calour.sorting.sort_abundance¶

calour.sorting.sort_abundance(exp: calour.experiment.Experiment, subgroup=None, **kwargs)[source]¶

Sort features based on their abundance in a subset of the samples.

This is a convenience wrapper for sort_by_data().

Note

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

Keyword Arguments:
Parameters:	exp (Experiment) – Input experiment object. subgroup (dict or None (default)) – None (default) to sort on all samples. Subset samples by columns (specified by dict keys) in sample metadata matching the dict values (a list). sorting is only on samples matching this list
	axis (0, 1, 's', or 'f') – Apply `key` function on row (sort the samples) (0 or ‘s’) or column (sort the features) (1 or ‘f’) subset (None, boolean mask, `slice`, or int indices, optional) – Sorting using only subset of the data. The subsetting occurs on the opposite of the specified axis. Default is to use the whole data set. key (str or callable) – If it is a callable, it should be a function accepts 1-D array of numeric and returns a comparative value (like `key` in builtin `sorted()`. For example, you can use `numpy.mean()` or `numpy.media()`. Alternatively it accepts the following strings: ’log_mean’: sort by the mean of the log; ’prevalence’: sort by the prevalence; inplace (bool, optional) – False (default) to create a copy. True to modify in place. reverse (bool, optional) – True to reverse the order of the sort. Similar to `sorted()` kwargs (dict) – keyword parameters passed to `key`
Returns:	with features sorted by abundance
Return type:	Experiment