def run(self, ko_file, wt_file, ts_file=None, kd_file=None, name=None):
import numpy
os.chdir(os.environ["gene_path"])
print "Reading in knockout data"
knockout_storage = ReadData(ko_file, "knockout")
knockout_storage.normalize()
wildtype_storage = ReadData(wt_file, "wildtype")
wildtype_storage.normalize()
knockdown_storage = ReadData(kd_file, "knockdown")
knockdown_storage.normalize()
wildtype_storage.combine(knockdown_storage)
timeseries_storage = None
if ts_file != None:
timeseries_storage = ReadData(ts_file, "timeseries")
for ts in timeseries_storage:
ts.normalize()
settings = {}
settings = ReadConfig(settings)
# TODO: CHANGE ME
settings["global"]["working_dir"] = os.getcwd() + '/'
# Setup job manager
print "Starting new job manager"
jobman = JobManager(settings)
# Make MCZ jobs
mczjob = MCZ()
mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, name)
print "Queuing job..."
jobman.queueJob(mczjob)
print jobman.queue
print "Running queue..."
jobman.runQueue()
jobman.waitToClear()
print "Queue finished"
job = jobman.finished[0]
print job.alg.gene_list
print job.alg.read_output(settings)
jobnet = job.alg.network
print "PREDICTED NETWORK:"
print job.alg.network.network
print jobnet.original_network
return jobnet.original_network
def run(self, name, datafiles, goldnet_file):
import numpy
os.chdir(os.environ["gene_path"])
datastore = ReadData(datafiles[0], "steadystate")
for file in datafiles[1:]:
datastore.combine(ReadData(file, "steadystate"))
datastore.normalize()
settings = {}
settings = ReadConfig(settings)
# TODO: CHANGE ME
settings["global"]["working_dir"] = os.getcwd() + '/'
# Setup job manager
print "Starting new job manager"
jobman = JobManager(settings)
# Make GENIE3 jobs
genie3 = GENIE3()
genie3.setup(datastore, settings, name)
print "Queuing job..."
jobman.queueJob(genie3)
print jobman.queue
print "Running queue..."
jobman.runQueue()
jobman.waitToClear()
print "Queue finished"
job = jobman.finished[0]
print job.alg.gene_list
print job.alg.read_output(settings)
jobnet = job.alg.network
print "PREDICTED NETWORK:"
print job.alg.network.network
print jobnet.original_network
if goldnet_file != None:
goldnet = Network()
goldnet.read_goldstd(goldnet_file)
print "GOLD NETWORK:"
print goldnet.network
print jobnet.analyzeMotifs(goldnet).ToString()
print jobnet.calculateAccuracy(goldnet)
return jobnet.original_network
for key in goldnets.keys():
goldnet = Network()
goldnet.read_goldstd(goldnets[key])
goldnets[key] = goldnet
genie3nets = {}
for i in range(20):
for name in data.keys():
ts_storage = data[name]
settings["global"]["time_series_delta_t"] = (1008.0 / (len(ts_storage[0].experiments)-1))
combined = ReadData(exp_data_directory + '/' + name + '/' + timeseries_filename, "timeseries")[0]
for ts in timeseries_as_steady_state[name][1:11]:
combined.combine(ts)
#combined.combine(knockouts[name])
combined.combine(multifactorials[name])
genie3job = GENIE3()
genie3job.setup(combined, settings, "Genie3_TimeSeries_{0}_{1}".format(name, i))
jobman.queueJob(genie3job)
genie3nets[name] = genie3job
genie3job.goldnet = goldnets[name]
jobman.runQueue()
jobman.waitToClear()
for job in jobman.finished:
goldnet = Network() goldnet.read_goldstd(settings["global"]["large_network_goldnet_file"]) ko_file = settings["global"]["large_network_knockout_file"].split() kd_file = settings["global"]["large_network_knockdown_file"].split() ts_file = settings["global"]["large_network_timeseries_file"].split() wt_file = settings["global"]["large_network_wildtype_file"].split() # Read data into program # Where the format is "FILENAME" "DATATYPE" knockout_storage = ReadData(ko_file[0], "knockout") knockdown_storage = ReadData(kd_file[0], "knockdown") timeseries_storage = ReadData(ts_file[0], "timeseries") wildtype_storage = ReadData(wt_file[0], "wildtype") wildtype_storage.combine(knockout_storage) wildtype_storage.combine(knockdown_storage) wildtype_storage.combine(timeseries_storage) # Setup job manager jobman = JobManager(settings) # Make BANJO jobs mczjob = MCZ() mczjob.setup(knockout_storage, wildtype_storage, settings, None, "mcz-test-run-1") jobman.queueJob(mczjob) print jobman.queue jobman.runQueue()
def get_network_results(name, settings, cache):
print "STARTING", name
if name in cache.keys():
print "CACHE HIT"
return cache[name]
