├── .gitignore ├── SampleDist.py ├── MakeBootstraps.py ├── io_methods.py ├── PseudoPvals.py ├── compositional_methods.py ├── analysis_methods.py ├── ReadMe.md ├── example ├── pvals │ ├── pvals.one_sided.txt │ ├── pvals.two_sided.txt │ └── perm_cor_3.txt ├── true_basis_cor.txt └── basis_corr │ ├── cor_pearson.out │ └── cor_spearman.out ├── Lineages.py ├── SparCC.py └── core_methods.py /.gitignore: -------------------------------------------------------------------------------- 1 | *.swp 2 | *.swo 3 | __pycache__/* 4 | -------------------------------------------------------------------------------- /SampleDist.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | ''' 4 | Created on Jun 20, 2011 5 | 6 | @author: jonathanfriedman 7 | 8 | Requires the scipy.cluster.hierarchy module! 9 | ''' 10 | 11 | 12 | from lib.SurveyMatrix import Survey_matrix as SM 13 | 14 | 15 | def kwargs_callback(option, opt, value, parser,**kwargs): 16 | d = kwargs['d'] 17 | d[option.dest] = value 18 | return d 19 | 20 | 21 | def Run(counts_file, metric = 'JSsqrt', **kwargs): 22 | ''' 23 | Compute the pairwise distance matrix between all sites and write it out as txt file. 24 | ''' 25 | ## read counts data 26 | temp = SM() 27 | counts = temp.from_file(counts_file) 28 | ## compute sample distances 29 | fracs = counts.to_fractions('normalize') 30 | D = fracs.dist_mat(metric = metric) 31 | ## write distance matrix 32 | out_file = kwargs.get('out_file', 'sample_dist_' + metric +'.out') 33 | D.writetxt(out_file) 34 | print('wrote ' + out_file) 35 | print('Done!') 36 | 37 | 38 | if __name__ == '__main__': 39 | ## parse input arguments 40 | from optparse import OptionParser 41 | kwargs = {} 42 | usage = ('Compute the distance matrix between samples.\n' 43 | 'By default uses the the square-root of the Jensen-Shannon divergence.\n' 44 | 'distance matrix is written out as txt files. \n' 45 | 'Requires the scipy.cluster.hierarchy module!\n' 46 | '\n' 47 | 'Usage: python SampleDist.py counts_file [options]\n' 48 | 'Example: python SampleDist.py example/fake_data.txt -m JSsqrt -o my_dist_mat.out') 49 | parser = OptionParser(usage) 50 | parser.add_option("-m", "--metric", dest="metric", default='JSsqrt', 51 | help="Distance metric to be utilized. JSsqrt (default) | any metric supported by scipy.cluster.hierarchy.") 52 | parser.add_option("-o", "--out_file", dest="out_file", type = 'str', 53 | action="callback", callback= kwargs_callback, callback_kwargs = {'d':kwargs}, 54 | help="File to which distance matrix will be written.") 55 | (options, args) = parser.parse_args() 56 | counts_file = args[0] 57 | metric = options.metric 58 | ## write sample distance 59 | Run(counts_file, metric = metric, **kwargs) 60 | 61 | 62 | -------------------------------------------------------------------------------- /MakeBootstraps.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | ''' 4 | @author: jonathanfriedman 5 | 6 | Script for making simulated datasets used to get pseudo p-values. 7 | ''' 8 | import os 9 | from analysis_methods import permute_w_replacement 10 | from io_methods import read_txt, write_txt 11 | 12 | def kwargs_callback(option, opt, value, parser,**kwargs): 13 | d = kwargs['d'] 14 | d[option.dest] = value 15 | return d 16 | 17 | def make_bootstraps(counts, nperm, perm_template, outpath='./', iprint=0): 18 | ''' 19 | Make n simulated datasets used to get pseudo p-values. 20 | Simulated datasets are generated by assigning each OTU in each sample 21 | an abundance that is randomly drawn (w. replacement) from the 22 | abundances of the OTU in all samples. 23 | Simulated datasets are either written out as txt files. 24 | 25 | Parameters 26 | ---------- 27 | counts : DataFrame 28 | Inferred correlations whose p-values are to be computed. 29 | nperm : int 30 | Number of permutations to produce. 31 | perm_template : str 32 | Template for the permuted data file names. 33 | Should not include the path, which is specified using the 34 | outpath parameter. 35 | The iteration number is indicated with a "#". 36 | For example: 'permuted/counts.permuted_#.txt' 37 | outpath : str (default './') 38 | The path to which permuted data will be written. 39 | If not provided files will be written to the cwd. 40 | iprint : int (default = 0) 41 | The interval at which iteration number is printed out. 42 | If iprint<=0 no printouts are made. 43 | ''' 44 | if not os.path.exists(outpath): os.makedirs(outpath) 45 | for i in range(nperm): 46 | if iprint>0: 47 | if not i%iprint: print(i) 48 | counts_perm = permute_w_replacement(counts, axis=1) 49 | ## write out cors 50 | outfile = outpath + perm_template.replace('#', '%d'%i) 51 | write_txt(counts_perm, outfile) 52 | 53 | def main(counts_file, nperm, perm_template, outpath='./'): 54 | ''' 55 | Make n simulated datasets used to get pseudo p-values. 56 | Simulated datasets are generated by assigning each OTU in each sample 57 | an abundance that is randomly drawn (w. replacement) from the 58 | abundances of the OTU in all samples. 59 | Simulated datasets are either written out as txt files. 60 | ''' 61 | if perm_template is None: 62 | perm_template = counts_file + '.permuted_#.txt' 63 | ## read counts data 64 | counts = read_txt(counts_file) 65 | ## make permutated data 66 | make_bootstraps(counts, nperm, perm_template, outpath=outpath) 67 | 68 | if __name__ == '__main__': 69 | ## parse input arguments 70 | from optparse import OptionParser 71 | kwargs = {} 72 | usage = ('Make n simulated datasets used to get pseudo p-values.\n' 73 | 'Simulated datasets are generated by assigning each OTU in each sample an abundance that is randomly drawn (w. replacement) from the abundances of the OTU in all samples.\n' 74 | 'Simulated datasets are either written out as txt files. \n' 75 | '\n' 76 | 'Usage: python MakeBootstraps.py counts_file [options]\n' 77 | 'Example: python MakeBootstraps.py example/fake_data.txt -n 5 -t permutation_#.txt -p example/pvals/') 78 | parser = OptionParser(usage) 79 | parser.add_option("-n", dest="n", default=100, type = 'int', 80 | help="Number of simulated datasets to create (100 default).") 81 | parser.add_option("-t", "--template", dest="perm_template", default=None, type = 'str', 82 | help="The template for the permuted data file names.\n" 83 | "Should not include the path, which is specified using the -p option.\n" 84 | 'The iteration number is indicated with a "#".\n' 85 | "For example: 'permuted/counts.permuted_#.txt'" 86 | "If not provided a '.permuted_#.txt' suffix will be added to the counts file name.\n") 87 | parser.add_option("-p", "--path", dest="outpath", default='./', type = 'str', 88 | help="The path to which permuted data will be written.\n" 89 | "If not provided files will be written to the cwd.\n") 90 | (options, args) = parser.parse_args() 91 | counts_file = args[0] 92 | n = options.n 93 | outpath = options.outpath 94 | perm_template = options.perm_template 95 | 96 | main(counts_file, n, perm_template, outpath) 97 | 98 | 99 | -------------------------------------------------------------------------------- /io_methods.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Dec 6, 2012 3 | 4 | @author: jonathanfriedman 5 | ''' 6 | import numpy as np 7 | from pandas.io.parsers import read_table 8 | from Lineages import Lineages 9 | #from pandas.util.decorators import Appender 10 | 11 | 12 | 13 | #@Appender(read_table.__doc__) 14 | def read_txt(file, T=True, lin=None, lin_label='lineage', 15 | format='QIIME', verbose=True, **kwargs): 16 | ''' 17 | Read general delimited file into DataFrame. 18 | 19 | This a wrapper around pandas' read_table function which adds 20 | optional parsing of lineage information, and sets some default 21 | parameter values. 22 | 23 | Note: 24 | By default the data is transposed! 25 | To avoid this behavior set the parameter 'T' to False. 26 | 27 | Parameters 28 | ---------- 29 | file : string 30 | Path to input file. 31 | T : bool (default True) 32 | Indicated whether the produced DataFrame will be transposed. 33 | lin : bool/None (default None) 34 | Indicated whether lineage information is given in the input file. 35 | If None, read_txt tries to infer the presence of 36 | lineage information automatically 37 | lin_label : string (default 'lineage') 38 | Label of the column containing the lineage information. 39 | format : string (default 'QIIME') 40 | Format of the lineage information. 41 | This argument is passed to the Lineage object constructor. 42 | verbose : bool (default True) 43 | Indicated whether to print to screen the parsed table stats. 44 | 45 | Returns 46 | ------- 47 | table : DataFrame 48 | Parsed table. 49 | lins : Lineages (optional) 50 | Parsed Lineages object. 51 | Returned only if lineage information was parsed. 