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 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]"
>> 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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