f_sem_sim = sys.argv[2]
f_dataset = sys.argv[3]
f_out = sys.argv[4]
bin_size = sys.argv[5]
bin_size = int(bin_size)
median_y_n = sys.argv[6]
# Redundant ens annotation
print "1) Read ens redundant annotations"
gene_convert = data.read_ens_rd(f_ens_rd)
print "Number of gene", len(gene_convert.keys())
# Read semantic similarity score (benchmark, y)
print "2) Read MGI similarity..."
sem_sim = data.read_sem_sim(f_sem_sim)
# Read genomic dataset (x) with gene pair benchmark value (y)
print "3) Read Score for pair with MGI Score..."
score_pair = data.read_score(f_dataset, gene_convert, sem_sim)
print "Number of gene pairs", len(score_pair.keys())
# sort pair according to score
print "4) Sort pair according Score..."
list_pair = functions.sort_by_val(score_pair)
print "Number of Pair", len(list_pair)
# Report dataset by bin versus semantic similarity
print "5) read and look at distribution mgi/hpo..."
data.report_data_ben_bin(f_out, score_pair, list_pair, sem_sim, bin_size,
median_y_n)
f_ens_rd=sys.argv[1] f_sem_sim=sys.argv[2] f_dataset=sys.argv[3] f_out=sys.argv[4] bin_size=sys.argv[5] bin_size=int(bin_size) median_y_n=sys.argv[6] # Redundant ens annotation print "1) Read ens redundant annotations" gene_convert=data.read_ens_rd(f_ens_rd) print "Number of gene",len(gene_convert.keys()) # Read semantic similarity score (benchmark, y) print "2) Read MGI similarity..." sem_sim=data.read_sem_sim(f_sem_sim) # Read genomic dataset (x) with gene pair benchmark value (y) print "3) Read Score for pair with MGI Score..." score_pair,list_pair=data.read_score(f_dataset,gene_convert) print "Number of gene pairs",len(score_pair.keys()) # Report dataset by bin versus semantic similarity print "4) read and look at distribution mgi/hpo..." data.report_data_ben_bin(f_out,score_pair,list_pair,sem_sim,bin_size,median_y_n)
f_dataset=sys.argv[3] f_out=sys.argv[4] bin_size=sys.argv[5] bin_size=int(bin_size) median_y_n=sys.argv[6] # Redundant ens annotation print "1) Read ens redundant annotations" gene_convert=data.read_ens_rd(f_ens_rd) print "Number of gene",len(gene_convert.keys()) # Read semantic similarity score (benchmark, y) print "2) Read MGI similarity..." sem_sim=data.read_sem_sim(f_sem_sim) # Read genomic dataset (x) with gene pair benchmark value (y) print "3) Read Score for pair with MGI Score..." score_pair=data.read_score(f_dataset,gene_convert,sem_sim) print "Number of gene pairs",len(score_pair.keys()) # sort pair according to score print "4) Sort pair according Score..." list_pair=functions.sort_by_val(score_pair) print "Number of Pair",len(list_pair) # Report dataset by bin versus semantic similarity print "5) read and look at distribution mgi/hpo..." data.report_data_ben_bin(f_out,score_pair,list_pair,sem_sim,bin_size,median_y_n)
import sys
import data, functions
# parameters
f_ens_rd = sys.argv[1]
f_sem_sim = sys.argv[2]
f_dataset = sys.argv[3]
f_out = sys.argv[4]
bin_size = sys.argv[5]
bin_size = int(bin_size)
median_y_n = sys.argv[6]
# Redundant ens annotation
print "1) Read ens redundant annotations"
gene_convert = data.read_ens_rd(f_ens_rd)
print "Number of gene", len(gene_convert.keys())
# Read semantic similarity score (benchmark, y)
print "2) Read MGI similarity..."
sem_sim = data.read_sem_sim(f_sem_sim)
# Read genomic dataset (x) with gene pair benchmark value (y)
print "3) Read Score for pair with MGI Score..."
score_pair, list_pair = data.read_score(f_dataset, gene_convert)
print "Number of gene pairs", len(score_pair.keys())
# Report dataset by bin versus semantic similarity
print "4) read and look at distribution mgi/hpo..."
data.report_data_ben_bin(f_out, score_pair, list_pair, sem_sim, bin_size,
median_y_n)