52 | ''' 53 | kwargs.setdefault('index_col',0) 54 | temp = read_table(file, **kwargs) 55 | # try to decide whether lineages are given, if not specified by user 56 | if lin is None: 57 | lin = False 58 | lin_labels = ('lin','lins','lineage','lineages', 59 | 'taxon','taxa','rdp') 60 | for c in temp.columns: 61 | if hasattr(c, 'lower'): 62 | if c.lower() in lin_labels: 63 | lin = True 64 | lin_label = c 65 | if lin: # parse lins if needed 66 | lins = Lineages.from_dict(temp[lin_label], format=format) 67 | temp = temp.drop(lin_label,axis=1) 68 | if T: 69 | temp = temp.T 70 | 71 | s = ['Finished parsing table.', 72 | 'Table dimensions: (%d,%d)' %temp.shape] 73 | if T: 74 | s += ['**** Data has been transposed! ****'] 75 | ncol = min(temp.shape[1],3) 76 | nrow = min(temp.shape[0],3) 77 | scol = tuple([ncol] + list(temp.columns[:ncol])) 78 | srow = tuple([nrow] + list(temp.index[:nrow])) 79 | s += [('First %d column labels are :' 80 | + ' ,'.join(['%s']*ncol)) %scol, 81 | ('First %d row labels are :' 82 | + ' ,'.join(['%s']*nrow)) %srow] 83 | 84 | table = temp 85 | if lin: 86 | if verbose: print('\n'.join(s), '\n') 87 | return table, lins 88 | else: 89 | if verbose: print('\n'.join(s), '\n') 90 | return table 91 | 92 | def write_txt(frame, file, T=True, lin=None, lin_label='lineage', **kwargs): 93 | ''' 94 | Write frame to txt file. 95 | 96 | This a wrapper around pandas' to_csv function which adds 97 | optional writing of lineage information, and sets some default 98 | parameter values. 99 | 100 | Note: 101 | By default the data is transposed! 102 | To avoid this behavior set the parameter 'T' to False. 103 | 104 | Parameters 105 | ---------- 106 | file : string 107 | Path to input file. 108 | T : bool (default True) 109 | Indicated whether the produced DataFrame will be transposed. 110 | lin : None/None (default None) 111 | Lineages object to be included in the output file. 112 | lin_label : string (default 'lineage') 113 | Label of the column containing the lineage information. 114 | ''' 115 | from pandas import Series 116 | kwargs.setdefault('sep','\t') 117 | if T: data = frame.T 118 | else: data = frame 119 | if lin is not None: 120 | d = {} 121 | for i in data.index: 122 | if i in lin: 123 | d[i] = lin[i].lin_str 124 | else: 125 | d[i] = None 126 | t = Series(d, name=lin_label) 127 | data = data.join(t) 128 | data.to_csv(file,**kwargs) 129 | 130 | 131 | if __name__ == '__main__': 132 | file = 'demo/data/fake_data_lin.counts' 133 | t, lin = read_txt(file) 134 | # write_txt(t*1.1, 'temp.txt', lin=lin) 135 | # print t 136 | # print read_txt.__doc__ 137 | 138 | -------------------------------------------------------------------------------- /PseudoPvals.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ''' 3 | Created on Apr 8, 2013 4 | 5 | @author: jonathanfriedman 6 | 7 | 8 | ''' 9 | import numpy as np 10 | from pandas import DataFrame as DF 11 | from io_methods import read_txt, write_txt 12 | 13 | def compare2sided(perm,real): 14 | return np.abs(perm) >= np.abs(real) 15 | 16 | def compare1sided(perm,real): 17 | inds_abs = compare2sided(perm,real) 18 | inds_sign = np.sign(perm) == np.sign(real) 19 | return inds_abs & inds_sign 20 | 21 | def get_pvalues(cor, perm_template, nperm, test_type='two_sided', 22 | iprint=0): 23 | ''' 24 | Compute pseudo p-vals from a set correlations obtained from permuted data' 25 | Pseudo p-vals are the percentage of times a correlation at least 26 | as extreme as the "real" one was observed in simulated datasets. 27 | 28 | Files containing the permuted correlations should be named with a 29 | consistent template, and these file names cannot contain any "#" characters. 30 | 31 | Parameters 32 | ---------- 33 | cor : DataFrame 34 | Inferred correlations whose p-values are to be computed. 35 | perm_template : str 36 | The template used for naming the correlation files of the 37 | permuted data. The iteration number is indicated with a "#". 38 | For example: 'permuted/cor.sparcc.permuted_#.txt' 39 | nperm : int 40 | Number of permutations available. 41 | test_type : 'two_sided' (default) | 'one_sided' 42 | two-sided = considering only the correlation magnitude. 43 | one-sided = accounting for the sign of correlations. 44 | iprint : int (default = 0) 45 | The interval at which iteration number is printed out. 46 | If iprint<=0 no printouts are made. 47 | 48 | Returns 49 | ------- 50 | p_vals: frame 51 | Computed pseudo p-values. 52 | ''' 53 | if test_type == 'two_sided': 54 | cmpfun = compare2sided 55 | elif test_type == 'one_sided': 56 | cmpfun = compare1sided 57 | else: 58 | raise ValueError('unsupported test type "%s"' %test_type) 59 | n_sig = DF(np.zeros(cor.shape), 60 | index=cor.index, 61 | columns=cor.columns) 62 | for i in range(nperm): 63 | if iprint>0: 64 | if not i%iprint: print(i) 65 | permfile = perm_template.replace('#', '%d'%i) 66 | cor_perm = read_txt(permfile).values 67 | n_sig[cmpfun(cor_perm, cor)] += 1 68 | p_vals = 1.*n_sig/nperm 69 | p_vals.values[np.diag_indices_from(p_vals.values)] = 1 70 | return p_vals 71 | 72 | 73 | def main(cor_file, perm_template, nperm, test_type='two_sided', outfile=None): 74 | ''' 75 | Compute pseudo p-vals from a set correlations obtained from permuted data' 76 | Pseudo p-vals are the percentage of times a correlation at least 77 | as extreme as the "real" one was observed in simulated datasets. 78 | 79 | Files containing the permuted correlations should be named with a 80 | consistent template, and these file names cannot contain any "#" characters. 81 | ''' 82 | cor = read_txt(cor_file) 83 | p_vals = get_pvalues(cor, perm_template, nperm, test_type) 84 | if outfile is None: 85 | outfile = cor_file +'.nperm_%d.pvals' %nperm 86 | write_txt(p_vals, outfile) 87 | 88 | 89 | if __name__ == '__main__': 90 | ## parse input arguments 91 | from optparse import OptionParser 92 | usage = ('Compute pseudo p-vals from a set correlations obtained from permuted data.\n' 93 | 'Pseudo p-vals are the percentage of times a correlation at least as extreme as the "real" one was observed in simulated datasets. \n' 94 | 'p-values can be either two-sided (considering only the correlation magnitude) or one-sided (accounting for the sign of correlations).\n' 95 | 'Files containing the permuted correlations should be named with a consistent template, where only the iteration number changes.\n' 96 | 'The permutation naming template is the second input argument with the iteration number replaced with a "#" character.\n' 97 | 'The template cannot contain additional "#" characters.\n' 98 | 'The total number of simulated sets is the third.\n' 99 | '\n' 100 | 'Usage: python PseudoPvals.py real_cor_file perm_template num_simulations [options]\n' 101 | 'Example: python PseudoPvals.py example/basis_corr/cor_sparcc.out example/pvals/perm_cor_#.txt 5 -o pvals.txt -t one_sided') 102 | parser = OptionParser(usage) 103 | parser.add_option("-t", "--type", dest="type", default='two_sided', type = 'str', 104 | help="Type of p-values to computed. oned-sided | two-sided (default).") 105 | parser.add_option("-o", "--outfile", dest="outfile", default=None, type = 'str', 106 | help="Name of file to which p-values will be written.") 107 | (options, args) = parser.parse_args() 108 | real_cor_file = args[0] 109 | perm_template = args[1] 110 | n = int(args[2]) 111 | test_type = options.type 112 | outfile = options.outfile 113 | 114 | main(real_cor_file, perm_template, n, test_type, outfile) 115 | 116 | -------------------------------------------------------------------------------- /compositional_methods.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Jun 24, 2012 3 | 4 | @author: jonathanfriedman 5 | ''' 6 | 7 | import numpy as np 8 | from pandas import DataFrame as DF 9 | from numpy import array, asarray, zeros, log, var, matrix, tile 10 | from core_methods import _get_axis 11 | 12 | def alr(frame, ref=None, axis=0): 13 | ''' 14 | Compute the additive log-ratio (alr) transformation 15 | with respect to the component given in ref. 16 | 17 | Parameters 18 | ---------- 19 | frame : DataFrame 20 | Frame to be transformed 21 | ref : valid label | None 22 | Label of component to be used as the normalization reference. 23 | i.e. values of other component will be divided by values of 24 | this reference component. 25 | IF None is passed (default), the last col/row is used as ref. 26 | axis : {0, 1} 27 | 0 : transform each row (default) 28 | 1 : transform each colum 29 | ''' 30 | if not isinstance(frame, DF): 31 | return alr_for_array(frame, ref, axis) 32 | axis = _get_axis(axis) 33 | if ref is None: 34 | label = frame._get_axis(1-axis)[-1] 35 | else: 36 | label = ref 37 | if axis==0: 38 | norm = 1.*frame[label] 39 | elif axis==1: 40 | norm = 1.*frame.xs(label) 41 | temp = frame.apply(lambda x: log(x/norm), axis=axis) 42 | return temp.drop(label,1-axis) 43 | 44 | def alr_for_array(frame, ref=None, axis=0): 45 | axis = _get_axis(axis) 46 | if ref is None: 47 | label = -1 48 | else: 49 | label = ref 50 | if axis==0: 51 | norm = 1.*frame[:,label] 52 | elif axis==1: 53 | norm = 1.*frame[label,:] 54 | temp = np.apply_along_axis(lambda x: log(x/norm), axis, frame) 55 | return np.delete(temp, label, 1-axis) 56 | 57 | def clr(frame, centrality='mean', axis=0): 58 | ''' 59 | Do the central log-ratio (clr) transformation of frame. 60 | 'centraility' is the metric of central tendency to divide by 61 | after taking the logarithm. 