def main():
""" Create sample-specific co-expression networks for one fold and one repeat
of a cross-validation for which fold indices have already been computed.
The data will be stored under
<data_dir>/repeat<repeat idx>
with the following structure:
edges.gz:
Gzipped file containing the list of edges of the co-expression networks.
Each line is an undirected edge, formatted as:
<index of gene 1> <index of gene 2>
By convention, the index of gene 1 is smaller than that of gene 2.
For k=0..(numFolds-1):
<k>/lioness/edge_weights.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the LIONESS co-expression networks
for the training samples.
<k>/lioness/edge_weights_te.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the LIONESS co-expression networks
for the test samples.
<k>/regline/edge_weights.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the Regline co-expression networks
for the training samples.
<k>/regline/edge_weights_te.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the Regline co-expression networks
for the test samples.
Parameters
----------
aces_dir: path
Path to the ACES folder.
data_dir: path
Path to the folder containing fold indices (under <data_dir>/repeat<repeat_idx>/fold<fold_idx>).
fold: int
Fold index.
repeat: int
Repeat index.
Example
-------
$ python setUpSubTypeStratifiedCV_computeNetworks.py ACES outputs/U133A_combat_RFS/subtype_stratified 0 0
Reference
---------
Allahyar, A., and Ridder, J. de (2015).
FERAL: network-based classifier with application to breast cancer outcome prediction.
Bioinformatics 31, i311--i319.
"""
parser = argparse.ArgumentParser(description="Build sample-specific co-expression networks" + \
"for a 10-fold sub-type stratified CV on the RFS data",
add_help=True)
parser.add_argument("aces_dir", help="Path to ACES data")
parser.add_argument("data_dir", help="Path to the fold indices")
parser.add_argument("fold", help="Index of the fold", type=int)
parser.add_argument("repeat", help="Index of the repeat", type=int)
args = parser.parse_args()
outDir = '%s/repeat%d' % (args.data_dir, args.repeat)
# Get expression data, sample labels.
# Do not normalize the data while loading it (so as not to use test data for normalization).
f = h5py.File("%s/experiments/data/U133A_combat.h5" % args.aces_dir)
expressionData = np.array(f['U133A_combat_RFS']['ExpressionData'])
sampleLabels = np.array(f['U133A_combat_RFS']['PatientClassLabels'])
f.close()
foldNr = args.fold
# Output directory
foldDir = "%s/fold%d" % (outDir, foldNr)
# Read train indices from file
trIndicesF = '%s/train.indices' % foldDir
trIndices = np.loadtxt(trIndicesF, dtype=int)
sys.stdout.write("Read training indices for fold %d from %s\n" % (foldNr, trIndicesF))
# Read test indices from file
teIndicesF = '%s/test.indices' % foldDir
teIndices = np.loadtxt(teIndicesF, dtype=int)
sys.stdout.write("Read training indices for fold %d from %s\n" % (foldNr, teIndicesF))
print teIndices
print teIndices.shape
# Create networks
CoExpressionNetwork.run_whole_data(expressionData, sampleLabels, foldDir,
trIndices=trIndices, teIndices=teIndices)
def main():
""" Create sample-specific co-expression networks for one fold and one repeat
of a cross-validation for which fold indices have already been computed.
The data will be stored under
<data_dir>/repeat<repeat idx>
with the following structure:
edges.gz:
Gzipped file containing the list of edges of the co-expression networks.
Each line is an undirected edge, formatted as:
<index of gene 1> <index of gene 2>
By convention, the index of gene 1 is smaller than that of gene 2.
For k=0..(numFolds-1):
<k>/lioness/edge_weights.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the LIONESS co-expression networks
for the training samples.
<k>/lioness/edge_weights_te.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the LIONESS co-expression networks
for the test samples.
<k>/regline/edge_weights.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the Regline co-expression networks
for the training samples.
<k>/regline/edge_weights_te.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the Regline co-expression networks
for the test samples.
Parameters
----------
aces_dir: path
Path to the ACES folder.
data_dir: path
Path to the folder containing fold indices (under <data_dir>/repeat<repeat_idx>/fold<fold_idx>).
fold: int
Fold index.
repeat: int
Repeat index.
