Building expressionsΒΆ

When doing a selection (using meta_select(), reg_select()) or a projection (using meta_project(), reg_project()) you are required to specify an expression on the metadata or region fields.

An expression can therefore use metadata attributes or region fields. Given a GMQLDataset dataset, one can access its region fields by typing:


and one can access its metadata attributes by typing:


The expressions in PyGMQL can be of two types:

  • Predicate: a logical condition that enables to select a portion of the dataset. This expression is used in selection. Some example of predicates follow:

    # region predicate
    (dataset.chr == 'chr1' || dataset.pValue < 0.9)
    # region predicate with access to metadata attributes
    dataset.score > dataset['size']

    It is possible, based on the function that requires a predicate, to mix region fields and metadata attributes in a region condition. Of course it is not possible to mix metadata and region conditions in a metadata selection (this is due to the fact that to each metadata attribute can be associated multiple values for each region field).

  • Extension: a mathematical expression describing how to build new metadata or region fields based on the existent ones. Some examples of expression follow:

    # region expression
    dataset.start + dataset.stop
    dataset.p_value / dataset.q_value
    # metadata expression
    dataset['size'] * 8.9
    dataset['score'] / dataset['size']

    It is possible to mix region fields and metadata attributes in region extensions:

    # region expression using metadata attributes
    (dataset.pvalue / 2) + dataset['metadata'] + 1