62 | 63 | Parameters 64 | ---------- 65 | centrality : 'mean' (default) | 'median' 66 | axis : {0, 1} 67 | 0 : transform each row (default) 68 | 1 : transform each colum 69 | ''' 70 | temp = log(frame) 71 | if centrality == 'mean': f = lambda x: x - x.mean() 72 | elif centrality == 'median': f = lambda x: x - x.median() 73 | if isinstance(frame, DF): 74 | z = temp.apply(f, axis=1-axis) 75 | else: 76 | z = np.apply_along_axis(f, 1-axis, temp) 77 | return z 78 | 79 | def variation_mat(frame): 80 | ''' 81 | Return the variation matrix of frame. 82 | Element i,j is the variance of the log ratio of components i and j. 83 | ''' 84 | x = 1.*asarray(frame) 85 | n,m = x.shape 86 | if m > 1000: 87 | return variation_mat_slow(frame) 88 | else: 89 | xx = tile(x.reshape((n,m,1)) ,(1,1,m)) 90 | xx_t = xx.transpose(0,2,1) 91 | try: 92 | l = log(1.*xx/xx_t) 93 | V = l.var(axis=0, ddof=1) 94 | return V 95 | except MemoryError: 96 | return variation_mat_slow(frame) 97 | 98 | def variation_mat_slow(frame, shrink=False): 99 | ''' 100 | Return the variation matrix of frame. 101 | Element i,j is the variance of the log ratio of components i and j. 102 | Slower version to be used in case the fast version runs out of memeory. 103 | ''' 104 | print('in slow') 105 | frame_a = 1.*asarray(frame) 106 | k = frame_a.shape[1] 107 | V = zeros((k,k)) 108 | for i in range(k-1): 109 | for j in range(i+1,k): 110 | y = array(log(frame_a[:,i]/frame_a[:,j])) 111 | v = var(y, ddof=1) # set ddof to divide by (n-1), rather than n, thus getting an unbiased estimator (rather than the ML one). 112 | V[i,j] = v 113 | V[j,i] = v 114 | return V 115 | 116 | def replace_zeros(frame, type='multiplicative', e=0.5): 117 | ''' 118 | Replace the zeros by a small value by imputation. 119 | Return new object. 120 | 121 | Inputs: 122 | e = [float] fraction of minimal value to use as imputed value delta. 123 | ''' 124 | new = 1.*frame.copy() 125 | for i, row in new.iterrows(): 126 | inds_z = (row ==0).nonzero()[0] # indices of zeros 127 | inds = (row > 0).nonzero()[0] # indices of no zeros 128 | delta = e * np.min(row[inds]) # imputed value for current sample 129 | row[inds_z] = delta # replace zeros by imputed values 130 | if type == 'simple': 131 | row /= row.sum() 132 | elif type == 'multiplicative': 133 | row[inds] *= (1-delta*len(inds_z)) 134 | new.ix[i] = row 135 | return new 136 | 137 | if __name__ == '__main__': 138 | rows = ['r1', 'r0', 'r2', 'r3'] 139 | cols = ['c0', 'c1', 'c2'] 140 | metac = DF([[np.nan,'big'], 141 | ['Entero','small'], 142 | ['Blautia','tiny']], 143 | columns=['name', 'Size'], 144 | index=cols) 145 | mat = np.array([[2., np.NAN,1], 146 | [1, 3, 2], 147 | [10, 15,3], 148 | [0,0,1]]) 149 | df = DF(mat, index=rows, columns=cols) 150 | # print df,'\n' 151 | # print filter_by_vals(df,[('sum','<=',3),('presence','>',1)],axis='rows'),'\n' 152 | 153 | 154 | 155 | -------------------------------------------------------------------------------- /analysis_methods.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Jun 24, 2012 3 | 4 | @author: jonathanfriedman 5 | ''' 6 | 7 | from pandas import DataFrame as DF 8 | from core_methods import _get_axis 9 | import numpy as np 10 | 11 | def basis_corr(frame, algo='SparCC', **kwargs): 12 | ''' 13 | Compute correlations between all columns of a counts frame. 14 | This is a wrapper around pysurvey.analysis.basis_correlations.main 15 | 16 | Parameters 17 | ---------- 18 | counts : array_like 19 | 2D array of counts. Columns are components, rows are samples. 20 | method : str {SparCC (default)| clr| pearson| spearman| kendall} 21 | The algorithm to use for computing correlation. 22 | 23 | Returns 24 | ------- 25 | cor_med: frame 26 | Estimated correlation matrix. 27 | Labels are column labels of input frame. 28 | cov_med: frame/None 29 | If method in {SparCC, clr} : Estimated covariance matrix. 30 | Labels are column labels of input frame. 31 | Otherwise: None. 32 | 33 | ======= ============ ======= ================================================ 34 | kwarg Accepts Default Desctiption 35 | ======= ============ ======= ================================================ 36 | iter int 20 number of estimation iteration to average over. 37 | oprint bool True print iteration progress? 38 | th 03.7, tested with 3.7.3), numpy (tested with version 1.17.4), and pandas 46 | (tested with version 0.25.3). 47 | 48 | 49 | ### Usage example: 50 | 51 | * The following lists the commands required for analyzing the included 'fake' 52 | dataset using the SparCC package, and generating all the files present in the 53 | subfolders of the example folder. 54 | 55 | * The fake dataset contains simulated abundances of 50 otus in 200 samples, 56 | drawn at random from a multinomial log-normal distribution. The true basis 57 | correlations used to generate the data are listed in 'true_basis_cor.txt' in 58 | the example folder. 59 | 60 | * Note that otu 0 is very dominant, and thus, using Pearson or Spearman 61 | correlations, appears to be negatively correlated with most other OTUs, though 62 | it is in fact not negatively correlated with any OTU. 63 | 64 | 65 | ### Correlation Calculation: 66 | 67 | First, we'll quantify the correlation between all OTUs, using SparCC, Pearson, 68 | and Spearman correlations: 69 | 70 | ``` 71 | python SparCC.py example/fake_data.txt -i 5 --cor_file=example/basis_corr/cor_sparcc.out 72 | python SparCC.py example/fake_data.txt -i 5 --cor_file=example/basis_corr/cor_pearson.out -a pearson 73 | python SparCC.py example/fake_data.txt -i 5 --cor_file=example/basis_corr/cor_spearman.out -a spearman 74 | ``` 75 | 76 | ### Pseudo p-value Calculation: 77 | 78 | Calculating pseudo p-values is done via a bootstrap procedure. 79 | First make shuffled (w. replacement) datasets: 80 | 81 | ``` 82 | python MakeBootstraps.py example/fake_data.txt -n 5 -t permutation_#.txt -p example/pvals/ 83 | ``` 84 | 85 | This will generate 5 shuffled datasets, which is clearly not enough to get 86 | meaningful p-values, and is used here for convenience. A more appropriate 87 | number of shuffles should be at least a 100, which is the default value. 88 | 89 | Next, you'll have to run SparCC on each of the shuffled data sets. Make sure to 90 | use the exact same parameters which you used when running SparCC on the real 91 | data, name all the output files consistently, numbered sequentially, and with a 92 | '.txt' extension. 93 | 94 | ``` 95 | python SparCC.py example/pvals/permutation_0.txt -i 5 --cor_file=example/pvals/perm_cor_0.txt 96 | python SparCC.py example/pvals/permutation_1.txt -i 5 --cor_file=example/pvals/perm_cor_1.txt 97 | python SparCC.py example/pvals/permutation_2.txt -i 5 --cor_file=example/pvals/perm_cor_2.txt 98 | python SparCC.py example/pvals/permutation_3.txt -i 5 --cor_file=example/pvals/perm_cor_3.txt 99 | python SparCC.py example/pvals/permutation_4.txt -i 5 --cor_file=example/pvals/perm_cor_4.txt 100 | ``` 101 | 102 | Above I'm simply called SparCC 5 separate times. However, it is much more 103 | efficient and convenient to write a small script that automates this, and 104 | submits these runs as separate jobs to a cluster (if one is available to you. 105 | Otherwise, this may take a while to run on a local machine...). 106 | 107 | Now that we have all the correlations computed from the shuffled datasets, we're 108 | ready to get the pseudo p-values. Remember to make sure all the correlation 109 | files are in the same folder, are numbered sequentially, and have a '.txt' 110 | extension. The following will compute both one and two sided p-values. 111 | 112 | ``` 113 | python PseudoPvals.py example/basis_corr/cor_sparcc.out example/pvals/perm_cor_#.txt 5 -o example/pvals/pvals.one_sided.txt -t one_sided 114 | python PseudoPvals.py example/basis_corr/cor_sparcc.out example/pvals/perm_cor_#.txt 5 -o example/pvals/pvals.one_sided.txt -t two_sided 115 | ``` 116 | 117 | 118 | 119 | LICENSE 120 | =================== 121 | 122 | The MIT License (MIT) 123 | 124 | Copyright (c) 2018-2020 Jonathan Friedman and Eric Alm 125 | 126 | Permission is hereby granted, free of charge, to any person obtaining a copy of 127 | this software and associated documentation files (the "Software"), to deal in 128 | the Software without restriction, including without limitation the rights to 129 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 130 | the Software, and to permit persons to whom the Software is furnished to do so, 131 | subject to the following conditions: 132 | 133 | The above copyright notice and this permission notice shall be included in all 134 | copies or substantial portions of the Software. 