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.
Data exploration by dimensionality reduction techniques
- How to perform [:DanieleMerico/HowtoDirectory/PCA: PCA] on a data matrix (e.g. expression matrix)
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]
System: R & the Mac
As a former Windows user, I spent an hour trying to answer the following question: what is the f. location of R executables on the Mac? (i.e. where the hell are R files installed?) (where, of course, "f." stands for funny). The answer is quite straightforward if, instead of wasting time looking for them all round your Mac, you just read the R Mac OS X FAQ, under the chapter "uninstalling R". In my system (Mac OS X 10.5.1), the funny location of R files is /library/frameworks/R.framework