def Run(counts_file, n, **kwargs):
'''
Compute the pairwise distance matrix between all sites and write it out as txt file.
'''
## read counts data
temp = SM()
counts = temp.from_file(counts_file)
## make simulated data
format = kwargs.get('format', 'txt')
out_file = kwargs.get('out_file', counts_file + '_sim_')
counts.sim_data(n, method='permute', base_file=out_file, format=format)
print 'wrote ' + out_file + '_[1...%d].txt' % n
print 'Done!'
def Run(counts_file, n, **kwargs):
'''
Compute the pairwise distance matrix between all sites and write it out as txt file.
'''
## read counts data
temp = SM()
counts = temp.from_file(counts_file)
## make simulated data
format = kwargs.get('format','txt')
out_file = kwargs.get('out_file', counts_file + '_sim_')
counts.sim_data(n, method = 'permute', base_file = out_file, format = format)
print 'wrote ' + out_file + '_[1...%d].txt' %n
print 'Done!'
def Run(counts_file, metric='JSsqrt', **kwargs):
'''
Compute the pairwise distance matrix between all sites and write it out as txt file.
'''
## read counts data
temp = SM()
counts = temp.from_file(counts_file)
## compute sample distances
fracs = counts.to_fractions('normalize')
D = fracs.dist_mat(metric=metric)
## write distance matrix
out_file = kwargs.get('out_file', 'sample_dist_' + metric + '.out')
D.writetxt(out_file)
print('wrote ' + out_file)
print('Done!')
def Run(counts_file, metric = 'JSsqrt', **kwargs):
'''
Compute the pairwise distance matrix between all sites and write it out as txt file.
'''
## read counts data
temp = SM()
counts = temp.from_file(counts_file)
## compute sample distances
fracs = counts.to_fractions('normalize')
D = fracs.dist_mat(metric = metric)
## write distance matrix
out_file = kwargs.get('out_file', 'sample_dist_' + metric +'.out')
D.writetxt(out_file)
print 'wrote ' + out_file
print 'Done!'
def RunSparCC(counts_file, algo='SparCC', **kwargs):
## read counts data
print 'reading data'
temp = SM()
counts = temp.from_file(counts_file)
## Calculate correlations between components using SparCC
print 'computing correlations'
cor, cov = counts.basis_corr(algo=algo, **kwargs)
## write out results
print 'writing results'
cor_file = kwargs.get('cor_file', 'cor_mat_' + algo + '.out')
cor.writetxt(cor_file)
print 'wrote ' + cor_file
if cov is not None:
cov_file = kwargs.get('cov_file', 'cov_mat_' + algo + '.out')
cov.writetxt(cov_file)
print 'wrote ' + cov_file
print 'Done!'
def RunSparCC(counts_file, algo='SparCC', **kwargs):
## read counts data
print 'reading data'
temp = SM()
counts = temp.from_file(counts_file)
## Calculate correlations between components using SparCC
print 'computing correlations'
cor, cov = counts.basis_corr(algo=algo , **kwargs)
## write out results
print 'writing results'
cor_file = kwargs.get('cor_file', 'cor_mat_' + algo + '.out')
cor.writetxt(cor_file)
print 'wrote ' + cor_file
if cov is not None:
cov_file = kwargs.get('cov_file', 'cov_mat_' + algo + '.out')
cov.writetxt(cov_file)
print 'wrote ' + cov_file
print 'Done!'
def RunSparCC(counts_file, algo="SparCC", **kwargs):
## read counts data
print "reading data"
temp = SM()
counts = temp.from_file(counts_file)
## Calculate correlations between components using SparCC
print "computing correlations"
cor, cov = counts.basis_corr(algo=algo, **kwargs)
## write out results
print "writing results"
cor_file = kwargs.get("cor_file", "cor_mat_" + algo + ".out")
cor.writetxt(cor_file)
print "wrote " + cor_file
if cov is not None:
cov_file = kwargs.get("cov_file", "cov_mat_" + algo + ".out")
cov.writetxt(cov_file)
print "wrote " + cov_file
print "Done!"
