[BioC] Timeseries loop design analysis using Limma or Maanova?
Pete
p.underhill at har.mrc.ac.uk
Wed Feb 15 16:38:57 CET 2006
Thanks for your response,
>>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.
>
I'm not quite sure I understand your point here? I was going to treat this
as a simple dye swap experiment, ignoring time and comparing mutant to WT.
Is this not a statistically valid approach? There are 3 independ mutant
samples compared in dyeswaps to the WT pool. I understand that there is no
biological replicate for the WT pool, however it is technically replicated
at the dyeswap level and cDNA synthesis level. The biological variation of
the WT population is not of immediate interest in this case, hence a pool
was used. Individual mutant samples were used instead of a pool, because
only a limited number of mutants were available.
>
>>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 agree with your statement about clustering, and prehaps I didn't word my
question very clearly. The timeseires clustering will indeed be performed on
genes selected as differential with respect to time. In the past I have used
the MAANOVA package to select these differential genes, however in that
particular case, the samples were all compared back to a single reference
sample rather than multiple references that are then compared to eachother
in an incomplete loop.
The issue I am concerned with is how to both, select genes that have a time
effect, and how/what to use as a standardised expression level for these
genes so that it can then be used in the clustering.
Cheers
Pete
>
>
>
>>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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>>Bioconductor at stat.math.ethz.ch
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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
>
> _______________________________________________
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