[BioC] use of voom function with attract package

Gordon K Smyth smyth at wehi.EDU.AU
Fri Apr 18 03:16:26 CEST 2014

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

> Date: Tue, 15 Apr 2014 09:44:42 -0700 (PDT)
> From: "Emmanouela Repapi [guest]" <guest at bioconductor.org>
> To: bioconductor at r-project.org, emmanouela.repapi at 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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