def main(args, config):
wDir = os.getcwd()
#Instance Preprocessing class
window = Preprocessing(args.fasta_file, config['win_length'], config['win_step'])
window.output_window()
print >> sys.stderr, "Creating windows_sequence.fasta"
#Instance Similarity and Composition class
sim = Similarity(args.fasta_file, config['score_adj'],wDir)
sim_matrix = sim.mcl_perform()
comp_results = Composition(config['kmer_len'])
comp_matrix = comp_results.joined()
#Join similarity and composition matrix for PCA
join = pd.concat([comp_matrix, sim_matrix], axis= 1, join='inner')
print >> sys.stderr, "Calculating similarity and composition matrix"
#Instance Reduction class
pca = Reduction(join, config['pca_comp'])
pca_data = pca.perform_pca()
print >> sys.stderr, "Performing PCA"
#Instance Clustering class
cluster = Clustering(pca_data)
clust_obj = cluster.plot()
print >> sys.stderr, "Performing clustering plot"
#Instance ClusterReport class
report = ClusterReport(clust_obj)
file_name, querySeq = report.output_queryseq()
print >> sys.stderr, "Doing report of clusters"
#Instance Validate class
valid = Validate(file_name, args.fasta_file,wDir)
jfileComp, jfileMinus = valid.roundTwo()
print >> sys.stderr, "Validation of results"
#Instance ParseJplace Class
parsing = ParseJplace(jfileComp, jfileMinus)
corrMat = parsing.correlation()
print >> sys.stderr, "Doing profiles"
#Instance Profile Class
ttest = Profiles(corrMat, querySeq)
bestWin = ttest.windowsAssigment()
print >>sys.stderr, "Doing permutations"
#Instance StatsBinom
finalResult = StatsBinom(args.fasta_file, config['win_length'],bestWin)
finalResult.binomial()
cleaning(file_name)
def basicMode(config, fasta_file, profilePath):
#create output folders
wDir = os.getcwd()
folders = ['pplacer', 'testing']
for folder in folders:
os.mkdir(os.path.join(wDir, folder))
#Instance Preprocessing class
window = Preprocessing(fasta_file, config['win_length'],
config['win_step'], "windows_sequence.fasta")
window.output_window()
reverseSeq = Preprocessing(fasta_file, config['win_length'],
config['win_step'], "reverse_windows.fasta")
reverseSeq.output_window()
print >> sys.stderr, "Creating windows_sequence.fasta"
#Instance Similarity and Composition class
sim = Similarity(fasta_file, config['score_adj'], wDir)
sim_matrix = sim.mcl_perform()
comp_results = Composition(config['kmer_len'])
comp_matrix = comp_results.joined()
#Join similarity and composition matrix for PCA
join = pd.concat([comp_matrix, sim_matrix], axis=1, join='inner')
print >> sys.stderr, "Calculating similarity and composition matrix"
#Instance Reduction class
pca = Reduction(join, config['pca_comp'])
pca_data = pca.perform_pca()
print >> sys.stderr, "Performing PCA"
#Instance Clustering class
cluster = Clustering(pca_data)
clust_obj = cluster.plot()
print >> sys.stderr, "Performing clustering plot"
#Instance ClusterReport class
report = ClusterReport(clust_obj)
file_name, querySeq = report.output_queryseq()
print >> sys.stderr, "Doing report of clusters"
#Instance Validate class
valid = Validate(file_name, fasta_file, wDir)
jfileComp, jfileMinus = valid.roundTwo()
print >> sys.stderr, "Validation of results"
#Instance ParseJplace Class
parsing = ParseJplace(jfileComp, jfileMinus)
corrMat = parsing.correlation()
print >> sys.stderr, "Doing profiles"
#Instance Profile Class
ttest = Profiles(corrMat, querySeq, wDir, profilePath)
bestWin = ttest.windowsAssigment()
print >> sys.stderr, "Doing permutations"
#Instance StatsBinom
finalResult = StatsBinom(fasta_file, config['win_length'], bestWin)
finalResult.binomial()
print >> sys.stderr, "Calculating p-value"
cleaning(file_name)
