## Please edit system and help pages ONLY in the moinmaster wiki! For more
## information, please see MoinMaster:MoinPagesEditorGroup.
##master-page:Unknown-Page
##master-date:Unknown-Date
#acl DanieleMerico:read,write,delete,revert All:read
#format wiki
#language en
== 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/AffyCelCalSig: 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]]
   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)

=== 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]