Daniele Merico - HowTo Directory

Affymetrix Microarray Analysis

Importing raw data and generating standard gene expression metrics (signals, calls, etc...)

• Importing Affymetrix CEL files, calculating MAS5 calls and signals
  CEL files are the almost-raw files generated after chip image processing by Affymetrix software;
  the "fun" usually starts from the CEL files onwards; here's is the simplest things you can do with CEL files.

• Importing Affymetrix CEL files, bothering about the R exprSet object, calculating MAS5 calls and signals
  if the experimental design is quite complex, or you are using a function requiring an expression set (exprSet),
  then, sorry, but you probably need to read this part instead of the previous one.

Data exploration by dimensionality reduction techniques

• How to perform on a data matrix (e.g. expression matrix)

  • using the stats-package function princomp (covariance matrix)

  • using the ade4-package function dudi.pca (covariance matrix)

Computing Differential Expression

• 2-class methods
  these methods require a dicotomic classification of the samples (e.g. case vs control), and reproducibility of samples belonging to the same class

  • PLGEM
    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)
      

    References
    • Pubmed.ID: 15606915 (main)
    • Pubmed.ID: 18029349 (proteomic application)

  • SAM
    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
      

    References:
    • Pubmed.ID: 11309499 (main)

General Computational Techniques

Computational Techniques for multi-dimensional data:

• A few tips on distances (especially for binary strings)

Tuning Visualization in R

My stuff:

• Hacks for boxplots tuning

• interacting with a scatter plot: the < identify > function

• drawing a legend in a plot, the < legend > function

For a more general reference:

• A graphical description of the main graphical parameters for R graphs
  and a broader how-to for R graphics

System: R & the Mac

• Where is R installed in 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:

  • Rgui:

  • other R files: /library/frameworks/R.framework

    • for arcane reasons, the R plugin for Eclipse requires as folder of R executables: /Library/Frameworks/R.framework/Versions/.../Resources (where "..." is the version currently under use)

The Eclipse Plug-in for Mac

• Eclipse can be used as a programming environment for R, and it can be also connected to Subversion (thus catching two pigeons with one bite)

  • how to install the R plugin for Eclipse

  • how to install the Subversion plugin for Eclipse

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