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 dirichlet_multi(a , n, N, **kwargs):
'''
Each sample is a sample from a multinomial with probabilities drawn from a dirichlet.
Inputs:
a = [array] alpha parameters of dirichlet. length determins the number of otus.
n = [int] number of samples.
N = [int/array] number of reads to take for each sample.
If int take same number of reads from each sample.
If list, length must be = n.
Optional inputs:
otu_labels = [list] row labels for Survey_matrix. Default to ['otu_1','otu_2',...,'otu_k'].
sample_labels = [list] col labels for Survey_matrix. Default to ['sample_1','sample_2',...,'sample_n'].
Return Survey_matrix of simulated data.
'''
k = len(a) # number of otus
if 'otu_labels' not in kwargs:
otu_labels = map(lambda i: 'otu_' + str(i) ,range(1,1+k))
else: otu_labels = kwargs['otu_labels']
if 'sample_labels' not in kwargs:
sample_labels = map(lambda i: 'sample_' + str(i) ,range(1,1+n))
else: sample_labels = kwargs['sample_labels']
if type(N) == int: N = [N]*n
probs = dirichlet(a,int(n)) # each row gives the probabilities of different otus in 1 sample.
mat = np.zeros((k,n))
for i in range(n):
mat[:,i] = multinomial(N[i],probs[i,:])
sim_data = Survey_matrix()
sim_data.from_matrix(mat ,row_labels = otu_labels, col_labels = sample_labels )
return sim_data
def dirichlet_multi(a, n, N, **kwargs):
'''
Each sample is a sample from a multinomial with probabilities drawn from a dirichlet.
Inputs:
a = [array] alpha parameters of dirichlet. length determins the number of otus.
n = [int] number of samples.
N = [int/array] number of reads to take for each sample.
If int take same number of reads from each sample.
If list, length must be = n.
Optional inputs:
otu_labels = [list] row labels for Survey_matrix. Default to ['otu_1','otu_2',...,'otu_k'].
sample_labels = [list] col labels for Survey_matrix. Default to ['sample_1','sample_2',...,'sample_n'].
Return Survey_matrix of simulated data.
'''
k = len(a) # number of otus
if 'otu_labels' not in kwargs:
otu_labels = map(lambda i: 'otu_' + str(i), range(1, 1 + k))
else:
otu_labels = kwargs['otu_labels']
if 'sample_labels' not in kwargs:
sample_labels = map(lambda i: 'sample_' + str(i), range(1, 1 + n))
else:
sample_labels = kwargs['sample_labels']
if type(N) == int: N = [N] * n
probs = dirichlet(a, int(
n)) # each row gives the probabilities of different otus in 1 sample.
mat = np.zeros((k, n))
for i in range(n):
mat[:, i] = multinomial(N[i], probs[i, :])
sim_data = Survey_matrix()
sim_data.from_matrix(mat, row_labels=otu_labels, col_labels=sample_labels)
return sim_data
def independent_kde(site, original):
from numpy.random import rand
counts, otus, samples = original.to_matrix()
k = len(otus)
n = len(samples)
# N = original.total_counts(rows = False).values()
# N_avg = np.mean(N)
N = [1000] * n
## load kde of each OTU
path = '../data/otu_dist/'
file = site + '_filtered_kde.pick'
f = open(path + file, 'r')
kdes, cdfs = pickle.load(f)
f.close()
## simulate each OTU
x = np.linspace(0, 1, 100)
simulated = np.zeros((k, n))
for i, otu in enumerate(otus):
cdf = cdfs[otu]
for j in range(n):
p = x[cdf > rand()][0]
simulated[i, j] = binomial(N[j], p)
## convert to Survey_matrix
sim = Survey_matrix()
sim.from_matrix(simulated, otus, samples)
return sim
def to_SurveyMat(self, **kwargs):
temp = Survey_matrix()
n, k = self.shape
row_labels = kwargs.get('row_labels', range(k))
col_labels = kwargs.get('col_labels', range(n))
temp.from_matrix(self.transpose(), row_labels, col_labels)
return temp
def independent_kde(site, original):
from numpy.random import rand
counts , otus, samples = original.to_matrix()
k = len(otus)
n = len(samples)
# N = original.total_counts(rows = False).values()
# N_avg = np.mean(N)
N = [1000] * n
## load kde of each OTU
path = '../data/otu_dist/'
file = site + '_filtered_kde.pick'
f = open(path + file,'r')
kdes, cdfs = pickle.load(f)
f.close()
## simulate each OTU
x = np.linspace(0,1,100)
simulated = np.zeros((k,n))
for i,otu in enumerate(otus):
cdf = cdfs[otu]
for j in range(n):
p = x[cdf>rand()][0]
simulated[i,j] = binomial(N[j],p)
## convert to Survey_matrix
sim = Survey_matrix()
sim.from_matrix(simulated, otus, samples)
return sim
def to_SurveyMat(self, **kwargs):
temp = Survey_matrix()
n, k = self.shape
row_labels = kwargs.get('row_labels', range(k))
col_labels = kwargs.get('col_labels', range(n))
temp.from_matrix(self.transpose(), row_labels, col_labels)
return temp
def test_simulate():
mat = Survey_matrix()
x = np.array([[20,20],[20,40]])
r_lab = ['r0','r1']
c_lab = ['c0','c1']
mat.from_matrix(x,r_lab,c_lab)
sims = simulate(mat, dirichlet_multi, num_sim = 10)
print sims
def test_simulate():
mat = Survey_matrix()
x = np.array([[20, 20], [20, 40]])
r_lab = ['r0', 'r1']
c_lab = ['c0', 'c1']
mat.from_matrix(x, r_lab, c_lab)
sims = simulate(mat, dirichlet_multi, num_sim=10)
print sims
def test_permute():
mat = Survey_matrix()
x = np.array([[1,2,3,4,5],[6,7,8,9,10]])
r_lab = ['r0','r1']
c_lab = ['c0','c1','c2','c3','c4']
mat.from_matrix(x,r_lab,c_lab)
sims = permute_w_replacement(mat)
print x
print sims.to_matrix()
def test_permute():
mat = Survey_matrix()
x = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
r_lab = ['r0', 'r1']
c_lab = ['c0', 'c1', 'c2', 'c3', 'c4']
mat.from_matrix(x, r_lab, c_lab)
sims = permute_w_replacement(mat)
print x
print sims.to_matrix()
def permute_w_replacement(original):
'''
Create simulated dataset were the count of each otu in each sample are randomly sampled from the all the counts of that otu in all samples.
