[BioC] Different levels of replicates and how to create a correct targets file out of that.

Gordon Smyth smyth at wehi.edu.au
Wed Mar 31 02:32:37 CEST 2004


At 11:51 PM 30/03/2004, Johan Lindberg wrote:
>Sorry, I forgot to have a subject on the mail I sent before.
>
>Hello everyone.
>I would really appreciate some comments/hints/help with a pretty long 
>question.

This question has been asked on the list before. See:

https://stat.ethz.ch/pipermail/bioconductor/2003-December/003277.html

The simplest treatment in limma is simply to treat your experiment as 
having two factors, one factor having 10 levels indicating the patient and 
one taking two levels, before or after. This treatment is analogous to a 
paired-test or to a two-way analysis of variance.

An alternative treatment would be to treat the patients as random effects. 
That would also be a correct treatment, and potentially a little more 
powerful, but also much more difficult and I don't think you gain very much.

>I have an experiment consisting of 18 hybridizations. On the 30K cDNA 
>arrays knee joint bioipsies (from different patients) before and after a 
>certain treatment is hybridized. What I want to find out is the effect of 
>the treatment, not the difference between the patients. The problem is how 
>to deal with different levels of replicates and how to create a correct 
>target file since I have no common reference?
>This is how the experimental set-up looks like.
>
>Patient Hybridization   Cy3                                     Cy5
>1               1A                      Biopsy 1 before 
>treatment       Biopsy 1 after treatment
>                 1B                      Biopsy 1 after 
> treatment        Biopsy 1 before treatment
>3               2A                      Biopsy 1 before 
>treatment       Biopsy 1 after treatment
>                 2B                      Biopsy 1 after 
> treatment        Biopsy 1 before treatment
>                 3A                      Biopsy 2 before 
> treatment       Biopsy 2 after treatment
>                 3B                      Biopsy 2 after 
> treatment        Biopsy 2 before treatment
>4               4A                      Biopsy 1 before 
>treatment       Biopsy 1 after treatment
>                 4B                      Biopsy 1 after 
> treatment        Biopsy 1 before treatment
>                 5A                      Biopsy 2 before 
> treatment       Biopsy 2 after treatment
>                 5B                      Biopsy 2 after 
> treatment        Biopsy 2 before treatment
>5               6A                      Biopsy 1 before 
>treatment       Biopsy 1 after treatment
>                 6B                      Biopsy 1 after 
> treatment        Biopsy 1 before treatment
>6               7A                      Biopsy 1 before 
>treatment       Biopsy 1 after treatment
>                 7B                      Biopsy 1 after 
> treatment        Biopsy 1 before treatment
>7               8A                      Biopsy 1 before 
>treatment       Biopsy 1 after treatment
>                 8B                      Biopsy 1 after 
> treatment        Biopsy 1 before treatment
>10              9A                      Biopsy 1 before 
>treatment       Biopsy 1 after treatment
>                 9B                      Biopsy 1 after 
> treatment        Biopsy 1 before treatment
>
>As you can see different patients have one or two biopsies taken from 
>them. Since I realize it would be a mistake to include all those into the 
>target file because if I have more measurements of a certain patient that 
>would bias the ranking of the B-stat towards the patient having the most 
>biopsies in the end, right? Or?
>Since the differentially expressed genes in the patient with more biopsies 
>will get smaller variance?
>
>My solution to the problem was just to create an artificial Mmatrix twice 
>as long as the original MA object. For the patients with two biopsies I 
>averaged over the technical replicates (dye-swaps) and put the values from 
>biopsy one and then the values from biopsy two in the matrix. From 
>patients with just a technical replicate I put the values from 
>hybridization 1A and then hybridization 1B into the matrix.
>
>The M-values of that matrix object would look something like:
>
>                         patient 
> 1               patient3                                        ....
>Rows 1-30000    Hybridization 1A        Average of hybridization 2A and 
>2B      ....
>Rows 30001-60000        Hybridization 1B        Average of hybridization 
>3A and 3B      ....
>
>After this I plan to use dupcor on the new matrix of M-values, as if I 
>would have a slide with replicate spots on it.
>
>So far so good or? Is this a good way of treating replicates on different 
>levels or has anyone else some better idea of how to do this. Comments 
>please.....
>
>
>And now, how to create a correct targets file since I have no common 
>reference.
>I guess it would look something like this:
>
>SlideNumber     Name    FileName        Cy3     Cy5
>1       pat1_p  test1.gpr       Before_p1       After_p1
>2       pat3_p  test2.gpr       Before_p2       After_p2
>3       pat4_p  test3.gpr       Before_p3       After_p3
>4       pat6_p  test4.gpr       Before_p4       After_p4
>5       pat7_p  test5.gpr       Before_p5       After_p5
>6       pat10_p test6.gpr       Before_p6       After_p6
>
>But when I want to make my contrast matrix I am lost since I do not have 
>anything to write as ref.
>design <- modelMatrix(targets, ref="????????")
>
>If I redo the matrix to
>
>SlideNumber     Name    FileName        Cy3     Cy5
>1       pat1_p  test1.gpr       Before_p        After_p
>2       pat3_p  test2.gpr       Before_p        After_p
>3       pat4_p  test3.gpr       Before_p        After_p
>4       pat6_p  test4.gpr       Before_p        After_p
>5       pat7_p  test5.gpr       Before_p        After_p
>6       pat10_p test6.gpr       Before_p        After_p
>
>wouldnt that be the same as treating this as a common reference design 
>when it is not? And wouldnt that effect the variance of the experiment? 
>How do I do this in a correct way.
>I looked at the Zebra fish example in the LIMMA user guide but isnt that 
>wrong as well. Because technical and biological replicates are treated the 
>same way in the targets file of the zebra fish.

Dye-swap pairs are not necessarily technical replicates.

>I realize that many of these questions should have been considered before 
>conducting the lab part but unfortunately they were not. So I will not be 
>surprised if someone sends me the same quote as I got yesterday from a friend:
>
>"To consult a statistician after an experiment is finished is often merely 
>to ask him to conduct a post mortem examination. He can perhaps say what 
>the experiment died of."
>- R.A. Fisher, Presidential Address to the First Indian Statistical 
>Congress, 1938
>
>Best regards

Gordon

>/Johan Lindberg
>
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