flotilla.compute.splicing module¶

Calculate modalities of splicing events.

class flotilla.compute.splicing.ModalityEstimator(step, vmax, logbf_thresh=3)[source]¶

Bases: object

Use Bayesian methods to estimate modalities of splicing events

Initialize an object with models to estimate splicing modality

Parameters:	step : float Distance between parameter values vmax : float Maximum parameter value logbf_thresh : float Minimum threshold at which the bayes factor difference is defined to be significant

fit_transform(data)[source]¶

Get the modality assignments of each splicing event in the data

Parameters:	data : pandas.DataFrame A (n_samples, n_events) dataframe of splicing events’ PSI scores. Must be psi scores which range from 0 to 1
Returns:	modality_assignments : pandas.Series A (n_events,) series of the estimated modality for each splicing event
Raises:	AssertionError If data does not fall only between 0 and 1.

class flotilla.compute.splicing.ModalityModel(alphas, betas)[source]¶

Bases: object

Object to model modalities from beta distributions

logliks(x)[source]¶

logsumexp_logliks(x)[source]¶

flotilla.compute.splicing.get_switchy_score_order(x)[source]¶

Apply switchy scores to a 2D array of data scores

Parameters:	x : numpy.array A 2-D numpy array in the shape [n_events, n_samples]
Returns:	score_order : numpy.array A 1-D array of the ordered indices, in switchy score order

flotilla.compute.splicing.switchy_score(array)[source]¶

Transform a 1D array of data scores to a vector of “switchy scores”

Calculates std deviation and mean of sine- and cosine-transformed versions of the array. Better than sorting by just the mean which doesn’t push the really lowly variant events to the ends.

Parameters:	array : numpy.array A 1-D numpy array or something that could be cast as such (like a list)
Returns:	switchy_score : float The “switchy score” of the study_data which can then be compared to other splicing event study_data