I have Bulk-RNAseq data from 3 drug exposures (vehicle, low and high dose) x 2 replicates per condition x 6 cell lines. So its a total of 36 samples.
I am interested in the Exposure effect.
I am using DuplicateCorrelation and limma voom, but my cell Line effect is eating all the effect (seen by variant partition).
info <- # is the metadata table
genes <- # counts table, rows are genes, columns are samples
design <- model.matrix( ~ Exposure+ CellLine ,info)
vobj_tmp = voom( genes, design, plot=TRUE)
dupcor <- duplicateCorrelation(vobj_tmp,design,block=info$SampleID) # I am blocking by sample, meaning each 2 replicates per condition
vobj = voom( genes, design, plot=TRUE, block=info$SampleID, correlation=dupcor$consensus)
fitDupCor <- lmFit(vobj, design, block=info$SampleID, correlation=dupcor$consensus.correlation)
fitDupCor <- eBayes( fitDupCor )
I got no significance because the effect of the cell lines i hugh (as I observed before in VariantPartition).
1) Am I using the blocking correctly? Or should I block by sampleID in duplicateCorrelation but block by CellLine in voom() ?
2) Is it correctly to use Variant Partition even considering the replicates as independent samples? I am not using duplicateCorrelation for Variant Partition.
gExpr <- DGEList(counts=counts)
gExpr <- calcNormFactors(gExpr)
design <- model.matrix( ~ Exposure+ CellLine ,info)
vobjGenes <- voom(gExpr, design)
geneExpres <-vobjGenes$E
varPart <- fitExtractVarPartModel( vobjGenes, form, info )
vp <- sortCols(varPart)
3) Any other idea on how can I rescue the exposure effect?
Thanks in advance Gimena
Hi James,
Yes, these are complete cases. every line has all the treatment groups and technical replicates.