Dear all,

Forgive me for asking help for such a basic issue. After following a course
 on R and microarray analysis, I am determined to get better at it.
 I believe I am very close to at least doing all the basic gene expression
 analysis (and hope to get more experienced from there), but I am stuck
 now at a relatively crucial stage.

My aim is straightforward: to extract sets of upregulated and downregulated
 genes between two conditions from experimental microarray data obtained
 with mouse430_2 arrays.

So, I obtained moderated t-stats after rma preprocessing:

> library(limma)
> design<-model.matrix(~factor(m31_eset$genotype))
> fit<-lmFit(m31_eset, design)
> ebayes<-eBayes(fit)
> etab<-topTable(ebayes, coef=2, number=50, adjust.method="fdr", p.value=0.05,lfc=0)
> etab
                ID      logFC   AveExpr          t      P.Value    adj.P.Val
37172   1452877_at -2.1867149 10.604163 -28.094315 4.561519e-10 2.057291e-05
17792   1433486_at -1.7610156  8.260051 -25.770171 9.826099e-10 2.215834e-05
25554   1441248_at -2.0141624  7.457400 -23.580934 2.159235e-09 3.246122e-05
17793   1433487_at -1.5573738  7.662227 -21.945441 4.078449e-09 4.598553e-05
14414   1430108_at -1.9558887  5.105592 -18.933815 1.497980e-08 1.288990e-04
941   1416610_a_at -1.6156069  8.287563 -18.644676 1.714805e-08 1.288990e-04
22672 1438366_x_at -1.6615440  9.385415 -16.024782 6.449002e-08 3.712528e-04
31808   1447502_at -1.9017788  6.208795 -15.986360 6.585269e-08 3.712528e-04
18573   1434267_at -1.3436798  7.025515 -13.057854 3.789018e-07 1.807532e-03
39737   1455442_at -1.4437945  6.134666 -12.972677 4.007742e-07 1.807532e-03
13110   1428804_at -1.1810039  4.567535 -12.800456 4.493937e-07 1.842555e-03
20345   1436039_at -0.9342354  8.330907 -10.566949 2.282503e-06 8.578598e-03
22898   1438592_at -1.5371677  4.240373 -10.361602 2.689797e-06 9.331735e-03
27128   1442822_at  0.7779881  6.374739  10.236780 2.976198e-06 9.587823e-03
21052   1436746_at  0.7136296  8.349428   9.664449 4.799472e-06 1.443073e-02
28394   1444088_at -0.8543858  6.742698  -9.354540 6.279629e-06 1.770110e-02
19280   1434974_at  0.6953667  6.055993   9.044643 8.278120e-06 2.077425e-02
12901   1428595_at -0.9757636  3.665744  -9.042911 8.291091e-06 2.077425e-02
25266   1440960_at  0.8017912  5.218203   8.634198 1.208269e-05 2.868112e-02
9419  1425113_x_at -0.8156575  8.968278  -8.348717 1.585442e-05 3.407913e-02
20142   1435836_at  0.6078861  7.724573   8.347830 1.586798e-05 3.407913e-02
             B
37172 6.877374
17792 6.751444
25554 6.602080
17793 6.464845
14414 6.132030
941   6.093091
22672 5.663773
31808 5.656270
18573 4.942105
39737 4.916286
13110 4.863031
20345 4.022656
22898 3.928929
27128 3.870369                         

The outcome looks fine, with genes with p-values below 0.05, independent
 of fold-change (and genes have been confirmed in 'Resolver'). 

However, I want separate files for probe sets with logFC>0 (upregulated)
 and logFC<0 (downregulated), but the option lfc=0 does not distinguish
 between positive or negative fold change. I have tried lfc>0 and <0,
 but the function does not recognise that.

There must be a simple way of obtaining these separate data files,
  but I have been banging my head over this for the whole weekend now,
 but cannot see the best solution. I have tried all kinds of things and feel
 I am really close, but nothing has done the job. I have also searched
 google and the bioconductor FAQs, but to no avail.

Any help is greatly appreciated, I do not want to give up, and I do not want
 to go back to 'Resolver' or 'Genespring' (even though they make some things
 very simple).

Thank you,

Patrick

sessionInfo()
R version 2.7.1 (2008-06-23)
x86_64-unknown-linux-gnu

locale:
LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C

attached base packages:
[1] splines   tools     stats     graphics  grDevices utils     datasets
[8] methods   base

other attached packages:
[1] vsn_3.6.0            lattice_0.17-8       genefilter_1.20.0
[4] survival_2.34-1      affy_1.18.2          preprocessCore_1.2.1
[7] affyio_1.8.1         Biobase_2.0.1        limma_2.14.5

loaded via a namespace (and not attached):
[1] annotate_1.18.0     AnnotationDbi_1.2.2 DBI_0.2-4
[4] grid_2.7.1          RSQLite_0.6-9


--
Patrick Wijchers
Gene control mechanisms and disease group
MRC Clinical Sciences Centre
Imperial College
Hammersmith Campus
Du Cane Road
London W12 0NN
Phone: +44 (0)20 8383 8317 (lab)
       +44 (0)20 8383 8500 (office)
Fax:   +44 (0)20 8383 8306
Email: patrick.wijchers@csc.mrc.ac.uk



--
Patrick Wijchers
Gene control mechanisms and disease group
MRC Clinical Sciences Centre
Imperial College
Hammersmith Campus
Du Cane Road
London W12 0NN
Phone: +44 (0)20 8383 8317 (lab)
       +44 (0)20 8383 8500 (office)
Fax:   +44 (0)20 8383 8306
Email: patrick.wijchers@csc.mrc.ac.uk


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