ko_file, kd_file, ts_file, wt_file, mf_file, goldnet = get_example_data_files(name, settings)
# Create date string to append to output_dir
t = datetime.now().strftime("%Y-%m-%d_%H.%M.%S")
settings["global"]["output_dir"] = settings["global"]["output_dir_save"] + "/" + \
settings["global"]["experiment_name"] + "-" + t + "-" + name + "/"
os.mkdir(settings["global"]["output_dir"])
# Get a list of the multifactorial files
# Read data into program
# Where the format is "FILENAME" "DATATYPE"
mf_storage = ReadData(mf_file[0], "multifactorial")
knockout_storage = ReadData(ko_file[0], "knockout")
knockdown_storage = ReadData(kd_file[0], "knockdown")
wildtype_storage = ReadData(wt_file[0], "wildtype")
timeseries_storage = ReadData(ts_file[0], "timeseries")
gene_list = knockout_storage.gene_list
# Setup job manager
jobman = JobManager(settings)
# MCZ
mczjob = MCZ()
mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "MCZ")
jobman.queueJob(mczjob)
# CLR
clrjob = CLR()
clrjob.setup(knockout_storage, settings, "CLR", "plos", 6)
jobman.queueJob(clrjob)
# GENIE3
mf_storage.combine(knockout_storage)
mf_storage.combine(wildtype_storage)
mf_storage.combine(knockdown_storage)
genie3job = GENIE3()
genie3job.setup(mf_storage, settings, "GENIE3")
jobman.queueJob(genie3job)
## TLCLR
tlclrjob = TLCLR()
tlclrjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "TLCLR")
jobman.queueJob(tlclrjob)
#if sys.argv[1] != "dream4100":
#cojob = ConvexOptimization()
#cojob.setup(knockout_storage, settings, "ConvOpt_T-"+ str(0.01),None, None, 0.01)
#jobman.queueJob(cojob)
### DFG4GRN
dfg = DFG4GRN()
settings["dfg4grn"]["eta_z"] = 0.01
settings["dfg4grn"]["lambda_w"] = 0.001
settings["dfg4grn"]["tau"] = 3
dfg.setup(timeseries_storage, TFList(timeseries_storage[0].gene_list), settings, "DFG", 20)
jobman.queueJob(dfg)
### Inferelator
### NIR
nirjob = NIR()
nirjob.setup(knockout_storage, settings, "NIR", 5, 5)
jobman.queueJob(nirjob)
#### TDARACNE
settings = ReadConfig(settings, "./config/default_values/tdaracne.cfg")
bjob = tdaracne()
settings["tdaracne"]["num_bins"] = 4
bjob.setup(timeseries_storage, settings, "TDARACNE")
jobman.queueJob(bjob)
print jobman.queue
jobman.runQueue()
jobman.waitToClear(name)
SaveResults(jobman.finished, goldnet, settings, name)
cache[name] = jobman.finished[:]
return cache[name]
kno3_1 = ReadData("datasets/RootArrayData/KNO3norm1.csv", "dex")
kno3_2 = ReadData("datasets/RootArrayData/KNO3norm2.csv", "dex")
kno3_3 = ReadData("datasets/RootArrayData/KNO3norm3.csv", "dex")
kno3_4 = ReadData("datasets/RootArrayData/KNO3norm4.csv", "dex")
settings["global"]["time_series_delta_t"] = "3 3 3 3 3 5"
dex_storage.filter(kno3_1.gene_list)
dexcombined.filter(kno3_1.gene_list)
dex_storage2.filter(kno3_1.gene_list)
cnlo_storage.filter(kno3_1.gene_list)
cnlo_no3_storage.filter(kno3_1.gene_list)
no3_1_storage.filter(kno3_1.gene_list)
no3_2_storage.filter(kno3_1.gene_list)
no3_3_storage.filter(kno3_1.gene_list)
dexcombined.combine(dex_storage2)
no3_storage = no3_1_storage
no3_storage.combine(no3_2_storage)
no3_storage.combine(no3_3_storage)
cnlo_no3_storage.combine(no3_storage)
#all_storage.combine(cnlo_no3_storage)
#dex_storage.combine(cnlo_storage)
#dex_storage.combine(no3_storage)
#dex_storage.normalize()
no3_storage.normalize()
cnlo_storage.normalize()
cnlo_no3_storage.normalize()
def run(self, kofile, tsfile, wtfile, datafiles, name, goldnet_file, normalize=False):
os.chdir(os.environ["gene_path"])
knockout_storage = ReadData(kofile, "knockout")
print "Reading in knockout data"
wildtype_storage = ReadData(wtfile, "steadystate")
if datafiles == []:
other_storage = None
else:
other_storage = ReadData(datafiles[0], "steadystate")
for file in datafiles[1:]:
other_storage.combine(ReadData(file, "steadystate"))
timeseries_storage = None
if tsfile != None:
timeseries_storage = ReadData(tsfile, "timeseries")
#for ts in timeseries_storage:
#ts.normalize()
#if normalize:
#knockout_storage.normalize()
#wildtype_storage.normalize()
#other_storage.normalize()
settings = {}
settings = ReadConfig(settings)
# TODO: CHANGE ME
settings["global"]["working_dir"] = os.getcwd() + '/'
# Setup job manager
print "Starting new job manager"
jobman = JobManager(settings)
# Make inferelator jobs
inferelatorjob = inferelator()
inferelatorjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, other_storage, name)
print "Queuing job..."
