Quality Control (QC), Visualization/plotting, and postprocessing software for Illumina methylation array data. See https://life-epigenetics-methylcheck.readthedocs-hosted.com/en/latest/ for full documentation and examples.
methylcheck is a Python-based package for filtering and visualizing Illumina methylation array data. The focus is on quality control.
This package contains high-level APIs for filtering processed data from local files. 'High-level' means that the details are abstracted away, and functions are designed to work with a minimum of knowledge and specification required. But you can always override the "smart" defaults with custom settings if things don't work. Before starting you must first process a set of
idat files with the associated
methylprep package or download processed data from the NIH GEO database. Refer to methylprep for instructions on this step.
This package is available in PyPi.
pip install methylcheck or
pip3 install methylcheck if your OS defaults to python2x. This package only works in python3.6+.
Importing your data
Methylcheck is designed to accept the output from the
methylprep package. If you have a bunch of
methylprep will return a single pickled pandas dataframe containing all the beta values for probes.
Load your data in a Jupyter Notebook like this:
mydata = pandas.read_pickle('beta_values.pkl')
pandas you must specify the file name.
If you used
methylprep process with the
--all option, there's a convenience function in
methylcheck (methylcheck.load) to open methylation files in a variety of formats.
import methycheck df = methylcheck.load('<path to folder with methylprep output>') # or df,meta = methylcheck.load_both('<path to folder with methylprep output>')
.load_both() also conveniently loads a dataframe of all meta data associated with the samples. If you are using public GEO data, it will load that collection's meta data. Some analysis functions require specifying which samples are part of a treatment group (vs control) and the
meta dataframe object can be used for this.
If you point to a folder with processed CSVs, this will load and combine these output files and return a dataframe:
import methycheck df = methylcheck.load('docs/example_data/GSE29852/9247377093/')
You can also use
.load() to read processed files in these formats:
('beta_value', 'm_value', 'meth', 'meth_df', 'noob_df', 'sesame')
Specify these using the
It will also load methylation data files processed using
df = methylcheck.load(<filename>, format='sesame')
For more, check out our examples of loading data into
Alternatively, you can download and process public GEO datasets. If you have a gzip of
idat files, process them first with
methylprep, or use the
methylprep download -i <GEO_ID> option to download and process public data.
In general, the best way to import data is to use
methylprep and run
import methylprep methylprep.run_pipeline(data_folder, betas=True) # or from the command line: python -m methylprep process -d <filepath to idats> --all
beta_values.pkl file it returns/saves to disk, and load that in a Jupyter notebook.
GEO (processed data in
csv, txt, xlsx formats)
If idats are not available on GEO, you can imported the processed tabular data using
methylcheck.read_geo. This will convert methylation/unmethylated signal intensities to beta values by default, returning a Pandas dataframe with samples in columns and probes in rows. As of version 0.6, it recognizes six different ways authors have organized their data, but does not handle these cases yet:
- Combining two files containing methylated and unmethylated values for a set of samples
- Reading GSM12345-tbl-1.txt type files (found in GSExxxx_family.tar.gz packages)
To obtain probe
beta values for samples or methylated/unmethylated signal intensities, download the file locally and run:
import methylcheck df = methylcheck.read_geo(filepath)
If you include
verbose=True it will explain what it is doing. You can also use
test_only=True to ensure the parser will work without loading the whole file (which can be several gigabytes in size). Test mode will only return the first 200 probes, but should parse the header and detect the file structure if it can.
From there, each data transformation is a single line of code using Pandas DataFrames.
methylcheck will keep track of the data format/structures for you, and you can visualize the effect of each filter as you go. You can also export images of your charts for publication.
Refer to the Jupyter notebooks on readthedocs for examples of filtering probes from a batch of samples, removing outlier samples, and generating plots of data.
Quality Control (QC) reports
The simplest way to generate a set of plots about the quality of your probes/array is to run this function in a Jupyter notebook:
import methylcheck methylcheck.run_qc('<path to your methylprep processed files>')
run_qc() is adapted from Illumina's Genome Studio QC functions.
A second, more customizable quality control pipeline is the
run_qc() but adds several sample outlier detection tools. One method, multi-dimensional scaling, is interactive, and allows you to identify samples within your batch that you can statistically reject as outliers. Note that
methylprep process automatically removes probes that fail the poobah p-value detection limit test by default;
run_pipeline() examines where samples with lots of unreliable probes should be disregarded entirely.
There is also a
methylcheck.qc_report.ReportPDF class that allows you to build your own QC report and save it to PDF. This is most useful if you process multiple batches of data and want to create a standardized, easy-to-read PDF report about the quality of samples in each batch.
methylcheck provides functions to
- predict the sex of samples (
- detect probes that differ between two sets of samples within a batch (
- remove sex-chromosome-linked probes and control probes
- remove "sketchy" probes, deemed unreliable by researchers
- filter sample outliers based on multi-dimensional scaling
- combine datasets for analysis
- plot sample beta or m-value distributions, or raw uncorrected probe channel intensities
Parts of this package were ported from
R package, and extended/developed by the team at Foxo Bioscience, who maintains it. You can write to
info@FOXOBioScience.com to give feedback, ask for help, or suggest improvements. For bugs, report issues on our github repo page.
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