Analysing Infinium MethylationEPIC v2.0 Kit already implemented?

[claus-h-g]

Hello Many thanks for providing methylprep. May I ask if the analysis of files originating form Infinium MethylationEPIC v2.0 chips is already possible?

[biopyju]

Hello,

Like claus-h-g, thank you for methylprep! Same question as claus-h-g. I tried to analyse .IDATs from Infinium MethylationEPIC v2.0, provided by a NextSeq 550 with methylprep.run_pipeline(path, export=True, betas=True, m_value=True, poobah=True, export_poobah=True, meta_data_frame=True), but I've an error :

`--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[3], line 1 ----> 1 methylprep.run_pipeline(path, export=True, betas=True, m_value=True, poobah=True, export_poobah=True, meta_data_frame=True)

File c:\Users\cal19499\Documents\A\anaconda3.10\lib\site-packages\methylprep\processing\pipeline.py:321, in run_pipeline(data_dir, array_type, export, manifest_filepath, sample_sheet_filepath, sample_name, betas, m_value, make_sample_sheet, batch_size, save_uncorrected, save_control, meta_data_frame, bit, poobah, export_poobah, poobah_decimals, poobah_sig, low_memory, sesame, quality_mask, pneg_ecdf, file_format, **kwargs) 318 missing_probe_errors = {'noob': [], 'raw':[]} 320 for batch_num, batch in enumerate(batches, 1): --> 321 idat_datasets = parse_sample_sheet_into_idat_datasets(sample_sheet, sample_name=batch, from_s3=None, meta_only=False, bit=bit) # replaces get_raw_datasets 322 # idat_datasets are a list; each item is a dict of {'green_idat': ..., 'red_idat':..., 'array_type', 'sample'} to feed into SigSet 323 #--- pre v1.5 --- raw_datasets = get_raw_datasets(sample_sheet, sample_name=batch) 324 if array_type is None: # use must provide either the array_type or manifest_filepath.

File c:\Users\cal19499\Documents\A\anaconda3.10\lib\site-packages\methylprep\models\sigset.py:106, in parse_sample_sheet_into_idat_datasets(sample_sheet, sample_name, from_s3, meta_only, bit) 104 batch_probe_counts.add(n_snps_read) 105 counts_per_sample[n_snps_read] += 1 --> 106 idat_datasets[idx]['array_type'] = ArrayType.from_probe_count(n_snps_read) 107 if len(batch_probe_counts) != 1: 108 array_types = Counter([dataset['array_type'] for dataset in idat_datasets])

File c:\Users\cal19499\Documents\A\anaconda3.10\lib\site-packages\methylprep\models\arrays.py:46, in ArrayType.from_probe_count(cls, probe_count) 43 LOGGER.warning(f'Probe count ({probe_count}) falls outside of normal range. Setting to newest array type: EPIC') 44 return cls.ILLUMINA_EPIC ---> 46 raise ValueError(f'Unknown array type: ({probe_count} probes detected)')

ValueError: Unknown array type: (1105209 probes detected)`

With GenomeStudio I don't have the same number of probes (937055)...

If someone know how to solve this issue... Thank you!

[Leo-GG]

Hi,

I ran into the same problem as biopyju using MethylationEPIC v2.0 data. Adding the manifest from Illumina didn't help, any ideas?

Thanks!

Leo

[parlar]

me too

[isycara]

I have the same problem. Did you find a solution?

[parlar]

I suspect (without knowing for sure) that it would not be too difficult to fork and make an updated version. In my case, i borrowed and adjusted code from methylprep to validate idat files and used R (via rpy2) for the rest..

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---

From: isycara @.> Sent: Wednesday, April 24, 2024 3:36:53 PM To: FoxoTech/methylprep @.> Cc: Pär Larsson @.>; Comment @.> Subject: Re: [FoxoTech/methylprep] Analysing Infinium MethylationEPIC v2.0 Kit already implemented? (Issue #122)

I have the same problem. Did you find a solution?

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[eliopato]

Hi, if this can be of any use, I forked the project and updated it to work with Epic v2 data there : https://github.com/eliopato/methylprep/tree/EpicV2 It's a work in progress so it's not guaranteed to work :grinning:

[brj0]

Hello, I wanted to share that I have developed a similar package, mepylome, which was inspired by this project and minfi/conumee. Mepylome supports 450k, EPIC, and EPICv2 arrays and offers a variety of methylation analysis options.