Hello. I am using Champ.DMP in Champ package to find differential methylation cpg sites in my recent work. I first sorted the pd files by the phenotype column (that is, Sample_Group 1, 2, 3), but then I found that the order of the columns in the beta matrix will affect the cpg sites finally found. When I matched the column names of the beta matrix identical with the row names of the PD files, I even got the 'Error: ChAMP.DMP Have not detected even one significant CpGs.'. Which should be the correct operation? What should I do? My code is below. Any help would be appreciated so much!

# only sort pd file by pheno, that is, Sample_ Group

newpd <- newpd[order(newpd$Sample_Group),]
myDMP <- champ.DMP(beta = correctedbeta, pheno = newpd$Sample_Group, arraytype="EPIC", adjPVal = 0.01)

[<<<<< 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 character type.
    Your pheno information contains following groups. >>
    <1>:55 samples.
    <2>:52 samples.
    <3>:56 samples.
    [The power of statistics analysis on groups contain very few samples may not strong.]
    pheno contains 3 phenotypes
    compare.group parameter is NULL, EACH PAIR of phenotypes will be added into Compare List.
    1_to_2 compare group : 1, 2
    1_to_3 compare group : 1, 3
    2_to_3 compare group : 2, 3

[ Section 1:  Check Input Pheno Done ]


[ Section 2:  Find Differential Methylated CpGs Start ]

  -----------------------------
  Start to Compare : 1, 2
  Contrast Matrix
      Contrasts
Levels p2-p1
    p1    -1
    p2     1
  You have found 130 significant MVPs with a BH adjusted P-value below 0.01.
  Calculate DMP for 1 and 2 done.
  -----------------------------
  Start to Compare : 1, 3
  Contrast Matrix
      Contrasts
Levels p3-p1
    p1    -1
    p3     1
  You have found 802 significant MVPs with a BH adjusted P-value below 0.01.
  Calculate DMP for 1 and 3 done.
  -----------------------------
  Start to Compare : 2, 3
  Contrast Matrix
      Contrasts
Levels p3-p2
    p2    -1
    p3     1
  You have found 1947 significant MVPs with a BH adjusted P-value below 0.01.
  Calculate DMP for 2 and 3 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.]

# sort pd file by pheno, and then make column of beta matrix identical with the row of pd file

newpd <- newpd[order(newpd$Sample_Group),]
correctedbeta <- correctedbeta[,newpd$Sample_Name]
myDMP <- champ.DMP(beta = correctedbeta, pheno = newpd$Sample_Group, arraytype="EPIC", adjPVal = 0.01)

[<<<<< 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 character type.
    Your pheno information contains following groups. >>
    <1>:55 samples.
    <2>:52 samples.
    <3>:56 samples.
    [The power of statistics analysis on groups contain very few samples may not strong.]
    pheno contains 3 phenotypes
    compare.group parameter is NULL, EACH PAIR of phenotypes will be added into Compare List.
    1_to_2 compare group : 1, 2
    1_to_3 compare group : 1, 3
    2_to_3 compare group : 2, 3

[ Section 1:  Check Input Pheno Done ]


[ Section 2:  Find Differential Methylated CpGs Start ]

  -----------------------------
  Start to Compare : 1, 2
  Contrast Matrix
      Contrasts
Levels p2-p1
    p1    -1
    p2     1
  You have found 0 significant MVPs with a BH adjusted P-value below 0.01.
  Calculate DMP for 1 and 2 done.
  -----------------------------
  Start to Compare : 1, 3
  Contrast Matrix
      Contrasts
Levels p3-p1
    p1    -1
    p3     1
  You have found 0 significant MVPs with a BH adjusted P-value below 0.01.
  Calculate DMP for 1 and 3 done.
  -----------------------------
  Start to Compare : 2, 3
  Contrast Matrix
      Contrasts
Levels p3-p2
    p2    -1
    p3     1
  You have found 0 significant MVPs with a BH adjusted P-value below 0.01.
  Calculate DMP for 2 and 3 done.

[ Section 2:  Find Numeric Vector Related CpGs Done ]

Error in champ.DMP(beta = a, pheno = newpd$Sample_Group, arraytype = "EPIC",  : 
  ChAMP.DMP Have not detected even one significant CpGs. You may try other threshold.