[BioC] Delta CT data distribution and cluster analyses; machine learning or other

[Richard Friedman]

John,

	I inadvertently took the correspondence off line.
By control gene do you mean something that is the same control in all  
experiments?
Or is the control gene the same as the Target gene under different  
conditions?
	

Best wishes,
Rich
On May 13, 2011, at 1:14 PM, john herbert wrote:

> The exact values I have are;
> Step 1 = Delta CT = CT of a target gene - CT of a control gene
>
> Normalised values in the data are 2^-(Delta CT) (2 to the power - DCT)
>
> I am wondering, as this data in this form, looks like microarray  
> data. So can I quantile normalise? And can I cluster based on the  
> normalised data?
>
> Sorry if I was not clear.
>
>
>
> On Fri, May 13, 2011 at 5:29 PM, Richard Friedman <friedman at cancercenter.columbia.edu 
> > wrote:
>
> On May 13, 2011, at 12:04 PM, john herbert wrote:
>
> Hi Richard,
> Thank you. It is from taqman real time PCR. I have sent a mail  
> asking how exactly they normalised the data.
> We only have biological replicates and no common reference, so I was  
> told we can only use Delta CT values.
>
> I make, maybe wrongly, that is Delta Delta CT values are normally  
> distributed that Delta CT values will also be normally distributed?
>
> I will make plots of the raw data and Delta CT as I know it.
>
>
>
>
> What does your Delta CT represent? The change of what and what?
>
>
>
>
>
>
>
>
> On Fri, May 13, 2011 at 3:53 PM, Richard Friedman <friedman at cancercenter.columbia.edu 
> > wrote:
> Dear John,
>
>       Is the Delta CT data from PCR or from some other method?
> If it is from PCR in my experience Delta Delta CT is usually  
> normally distributed.
> were the first delta references to the difference between the  
> experiment and internal reference
> (e.g. GAPDH) and the second delta refers to 2 experimental conditions.
>
> With hopes that the above helps,
> Rich
> ------------------------------------------------------------
> Richard A. Friedman, PhD
> Associate Research Scientist,
> Biomedical Informatics Shared Resource
> Herbert Irving Comprehensive Cancer Center (HICCC)
> Lecturer,
> Department of Biomedical Informatics (DBMI)
> Educational Coordinator,
> Center for Computational Biology and Bioinformatics (C2B2)/
> National Center for Multiscale Analysis of Genomic Networks (MAGNet)
> Room 824
> Irving Cancer Research Center
> Columbia University
> 1130 St. Nicholas Ave
> New York, NY 10032
> (212)851-4765 (voice)
> friedman at cancercenter.columbia.edu
> http://cancercenter.columbia.edu/~friedman/
>
> I am a Bayesian. When I see a multiple-choice question on a test and  
> I don't
> know the answer I say "eeney-meaney-miney-moe".
>
> Rose Friedman, Age 14
>
>
>
>
>
>
>
>
> On May 13, 2011, at 10:46 AM, john herbert wrote:
>
> Dear Bioconductors,
> I have a bunch of DeltaCT values for several tissues. If I boxplot  
> the data,
> it looks very similar to microarray data, a lot of congestion around  
> zero.
>
> Likewise, if I log2 the data, as in microarray, the distributions  
> looks
> close to normal and like microarray data.
>
> Please see the image here for different plots;
> https://docs.google.com/leaf?id=0B9IUGsKecS4GNDc0OWVlNzEtZjE5Yi00Y2Q4LWI0M2MtMGFiNzZhMDU0YTFm&hl=en
>
> My question is data manipulation in this manner OK for this type of  
> data and
> will it effect/invalidate any unsupervised machine learning/ 
> clustering?
>
> Can I quantile normalise the data and still do valid clustering?
>
>       [[alternative HTML version deleted]]
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
>
>
>
>