'''
from numpy.random import randint
counts , otus, samples = original.to_matrix()
n,k = counts.shape
new = np.zeros( (n,k) )
for row in range(n):
new[row] = counts[row, randint(0,k,(1,k)) ]
sim = Survey_matrix()
sim.from_matrix(new, otus, samples)
return sim
def permute_w_replacement(original):
'''
Create simulated dataset were the count of each otu in each sample are randomly sampled from the all the counts of that otu in all samples.
'''
from numpy.random import randint
counts, otus, samples = original.to_matrix()
n, k = counts.shape
new = np.zeros((n, k))
for row in range(n):
new[row] = counts[row, randint(0, k, (1, k))]
sim = Survey_matrix()
sim.from_matrix(new, otus, samples)
return sim
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 test_independent_betabinom():
n = 5
m = 50
mat = np.floor(100 * np.random.rand(n, m))
mat[1, :] = 100 - mat[0, :]
row_labels = range(n)
col_labels = range(m)
data = Survey_matrix()
data.from_matrix(mat, row_labels, col_labels)
c, p = data.correlation(type='pearsonr')
print c.to_matrix()
print p.to_matrix()
sim = independent_betabinom(data)
c_sim, p = sim.correlation(type='pearsonr')
print c_sim.to_matrix()
print p.to_matrix()
sim_norm = sim.normalize(rows=False)
c_sim_norm, p = sim_norm.correlation(type='pearsonr')
print c_sim_norm.to_matrix()
print p.to_matrix()
def test_independent_betabinom():
n = 5
m = 50
mat = np.floor(100*np.random.rand(n,m))
mat[1,:] = 100-mat[0,:]
row_labels = range(n)
col_labels = range(m)
data = Survey_matrix()
data.from_matrix(mat, row_labels, col_labels)
c,p = data.correlation(type = 'pearsonr')
print c.to_matrix()
print p.to_matrix()
sim = independent_betabinom(data)
c_sim, p = sim.correlation(type = 'pearsonr')
print c_sim.to_matrix()
print p.to_matrix()
sim_norm = sim.normalize(rows = False)
c_sim_norm, p = sim_norm.correlation(type = 'pearsonr')
print c_sim_norm.to_matrix()
print p.to_matrix()
def independent_betabinom(original):
counts , otus, samples = original.to_matrix()
k = len(otus)
n = len(samples)
N = original.total_counts(rows = False).values()
N_avg = np.mean(N)
## estimate beta-dist params for each OTU
a = []
tot = counts.sum(axis = 0)
for i in range(k):
c = np.c_[counts[i,:], tot- counts[i,:]]
a_opt, ll = estimate.dirmulti(c, symmetric = False)
a.append(a_opt)
## simulate each OTU
simulated = np.zeros((k,n))
for i in range(k):
probs = dirichlet(a[i],n)
for j,p in enumerate(probs):
simulated[i,j] = multinomial(N_avg,p)[0]
## convert to Survey_matrix
sim = Survey_matrix()
sim.from_matrix(simulated, otus, samples)
return sim
def independent_betabinom(original):
counts, otus, samples = original.to_matrix()
k = len(otus)
n = len(samples)
N = original.total_counts(rows=False).values()
N_avg = np.mean(N)
## estimate beta-dist params for each OTU
a = []
tot = counts.sum(axis=0)
for i in range(k):
c = np.c_[counts[i, :], tot - counts[i, :]]
a_opt, ll = estimate.dirmulti(c, symmetric=False)
a.append(a_opt)
## simulate each OTU
simulated = np.zeros((k, n))
for i in range(k):
probs = dirichlet(a[i], n)
for j, p in enumerate(probs):
simulated[i, j] = multinomial(N_avg, p)[0]
## convert to Survey_matrix
sim = Survey_matrix()
sim.from_matrix(simulated, otus, samples)
return sim
