def lw(data, alphas):
"""
Estimates the graph with Ledoit-Wolf estimator.
Parameters
----------
data: numpy ndarray
The input data for to reconstruct/estimate a graph on. Features as columns and observations as rows.
alphas: float
The threshold on the precision matrix to determine edges.
Returns
-------
adjacency matrix : the estimated adjacency matrix.
"""
alpha=alphas
scaler = StandardScaler()
data = scaler.fit_transform(data)
cov = LedoitWolf().fit(data)
precision_matrix = cov.get_precision()
n_features, _ = precision_matrix.shape
mask1 = np.abs(precision_matrix) > alpha
mask0 = np.abs(precision_matrix) <= alpha
adjacency_matrix = np.zeros((n_features,n_features))
adjacency_matrix[mask1] = 1
adjacency_matrix[mask0] = 0
adjacency_matrix[np.diag_indices_from(adjacency_matrix)] = 0
return adjacency_matrix
def main():
'''
Constructs a co-occurence network from gene expression data.
Main entry point to code.
'''
# Read in the data
if os.path.isfile(DATA_PICKLE):
print("reading previously saved data from pickle %s" % (DATA_PICKLE))
with open(DATA_PICKLE, 'rb') as file:
df = pickle.load(file)
lwe = pickle.load(file)
pmat = pickle.load(file)
pcore_indices = pickle.load(file)
pcor = pickle.load(file)
lfdr_pcor = pickle.load(file)
#prob = pickle.load(file)
else:
print("reading in data from %s" % (FILENAME))
df = pd.read_csv(FILENAME, sep='\t')
print("found %d rows and %d columns" % (df.shape[0], df.shape[1]))
# compute the row means and sort the data frame by descinding means
df['row_means'] = df.mean(axis=1)
df.sort_values('row_means', axis=0, ascending=False, inplace=True)
df.drop('row_means', axis=1, inplace=True)
# take the most abundant genes
df = df.head(PRUNE_GENES)
# Ledoit-Wolf optimal shrinkage coefficient estimate
print("computing Ledoit-Wolf optimal shrinkage coeffecient estimate")
lwe = LedoitWolf().fit(df.transpose())
pmat = lwe.get_precision()
# Convert symmetric matrix to array, first by getting indices
# of the off diagonal elements, second by pulling them into
# separate array (pcor).
print("extracting off diagnol elements of precision matrix")
pcor_indices = np.triu_indices(pmat.shape[0], 1)
pcor = pmat[pcor_indices]
# Determine edges by computing lfdr of pcor.
print("computing lfdr of partial correlations")
fdrtool = importr('fdrtool')
lfdr_pcor = fdrtool.fdrtool(FloatVector(pcor), statistic="correlation", plot=False)
#prob = 1-lfdr_pcor['lfdr']
with open(DATA_PICKLE, 'wb') as file:
pickle.dump(df, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(lwe, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pmat, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pcor_indices, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pcor, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(lfdr_pcor, file, pickle.HIGHEST_PROTOCOL)
#pickle.dump(prob, file, pickle.HIGHEST_PROTOCOL)
print("making 1-lfdr vs. pcor plot")
prob = 1-np.array(lfdr_pcor.rx2('lfdr'))
with PdfPages(PDF_FILENAME) as pdf:
plt.figure(figsize=(3, 3))
plt.plot(range(7), [3, 1, 4, 1, 5, 9, 2], 'r-o')
plt.title('Page One')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
plt.plot(pcor[0:10000:10], prob[0:10000:10], 'o', markeredgecolor='k', markersize=3)
plt.title("THIS IS A PLOT TITLE, YOU BET")
plt.xlabel('partial correlation')
plt.ylabel('lfdr')
pdf.savefig
plt.close()
spOrder = cs_amdW(sp, order, myCSparceLibP)
#estimate precision matrix from either Gaussian graphical model or
#the LedoitWolf regularisation (here we are using the second)
#note that with the typical correlation coefficients the estimation
#of the covariance matrix is not well regularised especially with a large number of rois.
