[BioC] PCA scree plot question

Ingunn Berget ingunn.berget at umb.no
Wed Jun 17 09:21:43 CEST 2009


Sometimes a look at the loadings (eigenvectors) can give information about which variables (genes) that contribute most to the different components.
Also you can scale the y-axis in the scree plot to say how much % of the variance that is explained by each component.

Best regards 
Ingunn



>-----Original Message-----
>From: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-
>bounces at stat.math.ethz.ch] On Behalf Of Paul Geeleher
>Sent: Tuesday, June 16, 2009 6:53 PM
>To: Naomi Altman
>Cc: bioconductor at stat.math.ethz.ch
>Subject: Re: [BioC] PCA scree plot question
>
>So it doesn't make sense to say that subset of genes is exclusively
>responsible for a single component? I take what you are saying to mean
>that every gene contributes to every component but to a different
>degree. I'll have to do some background reading on this PCA stuff...
>
>On Tue, Jun 16, 2009 at 4:34 PM, Naomi Altman<naomi at stat.psu.edu> wrote:
>> The fun thing about eigenvalues is that they are linear combinations
>of ALL
>> the genes.
>>
>> At 10:54 AM 6/16/2009, you wrote:
>>>
>>> Oh right, is there any way to figure out how many genes are involved
>>> in the component or does that even make sense?
>>>
>>> On Tue, Jun 16, 2009 at 3:39 PM, Naomi Altman<naomi at stat.psu.edu>
>wrote:
>>> > Y=variance of component
>>> >
>>> > units are measurement units^2
>>> >
>>> > --Naomi
>>> >
>>> > At 10:05 AM 6/16/2009, you wrote:
>>> >>
>>> >> Can anyone tell me what the units of the numbers on the Y-axis of
>the
>>> >> PCA plot are? I think its the number of genes involved in that
>>> >> particular component but maybe someone can tell me for sure? See
>here
>>> >> for an example of what I'm talking about:
>>> >>
>>> >> http://frink.nuigalway.ie/~pat/PCAScreePlotNorm.pdf
>>> >>
>>> >> Thanks alot,
>>> >>
>>> >> -Paul.
>>> >>
>>> >> _______________________________________________
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>>> >
>>> > Naomi S. Altman                                814-865-3791 (voice)
>>> > Associate Professor
>>> > Dept. of Statistics                              814-863-7114 (fax)
>>> > Penn State University                         814-865-1348
>(Statistics)
>>> > University Park, PA 16802-2111
>>> >
>>> >
>>
>> Naomi S. Altman                                814-865-3791 (voice)
>> Associate Professor
>> Dept. of Statistics                              814-863-7114 (fax)
>> Penn State University                         814-865-1348
>(Statistics)
>> University Park, PA 16802-2111
>>
>>
>
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