def make_network(self, algo = 'crude', **kwargs):
'''
'''
import SurveyStructures.OTUnetwork as ONet
n, k = self.shape
row_labels = kwargs.get('row_labels', range(k))
col_labels = kwargs.get('col_labels', range(n))
md = self.to_Surveymat(**kwargs)
if algo == 'crude': mat,pval = (md.log10()).correlation(type = 'pearsonr')
elif algo in set(['sparse', 'csi', 'clr']):
v_sparse, c_sparse, Cov_sparse = self.basis_corr(method = algo,**kwargs)
mat = MatrixDictionary()
mat.from_matrix(c_sparse, row_labels, row_labels)
pval = mat
elif algo == 'basis':
md = Survey_matrix()
n, k = self.shape
mat = np.log(self.basis)
md.from_matrix(mat.transpose(), row_labels, col_labels)
mat,pval = md.correlation(type = 'pearsonr')
elif algo in set(['pearsonr','kendalltau','spearmanr']):
abunds = self.to_Surveymat()
mat,pval = abunds.correlation(type = algo)
## make network
if algo == 'MI': net = ONet.OTUnetwork( abunds = abunds, lineages = lineages, matrix = mat.to_PairMatrix(), p_vals = {'direct':None}, algo = 'MI', lag = 0)
else: net = ONet.OTUnetwork(node_ids = mat.row_labels(), matrix = mat, p_vals = {'direct':pval}, algo = algo, lag = 0)
return net
def basis_corr(self, algo='SparCC', **kwargs):
'''
'''
import basis_correlations as basecor
counts_t, otus, samples = self.to_matrix()
counts = counts_t.transpose()
cor_med, cov_med = basecor.main(counts, algo=algo, **kwargs)
cor = MatrixDictionary()
cor.from_matrix(cor_med, otus, otus)
if cov_med is None:
cov = None
else:
cov = MatrixDictionary()
cov.from_matrix(cov_med, otus, otus)
return cor, cov
def basis_corr(self, algo='SparCC', **kwargs):
'''
'''
import basis_correlations as basecor
counts_t, otus, samples = self.to_matrix()
counts = counts_t.transpose()
cor_med, cov_med = basecor.main(counts, algo=algo, **kwargs)
cor = MatrixDictionary()
cor.from_matrix(cor_med, otus, otus)
if cov_med is None:
cov = None
else:
cov = MatrixDictionary()
cov.from_matrix(cov_med, otus, otus)
return cor, cov
def make_network(self, algo='crude', **kwargs):
'''
'''
import SurveyStructures.OTUnetwork as ONet
n, k = self.shape
row_labels = kwargs.get('row_labels', range(k))
col_labels = kwargs.get('col_labels', range(n))
md = self.to_Surveymat(**kwargs)
if algo == 'crude':
mat, pval = (md.log10()).correlation(type='pearsonr')
elif algo in set(['sparse', 'csi', 'clr']):
v_sparse, c_sparse, Cov_sparse = self.basis_corr(method=algo,
**kwargs)
mat = MatrixDictionary()
mat.from_matrix(c_sparse, row_labels, row_labels)
pval = mat
elif algo == 'basis':
md = Survey_matrix()
n, k = self.shape
mat = np.log(self.basis)
md.from_matrix(mat.transpose(), row_labels, col_labels)
mat, pval = md.correlation(type='pearsonr')
elif algo in set(['pearsonr', 'kendalltau', 'spearmanr']):
abunds = self.to_Surveymat()
mat, pval = abunds.correlation(type=algo)
## make network
if algo == 'MI':
net = ONet.OTUnetwork(abunds=abunds,
lineages=lineages,
matrix=mat.to_PairMatrix(),
p_vals={'direct': None},
algo='MI',
lag=0)
else:
net = ONet.OTUnetwork(node_ids=mat.row_labels(),
matrix=mat,
p_vals={'direct': pval},
algo=algo,
lag=0)
return net
