calour.sorting.cluster_data¶

calour.sorting.cluster_data(exp: calour.experiment.Experiment, transform=None, axis=1, metric='euclidean', inplace=False, **kwargs)[source]¶

Cluster the samples/features.

Reorder the features/samples so that ones with similar behavior (pattern across samples/features) are close to each other

Note

This function is also available as a class method Experiment.cluster_data()

Keyword Arguments:
Parameters:	exp (Experiment) – Input experiment object. aixs (0, 1, 's', or 'f', optional) – ‘f’ or 1 (default) means clustering features; ‘s’ or 0 means clustering samples transform (Callable) – a callable transform on a 2-d matrix. Input and output of transform are `Experiment`. The transform function can modify `Experiment.data` (it is a copy). It should not change the dimension of `Experiment.data`. metric (str or callable) – the clustering metric to use. It should be able to be passed to `scipy.spatial.distance.pdist`. inplace (bool, optional) – False (default) to create a copy. True to Replace data in exp.
	steps (list of callable) – each callable is a transformer that takes `Experiment` object as its 1st argument and has a boolean parameter of `inplace`. Each callable should return an `Experiment` object. inplace (bool) – transformation occuring in the original data or a copy kwargs (dict) – keyword arguments to pass to each transformers. The key should be in the form of “<transformer_name>__<param_name>”. For example, “transform(exp: Experiment, steps=[log_n], log_n__n=3)” will set “n” of function “log_n” to 3
Returns:	With samples/features clustered (reordered)
Return type:	Experiment