[BioC] limma: cannot repoduce older analysis

[Marcus Davy]

Additional to the points mentioned, you can check the log of changes to
limma, e.g.

changeLog(n=200)

To determine what functions may have changed in the time period.

When comparing your analysis from a reproducible research point of view if
you have any old objects stored (saved R session or objects dumped to disk)
you can using the functions "identical" and "all.equal" to examine where
analysis might deviate when trying to reproduce it.


Marcus



On 24/7/08 10:41 PM, "Wolfgang Huber" <huber at ebi.ac.uk> wrote:

> Hi Philipp
> 
> the quickest way to figure out what's going on may be to download and
> install an older version of R (as good as you can remember what it was),
> run
>  biocLite("limma")
> and then your script and see exactly at what point your results start to
> differ.
> 
> Is "targets" the same? (esp if you imported it from text file via
> read.table or the like, there can be surprises e.g. to do with
> locales/encoding, special characters...)
> 
> Best wishes
>  Wolfgang
> 
> ------------------------------------------------------------------
> Wolfgang Huber  EBI/EMBL  Cambridge UK  http://www.ebi.ac.uk/huber
> 
> 
> 24/07/2008 11:23 Philipp Pagel scripsit
>> Dear list,
>> 
>> About 3 months ago I analyzed a simple two-color array experiment and got
>> results that looked quite reasonable and biologically sound. For some reason
>> I
>> wanted to repeat the analysis and add a few plots that I had not included
>> before.
>> 
>> When I got VERY different results in my toptable, I assumed I must have
>> changed something in my approach so I simply ran my original analysis
>> script again and found I was unable to reproduce the original toptable.
>> I have spent quite some time trying to debug the problem and have to say
>> that I am stuck. I have the original data files and the original
>> R-script. The normalization is 100% reproducible - i.e. the normalized
>> MALists seem to be identical. Yet when searching for differential
>> expression I get totally different results.
>> 
>> The only difference between the two runs lies in updates to R and limma in
>> the meantime. Unfortunately, I did not record which version of R, limma etc.
>> I
>> had used, originally. My current environment is this:
>> 
>>> sessionInfo()
>> R version 2.7.1 (2008-06-23)
>> x86_64-pc-linux-gnu
>> 
>> locale:
>> LC_CTYPE=en_US.utf8;LC_NUMERIC=C;LC_TIME=en_US.utf8;LC_COLLATE=en_US.utf8;LC_
>> MONETARY=C;LC_MESSAGES=en_US.utf8;LC_PAPER=en_US.utf8;LC_NAME=C;LC_ADDRESS=C;
>> LC_TELEPHONE=C;LC_MEASUREMENT=en_US.utf8;LC_IDENTIFICATION=C
>> 
>> attached base packages:
>> [1] splines   stats     graphics  utils     datasets  grDevices methods
>> base
>> 
>> other attached packages:
>> [1] statmod_1.3.6   MASS_7.2-42     xtable_1.5-2    limma_2.14.2
>> lattice_0.17-10
>> [6] cairoDevice_2.8
>> 
>> loaded via a namespace (and not attached):
>> [1] grid_2.7.1  tools_2.7.1
>> 
>> 
>> My search for differential expression seems pretty standard to me:
>> 
>> MA$design <- modelMatrix(targets, ref="control")
>> # flag out controls etc.
>> MA$weights[MA$genes$Status != 'miRNA', ] = 0.0
>> # sort spots by ID to put replicates next to each other
>> MA2 <- MA[order(MA$genes$ID), ]
>> 
>> dupfit <- duplicateCorrelation(MA2, ndups=4)
>> fit <- lmFit(MA2, ndups=4, correlation=dupfit$consensus)
>> fit <- eBayes(fit)
>> tt <- topTable(fit, number=100)
>> 
>> I have siftet through the changelog of limma hoping to find a hint about
>> some changed default or behaviour in lmFit or eBayes but saw nothing
>> that seemed to expain my problem.
>> 
>> Any hints apprechiated.
>> 
>> cu
>> Philipp
>> 
> 
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