[BioC] Delta CT data distribution and cluster analyses; machine learning or other
Richard Friedman
friedman at cancercenter.columbia.edu
Sun May 15 20:01:50 CEST 2011
John,
Do not raise deltaCt to a power and do a t-test.
To test the hypothesis do deltaCt(condition 1)=deltaCt(condiiton 2) with a
t-test.
deltaCt=-log2M and will be closer to nornally distbutes that 2^-delatCt.
I hope this helps.
Best wishes,
Rich
On Sat, 14 May 2011, john herbert wrote:
> The range of Raw CT values is around 15 to 35The 2^-deltaCT are very small, less than zero. An example is 0.079703285
> I have 5 case samples and 5 control samples. For all samples, there are CT measures for target genes and house-keeper genes. Our approach is to
> use houskeeper on each sample as that used in Delta CT calculation.
>
> E.g.
> Sample Case 1 target CT = 15
> Sample Case 1 house keeper CT = 10
> Delta CT = 15-10 = 5
> A = 2 to the power of minus delta CT, as in Excel =power(2,-(-5)) = 0.03125
>
> Then normal sample is the same....
> Sample normal 1 target CT = 10
> Sample normal 1 house keeper CT = 4
> Delta CT = 10-4 = 6
> 2 to the power of minus delta CT, as in Excel =power(2,-(-6)) = 0.015625
>
> I have lots of these small values. These values don't look normally distributed.
>
> My view is maybe I should make an M value (log2 ratios) do ttests etc.
>
> Is this the best way to go for gene expression and subsequent clustering?.
>
> Thank you.
>
>
> On Fri, May 13, 2011 at 9:06 PM, Kevin R. Coombes <kevin.r.coombes at gmail.com> wrote:
> What is the range of the data that you received?
>
> In most TaqMan real-time PCR experiments, the Ct values range between about 10 (for really really abuindant things like 18S) to 40.
> These measurements are in cycles. In principle, if you had perfectly efficient probe-primer combination, the number of mRNA
> molecules present would double every cycle. As a result, cycle values are already essentially on the "negative log base two" scale.
>
> As Richard already pointed out, the Delta-Ct or Delta-Delta-Ct values on this scale are usually normal.
>
> If your data are not in a range that makes sense as cycles, then it is likely that someone exponentiated the data to get it back to
> the "raw" scale, and thus converted from normally distributed to log-normal.
>
> Kevin
>
>
>
> 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.
>
>
>
>
>
> 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
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> (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?
>
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>
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--
------------------------------------------------------------
Richard A. Friedman, PhD
Associate Research Scientist
Herbert Irving Comprehensive Cancer Center
Biomedical Informatics Shared Resource
Lecturer
Department of Biomedical Informatics
Box 95, Room 130BB or P&S 1-420C
Columbia University Medical Center
630 W. 168th St.
New York, NY 10032
(212)305-6901 (5-6901) (voice)
friedman at cancercenter.columbia.edu
http://cancercenter.columbia.edu/~friedman/
"The last 250 pages of the last Harry Potter
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