def process(args):
# Create the Genonets object. This will load the input file into
# memory.
gn = Genonets(args)
# Use 'gn' to create genotype networks for all genotype sets.
gn.create()
# Perform only 'Robustness' and 'Evolvability' analyses on just two of
# the genotype sets available in the input file, i.e., 'Foxa2' and 'Bbx'.
gn.analyze(["Foxa2", "Bbx"], analyses=[ac.ROBUSTNESS, ac.EVOLVABILITY])
# Write the given genotype networks to files in GML format.
# For a genotype network with two or more components, two files are generated:
# One corresponds to the entire network with all components, and the other
# corresponds to the dominant component only.
gn.save(["Foxa2", "Bbx"])
# Save genotype network level measures for the given genotype sets to
# 'Genotype_set_measures.txt'.
gn.save_network_results(["Foxa2", "Bbx"])
# Save all genotype level measures for the given genotype sets to
# 'Foxa2_genotype_measures.txt' and 'Bbx_genotype_measures.txt' files.
gn.save_genotype_results(["Foxa2", "Bbx"])
def process(args):
# Create the Genonets object. This will load the input file into
# memory.
gn = Genonets(args)
# Use 'gn' to create genotype networks for all genotype sets.
gn.create()
# Perform all available analyses on all genotype networks.
gn.analyze()
# Write all genotype networks to files in GML format. For a genotype network
# with two or more components, two files are generated: One corresponds to the
# entire network with all components, and the other corresponds to the dominant
# component only.
gn.save()
# Save all genotype network level measures to 'Genotype_set_measures.txt'.
gn.save_network_results()
# Save all genotype level measures to '<genotypeSetName>_genotype_measures.txt'
# files. One file per genotype set is generated.
gn.save_genotype_results()
def process(args):
# Create the Genonets object. This will load the input file into
# memory.
gn = Genonets(args)
# Use 'gn' to create genotype networks for all genotype sets.
gn.create()
# Perform 'Peaks' analysis
gn.analyze(analyses=[ac.PEAKS])
# At this point, the analysis is done. We now need to extract the
# data we need, i.e., the peaks dictionary.
# For each genotype set,
for genotypeSet in gn.genotype_sets():
# Get the igraph object for the giant
giant = gn.dominant_network(genotypeSet)
# Get the dict of peaks {key=peakId : value=[list of sequences in the peak]}
peaks = giant["Peaks"]
# Update the dict of peaks, so that instead of just a list of
# genotypes, we have a list of tuples (sequence, escore).
newPeaks = {}
# For each peak,
for peak in peaks:
# Initialize the list of tuples
seqScrTuples = []
# For each sequence in this peak,
for sequence in peaks[peak]:
# Find the corresponding vertex in the giant
try:
vertex = giant.vs.find(sequences=sequence)
except ValueError:
print("Oops! can't find " + sequence + " in giant.")
# Get the escore
score = giant.vs[vertex.index]["escores"]
# Add the tuple to the list of tuples
seqScrTuples.append((sequence, score))
# Add the peak and the corresponding list of tuples to the
# new peaks dict
newPeaks[peak] = seqScrTuples
# Replace the peaks dict in giant with the new dict. This is
# useful because now if you use the genonets functions below to
# save the network and results, the updated peaks dict with tuples
# will be written automatically to file.
giant["Peaks"] = newPeaks
# Save networks to file in GML format
gn.save()
# Save the results to file from network level analysis
gn.save_network_results()