ChAMP ebGSEA error
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Entering edit mode
2.5 years ago

Hello,

I have been running the ebGSEA method for my methylation samples and it works fine with all other phenotypes that I have but for one particular one I keep getting the following error:

[ Section 2: Running Global Test Start ]

Applying Binary Model on Global Test. It could be very slow... Error in eval(family$initialize) : y values must be 0 <= y <= 1 My phenotype values are between -1 and 1, I've tried the same with absolute values but also does not work. I am using the Beta values so they are between 0-1 I cant figure it our since my other phenotypes were definitely higher than 1 and all worked fine. ChAMP DNA Methylation • 845 views ADD COMMENT 0 Entering edit mode Encountering the same issue: Running: myebGSEA <- champ.ebGSEA(beta=myNorm, pheno=myLoad$pd$Sample_Group, arraytype="EPIC") Error: ebGSEA function require no NA in beta and pheno parameter. [ Section 1: Generate Annotation Start ] Extracting annotation from IlluminaHumanMethylationEPICilm10b4.hg19. Removing Non-CG probes out of annotation. Flat all genes on each CpG. Removing all duplicated CpG-genes. Annotation Prepared. [ Section 1: Generate Annotation Done ] [ Section 2: Running Global Test Start ] Applying Binary Model on Global Test. It could be very slow... Fehler in eval(family$initialize) : y values must be 0 <= y <= 1 Zusätzlich: Warnmeldung: In alias2SymbolTable(flat$symbol) : Multiple symbols ignored for one or more aliases ADD REPLY 0 Entering edit mode 12 weeks ago Josh • 0 (Answering this question because it's a top result on google when I encountered the same problem) This issue occurs when myLoad$pd$Sample_Group is non-binary. It appears that the ebGSEA function requires binary phenotypes. To get around this, you could do something like this: a_vs_b <- myLoad$pd$Sample_Group == 'a' | myLoad$pd$Sample_Group == 'b' pheno_a_vs_b <- myLoad$pd\$Sample_Group[a_vs_b]

myebGSEA <- champ.ebGSEA(beta=myNorm[,a_vs_b], pheno=pheno_a_vs_b, arraytype="EPIC")


where a and b binarize your phenotype values.