Hi guys,

I'm testing ChAMP package to analyse methylation EPIC-array data. I've a sample table sheet (.csv format) of 96 samples which has the following aspect:

Sample_Name                Sample_Well           Sample_group     

pool_women                    A01                      3
 213141                       B01                      0
pool_men                      C01                      3
253141                        D01                      0
202196                        E01                      1
200569                        F01                      0
242196                        G01                      1

Sentrix_ID  Sentrix_Position
    2,04426E+11    R01C01
    2,04426E+11    R02C01
    2,04426E+11    R03C01
    2,04426E+11    R04C01
    2,04426E+11    R05C01
    2,04426E+11    R06C01
    2,04426E+11    R07C01

where Sample_name is the name of my samples, Sample_Well represents the well plate in which the sample has been loaded on the plate, Sample_group represents a categorical variable with three possible values (1 = case, 0 = control, 3 = internal control). Finally, Sentrix_ID and Sentrix_Position report information about the Illumina chip for each sample.

I'm following the tutorial at the following link for realizing ChAMP pipeline: https://www.bioconductor.org/packages/release/bioc/vignettes/ChAMP/inst/doc/ChAMP.html#section-differential-methylation-probes

I'm arrived at the step in which I'd like to determine the Differentially Methylated Probes (DMPs) between samples belong to case and control phenotype using the champ.DMP() function.

Specifically, I ran the following commands:

# setting work directory
setwd("/home/platone1/MethylationData/_EPIPOLE_/20210304_QEIC21-03-2_pt1_1-12/")

# loading ChAMP package
library(ChAMP)

# creating dataDirectory
dataDirectory <- "./IDAT"

# loading raw methylation data from IDAT files
myImport <- champ.import(dataDirectory,arraytype="EPIC")

myLoad <- champ.filter(beta=myImport$beta,
             M=NULL,
             pd=myImport$pd,
             intensity=myImport$intensity,
             Meth=myImport$M,
             UnMeth=myImport$U,
             detP=myImport$detP,
             beadcount=myImport$bead,
             autoimpute=TRUE,
             filterDetP=TRUE,
             ProbeCutoff=0,
             SampleCutoff=0.1,
             detPcut=0.01,
             filterBeads=TRUE,
             beadCutoff=0.05,
             filterNoCG = TRUE,
             filterSNPs = TRUE,
             population = NULL,
             filterMultiHit = TRUE,
             filterXY = TRUE,
             fixOutlier = TRUE,
             arraytype = "EPIC")

# normalizing data
myNorm <- champ.norm(beta=myLoad$beta,
               rgSet=myLoad$rgSet,
               mset=myLoad$mset,
               resultsDir="/home/platone1/ChAMP/plate1//CHAMP_Normalization/",
               method="BMIQ",
               plotBMIQ=TRUE,
               arraytype="EPIC")

# different methylation probes (DMPs)
myDMP <- champ.DMP(beta = myNorm,
              pheno = myLoad$pd$Sample_group,
              compare.group = c("0", "1"),
              adjPVal = 0.05,
              adjust.method = "BH",
              arraytype = "EPIC")

When I arrived to the DMPs step, I found the following error message:

[===========================]
[<<<<< ChAMP.DMP START >>>>>]
-----------------------------
!!! Important !!! New Modification has been made on champ.DMP():

    (1): In this version champ.DMP() if your pheno parameter contains more than two groups of phenotypes, champ.DMP() would do pairewise differential methylated analysis between each pair of them. But you can also specify compare.group to only do comparasion between any two of them.

    (2): champ.DMP() now support numeric as pheno, and will do linear regression on them. So covariates like age could be inputted in this function. You need to make sure your inputted "pheno" parameter is "numeric" type.

--------------------------------

[ Section 1:  Check Input Pheno Start ]

  You pheno is integer type.
    Your pheno information contains following groups. >>
    <0>:NA samples.
    <NA>:NA samples.
    <1>:NA samples.
    [The power of statistics analysis on groups contain very few samples may not strong.]
    pheno contains 3 phenotypes
    Your compare.group parameter is in accord with your pheno. Two pheno types has been added into Compare List.
    0_to_1 compare group : 0, 1

[ Section 1:  Check Input Pheno Done ]


[ Section 2:  Find Differential Methylated CpGs Start ]

  -----------------------------
  Start to Compare : 0, 1
  Contrast Matrix
      Contrasts
Levels NA-p
     p   NA
Error in contrasts.fit(fit, contrast.matrix) :
  NAs not allowed in contrasts

Is the problem belonging to the variable chosen for making DMPs analysis?

Thank u!

Your pheno is integer type while in your case, as indicated in the tutorial, you need categorical covariates as “factor” or “character” type. A solution to this problem is to convert Sample_group to factor : myLoad$pd$Sample_group = as.factor(myLoad$pd$Sample_group)