[BioC] Do my Limma results look "normal"?

Sat Jun 7 18:48:57 CEST 2008

Paul Geeleher wrote:
> Gordon thanks for your advice again, excellent as always.
> 
> One more thing though, can you clear up for me if the standard
> deviation across all arrays is taken into account by Limma when
> calculating the p-values etc? I presume it is? The main reason I need
> to be clear on this is that my colleague using GeneSpring is booting
> my top miRNA (hsa-miR-451) out of his analysis completely based on
> standard deviation across the arrays.

   You are not telling us quite enough here, some folks filter because 
there is too little variability and others might filter for too much. 
Which is it (and a plot or some summary stats would not hurt, if you 
really want informed opinion).

   Other potential differences that are likely to have big impact with 
microRNA arrays are how it was normalized (as I tried to point out 
previously); almost all assumptions used to normalize mRNA expression 
arrays are not valid for miRNA arrays.  You seemed to have used vsn for 
normalization (I doubt that is an option in GeneSpring, but don't know 
as I don't have access to it).  So you might also want to see if you can 
run their pipeline on data normalized by vsn so you can more clearly see 
what the differences are between the algorithms for assessing DE (you 
have too many factors in the mix).

   But really you and your colleague have all the data (and we don't) 
and a few plots (as suggested earlier) should reveal what the real 
issues are here.

   best wishes
     Robert

> 
> -Paul
> 
> On Sat, Jun 7, 2008 at 7:37 AM, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>> Dear Paul,
>>
>>> Date: Thu, 5 Jun 2008 13:42:40 +0100
>>> From: "Paul Geeleher" <paulgeeleher at gmail.com>
>>> Subject: [BioC] Do my Limma results look "normal"?
>>> To: Bioconductor <bioconductor at stat.math.ethz.ch>
>>>
>>> Hi,
>>>
>>> This is the first time I've ever analyzed a microarray experiment
>>> using Limma (or anything else for that matter) and I was hoping that
>>> somebody could look at my results and tell me if they look normal.
>> You're asking a question that doesn't really have an answer, because all
>> experiments are different and give different results.  Your results suggest
>> a lot of probes are strongly DE, with a predominance of down over up
>> regulated results.  You're the only one who knows the background to your
>> experiment, so you're the only one who knows whether this makes sense from a
>> biological point of view.
>>
>>> The experiment is measuring differential expression between miRNAs of
>>> HER2+ and HER2- breast cancer tissue. There are 3 HER2+ arrays and 4
>>> HER2- arrays and each of the 399 miRNAs is replicated 4 times in each
>>> array.
>>>
>>> TopTable() reveals the following miRNAs with a fold change above 1.5,
>>> which I thought was a reasonable cutoff:
>> If you want a fold change of 1.5, you need lfc=log2(1.5) not lfc=1.5.
>>
>>>                 ID     logFC         t      P.Value    adj.P.Val
>>>  B
>>> 273      hsa-miR-451 -4.645060 -8.226854 4.510441e-09 9.246404e-07
>>> 10.8484797
>>> 128      hsa-miR-205  3.551495  7.574564 2.370061e-08 3.239083e-06
>>>  9.2222865
>>> 13       hsa-miR-101 -2.310652 -6.569497 3.374177e-07 2.567796e-05
>>>  6.6146751
>>> 282      hsa-miR-486 -2.686910 -6.542808 3.626060e-07 2.567796e-05
>>>  6.5439656
>>> 55       hsa-miR-144 -2.890719 -5.889594 2.152998e-06 1.261042e-04
>>>  4.7952480
>>> 387      mmu-miR-463 -2.609257 -5.764143 3.042120e-06 1.559086e-04
>>>  4.4561920
>>> 388      mmu-miR-464 -2.080402 -5.696976 3.662006e-06 1.668247e-04
>>>  4.2743601
>>> 151      hsa-miR-223 -1.722956 -5.637290 4.318942e-06 1.770766e-04
>>>  4.1126276
>>> 51    hsa-miR-142-3p -3.262824 -5.397809 8.386312e-06 3.125807e-04
>>>  3.4626378
>>> 14   hsa-miR-101_MM1 -1.922710 -5.224075 1.358743e-05 4.175776e-04
>>>  2.9905370
>>> 159  hsa-miR-26b_MM2 -2.221853 -5.206724 1.425875e-05 4.175776e-04
>>>  2.9433849
>>> 236 hsa-miR-376a_MM1 -1.633555 -4.653220 6.637043e-05 1.700742e-03
>>>  1.4433277
>>> 266     hsa-miR-432*  1.512622  4.627293 7.131510e-05 1.719952e-03
>>>  1.3734422
>>> 168      hsa-miR-29b -1.954087 -4.198854 2.323860e-04 4.763912e-03
>>>  0.2280262
>>> 31  hsa-miR-126*_MM2 -1.537988 -3.209957 3.233842e-03 5.099520e-02
>>> -2.2888897
>>> 52    hsa-miR-142-5p -1.881192 -2.831493 8.332384e-03 9.002153e-02
>>> -3.1731794
>>>
>>>
>>> Another person is sanity testing this data using GeneSpring and they
>>> are getting much higher p-values compared to mine.
>> This is not surprising considering that GeneSpring has chosen not to use any
>> statistical test invented since 1947.  In particular, it has not taken any
>> advantage of the last 8 years' intensive research on differential expression
>> for microarray data.
>>
>>> They are also taking the step of excluding quite a few of the miRNAs from
>>> the experiment based on their standard deviation across the arrays of each
>>> group. Should I be doing this also or is this taken into account by the
>>> eBayes() function or lmFit()?
>> You could choose to filter on raw standard deviation across all arrays. Some
>> authors recommend this.  Or you could filter on mean intensity. With only
>> 399 probes on your arrays, I doubt either of these things would make much
>> difference, but they might.
>>
>> It does not make sense to filter on standard deviation computed within
>> groups.
>>
>> Best wishes
>> Gordon
>>
>>> If you are interested the script I wrote to do the analysis is here:
>>>
>>> http://article.gmane.org/gmane.science.biology.informatics.conductor/18032/match=miRNA
>>>
>>> Thanks for any advice,
>>>
>>> -Paul.
> 
> 
> 

-- 
Robert Gentleman, PhD
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N, M2-B876
PO Box 19024
Seattle, Washington 98109-1024
206-667-7700
rgentlem at fhcrc.org