Difference between revisions of "Linear models for Microarray analysis"
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*Active development cycle | *Active development cycle | ||
[[Image:Limma_versions.tiff|thumb|200px|Linear development cycle]] | [[Image:Limma_versions.tiff|thumb|200px|Linear development cycle]] | ||
| + | ---- | ||
| + | ==== Statistical appreach ==== | ||
| + | *Parallel inference for each gene | ||
| + | **<font color="blue">univariate analysis approach</font> | ||
| + | *Computationally fast/robust | ||
| + | * Handles missing information/use defined flag information | ||
| + | *Linear models are essentially t-statistics for each spot/gene (signal/noise) | ||
| + | *Also makes use of between gene information (''moderated t-statistics'') | ||
---- | ---- | ||
Revision as of 04:46, 14 March 2006
Overview of Limma package for R
- Fits a linear model for each spot (gene)
- An open source software package for the R programming environment
- 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
Origin
- Written and maintained by Gordon Smyth with contributions From WEHI, Melbourne, Australia
- Software made public at the Australian Genstat Conference, Perth, in Dec 2002
- Became available in the Bioconductor open source bioinformatics project April 2003
- Limma integrates with other Bioconductor software packages, affy, marray, using convert package
- Active development cycle
Statistical appreach
- Parallel inference for each gene
- univariate analysis approach
- Computationally fast/robust
- Handles missing information/use defined flag information
- Linear models are essentially t-statistics for each spot/gene (signal/noise)
- Also makes use of between gene information (moderated t-statistics)
Object orintated programming environment
- Uploading data into R automatically populates elements of RGList
- R (Red foreground)
- Rb (Red background)
- G (Green foreground)
- Gb (Green background)
- genes (Spot annotation list)
- weights (prior weights weights given to each spot)
- MAList:
- M = log2(R) - log2(G) (minus)
- A = (log2(R) + log2(G))/2) (add - abundance)
- Backtransforming:
- log2(R') = A + M/2
- log2(G') = A - M/2
Scratchpad
- benefits/limitations?
- FDR control? → ranking better than selecting cutoff
