[BioC] Timeseries loop design analysis using Limma or Maanova?

[Jenny Drnevich]

At 10:07 AM 2/13/2006, Pete wrote:
>Hello all,
>
>I have been asked to analyse a set of timecourse data with an unusual
>incomplete loop design. This is the design of this type I have looked at
>and I'm not entirely sure how to treat it.
>
>The initial (and fairly easy) question asked of the data is, what are the
>differences between the mutant and the control animals at each timepoint?

I am interested in how you are going to analyze the differences between 
mutants and controls at each time point given that there is no replication 
of the control animals (only 1 control pool). I just advised a researcher 
against this kind of experimental design because I could not think offhand 
of a way to analyze it statistically. If there is a statistically valid 
method, I would like to know about it.


>The second question is how the mutant changes across the timeseries. The
>authors
>wish to use a bayesian timeseries clustering algorithmn to analyse this, but
>this requires a standardised measure for the mutant at each timepoint.

How are you going to implement this bayesian timeseries clustering? My 
interpretation of clustering algorithms in general is that they should not 
be used to determine which genes are "differentially" expressed, but rather 
one should first use a statistical model to determine differential 
expression, then only cluster the genes that show a significant difference 
somewhere along the time series to find groups of genes that show a similar 
expression pattern. My approach to this situation would be something along 
the lines of a single-channel analysis using a mixed model with array + dye 
+ treatment + time + treatment*time, and then cluster genes that showed a 
significant time effect, using the lsmeans for each mutant*timepoint group. 
The lack of replication of the controls may cause this not to work...

Cheers,
Jenny



>I am unsure quite how to achieve this second point and welcome any
>suggestions or references that may help. Is this something I could do in
>Limma or MAanova?
>
>
>The data are from spotted, two-colour, oligo arrays. There are 6 timepoints.
>At each timepoint, tissue samples from 3 individual mutant animals are
>compared to a control pool of WT animals at the same timepoint, with dye
>swaps. In addition each control pool has then been compared in a dye swap to
>the next timepoint control pool. See diagram below (if it comes out
>correctly!) or the table further below where a1 a2 a3 represent any 3
>individual animals.
>
>
>
>a1t1    a2t1    a3t1           a1t2    a2t2    a3t2      etc............
>     \\        ||        //                \\        ||        //
>      Control t1  ========= Control t2    ==== etc...............
>
>or
>
>SLIDE        CY3            CY5
>1                a1t1            control t1
>2                control t1    a1t1
>3                a2t1            control t1
>4                control t1     a2t1
>5                a3t1            control t1
>6                control t1    a3t1
>7                a1t2            control t2
>8                control t2    a1t2
>9                a2t2            control t2
>10                control t2        a2t2
>11                a3t2                control t2
>12                control t2        a3t2
>13                a1t3                control t3
>14                control t3        a1t3
>15                a2t3                control t3
>16                control t3        a2t3
>17                a3t3                control t3
>18                control t3        a3t3
>19                a1t4                control t4
>20                control t4        a1t4
>21                a2t4                control t4
>22                control t4        a2t4
>23                a3t4                control t4
>24                control t4        a3t4
>25                a1t5                control t5
>26                control t5        a1t5
>27                a2t5                control t5
>28                control t5        a2t5
>29                a3t5                control t5
>30                control t5        a3t5
>31                a1t6                control t6
>32                control t6        a1t6
>33                a2t6                control t6
>34                control t6        a2t6
>35                a3t6                control t6
>36                control t6        a3t6
>37                control t1        control t2
>38                control t2        control t1
>39                control t2        control t3
>40                control t3        control t2
>41                control t3        control t4
>42                control t4        control t3
>43                control t4        control t5
>44                control t5        control t4
>45                control t5        control t6
>46                control t6        control t5
>
>
>Many thanks
>
>Pete
>
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Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA

ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at uiuc.edu