jobman.queueJob(inferelatorjob)
print jobman.queue
print "Running queue..."
jobman.runQueue()
jobman.waitToClear()
print "Queue finished"
job = jobman.finished[0]
#print job.alg.gene_list
#print job.alg.read_output(settings)
jobnet = job.alg.network
#print "PREDICTED NETWORK:"
#print job.alg.network.network
print jobnet.original_network
if goldnet_file != None:
goldnet = Network()
goldnet.read_goldstd(goldnet_file)
#print "GOLD NETWORK:"
#print goldnet.network
#print jobnet.analyzeMotifs(goldnet).ToString()
print jobnet.calculateAccuracy(goldnet)
import AnalyzeResults
tprs, fprs, rocs = AnalyzeResults.GenerateMultiROC(jobman.finished, goldnet )
ps, rs, precs = AnalyzeResults.GenerateMultiPR(jobman.finished, goldnet)
print "Area Under ROC"
print rocs
print "Area Under PR"
print precs
return jobnet.original_network
c4d.filter(topgenes_list)
c4l.filter(topgenes_list)
c21d.filter(topgenes_list)
c21hl.filter(topgenes_list)
c21l.filter(topgenes_list)
c21ll.filter(topgenes_list)
c32l.filter(topgenes_list)
c32l2.filter(topgenes_list)
#for dataset in ts_storage:
#dataset.normalize()
combined.filter(topgenes_list)
combined.combine(c4l)
combined.combine(c21d)
combined.combine(c21hl)
combined.combine(c21l)
combined.combine(c21ll)
combined.combine(c32l)
combined.combine(c32l2)
# Remove the last time point for testing
leave_out = []
for i, ts in enumerate(ts_storage):
leave_out.append(ts.experiments[-1])
#goldnet = Network()
def run(self, datafiles=None, name=None, goldnet_file=None, topd=None, restk=None):
import numpy
os.chdir(os.environ["gene_path"])
print "Reading in data"
data_storage = ReadData(datafiles[0], "steadystate")
for file in datafiles[1:]:
data_storage.combine(ReadData(file, "steadystate"))
settings = {}
settings = ReadConfig(settings)
# TODO: CHANGE ME
settings["global"]["working_dir"] = os.getcwd() + "/"
# Setup job manager
print "Starting new job manager"
jobman = JobManager(settings)
# Make nir jobs
nirjob = NIR()
nirjob.setup(data_storage, settings, name, topd, restk)
print "Queuing job..."
jobman.queueJob(nirjob)
print jobman.queue
print "Running queue..."
