The range of Raw CT values is around 15 to 35
The 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@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@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@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@r-project.org
>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>>> Search the archives:
>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>>>
>>>>
>>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@r-project.org
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
>

	[[alternative HTML version deleted]]

