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!"