calour.filtering.is_prevalent¶

calour.filtering.is_prevalent(data, axis, cutoff=1, fraction=0.1)[source]¶

Check the prevalent of values above the cutoff.

present (abundance >= cutoff) in at least “fraction” of samples

Parameters:	data (numpy.ndarray or scipy.sparse.csr_matrix) – axis (int) – compute prevalence of each column (0) or row (1). cutoff (float) – the min threshold of abundance fraction (float) – [0, 1). the min threshold of presence (in fraction)
Returns:	bool array with True if prevalence >= cutoff.
Return type:	np.ndarray

Examples

>>> x = is_prevalent(np.array([[0, 1, 2], [0, 1, 2]]), 0, 2, 0.51)
>>> x.tolist()
[False, False, True]
>>> x = is_prevalent(np.array([[0, 1, 2], [0, 2, 2]]), 0, 2, 0.5)
>>> x.tolist()
[False, True, True]