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Applying methods

The available methods implement well established algorithms that transform and analyse data. Methods usually take as input predefined Dataset(s) and produce one or several Dataset(s) as output. This section explains how to apply methods to your data.

The full list of available methods can be found in the Python API documentation.

Apply a class method

Some methods are implemented as class methods. For example, the finemap method is a class method of the PICS class. This method performs fine-mapping using the PICS algorithm. These methods usually take as input one or several Dataset(s) and produce one or several Dataset(s) as output.

from gentropy.method.pics import PICS

finemapped_study_locus = PICS.finemap(
    study_locus_ld_annotated
).annotate_credible_sets()

Apply a Dataset instance method

Some methods are implemented as instance methods of the Dataset class. For example, the window_based_clumping method is an instance method of the SummaryStatistics class. This method performs window-based clumping on summary statistics.

# Perform window-based clumping on summary statistics
# By default, the method uses a 1Mb window and a p-value threshold of 5e-8
clumped_summary_statistics = summary_stats.window_based_clumping()

The window_based_clumping method is also available as a class method

The window_based_clumping method is also available as a class method of the WindowBasedClumping class. This method performs window-based clumping on summary statistics.

# Perform window-based clumping on summary statistics
from gentropy.method.window_based_clumping import WindowBasedClumping

clumped_summary_statistics = WindowBasedClumping.clump(
    summary_stats, distance=250_000
)

What's next?

Up next, we'll show you how to inspect your data to ensure its integrity and the success of your transformations.

  • 2024-02-05
  • 2024-02-05
  • Contributors
    • ireneisdoomed