jobman.runQueue()
jobman.waitToClear()
print "Queue finished"
job = jobman.finished[0]
print job.alg.gene_list
print job.alg.read_output(settings)
jobnet = job.alg.network
print "PREDICTED NETWORK:"
print job.alg.network.network
if goldnet_file != None:
goldnet = Network()
goldnet.read_goldstd(goldnet_file)
# print "GOLD NETWORK:"
# print goldnet.network
# print jobnet.analyzeMotifs(goldnet).ToString()
print jobnet.calculateAccuracy(goldnet)
import AnalyzeResults
tprs, fprs, rocs = AnalyzeResults.GenerateMultiROC(
jobman.finished, goldnet, True, job.alg.output_dir + "/ROC.pdf"
)
ps, rs, precs = AnalyzeResults.GenerateMultiPR(
jobman.finished, goldnet, True, job.alg.output_dir + "/PR.pdf"
)
print "Area Under ROC"
print rocs
print "Area Under PR"
print precs
return job.alg.network.network
jobman = JobManager(settings) # Make BANJO jobs mczjob = MCZ() mczjob.setup(knockout_storage, wildtype_storage, settings, timeseries_storage, knockdown_storage, "MCZ_Alone") jobman.queueJob(mczjob) clrjob = CLR() clrjob.setup(knockout_storage, settings, "clr_" + t + "_Bins-" + str(6), "plos", 6) jobman.queueJob(clrjob) #cojob = ConvexOptimization() #cojob.setup(knockout_storage, settings, "ConvOpt_T-Plos",None, None, 0.04) #jobman.queueJob(cojob) mf_storage.combine(knockout_storage) mf_storage.combine(wildtype_storage) mf_storage.combine(knockdown_storage) genie3job = GENIE3() genie3job.setup(mf_storage, settings, "MF_KO_WT_KD") jobman.queueJob(genie3job) print jobman.queue jobman.runQueue() jobman.waitToClear() accs = [] precs = [] settings["dfg4grn"]["eta_z"] = 0.001 settings["dfg4grn"]["lambda_w"] = 0.01 settings["dfg4grn"]["tau"] = 3
# Read data into program # Where the format is "FILENAME" "DATATYPE" mf_storage = ReadData(mf_file[0], "multifactorial") ko_storage = ReadData(ko_file[0], "knockout") kd_storage = ReadData(kd_file[0], "knockdown") wt_storage = ReadData(wt_file[0], "wildtype") # Setup job manager jobman = JobManager(settings) # Make GENIE3 jobs genie3job = GENIE3() genie3job.setup(mf_storage, settings, "MF") jobman.queueJob(genie3job) mf_storage.combine(ko_storage) genie3job = GENIE3() genie3job.setup(mf_storage, settings, "MF_KO") jobman.queueJob(genie3job) mf_storage.combine(wt_storage) genie3job = GENIE3() genie3job.setup(mf_storage, settings, "MF_KO_WT") jobman.queueJob(genie3job) mf_storage.combine(kd_storage) genie3job = GENIE3() genie3job.setup(mf_storage, settings, "MF_KO_WT_KD") jobman.queueJob(genie3job) print jobman.queue
if goldnet.network[gene1][gene2] > 0:
t.append(gene1)
tfs[name] = list(set(t))
goldnet = Network()
goldnet.read_goldstd(goldnets[data.keys()[0]])
genie3nets = {}
for name in data.keys():
for i in range(50):
ts_storage = data[name]
settings["global"]["time_series_delta_t"] = (1008.0 / (len(ts_storage[0].experiments)-1))
combined = ReadData(exp_data_directory + '/' + name + '/' + timeseries_filename, "timeseries")[0]
for ts in timeseries_as_steady_state[name][1:]:
combined.combine(ts)
combined.combine(multifactorials[name])
genie3job = GENIE3()
genie3job.setup(combined, settings, "Genie3_TimeSeries_SS_{0}-{1}".format(name, i))
jobman.queueJob(genie3job)
genie3nets[name] = genie3job
jobman.runQueue()
jobman.waitToClear()
for name in data.keys():
for i in range(50):
ts_storage = data[name]
settings["global"]["time_series_delta_t"] = (1008.0 / (len(ts_storage[0].experiments)-1))
kno3_3 = ReadData("datasets/RootArrayData/KNO3norm3.csv", "dex")
kno3_4 = ReadData("datasets/RootArrayData/KNO3norm4.csv", "dex")
dex_storage.filter(kno3_1.gene_list)
cnlo_storage.filter(kno3_1.gene_list)
cnlo_no3_storage.filter(kno3_1.gene_list)
no3_1_storage.filter(kno3_1.gene_list)
no3_2_storage.filter(kno3_1.gene_list)
no3_3_storage.filter(kno3_1.gene_list)
no3_storage = no3_1_storage
no3_storage.combine(no3_2_storage)
no3_storage.combine(no3_3_storage)
cnlo_no3_storage.combine(no3_storage)
#all_storage.combine(cnlo_no3_storage)
#dex_storage.combine(cnlo_storage)
#dex_storage.combine(no3_storage)
#dex_storage.normalize()
no3_storage.normalize()
cnlo_storage.normalize()
cnlo_no3_storage.normalize()
#all_storage.normalize()
# Set delta_t to be without the last time point
settings["global"]["time_series_delta_t"] = "3 3 3 3 3"
# Remove the last time point from each of these
