[BioC] is-normalisation-really-required
Gregor GORJANC
gregor.gorjanc at bfro.uni-lj.si
Thu May 12 08:26:45 CEST 2005
Naomi Altman wrote:
> I would add that the reason the TopTable results do not agree with the
> 2-fold or more results, is that generally statistical tests compare
> treatment mean differences
> to within treatment variation. Hence, if the results are not variable,
> you will have many statistically significant genes that have less than
> 2-fold difference. As mentioned many times on this list, statistical
> significance does not imply biological significance, but differences
> that are not statistically significant may be due to chance variation
> and thus are unlikely to have biological significance. The converse
> side of this is that if genes are highly variable, they may have more
> than 2-fold difference and not be statistically significant.
>
> The purpose of normalization is to remove biases that differ from array
> to array due to the hybridization and labeling processes, so that
> comparisons between conditions are free of this part of the experimental
> error. This improves our power to detect statistically significant
> differential expression.
>
Naomi, thanks for the update!
>> Gorjanc and Vijay
>>
>> This is a misconception as to why to normalize the data. It is not so
>> that we can get "pleasing" results or agreement between analytic
>> methods but because statistically it is the correct thing to do. If I
>> use the wrong statistical test on a set of data (e.g. parametric tests
>> on data that violates all the assumptions) and it gives the same
>> result as an appropriate non-parametric analysis that does not make it
>> "right" and ok to do again. It means I got lucky. If the analysis of
>> non-normalized data is the same as of normalized data you are lucky not
>> right. Sean is on target- if they agree normalize; if they do not agree
>> normalize. I would add to that why bother analyzing the non-normalized
>> data.
>>
>> Gordon
>>
>>
>> Gordon A. Barr, Ph.D.
>> Senior Research Scientist
>> NYS Psychiatric Institute
>> Columbia College of Physicians and Surgeons
>> 212-543-5694 (V)
>> 212-543-5467 (F)
>> "There is no flag large enough to cover the shame of killing innocent
>> people." -- Howard Zinn
>> _____________________________________________________
>> This e-mail is confidential and may be privileged. Use or disclosure
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>> you are not an intended recipient, please delete this e-mail.
>>
>> On Apr 13, 2005, at 8:29 AM, Gorjanc Gregor wrote:
>>
>>>> -----Original Message-----
>>>> From: Sean Davis [mailto:sdavis2 at mail.nih.gov]
>>>> Sent: sre 2005-04-13 14:16
>>>> To: Gorjanc Gregor
>>>> Cc: bioconductor at stat.math.ethz.ch
>>>> Subject: Re: [BioC] is-normalisation-really-required
>>>>
>>>> On Apr 13, 2005, at 8:03 AM, Gorjanc Gregor wrote:
>>>>
>>>>> Hi!
>>>>>
>>>>> You might try analysis with and without normalization and take
>>>>> a look at the results. If they say the same thing than I would
>>>>> say, no it is not necessary to do normalization.
>>>>
>>>>
>>>> So, if the two results agree, then the results with normalization are
>>>> correct; if not then the results with normalization are still correct.
>>>> Sounds like we are pretty much stuck with normalization....
>>>>
>>>> Sean
>>>
>>> Why should one do normalization if the results aren't different. But,
>>> in
>>> that case it really does not matter and one can do it or not.
>>>
>>>
>>>>> dear friends
>>>>> i have situation, where i thought its ok for me not to
>>>>> do normalisation, i am afraid i may be wrong. i want
>>>>> your advice in this regard.
>>>>>
>>>>> we performed a wild type - mutant, dye-swap
>>>>> experiment.
>>>>> when we analysed the intensity values, they were
>>>>> consistant among the two experiment (dye-swap). ie.,
>>>>> almost same values for mutants in both the experiments
>>>>> of the dye-swap.
>>>>> since the values are almost same, i thought there
>>>>> might not be any dye-bias, so i just went ahead,
>>>>> averaged the two values, found out their ratio and
>>>>> filtered genes with 2 fold change.
>>>>>
>>>>> so i have done this without normalisation.
>>>>> i am afraid, i might be wrong, my 2 fold chaging genes
>>>>> might be wrong...
>>>>> kindly give me your advice in this regard.
>>>>> i did analyse the data with limma, but the topTable
>>>>> genes there never correlates with my 2 fold genes.
>>>>>
>>>>> kindly correct me.
>>>>> thanks
>>>>>
>>>>> vijay
>>>>> graduate student
>>>>> department of biological sciences
>>>>> the university of southern mississippi
>>>>> MS, USA
>>>>
>>>>
>>>> --
>>>> Lep pozdrav / With regards,
>>>> Gregor Gorjanc
>>>>
>>>> ----------------------------------------------------------------------
>>>> -
>>>> -
>>>> University of Ljubljana
>>>> Biotechnical Faculty URI: http://www.bfro.uni-lj.si/MR/ggorjan
>>>> Zootechnical Department email: gregor.gorjanc <at> bfro.uni-lj.si
>>>> Groblje 3 tel: +386 (0)1 72 17 861
>>>> SI-1230 Domzale fax: +386 (0)1 72 17 888
>>>> Slovenia
>>>>
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>>
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>
>
> Naomi S. Altman 814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics 814-863-7114 (fax)
> Penn State University 814-865-1348 (Statistics)
> University Park, PA 16802-2111
>
>
--
Lep pozdrav / With regards,
Gregor Gorjanc
----------------------------------------------------------------------
University of Ljubljana
Biotechnical Faculty URI: http://www.bfro.uni-lj.si/MR/ggorjan
Zootechnical Department mail: gregor.gorjanc <at> bfro.uni-lj.si
Groblje 3 tel: +386 (0)1 72 17 861
SI-1230 Domzale fax: +386 (0)1 72 17 888
Slovenia, Europe
----------------------------------------------------------------------
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