Example
-------
$ python setUpSubTypeStratifiedCV_computeNetworks.py ACES outputs/U133A_combat_RFS/subtype_stratified 0 0
Reference
---------
Allahyar, A., and Ridder, J. de (2015).
FERAL: network-based classifier with application to breast cancer outcome prediction.
Bioinformatics 31, i311--i319.
"""
parser = argparse.ArgumentParser(description="Build sample-specific co-expression networks" + \
"for a 10-fold sub-type stratified CV on the RFS data",
add_help=True)
parser.add_argument("aces_dir", help="Path to ACES data")
parser.add_argument("data_dir", help="Path to the fold indices")
parser.add_argument("fold", help="Index of the fold", type=int)
parser.add_argument("repeat", help="Index of the repeat", type=int)
args = parser.parse_args()
outDir = '%s/repeat%d' % (args.data_dir, args.repeat)
# Get expression data, sample labels.
# Do not normalize the data while loading it (so as not to use test data for normalization).
f = h5py.File("%s/experiments/data/U133A_combat.h5" % args.aces_dir)
expressionData = np.array(f['U133A_combat_RFS']['ExpressionData'])
sampleLabels = np.array(f['U133A_combat_RFS']['PatientClassLabels'])
f.close()
foldNr = args.fold
# Output directory
foldDir = "%s/fold%d" % (outDir, foldNr)
# Read train indices from file
trIndicesF = '%s/train.indices' % foldDir
trIndices = np.loadtxt(trIndicesF, dtype=int)
sys.stdout.write("Read training indices for fold %d from %s\n" %
(foldNr, trIndicesF))
# Read test indices from file
teIndicesF = '%s/test.indices' % foldDir
teIndices = np.loadtxt(teIndicesF, dtype=int)
sys.stdout.write("Read training indices for fold %d from %s\n" %
(foldNr, teIndicesF))
print teIndices
print teIndices.shape
# Create networks
CoExpressionNetwork.run_whole_data(expressionData,
sampleLabels,
foldDir,
trIndices=trIndices,
teIndices=teIndices)
def main():
""" Create sample-specific co-expression networks for one fold and one repeat
of a subtype-stratified CV on the RFS data.
Meant to be run on the cluster.
The data will be stored under
$DATA_DIR/outputs/U133A_combat_RFS/subtype_stratified/repeat<repeat idx>
with the following structure:
For k=1..numFolds:
fold<k>/edges.gz:
Gzipped file containing the list of edges of the co-expression networks.
Each line is an undirected edge, formatted as:
<index of gene 1> <index of gene 2>
By convention, the index of gene 1 is smaller than that of gene 2.
fold<k>/lioness/edge_weights.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the LIONESS co-expression networks
for the training samples.
fold<k>/lioness/edge_weights_te.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the LIONESS co-expression networks
for the test samples.
fold<k>/regline/edge_weights.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the Regline co-expression networks
for the training samples.
fold<k>/regline/edge_weights_te.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the Regline co-expression networks
for the test samples.
Example:
$ python setUpSubTypeStratifiedCV_computeNetworks.py 0 0
Reference
---------
Allahyar, A., and Ridder, J. de (2015).
FERAL: network-based classifier with application to breast cancer outcome prediction.
Bioinformatics 31, i311--i319.
"""
parser = argparse.ArgumentParser(description="Build sample-specific co-expression networks" + \
"for a 10-fold sub-type stratified CV on the RFS data",
add_help=True)
parser.add_argument("repeat", help="Index of the repeat", type=int)
parser.add_argument("fold", help="Index of the fold", type=int)
parser.add_argument("-r",
"--refc_dir",
help="Reference data for network construction")
args = parser.parse_args()
out_dir = '%s/outputs/U133A_combat_RFS/subtype_stratified/repeat%d' % (
DATA_DIR, args.repeat)