135 | 136 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 137 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 138 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 139 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 140 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 141 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 142 | 143 | 144 | 145 | -------------------------------------------------------------------------------- /example/pvals/pvals.one_sided.txt: -------------------------------------------------------------------------------- 1 | OTU_id 0 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27 28 29 3 30 31 32 33 34 35 36 37 38 39 4 40 41 42 43 44 45 46 47 48 49 5 6 7 8 9 2 | 0 1.0 0.0 0.4 0.6 0.2 0.2 0.6 0.6 0.2 0.6 0.8 0.4 1.0 0.2 0.8 0.2 0.8 0.0 0.2 0.2 0.2 0.6 1.0 0.0 0.4 0.0 0.8 0.2 0.0 0.2 1.0 0.6 0.0 0.6 0.0 0.2 0.4 1.0 0.8 1.0 0.8 0.4 1.0 0.8 0.2 1.0 0.4 0.0 0.0 0.4 3 | 1 0.0 1.0 0.6 1.0 0.8 0.2 0.8 0.0 1.0 0.2 0.6 0.0 0.6 0.6 0.2 0.6 0.2 0.8 0.0 1.0 0.4 0.6 0.8 0.4 0.2 0.4 0.2 0.6 0.0 0.8 1.0 0.2 0.0 0.4 1.0 1.0 0.0 0.4 0.6 0.6 1.0 0.4 0.8 1.0 0.6 0.4 0.6 0.0 0.2 0.2 4 | 10 0.4 0.6 1.0 0.0 0.0 0.2 0.8 0.6 0.6 0.2 1.0 0.2 1.0 0.4 0.4 1.0 1.0 0.4 0.4 0.8 0.0 1.0 0.4 0.2 0.0 0.6 1.0 1.0 0.2 0.6 1.0 1.0 0.8 0.0 1.0 0.6 0.6 0.4 0.4 0.8 0.8 0.8 0.4 0.6 1.0 0.0 0.8 1.0 0.4 0.2 5 | 11 0.6 1.0 0.0 1.0 0.0 0.0 0.6 0.4 0.6 0.2 0.6 0.4 1.0 0.2 0.2 1.0 0.6 0.4 0.8 0.2 0.0 0.2 1.0 0.2 0.6 0.2 0.6 0.6 1.0 0.4 0.8 0.4 0.8 1.0 0.6 0.2 0.4 0.8 0.8 0.0 0.6 0.8 0.2 0.8 0.6 0.0 0.2 0.4 0.8 0.4 6 | 12 0.2 0.8 0.0 0.0 1.0 0.0 1.0 0.2 1.0 0.2 0.8 0.4 0.4 0.2 0.6 0.0 0.6 0.8 0.4 0.2 0.2 0.2 0.8 0.0 0.4 1.0 0.0 0.2 1.0 0.4 1.0 0.6 0.6 0.8 0.2 0.6 0.0 0.2 0.2 0.6 0.0 0.8 0.4 0.4 0.6 0.8 0.8 0.8 0.6 1.0 7 | 13 0.2 0.2 0.2 0.0 0.0 1.0 0.2 0.0 0.0 0.2 0.4 0.8 0.8 1.0 0.2 0.4 0.8 0.6 0.8 1.0 0.2 0.2 0.8 0.4 0.6 0.2 0.2 0.8 0.0 0.2 0.6 0.4 0.6 0.0 1.0 1.0 0.4 0.2 0.0 1.0 0.4 0.2 0.0 0.8 0.2 0.2 0.6 0.8 0.6 0.6 8 | 14 0.6 0.8 0.8 0.6 1.0 0.2 1.0 0.0 0.2 0.8 0.6 0.6 0.4 0.8 0.0 0.6 0.0 1.0 0.2 0.8 0.0 0.8 0.4 0.2 0.8 0.8 0.4 1.0 1.0 0.0 0.2 1.0 0.0 0.8 0.4 1.0 0.8 0.8 0.6 0.0 0.2 0.6 1.0 1.0 0.6 1.0 0.2 0.6 0.0 0.2 9 | 15 0.6 0.0 0.6 0.4 0.2 0.0 0.0 1.0 0.4 0.2 0.4 0.6 0.8 0.6 0.8 0.0 0.8 0.4 0.6 0.0 0.0 0.2 1.0 1.0 0.0 1.0 0.8 0.4 0.2 0.8 0.4 0.0 0.2 0.4 0.6 0.6 0.8 0.4 0.6 0.8 0.6 0.4 0.8 0.8 0.4 0.2 0.8 0.2 0.0 0.4 10 | 16 0.2 1.0 0.6 0.6 1.0 0.0 0.2 0.4 1.0 0.8 0.8 0.0 0.6 0.6 1.0 0.8 1.0 0.2 1.0 0.2 0.4 0.6 0.2 0.4 1.0 0.4 0.0 1.0 0.2 0.0 0.8 0.4 0.4 1.0 0.6 0.2 0.2 0.4 1.0 0.0 0.6 0.2 0.4 0.2 0.6 0.2 0.2 0.0 0.6 1.0 11 | 17 0.6 0.2 0.2 0.2 0.2 0.2 0.8 0.2 0.8 1.0 0.2 0.4 0.2 1.0 1.0 0.6 0.2 0.8 0.6 0.6 0.4 0.8 0.0 1.0 0.8 0.0 0.2 0.4 1.0 0.0 0.8 0.8 0.4 0.8 0.6 0.6 0.0 0.2 0.4 0.8 0.4 0.4 0.2 1.0 0.6 0.2 0.4 1.0 0.4 0.8 12 | 18 0.8 0.6 1.0 0.6 0.8 0.4 0.6 0.4 0.8 0.2 1.0 0.4 0.4 0.4 0.2 0.0 0.4 0.0 1.0 0.8 0.4 0.6 1.0 0.2 0.4 0.4 0.4 0.0 1.0 0.0 0.8 0.2 0.2 0.2 0.6 0.6 1.0 0.4 0.6 0.0 0.4 1.0 0.0 0.6 0.8 0.6 0.2 0.0 0.6 0.8 13 | 19 0.4 0.0 0.2 0.4 0.4 0.8 0.6 0.6 0.0 0.4 0.4 1.0 0.2 0.4 0.2 0.4 0.8 0.8 0.0 0.4 1.0 0.4 0.6 0.0 0.6 0.8 1.0 0.2 0.0 0.6 0.4 1.0 0.4 0.0 0.6 1.0 1.0 0.4 0.2 0.0 0.0 0.8 1.0 0.6 0.0 0.4 0.0 0.2 1.0 0.0 14 | 2 1.0 0.6 1.0 1.0 0.4 0.8 0.4 0.8 0.6 0.2 0.4 0.2 1.0 0.8 0.8 1.0 0.2 0.6 1.0 0.8 1.0 1.0 1.0 0.6 0.8 0.6 0.0 0.2 0.0 0.2 0.4 1.0 0.6 1.0 0.4 0.2 0.2 0.0 0.4 0.0 0.6 0.4 0.2 0.2 1.0 0.2 0.2 0.0 0.2 0.8 15 | 20 0.2 0.6 0.4 0.2 0.2 1.0 0.8 0.6 0.6 1.0 0.4 0.4 0.8 1.0 0.0 0.4 0.6 0.4 0.4 0.8 0.4 0.4 1.0 0.2 0.2 0.0 0.4 0.2 1.0 0.6 0.6 1.0 0.4 0.4 0.4 0.8 0.6 0.2 0.4 0.2 1.0 0.4 1.0 1.0 1.0 0.4 1.0 1.0 1.0 0.6 16 | 21 0.8 0.2 0.4 0.2 0.6 0.2 0.0 0.8 1.0 1.0 0.2 0.2 0.8 0.0 1.0 0.2 0.0 0.0 0.6 0.6 0.8 0.4 0.6 1.0 0.6 0.2 0.2 1.0 0.4 0.8 0.6 0.8 0.6 0.6 0.6 0.4 1.0 0.2 0.2 0.6 0.8 1.0 0.2 0.4 0.8 1.0 0.4 0.0 0.4 1.0 17 | 22 0.2 0.6 1.0 1.0 0.0 0.4 0.6 0.0 0.8 0.6 0.0 0.4 1.0 0.4 0.2 1.0 0.2 0.8 0.6 0.8 0.6 0.4 0.2 0.4 0.8 0.8 0.4 1.0 0.6 0.0 1.0 0.4 0.6 0.8 0.6 0.0 1.0 0.8 0.6 0.2 0.8 0.2 0.2 1.0 1.0 0.8 0.4 1.0 1.0 0.2 18 | 23 0.8 0.2 1.0 0.6 0.6 0.8 0.0 0.8 1.0 0.2 0.4 0.8 0.2 0.6 0.0 0.2 1.0 0.6 0.4 0.4 0.0 0.2 0.0 0.2 0.2 0.6 0.2 0.8 1.0 0.6 0.8 0.4 0.6 0.8 1.0 0.4 0.2 0.2 0.4 0.2 0.4 1.0 1.0 0.0 0.6 1.0 0.2 0.2 0.2 0.0 19 | 24 0.0 0.8 0.4 0.4 0.8 0.6 1.0 0.4 0.2 0.8 0.0 0.8 0.6 0.4 0.0 0.8 0.6 1.0 0.6 0.4 0.8 0.2 1.0 0.2 0.6 1.0 0.0 0.4 1.0 0.4 0.4 0.4 1.0 0.4 0.4 0.0 0.2 0.6 1.0 0.6 0.0 0.4 0.4 0.2 0.4 0.4 0.8 0.6 0.4 0.0 20 | 25 0.2 0.0 0.4 0.8 0.4 0.8 0.2 0.6 1.0 0.6 1.0 0.0 1.0 0.4 0.6 0.6 0.4 0.6 1.0 0.8 0.8 0.8 0.8 1.0 0.6 0.0 0.2 1.0 0.2 0.0 0.2 1.0 0.4 0.4 0.6 0.8 0.6 0.2 0.2 1.0 0.2 0.6 0.0 1.0 0.8 0.8 1.0 0.2 0.8 0.0 21 | 26 0.2 1.0 0.8 0.2 0.2 1.0 0.8 0.0 0.2 0.6 0.8 0.4 0.8 0.8 0.6 0.8 0.4 0.4 0.8 1.0 0.4 0.8 0.6 0.6 0.6 0.8 0.8 0.0 1.0 0.2 0.8 0.4 1.0 1.0 1.0 0.4 1.0 0.6 0.8 0.4 0.2 1.0 0.6 0.0 0.6 0.6 0.6 0.2 0.8 0.6 22 | 27 0.2 0.4 0.0 0.0 0.2 0.2 0.0 0.0 0.4 0.4 0.4 1.0 1.0 0.4 0.8 0.6 0.0 0.8 0.8 0.4 1.0 0.8 1.0 0.6 0.4 0.4 1.0 0.2 0.0 0.0 1.0 0.0 0.2 0.6 1.0 0.6 0.6 0.2 0.6 0.0 0.8 0.6 0.8 1.0 0.2 0.6 0.2 0.4 1.0 0.8 23 | 28 0.6 0.6 1.0 0.2 0.2 0.2 0.8 0.2 0.6 0.8 0.6 0.4 1.0 0.4 0.4 0.4 0.2 0.2 0.8 0.8 0.8 1.0 0.8 0.4 0.8 0.2 0.2 0.2 0.8 0.2 0.4 0.6 1.0 1.0 1.0 0.6 0.4 0.0 0.4 0.2 0.2 0.4 0.2 1.0 1.0 0.6 0.8 0.8 1.0 0.4 24 | 29 1.0 0.8 0.4 1.0 0.8 0.8 0.4 1.0 0.2 0.0 1.0 0.6 1.0 1.0 0.6 0.2 0.0 1.0 0.8 0.6 1.0 0.8 1.0 0.0 0.6 0.2 0.0 0.2 0.0 0.2 0.6 0.2 0.2 0.2 0.6 0.6 0.6 0.0 0.4 0.6 0.8 0.2 0.6 0.8 0.0 1.0 0.0 0.2 0.4 1.0 25 | 3 0.0 0.4 0.2 0.2 0.0 0.4 0.2 1.0 0.4 1.0 0.2 0.0 0.6 0.2 1.0 0.4 0.2 0.2 1.0 0.6 0.6 0.4 0.0 1.0 0.6 0.2 0.4 0.2 1.0 0.2 1.0 0.6 0.6 1.0 0.2 0.8 0.2 0.4 0.4 1.0 0.8 0.6 0.8 0.4 1.0 0.4 0.4 0.6 1.0 0.6 26 | 30 0.4 0.2 0.0 0.6 0.4 0.6 0.8 0.0 1.0 0.8 0.4 0.6 0.8 0.2 0.6 0.8 0.2 0.6 0.6 0.6 0.4 0.8 0.6 0.6 1.0 0.8 0.4 1.0 0.0 0.6 1.0 0.0 0.0 0.4 0.2 0.0 0.2 0.4 1.0 0.8 0.0 0.0 0.2 1.0 1.0 0.8 0.8 1.0 0.8 0.0 27 | 31 0.0 0.4 0.6 0.2 1.0 0.2 0.8 1.0 0.4 0.0 0.4 0.8 0.6 0.0 0.2 0.8 0.6 1.0 0.0 0.8 0.4 0.2 0.2 0.2 0.8 1.0 0.0 0.0 0.4 0.8 0.4 1.0 0.6 0.8 1.0 0.0 0.2 0.4 0.2 0.4 1.0 0.4 0.0 0.4 0.2 0.0 0.0 0.8 0.8 0.4 28 | 32 0.8 0.2 1.0 0.6 0.0 0.2 0.4 0.8 0.0 0.2 0.4 1.0 0.0 0.4 0.2 0.4 0.2 0.0 0.2 0.8 1.0 0.2 0.0 0.4 0.4 0.0 1.0 0.6 0.6 0.8 0.6 0.2 1.0 0.0 0.4 0.4 0.0 0.0 0.6 0.0 0.2 0.4 1.0 0.8 0.6 0.4 0.4 0.0 1.0 0.4 29 | 33 0.2 0.6 1.0 0.6 0.2 0.8 1.0 0.4 1.0 0.4 0.0 0.2 0.2 0.2 1.0 1.0 0.8 0.4 1.0 0.0 0.2 0.2 0.2 0.2 1.0 0.0 0.6 1.0 0.8 0.0 1.0 0.0 0.2 0.2 0.4 0.6 0.2 0.8 0.0 0.2 0.2 0.8 0.4 0.2 0.4 0.4 0.0 0.8 0.2 1.0 30 | 34 0.0 0.0 0.2 1.0 1.0 0.0 1.0 0.2 0.2 1.0 1.0 0.0 0.0 1.0 0.4 0.6 1.0 1.0 0.2 1.0 0.0 0.8 0.0 1.0 0.0 0.4 0.6 0.8 1.0 0.0 0.4 0.0 1.0 0.2 0.4 0.6 0.4 0.6 1.0 0.0 0.0 0.6 1.0 0.2 0.4 0.0 0.6 0.8 0.4 0.8 31 | 35 0.2 0.8 0.6 0.4 0.4 0.2 0.0 0.8 0.0 0.0 0.0 0.6 0.2 0.6 0.8 0.0 0.6 0.4 0.0 0.2 0.0 0.2 0.2 0.2 0.6 0.8 0.8 0.0 0.0 1.0 0.0 1.0 0.0 0.8 0.4 0.4 0.8 0.2 0.6 0.8 0.0 0.4 1.0 0.4 0.0 0.6 0.6 0.8 0.2 0.0 32 | 36 1.0 1.0 1.0 0.8 1.0 0.6 0.2 0.4 0.8 0.8 0.8 0.4 0.4 0.6 0.6 1.0 0.8 0.4 0.2 0.8 1.0 0.4 0.6 1.0 1.0 0.4 0.6 1.0 0.4 0.0 1.0 1.0 0.6 0.0 1.0 0.6 0.6 0.2 0.4 0.2 0.6 0.6 0.8 1.0 0.8 0.4 0.0 0.0 0.0 0.2 33 | 37 0.6 0.2 1.0 0.4 0.6 0.4 1.0 0.0 0.4 0.8 0.2 1.0 1.0 1.0 0.8 0.4 0.4 0.4 1.0 0.4 0.0 0.6 0.2 0.6 0.0 1.0 0.2 0.0 0.0 1.0 1.0 1.0 0.0 0.2 0.2 0.8 1.0 0.0 0.4 0.6 1.0 0.2 0.4 1.0 1.0 0.6 1.0 0.2 0.8 0.4 34 | 38 0.0 0.0 0.8 0.8 0.6 0.6 0.0 0.2 0.4 0.4 0.2 0.4 0.6 0.4 0.6 0.6 0.6 1.0 0.4 1.0 0.2 1.0 0.2 0.6 0.0 0.6 1.0 0.2 1.0 0.0 0.6 0.0 1.0 1.0 0.4 1.0 0.4 0.6 0.0 1.0 0.4 0.2 0.4 0.6 1.0 0.4 0.6 0.8 0.8 0.2 35 | 39 0.6 0.4 0.0 1.0 0.8 0.0 0.8 0.4 1.0 0.8 0.2 0.0 1.0 0.4 0.6 0.8 0.8 0.4 0.4 1.0 0.6 1.0 0.2 1.0 0.4 0.8 0.0 0.2 0.2 0.8 0.0 0.2 1.0 1.0 0.0 0.2 0.6 0.6 1.0 0.0 0.2 0.6 0.0 0.6 0.8 0.0 0.8 0.4 1.0 0.2 36 | 4 0.0 1.0 1.0 0.6 0.2 1.0 0.4 0.6 0.6 0.6 0.6 0.6 0.4 0.4 0.6 0.6 1.0 0.4 0.6 1.0 1.0 1.0 0.6 0.2 0.2 1.0 0.4 0.4 0.4 0.4 1.0 0.2 0.4 0.0 1.0 0.6 0.2 1.0 0.6 0.2 1.0 0.6 0.6 1.0 0.8 0.0 0.6 0.2 0.8 0.0 37 | 40 0.2 1.0 0.6 0.2 0.6 1.0 1.0 0.6 0.2 0.6 0.6 1.0 0.2 0.8 0.4 0.0 0.4 0.0 0.8 0.4 0.6 0.6 0.6 0.8 0.0 0.0 0.4 0.6 0.6 0.4 0.6 0.8 1.0 0.2 0.6 1.0 0.8 0.2 0.0 1.0 0.0 0.0 0.6 0.2 1.0 1.0 0.2 1.0 1.0 0.4 38 | 41 0.4 0.0 0.6 0.4 0.0 0.4 0.8 0.8 0.2 0.0 1.0 1.0 0.2 0.6 1.0 1.0 0.2 0.2 0.6 1.0 0.6 0.4 0.6 0.2 0.2 0.2 0.0 0.2 0.4 0.8 0.6 1.0 0.4 0.6 0.2 0.8 1.0 0.6 0.0 1.0 0.4 1.0 0.0 0.8 0.2 0.6 0.2 0.4 0.2 0.6 39 | 42 1.0 0.4 0.4 0.8 0.2 0.2 0.8 0.4 0.4 0.2 0.4 0.4 0.0 0.2 0.2 0.8 0.2 0.6 0.2 0.6 0.2 0.0 0.0 0.4 0.4 0.4 0.0 0.8 0.6 0.2 0.2 0.0 0.6 0.6 1.0 0.2 0.6 1.0 0.4 0.8 0.4 0.8 0.6 0.8 0.2 0.4 0.0 0.6 0.0 0.0 40 | 43 0.8 0.6 0.4 0.8 0.2 0.0 0.6 0.6 1.0 0.4 0.6 0.2 0.4 0.4 0.2 0.6 0.4 1.0 0.2 0.8 0.6 0.4 0.4 0.4 1.0 0.2 0.6 0.0 1.0 0.6 0.4 0.4 0.0 1.0 0.6 0.0 0.0 0.4 1.0 0.6 0.8 0.4 0.2 0.6 1.0 0.2 0.8 0.0 0.8 1.0 41 | 44 1.0 0.6 0.8 0.0 0.6 1.0 0.0 0.8 0.0 0.8 0.0 0.0 0.0 0.2 0.6 0.2 0.2 0.6 1.0 0.4 0.0 0.2 0.6 1.0 0.8 0.4 0.0 0.2 0.0 0.8 0.2 0.6 1.0 0.0 0.2 1.0 1.0 0.8 0.6 1.0 0.6 0.6 0.8 0.0 0.0 0.2 0.6 1.0 1.0 0.4 42 | 45 0.8 1.0 0.8 0.6 0.0 0.4 0.2 0.6 0.6 0.4 0.4 0.0 0.6 1.0 0.8 0.8 0.4 0.0 0.2 0.2 0.8 0.2 0.8 0.8 0.0 1.0 0.2 0.2 0.0 0.0 0.6 1.0 0.4 0.2 1.0 0.0 0.4 0.4 0.8 0.6 1.0 0.6 0.8 0.8 1.0 1.0 0.0 0.8 0.2 0.8 43 | 46 0.4 0.4 0.8 0.8 0.8 0.2 0.6 0.4 0.2 0.4 1.0 0.8 0.4 0.4 1.0 0.2 1.0 0.4 0.6 1.0 0.6 0.4 0.2 0.6 0.0 0.4 0.4 0.8 0.6 0.4 0.6 0.2 0.2 0.6 0.6 0.0 1.0 0.8 0.4 0.6 0.6 1.0 0.0 0.8 0.4 1.0 0.4 0.8 0.0 0.2 44 | 47 1.0 0.8 0.4 0.2 0.4 0.0 1.0 0.8 0.4 0.2 0.0 1.0 0.2 1.0 0.2 0.2 1.0 0.4 0.0 0.6 0.8 0.2 0.6 0.8 0.2 0.0 1.0 0.4 1.0 1.0 0.8 