Hello All,

I am using ChAMP do run a methylation analysis on the 450k dataset. I am not running every step individually but using the champ.process function to run the entire pipeline just by providing location to my IDAT directory which has my the sample_sheet.csv file. The program does run for a while, it produces the CHAMP_RESULTS directory and few other files but dies at or during normalization. Below is the a snippet of my error log file. Could you please tell me what happening here and is there a way to fix this?

Running champ.norm()...

[===========================]

[>>>>> ChAMP.NORM START <<<<<<]

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

champ.norm Results will be saved in ./CHAMP_RESULT//CHAMP_Normalization/

[ SWAN method call for BOTH rgSet and mset input, FunctionNormalization call for rgset only , while PBC and BMIQ only needs beta value. Please set parameter correctly. ]

<< Normalizing data with BMIQ Method >>

Note that,BMIQ function may fail for bad quality samples (Samples did not even show beta distribution).

3 cores will be used to do parallel BMIQ computing.

Error in socketConnection("localhost", port = port, server = TRUE, blocking = TRUE,  : 

  internet routines cannot be loaded

Calls: champ.process ... makePSOCKcluster -> newPSOCKnode -> socketConnection

Execution halted

Hello:

Thanks for using ChAMP for your analysis. To be honest, I don't suggest scientist use champ.process() to analysis Methylation Array, because you can not know the problem and result for each step. Thus I suggest you to use separate steps: champ.load(), champ.QC(), champ.norm()... to analysis it one by one.

Based on your current error, I suppose it looks like some error related to your computer/server setting, since the BMIQ function use parallel running by default. And I assigned three cores for default function. So BMIQ function would automatically open two other more thread and run BMIQ on all samples at the same time. It looks your server does not support parallel...

Here I suggest you to do following two things:

(1): run your data step by step, and use champ.norm() to do normalization on your methyhlation dataset.

(2): Set cores in champ.norm() as 1, which will not trigger paralleling. Then it should be OK.

You may try above two suggestions.

Best

Yuan Tian