# Get expression data, sample labels.
# Do not normalize the data while loading it (so as not to use test data for normalization).
f = h5py.File("%s/ACES/experiments/data/U133A_combat.h5" % DATA_DIR)
expression_data = np.array(f['U133A_combat_RFS']['ExpressionData'])
sample_labels = np.array(f['U133A_combat_RFS']['PatientClassLabels'])
f.close()
fold_nr = args.fold
# Output directory
fold_dir = "%s/fold%d" % (out_dir, fold_nr)
# Read train indices from file
tr_indices_f = '%s/train.indices' % fold_dir
tr_indices = np.loadtxt(tr_indices_f, dtype=int)
sys.stdout.write("Read training indices for fold %d from %s\n" %
(fold_nr, tr_indices_f))
# Read test indices from file
te_indices_f = '%s/test.indices' % fold_dir
te_indices = np.loadtxt(te_indices_f, dtype=int)
sys.stdout.write("Read training indices for fold %d from %s\n" %
(fold_nr, te_indices_f))
print te_indices
print te_indices.shape
# Create networks
CoExpressionNetwork.run_whole_data(expression_data,
sample_labels,
fold_dir,
reference_data=args.refc_dir,
tr_indices=tr_indices,
te_indices=te_indices)
def main():
""" Create sample-specific co-expression networks for one fold and one repeat
of a subtype-stratified CV on the RFS data.
Meant to be run on the cluster.
The data will be stored under
$DATA_DIR/outputs/U133A_combat_RFS/subtype_stratified/repeat<repeat idx>
with the following structure:
For k=1..numFolds:
fold<k>/edges.gz:
Gzipped file containing the list of edges of the co-expression networks.
Each line is an undirected edge, formatted as:
<index of gene 1> <index of gene 2>
By convention, the index of gene 1 is smaller than that of gene 2.
fold<k>/lioness/edge_weights.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the LIONESS co-expression networks
for the training samples.
fold<k>/lioness/edge_weights_te.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the LIONESS co-expression networks
for the test samples.
fold<k>/regline/edge_weights.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the Regline co-expression networks
for the training samples.
fold<k>/regline/edge_weights_te.gz:
gzipped file containing the (self.numSamples, numEdges) array
describing the edge weights of the Regline co-expression networks
for the test samples.
Example:
$ python setUpSubTypeStratifiedCV_computeNetworks.py 0 0
Reference
---------
Allahyar, A., and Ridder, J. de (2015).
FERAL: network-based classifier with application to breast cancer outcome prediction.
Bioinformatics 31, i311--i319.
"""
parser = argparse.ArgumentParser(description="Build sample-specific co-expression networks" + \
"for a 10-fold sub-type stratified CV on the RFS data",
add_help=True)
parser.add_argument("repeat", help="Index of the repeat", type=int)
parser.add_argument("fold", help="Index of the fold", type=int)
parser.add_argument("-r", "--refc_dir",
help="Reference data for network construction")
args = parser.parse_args()
out_dir = '%s/outputs/U133A_combat_RFS/subtype_stratified/repeat%d' % (DATA_DIR, args.repeat)
# Get expression data, sample labels.
# Do not normalize the data while loading it (so as not to use test data for normalization).
f = h5py.File("%s/ACES/experiments/data/U133A_combat.h5" % DATA_DIR)
expression_data = np.array(f['U133A_combat_RFS']['ExpressionData'])
sample_labels = np.array(f['U133A_combat_RFS']['PatientClassLabels'])
f.close()
fold_nr = args.fold
# Output directory
fold_dir = "%s/fold%d" % (out_dir, fold_nr)
# Read train indices from file
tr_indices_f = '%s/train.indices' % fold_dir
tr_indices = np.loadtxt(tr_indices_f, dtype=int)
sys.stdout.write("Read training indices for fold %d from %s\n" % (fold_nr, tr_indices_f))
# Read test indices from file
te_indices_f = '%s/test.indices' % fold_dir
te_indices = np.loadtxt(te_indices_f, dtype=int)
sys.stdout.write("Read training indices for fold %d from %s\n" % (fold_nr, te_indices_f))
print te_indices
print te_indices.shape
# Create networks
CoExpressionNetwork.run_whole_data(expression_data, sample_labels, fold_dir,
reference_data=args.refc_dir,
tr_indices=tr_indices, te_indices=te_indices)