0.4 0.4 0.0 0.6 0.6 0.0 0.6 0.2 0.8 0.8 0.0 1.0 0.4 0.4 0.8 0.0 0.2 0.8 0.2 45 | 48 0.8 1.0 0.6 0.8 0.4 0.8 1.0 0.8 0.2 1.0 0.6 0.6 0.2 1.0 0.4 1.0 0.0 0.2 1.0 0.0 1.0 1.0 0.8 0.4 1.0 0.4 0.8 0.2 0.2 0.4 1.0 1.0 0.6 0.6 1.0 0.2 0.8 0.8 0.6 0.0 0.8 0.8 0.4 1.0 0.8 0.4 1.0 0.2 1.0 0.8 46 | 49 0.2 0.6 1.0 0.6 0.6 0.2 0.6 0.4 0.6 0.6 0.8 0.0 1.0 1.0 0.8 1.0 0.6 0.4 0.8 0.6 0.2 1.0 0.0 1.0 1.0 0.2 0.6 0.4 0.4 0.0 0.8 1.0 1.0 0.8 0.8 1.0 0.2 0.2 1.0 0.0 1.0 0.4 0.4 0.8 1.0 1.0 0.0 0.2 0.8 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29 | 33 0.2 0.6 0.6 1.0 0.2 0.6 0.8 1.0 0.8 0.4 0.6 0.0 1.0 0.2 1.0 0.6 0.6 0.8 0.4 1.0 0.2 0.4 0.2 0.8 1.0 0.4 1.0 1.0 0.6 0.0 0.6 0.2 0.6 0.8 0.2 0.6 0.0 0.8 0.2 0.6 0.8 0.2 1.0 0.2 0.8 0.6 0.2 1.0 0.6 0.8 30 | 34 0.0 0.4 0.4 1.0 0.6 0.0 0.6 0.6 0.0 0.4 0.8 0.0 0.6 0.8 0.8 0.8 0.6 1.0 1.0 0.6 1.0 0.8 0.0 1.0 0.6 0.8 0.6 0.6 1.0 0.2 0.8 0.4 1.0 0.0 1.0 0.2 0.8 0.2 1.0 0.0 0.6 0.6 0.8 1.0 0.8 0.4 0.8 0.6 0.4 1.0 31 | 35 0.6 0.6 1.0 0.8 0.4 0.6 0.6 1.0 0.4 0.6 0.8 0.6 0.8 1.0 0.4 0.2 1.0 0.2 0.0 0.4 0.0 0.0 0.0 0.4 0.8 0.4 0.8 0.0 0.2 1.0 0.2 0.4 0.4 0.8 0.2 0.4 1.0 0.2 0.4 0.4 0.0 0.0 1.0 1.0 0.4 0.8 1.0 0.4 0.2 0.4 32 | 36 0.6 0.6 0.8 0.6 1.0 0.6 1.0 1.0 0.6 0.8 0.8 1.0 0.0 0.6 0.8 0.8 0.2 0.6 1.0 0.6 0.8 0.2 0.8 1.0 0.8 0.4 0.6 0.6 0.8 0.2 1.0 0.6 0.4 0.0 1.0 1.0 1.0 0.4 1.0 0.2 1.0 0.8 0.0 1.0 1.0 0.6 0.0 0.0 0.0 0.6 33 | 37 1.0 0.4 1.0 0.2 1.0 0.6 0.6 0.2 1.0 0.4 0.8 0.2 0.8 0.8 0.8 0.6 0.8 0.2 0.0 0.6 0.0 0.8 0.2 0.6 0.8 0.6 0.2 0.2 0.4 0.4 0.6 1.0 0.6 0.2 0.4 1.0 0.6 0.0 0.4 1.0 0.2 1.0 0.6 1.0 0.8 0.4 0.8 0.8 1.0 1.0 34 | 38 0.4 0.2 0.6 0.6 0.6 0.0 1.0 0.6 0.0 1.0 0.2 0.2 0.6 0.8 1.0 1.0 1.0 0.8 0.6 0.6 0.0 1.0 0.2 0.2 0.0 1.0 0.8 0.6 1.0 0.4 0.4 0.6 1.0 0.6 0.0 1.0 0.4 0.6 0.2 0.0 0.4 0.0 0.8 0.6 0.8 0.2 0.0 1.0 1.0 0.0 35 | 39 0.6 1.0 0.6 0.2 1.0 0.4 0.2 0.2 0.6 0.0 1.0 0.2 0.2 0.8 0.2 0.6 0.0 0.8 0.6 1.0 1.0 1.0 0.2 0.4 0.4 1.0 1.0 0.8 0.0 0.8 0.0 0.2 0.6 1.0 0.2 0.8 0.2 0.6 0.4 0.0 0.0 0.6 0.0 0.8 0.4 0.0 1.0 0.2 0.6 0.8 36 | 4 0.4 1.0 0.2 0.4 0.6 0.6 0.2 0.6 0.6 0.0 0.2 0.2 0.4 0.0 0.8 1.0 0.2 1.0 0.8 0.8 0.8 0.8 0.4 0.6 0.4 0.8 0.6 0.2 1.0 0.2 1.0 0.4 0.0 0.2 1.0 1.0 0.4 0.8 0.6 0.6 0.8 1.0 0.6 0.4 0.8 0.0 1.0 0.4 0.8 0.0 37 | 40 0.0 0.6 0.4 0.4 0.8 1.0 0.4 0.4 0.8 0.2 0.4 1.0 0.6 1.0 1.0 0.2 0.8 0.2 0.4 1.0 1.0 0.6 0.4 0.4 0.2 0.8 0.2 0.6 0.2 0.4 1.0 1.0 1.0 0.8 1.0 1.0 0.4 0.0 0.4 0.4 0.2 0.0 0.4 0.8 1.0 1.0 0.2 1.0 0.6 0.2 38 | 41 0.4 0.2 0.8 0.2 0.0 0.6 1.0 1.0 0.2 0.0 1.0 0.6 1.0 0.6 1.0 0.8 1.0 0.8 0.4 0.0 0.0 0.2 1.0 0.2 0.8 0.0 0.0 0.0 0.8 1.0 1.0 0.6 0.4 0.2 0.4 0.4 1.0 1.0 0.0 0.6 1.0 0.8 0.4 1.0 0.6 1.0 0.8 0.4 0.8 1.0 39 | 42 1.0 0.6 1.0 0.8 0.8 0.0 0.4 1.0 0.6 0.8 0.2 0.0 0.0 0.4 0.6 0.8 0.6 0.2 0.0 0.2 0.4 0.2 0.2 0.8 0.4 0.4 0.0 0.8 0.2 0.2 0.4 0.0 0.6 0.6 0.8 0.0 1.0 1.0 0.6 0.8 1.0 0.8 0.4 1.0 0.2 0.2 0.6 0.8 0.8 0.2 40 | 43 0.6 0.4 0.6 0.2 0.4 0.2 1.0 0.4 1.0 0.0 0.2 0.8 0.2 0.0 1.0 0.6 0.8 1.0 0.2 1.0 0.6 0.8 0.0 0.4 1.0 0.0 0.8 0.2 1.0 0.4 1.0 0.4 0.2 0.4 0.6 0.4 0.0 0.6 1.0 1.0 1.0 0.2 0.4 1.0 0.6 0.8 1.0 0.4 0.4 0.8 41 | 44 0.6 0.8 1.0 0.6 1.0 1.0 0.4 0.8 0.0 0.8 0.6 0.2 0.0 0.2 0.4 0.0 0.2 0.4 0.2 0.6 0.2 0.8 0.0 1.0 1.0 0.2 0.6 0.6 0.0 0.4 0.2 1.0 0.0 0.0 0.6 0.4 0.6 0.8 1.0 1.0 0.8 0.4 1.0 0.6 0.8 0.4 0.8 1.0 1.0 1.0 42 | 45 1.0 1.0 0.8 1.0 0.0 0.8 0.6 0.6 0.4 0.0 0.2 0.4 0.4 0.6 0.0 0.6 1.0 0.4 0.0 0.6 0.6 0.6 1.0 0.8 0.2 1.0 0.0 0.8 0.6 0.0 1.0 0.2 0.4 0.0 0.8 0.2 1.0 1.0 1.0 0.8 1.0 0.4 0.4 0.6 1.0 0.2 0.0 0.6 0.8 1.0 43 | 46 0.0 0.4 0.6 0.6 0.2 0.0 0.8 0.4 0.0 0.4 0.6 0.8 0.6 0.4 1.0 0.6 1.0 0.2 0.6 1.0 0.4 1.0 0.6 0.6 0.2 1.0 0.8 0.2 0.6 0.0 0.8 1.0 0.0 0.6 1.0 0.0 0.8 0.8 0.2 0.4 0.4 1.0 0.2 0.6 0.6 1.0 0.0 0.6 0.0 0.6 44 | 47 1.0 0.6 1.0 0.8 0.0 0.0 0.6 0.2 1.0 0.2 0.0 0.6 0.2 0.8 1.0 1.0 0.4 0.6 0.4 0.4 0.8 0.4 0.4 0.6 0.2 0.0 1.0 1.0 0.8 1.0 0.0 0.6 0.8 0.0 0.6 0.4 0.4 0.4 0.4 1.0 0.4 0.2 1.0 0.4 0.0 1.0 0.8 0.4 0.8 0.2 45 | 48 0.4 1.0 0.4 0.2 0.2 0.6 1.0 1.0 0.2 0.8 0.6 0.2 0.0 0.8 1.0 0.8 0.4 0.2 1.0 0.0 1.0 1.0 0.8 0.2 1.0 0.6 1.0 0.2 1.0 1.0 1.0 1.0 0.6 0.8 0.4 0.8 1.0 1.0 1.0 0.6 0.6 0.6 0.4 1.0 0.0 0.4 1.0 0.0 0.0 1.0 46 | 49 0.0 1.0 0.8 0.6 0.0 0.2 1.0 0.4 1.0 0.2 0.4 0.0 0.0 0.8 1.0 1.0 1.0 0.4 0.8 1.0 0.6 0.8 0.0 1.0 0.6 0.0 0.2 0.8 0.8 0.4 1.0 0.8 0.8 0.4 0.8 1.0 0.6 0.2 0.6 0.8 1.0 0.6 0.0 0.0 1.0 1.0 0.0 0.2 0.2 0.4 47 | 5 1.0 0.8 0.6 0.0 0.6 0.6 0.8 0.2 1.0 0.4 0.4 1.0 0.2 0.2 0.8 0.6 1.0 0.6 1.0 0.8 0.6 0.2 0.8 0.6 0.8 0.2 1.0 0.6 0.4 0.8 0.6 0.4 0.2 0.0 0.0 1.0 1.0 0.2 0.8 0.4 0.2 1.0 1.0 0.4 1.0 1.0 0.4 0.8 0.8 1.0 48 | 6 1.0 0.6 1.0 0.6 0.6 0.8 0.2 0.8 0.2 1.0 0.6 0.2 0.2 0.4 0.6 0.2 0.6 0.8 0.6 1.0 0.4 0.4 0.0 0.6 0.2 0.6 0.4 0.2 0.8 1.0 0.0 0.8 0.0 1.0 1.0 0.2 0.8 0.6 1.0 0.8 0.0 0.0 0.8 1.0 0.0 0.4 1.0 0.0 0.2 0.4 49 | 7 0.0 0.0 1.0 0.2 0.6 0.8 1.0 0.0 0.4 0.8 0.2 0.6 0.4 0.6 0.0 0.2 0.2 0.8 0.4 0.0 0.6 1.0 0.2 0.4 1.0 0.6 0.4 1.0 0.6 0.4 0.0 0.8 1.0 0.2 0.4 1.0 0.4 0.8 0.4 1.0 0.6 0.6 0.4 0.0 0.2 0.8 0.0 1.0 0.6 0.4 50 | 8 0.0 0.0 0.4 0.4 1.0 0.8 0.6 1.0 0.2 0.0 0.6 1.0 0.0 1.0 0.4 1.0 0.8 0.4 1.0 0.6 0.6 0.8 0.6 1.0 1.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 1.0 0.6 0.8 0.6 0.8 0.8 0.4 1.0 0.8 0.0 0.8 0.0 0.2 0.8 0.2 0.6 1.0 0.2 51 | 9 0.6 0.2 0.8 0.4 0.8 1.0 0.6 0.6 0.6 0.6 0.6 1.0 0.4 0.2 0.2 0.2 0.0 0.0 0.0 0.4 1.0 0.6 0.6 0.2 0.2 0.4 1.0 0.8 1.0 0.4 0.6 1.0 0.0 0.8 0.0 0.2 1.0 0.2 0.8 1.0 1.0 0.6 0.2 1.0 0.4 1.0 0.4 0.4 0.2 1.0 52 | -------------------------------------------------------------------------------- /Lineages.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Jun 27, 2011 3 | 4 | @author: jonathanfriedman 5 | ''' 6 | 7 | import pickle as pickle 8 | import numpy as np 9 | 10 | _levels = ['k', 'p', 'c', 'o', 'f', 'g' , 's'] 11 | _levels_RDP = {'domain':'k', 'phylum':'p', 'class':'c', 'order':'o', 'family':'f', 'genus':'g','species':'s'} 12 | _unassigned_str = 'unassigned' 13 | 14 | class Lineage(object): 15 | ''' 16 | Class containing information regarding the taxonomic 17 | assignment of an OTU. 18 | 19 | Class Attributes 20 | ----------------- 21 | lin_str : str 22 | The lineage line used to create self. 23 | lin : dict 24 | Lineage assignments. keyed by taxonomic level 25 | (1 letter abbreviations. See Lineages._levels) 26 | conf : dict 27 | confidence level of the assignments. 28 | Same keys as self.lin 29 | ''' 30 | 31 | def __init__(self, id=None, lin_str=None, format='QIIME'): 32 | ''' 33 | 34 | Parameters 35 | ---------- 36 | id : hashable 37 | id of the current lineage. typically an OTU id. 38 | lin_str : str 39 | line containing the lineage assignment information. 40 | format : {'QIIME' (default)| 'HMP' | 'RDP'} 41 | The format of the lin_str. 42 | - QIIME : pairs of '[levelName]__[assignment]' 43 | separated by semicolons. Level names are displayed even for 44 | unassigned levels. Examples: 45 | * `k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Veillonellaceae;g__;s__` 46 | * `k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__;f__;g__;s__` 47 | - HMP : pairs of '[assignment]([confidence])' separated by 48 | semicolons. Unassigned levels are omitted. Examples: 49 | * `Root(100);Bacteria(100);"Firmicutes"(100);"Bacilli"(100);Bacillales(100);Bacillaceae(99);Bacillus(99);` 50 | * `Root(100);Bacteria(100);"Firmicutes"(100);"Clostridia"(100);Clostridiales(100);` 51 | - RDP : triplets of '[assignment]\\t[levelName]\t[confidence]', 52 | separated by tabs. Unassigned levels are blanks, and all 53 | tabs are retained! Examples: 54 | * `Bacteria\\tdomain\\t0.98\\tOD1\\tphylum\\t0.47\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tOD1_genera_incertae_sedis\\tgenus\\t0.47` 55 | ''' 56 | ## init id 57 | if not id: id = hash(self) 58 | self.id = id 59 | ##init taxonomy 60 | n_levels = len(_levels) 61 | lin_str = lin_str.replace('\t',';') 62 | self.lin_str = lin_str 63 | self.lin = dict.fromkeys(_levels, _unassigned_str) # lineage information 64 | self.conf = dict.fromkeys(_levels, _unassigned_str) # assignment confidence 65 | if lin_str: 66 | if format.upper() == 'QIIME': 67 | fields = lin_str.strip().split(';') 68 | for field in fields: 69 | field = field.strip() 70 | if field.lower() == 'root': 71 | continue 72 | level, lin = field.split('__') 73 | if lin == '': lin = _unassigned_str 74 | self.lin[level] = lin.strip('"') 75 | elif format.upper() == 'HMP': 76 | fields = lin_str.strip().split(';')[1:-1] #ignore first assignment to root. 77 | n_fields = len(fields) 78 | fields += ['unassigned(0)'] * (n_levels-n_fields) 79 | for i,level in enumerate(_levels): 80 | temp = fields[i].split('(') 81 | assignment = temp[0].strip('"') 82 | if assignment == 'unclassified': 83 | assignment = _unassigned_str 84 | self.lin[level] = assignment 85 | self.conf[level] = int(temp[-1][:-1]) 86 | elif format.upper() == 'RDP': 87 | fields = lin_str.strip().split(';') 88 | for i,level in enumerate(_levels): 89 | try: 90 | lin = fields[3*i] 91 | conf = float(fields[3*i+2]) 92 | if not lin: 93 | lin = _unassigned_str 94 | conf = 0 95 | except: 96 | lin = _unassigned_str 97 | conf = 0 98 | self.lin[level] = lin 99 | self.conf[level] = conf 100 | else: 101 | raise ValueError("Unsuppoerted format: '%s'." %format) 102 | 103 | def __repr__(self): return repr(self.lin_str) 104 | 105 | def __eq__(self, other): 106 | if type(self) != type(other): 107 | return False 108 | attrs = ('lin','lin_str','id','conf') 109 | return all( getattr(self,attr)==getattr(other,attr) for attr in attrs ) 110 | 111 | def __ne__(self, other): 112 | return not self.