Can someone please help to debug this error using the gseKEGG function in clusterProfiler ?

This is how I prepare the ranked gene list

> y=geneset$log2FoldChange
> names(y)=as.character(geneset$Entrez_Gene_ID)
> y<-sort(y, decreasing=TRUE)

> head(y)
  362893    29219   116568    83585    83500   366957
7.763638 7.740288 7.731271 7.630551 7.594507 7.535680

and this is what I get from gseKEGG

> kk2 <- gseKEGG(geneList     = y,
+                organism     = 'rno',
+                keyType="ncbi-geneid",
+                nPerm        = 1000,
+                minGSSize    = 120,
+                pvalueCutoff = 0.05,
+                verbose      = TRUE)
preparing geneSet collections...
GSEA analysis...
Error in data.frame(ID = as.character(tmp_res$pathway), Description = Description,  :
  arguments imply differing number of rows: 0, 1

Thank you

You very likely have to decrease minGSSize to a lower number...

Using the provided, human sample dataset:

>library(clusterProfiler)
>data(geneList)

> kk <- gseKEGG(geneList     = geneList,
+                 organism     = 'hsa',
+                 keyType="ncbi-geneid",
+                 nPerm        = 1000,
+                 minGSSize    = 500,
+                 pvalueCutoff = 0.05,
+                 verbose      = TRUE)
preparing geneSet collections...
GSEA analysis...
Error in data.frame(ID = as.character(tmp_res$pathway), Description = Description,  :
  arguments imply differing number of rows: 0, 1
In addition: Warning messages:
1: In min(p) : no non-missing arguments to min; returning Inf
2: In max(p) : no non-missing arguments to max; returning -Inf
3: In min(p) : no non-missing arguments to min; returning Inf
4: In max(p) : no non-missing arguments to max; returning -Inf

> kk <- gseKEGG(geneList     = geneList,
+                 organism     = 'hsa',
+                 keyType="ncbi-geneid",
+                 nPerm        = 1000,
+                 minGSSize    = 100,
+                 pvalueCutoff = 0.05,
+                 verbose      = TRUE)
preparing geneSet collections...
GSEA analysis...
leading edge analysis...
done...
>

That's it! Thank you