epitoolkit 0.0.1

EpiToolkit is a set of tools useful in the analysis of data from EPIC / 450K microarrays.

EpiGenToolKit

Is a small library created to deal with data from EPIC / 450K microarrays. The tool allows to:

a) Simply visualize methylation levels of specific CpG or genomic region. 

b) Perform enrichment analysis of a selected subset of CpG against the whole array. In this type of analysis expected frequency [%] (based on mynorm) of genomic regions is compared to observed (based on provided cpgs set), results are comapred using chi-square test.

How to start?

a) using env

    python -m venv env 
    source env/bin/activate # Windows: env\Scripts\activate
    pip install epitoolkit

b) using poetry

    poetry new .
    poetry add epitoolkit

c) or just clone the repository:

    git clone https://github.com/ClinicalEpigeneticsLaboratory/EpiGenToolKit.git
    cd EpiGenToolKit && poetry install

How to use?

Visualization

To visualize single CpG site or specific genomic region initialize Visualise object:

    from epitoolkit.tools import Visualize
    viz = Visualize(manifest=<path_to_array_manifest>,
                    mynorm=<path_to_mynorm_file>,
                    poi=<path_to_poi_file>,
                    skiprows=7) # many manifest contains headers, set skiprows argument to ignore few first rows.

all files must have *.csv extension, mynorm must contain samples [columns] and cpgs [rows], the proper EPIC manifest may be downloaded from the link, poi file must contain sample names [rows] (the same as in mynorm file) and POI (phenotype of interest) column containing names of phenotype e.g. Control and Case.

To visualize single CpG:

viz.plot_CpG("cg07881041", # cpg ID 
         static=False, # plot type static / interactive
         height=400, # plot size
         width=700, # plot size
         title="", # plot title
         legend_title="", # legend title
         font_size=22, # font size
         show_legend=True, # False to hide legedn
         x_axis_label="CpG", # x axsis label
         y_axis_label="beta-values") # y axis label

To visualize specific genomic region:

    vis.plot_Range(chr=17, start=5999, end=6770) # All arguments describing plot details such as height or width (described above) are also available in this method.

To visualize specific CpGs in genomic order, instead of whole region pass collection of CpGs:

    viz.plot_Range(cpgs=["cg04594855", "cg09002677"]) 

To save plots use export argument for instance:

    viz.plot_Range(chr=17, start=5999, end=6770, export="plot.html") # if static = False only html format is supported if static = True, use png extension.

Enrichment analysis

To perform enrichment analysis against any type of genomic region specified in the manifest file, the user needs to initialize EnrichemntAnalysis object.

        from src.epitoolkit.tools import EnrichemntAnalysis
        ea = EnrichemntAnalysis(manifest=<path_to_array_manifest>,
                    mynorm=<path_to_mynorm_file>)

or if Analysis object already exists:

        ea = EnrichemntAnalysis(manifest=viz.manifest,
                    mynorm=viz.mynorm)

To start analysis:

        ea.enrichemntAnalysis(categories_to_analyse=["UCSC_RefGene_Group", "Relation_to_UCSC_CpG_Island"],  # list of categories to analyse
                          cpgs=cpgs) # list of cpgs to analyse against backgorund