__eq__(other) 113 | 114 | def get_assignment(self, level=None, best=False): 115 | ''' 116 | If best is True, return the most resolved lineage assignment 117 | and its level. 118 | ''' 119 | if level is None: # return most resolved assignment 120 | for l in _levels[::-1]: 121 | if self.lin[l] != _unassigned_str: 122 | return l + '__' + self.lin[l] 123 | return l + '__' + self.lin[l] 124 | else: 125 | if best: # return assignment at given level if assigned, o.w. return most resolved assignment. 126 | if self.lin[level] == _unassigned_str: 127 | return self.get_assignment(level=None,best=best) 128 | else: 129 | return self.get_assignment(level=level,best=False) 130 | else: # return assignment at given level (assigned or not) 131 | return level + '__' + self.lin[level] 132 | 133 | 134 | class Lineages(dict): 135 | ''' 136 | A dictionary of Lineage objects with dedicated methods. 137 | ''' 138 | ## override some dict methods to support checking that all items are of type Lineage. 139 | ## See: http://stackoverflow.com/questions/2060972/subclassing-python-dictionary-to-override-setitem 140 | def __init__(self, *args, **kwargs): 141 | ''' 142 | A subcalss of :class:`dict` for holding :class:`Lineage` 143 | objects. 144 | ''' 145 | self.update(*args, **kwargs) 146 | 147 | def __setitem__(self, key, value): 148 | if not isinstance(value, Lineage): 149 | t = type(value) 150 | raise TypeError("Item corresponding to key '%s' is of type '%s instead of a Lineage object'." %(key, t)) 151 | if key != value.id: 152 | raise ValueError("Item with ID '%s' has key '%s' instead of its own ID." %(value.id,key)) 153 | super(Lineages, self).__setitem__(key, value) 154 | 155 | def update(self, *args, **kwargs): 156 | if args: 157 | if len(args) > 1: 158 | raise TypeError("update expected at most 1 arguments, got %d" % len(args)) 159 | other = dict(args[0]) 160 | for key in other: 161 | self[key] = other[key] 162 | for key in kwargs: 163 | self[key] = kwargs[key] 164 | 165 | def setdefault(self, key, value=None): 166 | if key not in self: 167 | self[key] = value 168 | return self[key] 169 | 170 | @classmethod 171 | def from_txt(cls, file, n_skip=0, format='QIIME', **kwargs): 172 | ''' 173 | Create lineages object from txt file containing 2 columns: 174 | id and lineage string. 175 | ''' 176 | f = open(file,'r') 177 | lines = f.readlines() 178 | delim_def = '\t' 179 | if format.upper() == 'RDP': 180 | delim_def = '\t\t\t\t\t' 181 | delim = kwargs.get('delim', delim_def) 182 | n = len(delim) 183 | d = {} 184 | for line in lines[n_skip:]: 185 | l = line.strip().replace('-','') 186 | i = l.index(delim) 187 | d[l[:i]] = l[i+n:] 188 | # d = dict([ line.strip().split(delim) for line in lines[n_skip:] ]) 189 | return cls.from_dict(d, format) 190 | 191 | 192 | def to_txt(self, file, sort_fun=str): 193 | ''' 194 | Write all lineage strings to file, sorted by ids. 195 | ''' 196 | ids = sorted(list(self.keys()), key=sort_fun) 197 | f = open(file,'w') 198 | header = 'ID' +'\t' + 'Lineage' + '\n' 199 | f.write(header) 200 | for i in ids: 201 | line = str(i) +'\t' + self[i].lin_str + '\n' 202 | f.write(line) 203 | f.close() 204 | 205 | 206 | def to_pickle(self,file): 207 | ''' pickles into file''' 208 | f=open(file,'w') 209 | pickle.dump(self,f) 210 | f.close() 211 | 212 | save = to_pickle 213 | 214 | @classmethod 215 | def from_pickle(cls,file): 216 | ''' unpickles from file''' 217 | f=open(file,'r') 218 | temp=pickle.load(f) 219 | f.close() 220 | return temp 221 | 222 | @classmethod 223 | def from_dict(cls, d, format = 'QIIME'): 224 | ''' 225 | Make a Lineages object from a dictionary whose keys are ids 226 | and values are lineage strings. 227 | ''' 228 | lins = Lineages() 229 | for id, s in d.items(): 230 | lin = Lineage(id = id, lin_str = s, format = format) 231 | lins[id] = lin 232 | return lins 233 | 234 | def get_assignments(self,level, best=False, ids='all'): 235 | ''' 236 | Get the assignment of all lineages at given taxonomic level. 237 | 238 | Parameters 239 | ---------- 240 | level: str 241 | Desired taxonomic level. 242 | best: bool 243 | If True, return best assignment if less resolved than required level. 244 | Output in formal level_assignment 245 | ids : iterable/str 246 | Ids for which to get taxonomic assignment. 247 | 248 | Returns 249 | ------- 250 | A list of assignments keyed by ids of :class:`Lineage` objects. 251 | ''' 252 | if level not in _levels: 253 | raise ValueError("Level '%s' is not one of the allowed taxonomic leveles: 'k', 'p', 'c', 'o', 'f', 'g' , 's'." %level) 254 | if isinstance(ids, str): 255 | if ids=='all': 256 | a = [l.get_assignment(level, best=best) for l in self.values()] 257 | return a 258 | else: 259 | ids = (ids,) 260 | a = [self[id].get_assignment(level, best=best) for id in ids] 261 | return a 262 | 263 | 264 | def get_ids(self, level, assignment, best=True, complement = False): 265 | ''' 266 | Get the ids of all items with a given taxonomy. 267 | If complement is True, get the ids of otus with assignment 268 | different than given. 269 | ''' 270 | if level not in _levels: raise ValueError("Level '%s' is not one of the allowed taxonomic leveles: 'k', 'p', 'c', 'o', 'f', 'g' , 's'." %level) 271 | if complement: ids = [i for i in iter(self.keys()) if self[i].get_assignment(level=level, best=best) != assignment] 272 | else: ids = [i for i in iter(self.keys()) if self[i].get_assignment(level=level, best=best) == assignment] 273 | return ids 274 | 275 | def get_assigned(self, level): 276 | ''' 277 | Get the ids of items that have an assigned taxonmy at given level. 278 | ''' 279 | if level not in _levels: raise ValueError("Level '%s' is not one of the allowed taxonomic leveles: 'k', 'p', 'c', 'o', 'f', 'g' , 's'." %level) 280 | return self.get_ids(level, level + '.' + _unassigned_str, complement = True, best=False) 281 | 282 | 283 | def filter(self, ids): 284 | ''' 285 | Return new instance with only given ids 286 | ''' 287 | ids = [i for i in ids if i in self] 288 | new_d = dict([ (id,self[id]) for id in ids ]) 289 | new = Lineages(new_d) 290 | return new 291 | 292 | 293 | if __name__ == '__main__': 294 | pass 295 | # test_Lineage() 296 | 297 | 298 | 299 | -------------------------------------------------------------------------------- /SparCC.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | ''' 4 | @author: jonathanfriedman 5 | 6 | Module for estimating the correlations in the basis when only compositional data is available. 7 | ''' 8 | 9 | import warnings 10 | import numpy as np 11 | from numpy import (unravel_index, argmax, ones, corrcoef, cov, r_, 12 | diag, sqrt, where, nan) 13 | from core_methods import to_fractions 14 | from compositional_methods import variation_mat, clr 15 | from analysis_methods import correlation 16 | from pandas import DataFrame as DF 17 | try: 18 | from scipy.stats import nanmedian 19 | except ImportError: 20 | from numpy import nanmedian 21 | 22 | 23 | def append_indices(excluded,exclude): 24 | ''' 25 | Append the indx of current excluded value to tuple of previously excluded values. 26 | ''' 27 | if excluded is None: inds = exclude 28 | else: inds = (r_[excluded[0],exclude[0]], r_[excluded[1],exclude[1]]) 29 | return inds 30 | 31 | def new_excluded_pair(C, th=0.1, previously_excluded=[]): 32 | ''' 33 | Find component pair with highest correlation among pairs that 34 | weren't previously excluded. 35 | Return the i,j of pair if it's correlaiton >= than th. 36 | Otherwise return None. 37 | ''' 38 | # C_temp = abs(C - diag(diag(C)) ) 39 | C_temp = np.triu(abs(C),1) # work only on upper triangle, excluding diagonal 40 | C_temp[tuple(zip(*previously_excluded))] = 0 41 | i,j = unravel_index(argmax(C_temp), C_temp.shape) 42 | cmax = C_temp[i,j] 43 | if cmax > th: 44 | return i,j 45 | else: 46 | return None 47 | 48 | def basis_var(f, Var_mat, M, **kwargs): 49 | ''' 50 | Estimate the variances of the basis of the compositional data x. 51 | Assumes that the correlations are sparse (mean correlation is small). 52 | The element of V_mat are refered to as t_ij in the SparCC paper. 53 | ''' 54 | ## compute basis variances 55 | try: M_inv = np.linalg.inv(M) 56 | except: M_inv = np.linalg.pinv(M) 57 | V_vec = Var_mat.sum(axis=1) # elements are t_i's of SparCC paper 58 | V_base = np.dot(M_inv, V_vec) # basis variances. 59 | ## if any variances are <0 set them to V_min 60 | V_min = kwargs.get('V_min', 1e-10) 61 | V_base[V_base <= 0] = V_min 62 | return V_base 63 | 64 | def C_from_V(Var_mat, V_base): 65 | ''' 66 | Given the estimated basis variances and observed fractions variation matrix, 67 | compute the basis correlation & covaraince matrices. 