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EpigeneticPacemaker

Parameters


iter_limit: int, default=100

The maximum number of iterations

error_tolerance: float, default=0.00001

Optimization tolerance, if the error between two consecutive iterations is below the tolerance threshold the model will stop fitting


Attributes

EPM: Dict[model_parameters]

  • MC_error - initial model error after first model iteration
  • MC_rates - initial site rates after first model iteration
  • MC_intercepts - initial sites intercepts after first model iteration
  • MC_rss - initial residual sum of squares
  • EPM_error - observed fit model error
  • EPM_rates - fit model rates
  • EPM_intercepts - fit model intercepts
  • EPM_iter - number of iteration used to fit model
  • EPM_rss - fit model residual of sum squares
  • chi2 - log(MC_rss / EPM_rss) * number_sites * number_samples
  • pval - chi2 p

Methods

fit(meth_array: numpy.array, states: numpy.array)

Fit an EPM model with an m x n methylation array and n methylation states, where m is the number of methylation sites and n is the number of samples. The meth_array and states should be passed an numpy arrays or an assertion error will be thrown.

predict(meth_array: numpy.array) -> predicted epigenetic states: numpy.array

Predict n epigenetic states given an m x n methylation array

score(meth_array: numpy.array, states: numpy.array) -> Pearson R: Tuple[significance, R value]

Predict n epigenetic states given an m x n methylation array and compare against known values. Note, predicted values may not be linearly dependent on input values.


EpigeneticPacemakerCV

Parameters


iter_limit: int, default=100

The maximum number of iterations

error_tolerance: float, default=0.00001

Optimization tolerance, if the error between two consecutive iterations is below the tolerance threshold the model will stop fitting

verbose: bool, default=False

Verbose model fitting

cv_folds: int, default=3

Number of cross validation folds

randomize_order: bool, default=True

Randomize the test folds, otherwise step through the CV folds in order


Attributes

EPM: Dict[model_parameters]

  • EPM_rates - CV model rates
  • EPM_intercepts - CV model intercepts

models: Dict[cv models]

Cross validation model attributes for each fold

predicted_states: numpy.ndarray Predicted epigenetic state for test samples, ie. samples left out of model fitting for a particular fold


Methods

fit(meth_array: numpy.array, states: numpy.ndarray)

Fit an EPM model with an m \times n methylation array and n methylation states, where m is the number of methylation sites and n is the number of samples. The meth_array and states should be passed an numpy arrays or an assertion error will be thrown.

predict(meth_array: numpy.array) -> predicted epigentic states: numpy.ndarray

Predict n epigenetic states given an m x n methylation array

score(meth_array: numpy.ndarray, states: numpy.ndarray) -> Pearson R: Tuple[significance, R value]

Predict n epigenetic states given an m x n methylation array and compare against known values. Note, predicted values may not be linearly dependent on input values.