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| Importing raw data and generating standard gene expression metrics (signals, calls, etc...) | __Importing raw data and generating standard gene expression metrics__ (signals, calls, etc...) |
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| Computing Differential Expression * __2-class methods__ [[BR]] |
__Computing Differential Expression__ * '''2-class methods''' [[BR]] |
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| PLGEM is an error-model based method; it is recommended when the number of replicates is uneven between case and control; it has been successfully applied to tandem mass-spec proteomics data, where the signal was generated as abundancy-normalized peptide counts (NSAF) * PubMed.ID: 15606915 (main) * PubMed.ID: 18029349 (proteomic application) * SAM |
Features: * statistic used: corrected signal-to-noise, every gene treated as an independent entity; signal-to-noise is corrected according to an error model for the global estimation of varibility; * error model requires: linear relation between signal mean and standard deviation * significance: estimated by randomly permuting the data (by column), and computing the statistic; * recommended when: the number of replicates is uneven between case and control, with one of the two having very few, or just one replicate; * proteomics: successfully applied to tandem mass-spec proteomics data, where the signal was generated as abundancy-normalized peptide counts (NSAF)[[BR]] References * Pubmed.ID: 15606915 (main) * Pubmed.ID: 18029349 (proteomic application) * SAM[[BR]] Features: * statistic used: corrected signal-to-noise, every gene treated as an independent entity; * significance: estimated by randomly permuting the data (by column), and computing the statistic; * recommended when: the number of replicates is 3 or more, and even between case and control; * proteomics: unknown[[BR]] References: * Pubmed.ID: 11309499 (main) |
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| Computational Techniques for multi-dimensional data: | __Computational Techniques for multi-dimensional data__: |
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=== Tuning Visualization in R === * [:DanieleMerico/HowtoDirectory/Boxplots: Hacks for boxplots tuning] * [http://research.stowers-institute.org/efg/R/Graphics/Basics/mar-oma/index.htm A graphical description of the main graphical parameters for R graphs][[BR]] and [http://research.stowers-institute.org/efg/R/ a broader how-to for R graphics] |
Daniele Merico - HowTo Directory
Affymetrix Microarray Analysis
Importing raw data and generating standard gene expression metrics (signals, calls, etc...)
[:DanieleMerico/HowtoDirectory/AffyCelCalSig: Importing Affymetrix CEL files, calculating MAS5 calls and signals]BR CEL files are the almost-raw files generated after chip image processing by Affymetrix software; BR the "fun" usually starts from the CEL files onwards; here's is the simplest things you can do with CEL files.
[:DanieleMerico/HowtoDirectory/ExprSet: Importing Affymetrix CEL files, bothering about the R exprSet object, calculating MAS5 calls and signals]BR if the experimental design is quite complex, or you are using a function requiring an expression set (exprSet),BR then, sorry, but you probably need to read this part instead of the previous one.
Computing Differential Expression
2-class methods BR these methods require a dicotomic classification of the samples (e.g. case vs control), and reproducibility of samples belonging to the same class
[:DanieleMerico/HowtoDirectory/PLGEM: PLGEM]BR Features:
- statistic used: corrected signal-to-noise, every gene treated as an independent entity; signal-to-noise is corrected according to an error model for the global estimation of varibility;
- error model requires: linear relation between signal mean and standard deviation
- significance: estimated by randomly permuting the data (by column), and computing the statistic;
- recommended when: the number of replicates is uneven between case and control, with one of the two having very few, or just one replicate;
proteomics: successfully applied to tandem mass-spec proteomics data, where the signal was generated as abundancy-normalized peptide counts (NSAF)BR
- Pubmed.ID: 15606915 (main)
- Pubmed.ID: 18029349 (proteomic application)
- statistic used: corrected signal-to-noise, every gene treated as an independent entity; signal-to-noise is corrected according to an error model for the global estimation of varibility;
SAMBR Features:
- statistic used: corrected signal-to-noise, every gene treated as an independent entity;
- significance: estimated by randomly permuting the data (by column), and computing the statistic;
- recommended when: the number of replicates is 3 or more, and even between case and control;
proteomics: unknownBR
- Pubmed.ID: 11309499 (main)
General Computational Techniques
Computational Techniques for multi-dimensional data:
- [:DanieleMerico/HowtoDirectory/Distances: A few tips on distances] (especially for binary strings)
Tuning Visualization in R
- [:DanieleMerico/HowtoDirectory/Boxplots: Hacks for boxplots tuning]
[http://research.stowers-institute.org/efg/R/Graphics/Basics/mar-oma/index.htm A graphical description of the main graphical parameters for R graphs]BR and [http://research.stowers-institute.org/efg/R/ a broader how-to for R graphics]