Hi Kasper,
I am working on EPIC array data. I came to know that minfi package works well for EPIC data. I am using R3.3.0 and minfi_1.18.2 versions. My sample_annotation file looks like following.
| Sample_Name | Sample_Well | Sample_Plate | Sample_Group | Pool_ID | Sentrix_ID | Sentrix_Position |
| 1 | MCF10A_NT_60 | 2.00E+11 | R01C01 | |||
| 2 | MCF10A_BPA_60 | 2.00E+11 | R02C01 | |||
| 3 | MCF10A_BaP_60 | 2.01E+11 | R01C01 | |||
| 4 | MCF10A_BPA-BaP_60 | 2.01E+11 | R02C01 | |||
sheet <- read.metharray.sheet("data/idat")
[read.metharray.sheet] Found the following CSV files:
[1] "data/idat/sample_annotation.csv"
> epicData <- read.metharray.exp(targets=sheet)
> epicData@annotation <- c(array="IlluminaHumanMethylationEPIC", annotation="ilm10b2.hg19")
> sheet
Sample_Name Sample_Well Sample_Plate Sample_Group Pool_ID Array
1 1 NA NA MCF10A_NT_60 NA R01C01
2 2 NA NA MCF10A_BPA_60 NA R02C01
3 3 NA NA MCF10A_BaP_60 NA R01C01
When I got the MDS plot I couldnt identify the outliers because Sample_Name is given with numbers 1-27. So, I changed Sample_Name column in sample_annotation.csv like the following
| Sample_Name | Sample_Well | Sample_Plate | Sample_Group | Pool_ID | Sentrix_ID | Sentrix_Position |
| A_NT_1 | MCF10A_NT_60 | 2.00E+11 | R01C01 | |||
| A_BPA_1 | MCF10A_BPA_60 | 2.00E+11 | R02C01 | |||
| A_BaP_1 | MCF10A_BaP_60 | 2.01E+11 | R01C01 | |||
And when I run again I am getting an error.
sheet <- read.metharray.sheet("data/idat")
[read.metharray.sheet] Found the following CSV files:
[1] "data/idat/sample_annotation.csv"
> epicData <- read.metharray.exp(targets=sheet)
Error in read.metharray(files, extended = extended, verbose = verbose) :
The following specified files do not exist:character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat, character(0)_Grn.idat
Could you please help me in this? Thank you
Dear Kasper,
I had sent an email to you with two csv files. please check it and let me know. Thank you
Dear Kasper,
I found a solution to this problem from a recent paper "A cross-package Bioconductor workflow for analysing
methylation array data". But I would like to know which normalization function is better for cancer/normal samples. And to find DMR which package should be used? buphunter or DMRcate? Thank you in Advance.