68 | ''' 69 | Vi, Vj = np.meshgrid(V_base, V_base) 70 | Cov_base = 0.5*(Vi + Vj - Var_mat) 71 | C_base = Cov_base/ sqrt(Vi) / sqrt(Vj) 72 | return C_base, Cov_base 73 | 74 | def run_sparcc(f, **kwargs): 75 | ''' 76 | Estimate the correlations of the basis of the compositional data f. 77 | Assumes that the correlations are sparse (mean correlation is small). 78 | ''' 79 | th = kwargs.get('th', 0.1) 80 | xiter = kwargs.get('xiter', 10) 81 | ## observed log-ratio variances 82 | Var_mat = variation_mat(f) 83 | Var_mat_temp = Var_mat.copy() 84 | ## Make matrix from eqs. 13 of SparCC paper such that: t_i = M * Basis_Varainces 85 | D = Var_mat.shape[0] # number of components 86 | M = ones((D,D)) + diag([D-2]*D) 87 | ## get approx. basis variances and from them basis covariances/correlations 88 | V_base = basis_var(f, Var_mat_temp, M) 89 | C_base, Cov_base = C_from_V(Var_mat, V_base) 90 | ## Refine by excluding strongly correlated pairs 91 | excluded_pairs = [] 92 | excluded_comp = np.array([]) 93 | for xi in range(xiter): 94 | # search for new pair to exclude 95 | to_exclude = new_excluded_pair(C_base, th, excluded_pairs) #i,j pair, or None 96 | if to_exclude is None: #terminate if no new pairs to exclude 97 | break 98 | # exclude pair 99 | excluded_pairs.append(to_exclude) 100 | i,j = to_exclude 101 | M[i,j] -= 1 102 | M[j,i] -= 1 103 | M[i,i] -= 1 104 | M[j,j] -= 1 105 | inds = tuple(zip(*excluded_pairs)) 106 | Var_mat_temp[inds] = 0 107 | Var_mat_temp.T[inds] = 0 108 | # search for new components to exclude 109 | nexcluded = np.bincount(np.ravel(excluded_pairs)) #number of excluded pairs for each component 110 | excluded_comp_prev = set(excluded_comp.copy()) 111 | excluded_comp = where(nexcluded>=D-3)[0] 112 | excluded_comp_new = set(excluded_comp) - excluded_comp_prev 113 | if len(excluded_comp_new)>0: 114 | print(excluded_comp) 115 | # check if enough components left 116 | if len(excluded_comp) > D-4: 117 | warnings.warn('Too many component excluded. Returning clr result.') 118 | return run_clr(f) 119 | for xcomp in excluded_comp_new: 120 | Var_mat_temp[xcomp,:] = 0 121 | Var_mat_temp[:,xcomp] = 0 122 | M[xcomp,:] = 0 123 | M[:,xcomp] = 0 124 | M[xcomp,xcomp] = 1 125 | # run another sparcc iteration 126 | V_base = basis_var(f, Var_mat_temp, M) 127 | C_base, Cov_base = C_from_V(Var_mat, V_base) 128 | # set excluded components infered values to nans 129 | for xcomp in excluded_comp: 130 | V_base[xcomp] = nan 131 | C_base[xcomp,:] = nan 132 | C_base[:,xcomp] = nan 133 | Cov_base[xcomp,:] = nan 134 | Cov_base[:,xcomp] = nan 135 | return V_base, C_base, Cov_base 136 | 137 | def run_clr(f): 138 | ''' 139 | Estimate the correlations of the compositional data f. 140 | Data is transformed using the central log ratio (clr) transform. 141 | ''' 142 | z = clr(f) 143 | Cov_base = cov(z, rowvar=0) 144 | C_base = corrcoef(z, rowvar=0) 145 | V_base = diag(Cov_base) 146 | return V_base, C_base, Cov_base 147 | 148 | def basis_corr(f, method='sparcc', **kwargs): 149 | ''' 150 | Compute the basis correlations between all components of 151 | the compositional data f. 152 | 153 | Parameters 154 | ---------- 155 | f : array_like 156 | 2D array of relative abundances. 157 | Columns are counts, rows are samples. 158 | method : str, optional (default 'SparCC') 159 | The algorithm to use for computing correlation. 160 | Supported values: SparCC, clr, pearson, spearman, kendall 161 | Note that the pearson, spearman, kendall methods are not 162 | altered to account for the fact that the data is compositional, 163 | and are provided to facilitate comparisons to 164 | the clr and sparcc methods. 165 | 166 | Returns 167 | ------- 168 | V_base: array 169 | Estimated basis variances. 170 | C_base: array 171 | Estimated basis correlation matrix. 172 | Cov_base: array 173 | Estimated basis covariance matrix. 174 | 175 | ======= ============ ======= ================================================ 176 | kwarg Accepts Default Desctiption 177 | ======= ============ ======= ================================================ 178 | th 0 1 + tol: 193 | warnings.warn('Sparcity assumption violated. Returning clr result.') 194 | V_base, C_base, Cov_base = run_clr(f) 195 | else: 196 | raise ValueError('Unsupported basis correlation method: "%s"' %method) 197 | return V_base, C_base, Cov_base 198 | 199 | def main(counts, method='SparCC', **kwargs): 200 | ''' 201 | Compute correlations between all components of counts matrix. 202 | Run several iterations, in each the fractions are re-estimated, 203 | and return the median of all iterations. 204 | Running several iterations is only helpful with 'dirichlet' 205 | normalization method, as with other methods all iterations 206 | will give identical results. Thus, if using other normalizations 207 | set 'iter' parameter to 1. 208 | 209 | Parameters 210 | ---------- 211 | counts : DataFrame 212 | 2D array of counts. Columns are components, rows are samples. 213 | If using 'dirichlet' or 'pseudo' normalization, 214 | counts (positive integers) are required to produce meaningful results, 215 | though this is not explicitly checked by the code. 216 | method : str, optional (default 'SparCC') 217 | The algorithm to use for computing correlation. 218 | Supported values: SparCC, clr, pearson, spearman, kendall 219 | Note that the pearson, spearman, kendall methods are not 220 | altered to account for the fact that the data is compositional, 221 | and are provided to facilitate comparisons to 222 | the clr and sparcc methods. 223 | 224 | Returns 225 | ------- 226 | cor_med: array 227 | Estimated correlation values. 228 | cov_med: array 229 | Estimated covariance matrix if method in {SparCC, clr}, 230 | None otherwise. 231 | 232 | ======= ============ ======= ================================================ 233 | kwarg Accepts Default Desctiption 234 | ======= ============ ======= ================================================ 235 | iter int 20 number of estimation iteration to average over. 236 | oprint bool True print iteration progress? 237 | th 0', '<', '>=', '<=', 'in'. 106 | - A function that accepts a Series and returns a bool. 107 | - A pysurvey.util.filters.Filter object. 108 | axis : {0 | 1} 109 | 0 : filter rows. 110 | 1 : filter columns. 111 | verbose : bool (default True) 112 | Determines whether to print filtering info. 113 | how : {'all' (default) | 'any' | callable} 114 | 'all' - Keep row/cols that pass all filtering criteria. 115 | 'any' - Keep row/cols that pass any of the filtering criteria. 116 | callable - to be used to reduce the list of bools returned by the filters 117 | for each row/col. 118 | nan_val : bool/None (default None) 119 | Value to be returned by filter if a nan is encountered. 120 | If None is given, nan are not treated separately. 121 | norm : bool (default False) 122 | Indicates whether to normalize the frame before evaluating the filters. 123 | The filtering itself is always conducted on the unnormalized frame. 124 | 125 | Returns 126 | ------- 127 | filtered: frame 128 | Filtered frame (new instance). 129 | ''' 130 | from pysurvey.util.filters import parse_filters 131 | axis = _get_axis(axis) 132 | if norm: 133 | x = normalize(frame) 134 | else: 135 | x = frame 136 | reducer = parse_reducer(how) 137 | ## create filters 138 | filters = parse_filters(criteria, nan_val) 139 | ## find labels to drop 140 | selectors = (x.apply(fil, axis=1-axis) for fil in filters) 141 | selector = reduce(reducer, selectors) 142 | drop = selector[selector==False].index 143 | ## do filtering 144 | filtered = frame.drop(drop, axis=axis) 145 | ## print message 146 | if verbose: 147 | axis_s = {0:'rows',1:'columns'} 148 | s = ['Dropped %d %s' %(len(drop),axis_s[axis]), 149 | 'Resulting size is (%d,%d)' %filtered.shape] 150 | print('\n'.join(s) +'\n') 151 | return filtered 152 | 153 | def keep(frame, n, criterion='sum', axis=0, which='first', sort=True): 154 | ''' 155 | Create a new frame with only the n most extreme rows/cols. 156 | 157 | -------- NO UNITTEST --------- 158 | 159 | Parameters 160 | ---------- 161 | frame : frame 162 | Frame to be filtered 163 | n : int 164 | Number of row/cols to be kept. 165 | criterion : {'sum' (default) | 'avg' | 'med' | 'std' | 'presence' | 'var' | label | callable} 166 | Criterion by which the row/columns will be ordered. 167 | See pysurvey.util.filters.parse_actor for more information. 168 | axis : {0 | 1} 169 | 0 : keep only n rows. 170 | 1 : keep only n cols. 171 | which : {'first' (default) | last} 172 | Indicates whether to keep the first or last n elements after sorting by criterion. 173 | sort : bool (default False) 174 | Indicates whether to sort the kept n rows/cols by the given criterion, 175 | or retain the order in which they appear in the given frame. 176 | 177 | Returns 178 | ------- 179 | filtered: frame 180 | Filtered frame (new instance). 181 | ''' 182 | from pysurvey.util.filters import parse_actor 183 | axis = _get_axis(axis) 184 | if axis == 1: data = frame 185 | elif axis == 0: data = frame.T 186 | f = parse_actor(criterion) 187 | 188 | # biggest = kwargs.get('biggest', True) # if true return the n cols with the biggest values for criterion, else return the n cols with the smallest values. 