Dear Gordon and Ryan,

Thank you both very much for your replies, using the F statistic from the lmFit and the geneSetTest seems like the right way to go for what I need to do. 
On a similar note, do you have a function in mind for finding the genes with similar expression profiles within a specific pathway (as a replacement of the other function of attract, findSynexprs)?

Thank you again for your help.

Best wishes,
Emmanouela

On 18 Apr 2014, at 02:16, Gordon K Smyth <smyth@wehi.EDU.AU> wrote:

> Dear Emmanouela,
> 
> The limma package is designed to fit linear models, and it can compute t-statistics and F-statistics faster than making your own loop to lm(). If you want F-statistics for distinguishing the cell types, why not:
> 
>  fit <- lmFit(anal_voom, design)
>  fit <- eBayes(fit[,-1])
> 
> Then the F-statistics will be in fit$F.
> 
> If you want to know whether a particular KEGG pathway tends to have larger F-statistics, you could also use:
> 
>  geneSetTest(index, fit$F)
> 
> where index selects genes in the pathway.  If there are only two cell types, a better way would be:
> 
>  camera(anal_voom, index, design)
> 
> With camera, index could be a list of index vectors for all the KEGG pathways at once.
> 
> Best wishes
> Gordon
> 
>> Date: Tue, 15 Apr 2014 09:44:42 -0700 (PDT)
>> From: "Emmanouela Repapi [guest]" <guest@bioconductor.org>
>> To: bioconductor@r-project.org, emmanouela.repapi@imm.ox.ac.uk
>> Subject: [BioC] use of voom function with attract package
>> 
>> 
>> Dear Bioconductor,
>> 
>> I am trying to use the attract package to find the processes that are differentially activated between cell types of the same lineage, using RNA-Seq data. Since the attract package is designed to work with microarray data, I decided to use the voom function to transform my data and change the findAttractors() function accordingly, to accommodate this type of data. Since this is not trivial, I want to make sure that I am using the output from the voom function correctly.
>> 
>> The main part of the findAttractors() uses lm to model the expression in relation to the cell type (group) and then an anova to get the F statistic for each gene:
>>   fstat <- apply(dat.detect.wkegg, 1, function(y, x) {
>>       anova(lm(y ~ x))[[4]][1]}, x = group)
>> where dat.detect.wkegg is the matrix of the normalized expression values with the genes per row and the samples per column.
>> (To give some more context, the function then uses the log2 values of the fstat and does a t test between the gene values of a specific pathway vs all the gene values to identify the significant pathways. )
>> 
>> What I want to do is change the above to:
>> 
>> counts_data <- DGEList(counts=rnaseq,group=celltype)
>> counts_data_norm <- calcNormFactors(counts_data)
>> 
>> design <- model.matrix( ~ celltype)
>> anal_voom <- voom(counts_data_norm, design)
>> dat.detect.wkegg <- as.list(as.data.frame(t(anal_voom$E)))
>> voom_weights <- as.list(as.data.frame(t(anal_voom$weights)))
>> 
>> fstat <- mapply(function(y, w, group) {anova(lm(y ~ group, weights=w))[[4]][1]},
>> 	dat.detect.wkegg, voom_weights, MoreArgs = list(group=celltype))
>> 
>> Is this the way to go with using the weights from voom, or am I getting this very wrong?
>> 
>> Many thanks in advance for your reply!
>> 
>> Best wishes,
>> Emmanouela
>> 
>> 
>> 
>> 
>> -- output of sessionInfo():
>> 
>> sessionInfo()
>> R version 3.0.1 (2013-05-16)
>> Platform: x86_64-unknown-linux-gnu (64-bit)
>> 
>> locale:
>> [1] LC_CTYPE=en_GB.ISO-8859-1       LC_NUMERIC=C                    LC_TIME=en_GB.ISO-8859-1        LC_COLLATE=en_GB.ISO-8859-1     LC_MONETARY=en_GB.ISO-8859-1    LC_MESSAGES=en_GB.ISO-8859-1
>> [7] LC_PAPER=C                      LC_NAME=C                       LC_ADDRESS=C                    LC_TELEPHONE=C                  LC_MEASUREMENT=en_GB.ISO-8859-1 LC_IDENTIFICATION=C
>> 
>> attached base packages:
>> [1] parallel  stats     graphics  grDevices utils     datasets  methods   base
>> 
>> other attached packages:
>> [1] attract_1.14.0       GOstats_2.28.0       graph_1.40.1         Category_2.28.0      GO.db_2.10.1         Matrix_1.1-3         cluster_1.15.2       annotate_1.40.1      org.Mm.eg.db_2.10.1
>> [10] KEGG.db_2.10.1       RSQLite_0.11.4       DBI_0.2-7            AnnotationDbi_1.24.0 Biobase_2.22.0       BiocGenerics_0.8.0   edgeR_3.4.2          limma_3.18.13
>> 
>> loaded via a namespace (and not attached):
>> [1] AnnotationForge_1.4.4 genefilter_1.44.0     grid_3.0.1            GSEABase_1.24.0       IRanges_1.20.7        lattice_0.20-29       RBGL_1.38.0           splines_3.0.1
>> [9] stats4_3.0.1          survival_2.37-7       tcltk_3.0.1           tools_3.0.1           XML_3.98-1.1          xtable_1.7-3
> 
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