + | ===== Overview of Limma package for R===== |
*Fits a linear model for each spot (''gene'') | *Fits a linear model for each spot (''gene'') | ||
*Focus on normalization and statistical analysis of cDNA microarray gene expression data | *Focus on normalization and statistical analysis of cDNA microarray gene expression data | ||
Revision as of 04:12, 14 March 2006
Overview of Limma package for R
- 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
