Deleted:No significant MVPs with a BH adjusted P-value below 0.05
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486342583 • 0
@cc99edda
Last seen 10 weeks ago
Hong Kong

Enter the body of text here Dear, everyone.

Currently, I am doing find out DMPs in Champ using Illumina EPIC methylation array. And I am doing case-control studies

But I got the error message Error in champ.DMP() : ChAMP.DMP Have not detected even one significant CpGs. You may try other threshold. Then I found the reason why I got the error message due to the Benjamini-Hochberg correction, and my lowest adjusted p-values are 0.999996913. (adj.P.Val)

Therefore, when I am looking at GUI there is no result when the threshold is <0.05. When the threshold should be raised to 1.0, I can see the result.

(1) Threshold p<0.05 Code should be placed in three backticks as shown below

```rmyDMP <- champ.DMP()

[ Section 1: Check Input Pheno Start ]

You pheno is character type. Your pheno information contains following groups. >> <Sarcopenia>:9 samples. <Normal>:9 samples. [The power of statistics analysis on groups contain very few samples may not strong.] pheno contains only 2 phenotypes compare.group parameter is NULL, two pheno types will be added into Compare List. Sarcopenia_to_Normal compare group : Sarcopenia, Normal

[ Section 1: Check Input Pheno Done ]

[ Section 2: Find Differential Methylated CpGs Start ]


Start to Compare : Sarcopenia, Normal Contrast Matrix Contrasts Levels pSarcopenia-pNormal pNormal -1 pSarcopenia 1 You have found 0 significant MVPs with a BH adjusted P-value below 0.05. Calculate DMP for Sarcopenia and Normal done.

[ Section 2: Find Numeric Vector Related CpGs Done ]

Error in champ.DMP() : ChAMP.DMP Have not detected even one significant CpGs. You may try other threshold.

(2) Threshold p=1.0

myDMP <- champ.DMP(beta = myNorm, pheno = myLoad$pd$Sample_Group, compare.group = NULL, adjPVal = 1.0, adjust.method = "BH", arraytype = "EPIC")

[ Section 1: Check Input Pheno Done ] [ Section 2: Find Differential Methylated CpGs Start ]


Start to Compare : Sarcopenia, Normal Contrast Matrix Contrasts Levels pSarcopenia-pNormal pNormal -1 pSarcopenia 1 You have found 727958 significant MVPs with a BH adjusted P-value below 1. Calculate DMP for Sarcopenia and Normal done.

[ Section 2: Find Numeric Vector Related CpGs Done ]

[ Section 3: Match Annotation Start ]

[ Section 3: Match Annotation Done ]

[<<<<<< ChAMP.DMP END >>>>>>] [===========================] [You may want to process DMP.GUI() or champ.GSEA() next.]

include your problematic code here with any corresponding output

please also include the results of running the following in an R session

sessionInfo( )

```Is there any idea what the problem can be here? or should I use another correction method?

Any comments that help me are welcome. Any help would be greatly appreciated.

ChAMP • 457 views
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I realized that someone had asked the same question, but he seemed to have closed the issue and I didn't see a solution. I hope to receive help. Thank you very much

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There is no problem, there is not any significant DMP between the groups compared.

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Thank you for your reply, but for some reason, when I ran this code yesterday, it was normal

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I don't follow what you mean by "Normal" but in general, when you run code and it returns different results indicates that you might have old variables still in the environment or some other leftovers. Be sure to properly script your analysis, and when you're done coding run it from a clean environment and version-control analysis with something like Git(Hub).

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enter image description here Thank you for your reply. I am a beginner. What I meant was that I could have obtained this image yesterday, but running the same code today would result in an error. (I'm not sure if you can see the picture I sent, but I think what you said makes sense.)

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This is my running code

myDMP <- champ.DMP(beta = myNormA,
                    pheno = myLoad$pd$Sample_Group,
                    arraytype = "450K" 
                    )

but when using "myNormA<- champ.norm (arraytype=" 450K ")" code to generate "myNormA", it always takes a long time. Will this lead to errors in "myNormA" and subsequent errors?

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The image is blurred, but you need to follow ATpoint advice and restart the entire analysis using a clean environment.

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