Difference between revisions of "Linear models for Microarray analysis"
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**log2(G') = A - M/2 | **log2(G') = A - M/2 | ||
---- | ---- | ||
| − | + | ====Advantages using Limma==== | |
| + | *Nice organisational framework for handling cDNA expression data using object orientated programming | ||
| + | *Flexible methods to handle weighting of poor quality spots | ||
| + | *Encorporates cDNA normalization routines with a proven track record | ||
| + | *Robust statistical analysis approach | ||
| + | ::<font color="blue">Can analyze cDNA microarray slides possessing large amounts of missing information</font> | ||
| + | *Analysis methods able to encorporate duplicate spots from either technical or biological sources | ||
| + | ---- | ||
| + | ====Limitations==== | ||
| + | *Experiments with different spotting templates cannot easily be combined for analysis | ||
| + | *Statistical analysis cannot pool information together when there are variable numbers of the same replicated spots | ||
| + | ::must analyze spot information about the same transcript independently | ||
| + | *Linear models cannot encorporate error model structures from time series designs | ||
[[Category:Sven/Rosaceae]] | [[Category:Sven/Rosaceae]] | ||
Revision as of 04:08, 15 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 approach
- Parallel inference for each gene
- 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 the R programming language automatically populates elements of RGList
- R (Red foreground)
- G (Green foreground)
- Foreground intensities range ~ 1 → 65535
- Rb (Red background)
- Gb (Green background)
- Background intensities range ~ 1 → 1000
- genes (Spot annotation list)
- weights (prior weights weights given to each spot)
- MAList data transformation
- M = log2(R) - log2(G) (minus)
- A = (log2(R) + log2(G))/2) (add - abundance)
- Backtransforming to Normalized R', G' values
- log2(R') = A + M/2
- log2(G') = A - M/2
Advantages using Limma
- Nice organisational framework for handling cDNA expression data using object orientated programming
- Flexible methods to handle weighting of poor quality spots
- Encorporates cDNA normalization routines with a proven track record
- Robust statistical analysis approach
- Can analyze cDNA microarray slides possessing large amounts of missing information
- Analysis methods able to encorporate duplicate spots from either technical or biological sources
Limitations
- Experiments with different spotting templates cannot easily be combined for analysis
- Statistical analysis cannot pool information together when there are variable numbers of the same replicated spots
- must analyze spot information about the same transcript independently
- Linear models cannot encorporate error model structures from time series designs
