best way to do Principal Component Analysis (PCA)

[james.odon]

Hello all,

I need to do a principal component analysis on my data, but I have no experience with this and do not know anyone who has done it.

Is it possible to do with QIIME?  If not, what is the best way to go about doing this.  What programming languages or packages are typically used to do this?

Thanks in advance!

Jim

[John Chase]

Hi,

Yes, you can run principle components analysis is possible with QIIME. This would be done by running beta_diversity.py with the euclidean distance metric, and then running principal_coordinates.py on the distance matrix created in the first step. 

R is commonly used for this type of analysis, though I don't have enough of a background in R to comment on that. 

If anyone has any other suggestions, they would be welcome.

John

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[James O'Donnell]

So that will give you a principal components analysis (PCA) rather than a principal coordinates analysis (PCoA) ?

-Jim

[John Chase]

Yes, that's correct. Principle components is a principle coordinates analysis where the distance metric is specifically Euclidean distance. 

John

[spol...@gmail.com]

When I compare my results with beta_div.py using binary_euclidean vs euclidian, the binary_euclidian looks a lot more like what I expected once it is displayed with Emperor.   The MDS plot I got with cummeRbund looks like a 2D projection of the 3D plot obtained with the binary_euclidian.  On the page http://qiime.org/scripts/beta_diversity.html it says that binary_ "specifies that a metric is qualitative, and considers only the presence or absence of each taxon". I am now not sure if you meant to say euclidian or binary_euclidan.   Can you comment on this, and help explain the difference?

Thanks

Bill

[Antonio González Peña]

Well, PCA is a special case of PCoA using euclidean distances. Now,
you are comparing your results from euclidean and binary_euclidean to
cummeRbund and to be honest I don't know what that script uses.
Furthermore, the documentation is not explicit abot it:
http://bioconductor.org/packages/release/bioc/manuals/cummeRbund/man/cummeRbund.pdf.
Anyway, my guess is that they are using euclidean but they are log
transforming the values: logMode is set to TRUE by default. Thus,
suggest trying to set that value to FALSE and check if the plots now
look more similar.

Antonio