[BioC] Microarray PCA/MDS/SVD

Wolfgang Huber huber at ebi.ac.uk
Tue Dec 9 15:37:21 CET 2008

Hi Yannick

this one is a good start:

The Elements of Statistical Learning
Data Mining, Inference, and Prediction
Series: Springer Series in Statistics
Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome
1st ed. 2001. Corr. 3rd printing, 2003, 552 p., Hardcover
ISBN: 978-0-387-95284-0

A second edition is coming out early next year.

Re your points - see below:

> could someone recommend a review or book on PCA/MDS/SVD/Factor Analysis 
> techniques & best practices for gene expression data?
> I want to visualize each of my samples (3 different conditions; 20 
> timepoints - no need to visualize replication because things will become 
> too messy) . 

I am not sure I understand: making sure that variation between 
replicates is small compared to the variation between your conditions 
and timepoints seems like a basic sanity test  - without which anything 
that follows would be waste of time.

> But I'm frankly a bit overwhelmed by the plethora of options.
> My doubts include:
>     - when it's appropriate to use which technique
>     - should I use it on my complete normalized gene expression data 
> set? Or only on significant genes? 

Depends on what you want to see. The two options look for different things.

Best wishes

Wolfgang Huber, EMBL-EBI, http://www.ebi.ac.uk/huber

> Or on the covariance matrix between 
> microarrays?
>     - even for a simple PCA, there are an overwhelming number of 
> implementations in R (ade's dudi.pca, prcomp, princomp, several in MASS, 
> several in pcaMethods)
> thanks :o)
> yannick
> --------------------------------------------
>          yannick . wurm @ unil . ch
> Ant Genomics, Ecology & Evolution @ Lausanne
>   http://www.unil.ch/dee/page28685_fr.html

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