#This problem is exacerbated with the inversion of the correlation matrix
print "Loading fMRI averaged signal ..."
print "Estimate the precision matrix ..."
precAll = np.empty([lenR,lenR,subjNum])
for ii,subj in enumerate(folder_names):
inputP = pathfMRI + 'timeSeriesR' + prefS + '_' + subj + '.txt'
signal = np.loadtxt(inputP)
LedW = LedoitWolf(store_precision=True, assume_centered=False)
LedW.fit(signal.T)
prec = LedW.get_precision() #estimate the precision matrix based on the LedoitWolf regularisation
precAll[:,:,ii] = prec
print "Preparing Data for randomised Lasso: Cholesky decomposition, scaling diagonal and so on..."
#prepare data for ransomised Lasso
Y,X,ri0,ci0 = prepChol(scAll,precAll,sp,spOrder)
print "Randomised Lasso is running ..."
prob_all = run_randomisedLasso(X.T,Y)
print "Reorder data and visualise ..."
adj2,adj = visual_res(prob_all,lenR,roisF,ri0,ci0,thres)
pl.matshow(adj2)
pl.colorbar()
pl.show()
def main():
'''
Constructs a co-occurence network from gene expression data.
Main entry point to code.
'''
# Read in the data
if os.path.isfile(DATA_PICKLE):
print("reading previously saved data from pickle %s" % (DATA_PICKLE))
with open(DATA_PICKLE, 'rb') as file:
df = pickle.load(file)
lwe = pickle.load(file)
pmat = pickle.load(file)
pcore_indices = pickle.load(file)
pcor = pickle.load(file)
lfdr_pcor = pickle.load(file)
#prob = pickle.load(file)
else:
print("reading in data from %s" % (FILENAME))
df = pd.read_csv(FILENAME, sep='\t')
# TODO: remove this experimental data-trimming
#if NUM_ROWS_DEV_SCALE is not None:
# old_shape = df.shape
# df = df.iloc[0:NUM_ROWS_DEV_SCALE, ]
# print('DEV MODE: TRIMED DATA FROM {} to {}'.format(old_shape, df.shape))
print("found %d rows and %d columns" % (df.shape[0], df.shape[1]))
# compute the row means and sort the data frame by descinding means
df['row_means'] = df.mean(axis=1)
df.sort_values('row_means', axis=0, ascending=False, inplace=True)
df.drop('row_means', axis=1, inplace=True)
# take the most abundant genes
#df = df.head(PRUNE_GENES)
# Ledoit-Wolf optimal shrinkage coefficient estimate
print("computing Ledoit-Wolf optimal shrinkage coeffecient estimate")
start_time = datetime.now()
print('time: {}'.format(str(start_time)))
lwe = LedoitWolf().fit(df.transpose())
end_time = datetime.now()
total_time = end_time - start_time
print('LedoitWolf time for {} genes: {}'.format(df.shape[0], str(total_time)))
pmat = lwe.get_precision()
# Convert symmetric matrix to array, first by getting indices
# of the off diagonal elements, second by pulling them into
# separate array (pcor).
print("extracting off diagnol elements of precision matrix")
pcor_indices = np.triu_indices(pmat.shape[0], 1)
pcor = pmat[pcor_indices]
# Determine edges by computing lfdr of pcor.
print("computing lfdr of partial correlations")
fdrtool = importr('fdrtool')
lfdr_pcor = fdrtool.fdrtool(FloatVector(pcor), statistic="correlation", plot=False)
#prob = 1-lfdr_pcor['lfdr']
with open(DATA_PICKLE, 'wb') as file:
pickle.dump(df, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(lwe, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pmat, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pcor_indices, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pcor, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(lfdr_pcor, file, pickle.HIGHEST_PROTOCOL)
#pickle.dump(prob, file, pickle.HIGHEST_PROTOCOL)
print("making 1-lfdr vs. pcor plot")
prob = 1-np.array(lfdr_pcor.rx2('lfdr'))
with PdfPages(PDF_FILENAME) as pdf:
plt.figure(figsize=(3, 3))
plt.plot(range(7), [3, 1, 4, 1, 5, 9, 2], 'r-o')
plt.title('Page One')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
plt.plot(pcor[0:10000:10], prob[0:10000:10], 'o', markeredgecolor='k', markersize=3)
plt.title("THIS IS A PLOT TITLE, YOU BET")
plt.xlabel('partial correlation')
plt.ylabel('lfdr')
pdf.savefig
plt.close()
