methylprep is a python package for processing Illumina methylation array data. View on ReadTheDocs.

methylprep is part of a methyl-suite of python packages that provide functions to process and analyze DNA methylation data from Illumina arrays (27, 450k, and EPIC/850k supported). The methylprep package contains functions for processing raw data files from arrays, or downloading (and processing) public data sets from GEO (the NIH Gene Expression Omnibus is a database repository), or from ArrayExpress. It contains both a command line interface (CLI) for processing data from local files, and a set of functions for building a custom pipeline in a jupyter notebook or python scripting environment. The aim is to offer a standard process, with flexibility for those who want it.

You should install all three components, as they work together.

• methylcheck includes
  • quality control (QC) functions for filtering out unreliable probes, based on the published literature and outlier detection.
  • sample outlier detection
  • array level QC plots, based on Genome Studio functions
  • data visualization functions based on seaborn and matplotlib graphic libraries.
  • predict sex of human samples from probes
  • interactive method for assigning samples to groups, based on array data, in a Jupyter notebook
• methylize provides analysis functions
  • differentially methylated probe statistics (between treatment and control samples)
  • volcano plots (which probes are the most different)
  • manhattan plot (where in genome are the differences)

methylprep maintains configuration files for your Python package manager of choice: pipenv or pip. Conda install is coming soon.

pip install methylprep

The most common use case is processing .idat files on a computer within a command line interface. This can also be done in a Jupyter notebook, but large data sets take hours to run and Jupyter will take longer to run these than command line.

python -m methylprep -v process -d <filepath> --all

The --all option applies the most common settings. Here are some specific options:

data_dir is the one required field. If you do not provide the file path for the project's sample_sheet, it will find one based on the supplied data directory path. It will also auto detect the array type and download the corresponding manifest file for you.

Run the complete methylation processing pipeline for the given project directory, optionally exporting the results to file.

Returns: A collection of DataContainer objects for each processed sample

from methylprep import run_pipeline

data_containers = run_pipeline(data_dir, array_type=None, export=False, manifest_filepath=None, sample_sheet_filepath=None, sample_names=None)

Note: All the same input parameters from command line apply to run_pipeline, except --all. Type dir(methylprep.run_pipeline) in an interactive python session to see details.

Note: By default, if run_pipeline is called as a function in a script, a list of SampleDataContainer objects is returned. However, if you specify betas=True or m_value=True, a dataframe of beta values or m-values is returned instead. All methylcheck functions are designed to work on a dataframe or a folder to the processed data generated by run_pipeline.

methylprep provides a command line interface (CLI) so the package can be used directly in bash/batchfile or windows/cmd scripts as part of building your custom processing pipeline.

All invocations of the methylprep CLI will provide contextual help, supplying the possible arguments and/or options available based on the invoked command. If you specify verbose logging the package will emit log output of DEBUG levels and above.

python -m methylprep

usage: methylprep [-h] [-v] {process,sample_sheet} ...

Utility to process methylation data from Illumina IDAT files

positional arguments:
  {process,sample_sheet}
    process             process help
    sample_sheet        sample sheet help

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Enable verbose logging

The methylprep cli provides these top-level commands:

• process the main function: processing methylation data from idat files. Covered already.
• sample_sheet to find/read/validate a sample sheet and output its contents
• download download and process public data sets in NIH GEO or ArrayExpress collections. Provide the public Accession ID and it will handle the rest.
• composite download a bunch of datasets from a list of GEO ids, process them all, and combine into a large dataset
• alert scan GEO database and construct a CSV / dataframe of sample meta data and phenotypes for all studies matching a keyword

There are thousands of publically accessible DNA methylation data sets available via the GEO (US NCBI NIH) https://www.ncbi.nlm.nih.gov/geo/ and ArrayExpress (UK) https://www.ebi.ac.uk/arrayexpress/ websites. This function makes it easy to import them and build a reference library of methylation data.

The CLI now includes a download option. Supply the GEO ID or ArrayExpress ID and it will locate the files, download the idats, process them, and build a dataframe of the associated meta data. This dataframe format should be compatible with methylcheck and methylize.

When processing large batches of raw .idat files, specify --batch_size to break the processing up into smaller batches so the computer's memory won't overload. This is off by default when using process but is ON when using download and set to batch_size of 100. Set to 0 to force processing everything as one batch. The output files will be split into multiple files afterwards, and you can recomine them using methylcheck.load.

Find and parse the sample sheet in a given directory and emit the details of each sample. This is not required for actually processing data.

>>> python -m methylprep sample_sheet

usage: methylprep sample_sheet -d DATA_DIR

optional arguments:

~/methylprep$ python -m methylprep -v sample_sheet -d ~/GSE133062/GSE133062 --create
INFO:methylprep.files.sample_sheets:[!] Created sample sheet: ~/GSE133062/GSE133062/samplesheet.csv with 70 GSM_IDs
INFO:methylprep.files.sample_sheets:Searching for sample_sheet in ~/GSE133062/GSE133062
INFO:methylprep.files.sample_sheets:Found sample sheet file: ~/GSE133062/GSE133062/samplesheet.csv
INFO:methylprep.files.sample_sheets:Parsing sample_sheet
200861170112_R01C01
200882160083_R03C01
200861170067_R02C01
200498360027_R04C01
200498360027_R08C01
200861170067_R01C01
200861170072_R05C01
200498360027_R06C01
200861170072_R01C01
200861170067_R03C01
200882160070_R02C01
...

A tool to build a data set from a list of public datasets. optional arguments:

Function to check for new datasets on GEO and update a csv each time it is run. Usable as a weekly cron command line function. Saves data to a local csv to compare with old datasets in _meta.csv. Saves the dates of each dataset from GEO; calculates any new ones as new rows. updates csv.