**Nota Bene**

- If you must use RPKM / FPKM data for any downstream analyses, I would
transform these to Z-scale first with
*zFPKM* package, and then use
'gaussian' kernel with `gsva()`

(depending on your version of *gsva*)
- you can still use RPKM / FPKM with 'poisson' kernel

See: GSVA kernels: Gaussian or Poisson?

A recent protocol that I did for a simple disease versus control comparison:

```
#Perform GSVA
topMatrixGSVA <- gsva(topMatrix, c2BroadSets, min.sz=10, max.sz=999999, abs.ranking=FALSE, verbose=TRUE)
design <- model.matrix(~ factor(metadata$DiseaseStatus, levels=c("Control", "Disease")))
colnames(design) <- c("Intercept", "DiseaseVsControl")
fit <- lmFit(topMatrixGSVA, design)
fit <- eBayes(fit)
sigPathways <- topTable(fit, coef="DiseaseVsControl", number=Inf, p.value=0.05, adjust="BH")
sigPathways <- sigPathways[abs(sigPathways$logFC)>1,]
res <- decideTests(fit, p.value=0.05)
#Create an object that can easily be written to disc
wObject <- data.frame(rownames(sigPathways), sigPathways)
colnames(wObject) <- c("Pathway","Log2FoldChange","MeanExpression","tStat","Pvalue","AdjustedPval","Bvalue")
#Filter the GSVA object to only include significant pathways
topMatrixGSVA <- topMatrixGSVA[rownames(sigPathways),]
#Set colour for heatmap
require(RColorBrewer)
myCol <- colorRampPalette(c("dodgerblue", "black", "yellow"))(100)
myBreaks <- seq(-1.5, 1.5, length.out=101)
heat <- t(scale(t(topMatrixGSVA)))
heatmap.2(heat, col=myCol, breaks=myBreaks, main="Title", key=TRUE, keysize=1.0, key.title="", key.xlab="Enrichment Z-score", scale="none", density.info="none", reorderfun=function(d,w) reorder(d, w, agglo.FUN=mean), trace="none", cexRow=1.0, cexCol=1.0, distfun=function(x) dist(x, method="euclidean"), hclustfun=function(x) hclust(x, method="ward.D2"), margin=c(10,25))
```

Hi! I would like to ask here why you use the p.value to discriminate between significant and non-significant results? What about the adjusted p-value?

Hi, adjusted p-values are being used. Please read the documentation for

`topTable()`

and`decideTests()`