189 | temp = data.apply(f) 190 | temp.sort() 191 | temp = temp[::-1] 192 | which = which.strip().lower() 193 | if which == 'first': 194 | inds = temp.index[:n] 195 | elif which == 'last': 196 | inds = temp.index[-n:] 197 | else: 198 | raise ValueError("Unsupported value for 'which' parameter: %s" %which) 199 | filtered = data.filter(items=inds) 200 | if not sort: filtered = filtered.reindex_like(data).dropna(how='all', axis=axis) 201 | if axis == 0: filtered = filtered.T 202 | return filtered 203 | 204 | def vals_by_keys(frame, key_pairs): 205 | ''' 206 | Return a list of values corresponding to key_pairs. 207 | Inputs: 208 | key_pairs = [list] each element = [col_key, row_key]. 209 | Outputs: 210 | vals = [list] values for each pair in key_pairs, in corresponding order. 211 | ''' 212 | vals = [frame[pair[0]][pair[1]] for pair in key_pairs] 213 | return vals 214 | 215 | def to_binary(frame, th=0): 216 | ''' 217 | Discretize matrix s.t. matrix[matrix > th] = 1, matrix[matrix <= th] = 0. 218 | Return new instance. 219 | ''' 220 | bin = frame.copy() 221 | ind = frame > th 222 | bin[ind] = 1 223 | bin[-ind] = 0 224 | return bin 225 | 226 | 227 | #------------------------------------------------------------------------------- 228 | # Methods for counts data 229 | 230 | def normalize(frame, axis=0): 231 | ''' 232 | Normalize counts by sample total. 233 | 234 | Parameters 235 | ---------- 236 | axis : {0, 1} 237 | 0 : normalize each row 238 | 1 : normalize each column 239 | 240 | Returns new instance of same class as input frame. 241 | ''' 242 | axis = _get_axis(axis) 243 | tmp = np.apply_along_axis(lambda x:1.*x/x.sum(), 1-axis, frame) 244 | return DF(tmp) 245 | 246 | def to_fractions(frame, method='dirichlet', p_counts=1, axis=0): 247 | ''' 248 | Covert counts to fraction using given method. 249 | 250 | Parameters 251 | ---------- 252 | method : string {'dirichlet' (default) | 'normalize' | 'pseudo'} 253 | dirichlet - randomly draw from the corresponding posterior 254 | Dirichlet distribution with a uniform prior. 255 | That is, for a vector of counts C, 256 | draw the fractions from Dirichlet(C+1). 257 | normalize - simply divide each row by its sum. 258 | pseudo - add given pseudo count (defualt 1) to each count and 259 | do simple normalization. 260 | p_counts : int/float (default 1) 261 | The value of the pseudo counts to add to all counts. 262 | Used only if method is dirichlet 263 | axis : {0 | 1} 264 | 0 : normalize each row. 265 | 1 : normalize each column. 266 | 267 | Returns 268 | ------- 269 | fracs: frame/array 270 | Estimated component fractions. 271 | Returns new instance of same class as input frame. 272 | ''' 273 | axis = _get_axis(axis) 274 | if method == 'normalize': 275 | fracs = normalize(frame, axis) 276 | return fracs 277 | 278 | ## if method is not normalize, get the pseudo counts (dirichlet prior) 279 | from numbers import Number 280 | if not isinstance(p_counts, Number): 281 | p_counts = np.asarray(p_counts) 282 | 283 | if method == 'pseudo': 284 | fracs = normalize(frame+p_counts, axis) 285 | elif method == 'dirichlet': 286 | from numpy.random.mtrand import dirichlet 287 | def dir_fun(x): 288 | a = x+p_counts 289 | f = dirichlet(a) 290 | return f 291 | fracs = np.apply_along_axis(dir_fun, 1-axis, frame) 292 | fracs = DF(fracs) 293 | else: 294 | raise ValueError('Unsupported method "%s"' %method) 295 | return fracs 296 | 297 | def rarefy(frame,n, replace=False, remove_shallow=None): 298 | ''' 299 | Down-sample all rows to have exactly n counts in total for each row. 300 | if remove_shallow, samples with less than n total counts are excluded. 301 | 302 | Parameters 303 | ---------- 304 | n : int 305 | Rows will be down-sampled to this total number of counts. 306 | replace : bool (default False) 307 | Indicates whether sampling is done with or without replacement. 308 | remove_shallow : bool/None (default None) 309 | Indicates whether to remove rows that have less than n total counts to 310 | begin with. 311 | If None is given, remove_shallow is set to be False for sampling with replacement 312 | and True for sampling without replacement. 313 | If remove_shallow is set to False, and sampling is without replacement, 314 | rows that have less than the desired total-number of counts are left unchanged. 315 | 316 | Returns 317 | ------- 318 | deep_rarefied: frame 319 | Rarefied frame (new instance). 320 | ''' 321 | ## decide whether to remove 'shallow' samples 322 | if remove_shallow is None: 323 | remove_shallow = not replace 324 | if remove_shallow: 325 | deep = filter_by_vals(frame, ('sum','>=', n), axis='rows') 326 | else: 327 | deep = frame 328 | deep_rarefied = deep.copy() 329 | ## perform rarefaction 330 | if replace: 331 | from numpy.random.mtrand import multinomial 332 | def draw(x): 333 | p = x/float(x.sum()) 334 | f = 1.*multinomial(n,p) 335 | return f 336 | else: 337 | from numpy.random import rand 338 | def draw(x): 339 | k = len(x) 340 | nt = x.sum() 341 | if nt < n: 342 | return x 343 | new = np.zeros(k) 344 | counts = 1.*x 345 | for j in range(n): 346 | p = counts/nt 347 | i = np.where((p.cumsum() - rand())>0)[0][0] 348 | nt-=1 349 | counts[i]-=1 350 | new[i]+=1 351 | return new 352 | deep_rarefied = (deep_rarefied.T.apply(draw)).T 353 | return deep_rarefied 354 | 355 | def group_taxa(frame, lins, level='p', best=True): 356 | ''' 357 | Return a new instance with cols corresponding to counts aggregated at the 358 | desired taxonomic level (e.g. phylum). 359 | OTUs that are missing from lin are not accounted for. 360 | OTUs that are not assigned at desired level are aggregated into the 'unassigned' row. 361 | 362 | Parameters 363 | ---------- 364 | lins : Lineages 365 | Lineage info of OTUs in frame. 366 | level : str {'k' | 'p' (default) | 'c' | 'o' | 'f' | 'g' | 's'} 367 | Desired taxonomic level of aggregation 368 | best : bool (default True) 369 | Indicates whether to return the best assigned taxonomy 370 | (at the desired level or above), or return the taxonomy at the desired level, 371 | even if it is unassigned. 372 | 373 | Returns 374 | ------- 375 | Grouped frame (new instance). 376 | ''' 377 | old = frame 378 | new = old.filter(items = []) # create new object of same class as frame, with same samples but now otus. 379 | taxa = set(lins.get_assignments(level, best=best)) # set of all taxa present in lin. 380 | for t in taxa: 381 | otus = lins.get_ids(level, t, best=best) 382 | temp = old.filter(items = otus) # matrix with only otus of given taxa 383 | new[t] = temp.sum(axis = 1) 384 | return new.dropna(axis=0) 385 | 386 | 387 | if __name__ == '__main__': 388 | rows = ['r1', 'r0', 'r2', 'r3'] 389 | cols = ['c0', 'c1', 'c2'] 390 | metac = DF([[np.nan,'big'], 391 | ['Entero','small'], 392 | ['Blautia','tiny']], 393 | columns=['name', 'Size'], 394 | index=cols) 395 | mat = np.array([[2., np.NAN,1], 396 | [1, 3, 2], 397 | [10, 15,3], 398 | [0,0,1]]) 399 | df = DF(mat, index=rows, columns=cols) 400 | # print df,'\n' 401 | # print filter_by_vals(df,[('sum','<=',3),('presence','>',1)],axis='rows'),'\n' 402 | print(metac, '\n') 403 | actor = lambda x: x['Size'] 404 | filter1 = lambda x: isinstance(x['name'], str) 405 | filter2 = (actor,'in',['big','tiny']) 406 | filter3 = ('Size','in',['big','tiny']) 407 | filter4 = ('name','in',['Entero','Blautia']) 408 | print(filter_by_vals(metac, filter1, axis=0),'\n') 409 | print(filter_by_vals(metac, filter2, axis=0),'\n') 410 | print(filter_by_vals(metac, filter3, axis=0),'\n') 411 | print(filter_by_vals(metac,[filter1,filter2], axis=0),'\n') 412 | print(filter_by_vals(metac, filter4, axis=0, nan_val=True),'\n') 413 | 414 | df = DF([[1,3,2],[4,6,5]], columns=['a','b','c'], index=['r1','r2']) 415 | print(df,'\n') 416 | print(rarefy(df,7, replace=True)) 417 | 418 | 419 | -------------------------------------------------------------------------------- /example/true_basis_cor.txt: -------------------------------------------------------------------------------- 1 | OTU_id 0 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27 28 29 3 30 31 32 33 34 35 36 37 38 39 4 40 41 42 43 44 45 46 47 48 49 5 6 7 8 9 2 | 0 1.000 0.841 0.097 -0.057 0.053 0.052 0.007 0.005 -0.080 -0.063 0.034 -0.049 -0.036 -0.095 -0.031 0.097 -0.042 -0.071 -0.120 -0.083 0.091 0.061 -0.015 -0.103 -0.061 0.062 -0.023 -0.055 0.133 0.045 -0.004 -0.026 -0.088 0.069 -0.027 0.067 0.059 -0.049 0.033 -0.011 0.011 0.103 -0.014 -0.008 -0.091 0.029 0.016 0.129 0.163 -0.056 3 | 1 0.841 1.000 0.083 -0.020 0.051 0.028 0.019 -0.040 -0.048 -0.089 0.082 -0.128 -0.099 -0.076 -0.012 0.042 0.017 -0.047 -0.123 -0.017 0.105 0.044 0.017 -0.008 -0.033 0.012 -0.059 0.029 0.105 0.051 0.012 -0.061 -0.138 0.013 0.034 0.039 0.102 -0.075 0.052 -0.006 0.006 0.086 -0.019 -0.005 -0.028 -0.002 0.032 0.133 0.191 -0.082 4 | 10 0.097 0.083 1.000 -0.825 0.187 0.100 0.069 0.005 -0.048 -0.083 -0.033 -0.084 0.012 -0.066 0.163 0.013 -0.101 -0.039 -0.106 0.056 0.123 -0.040 0.014 -0.078 -0.038 -0.091 -0.061 -0.004 0.038 0.007 0.034 -0.054 0.004 0.091 -0.049 -0.036 -0.004 -0.053 -0.045 -0.017 -0.017 0.009 -0.031 -0.069 -0.010 0.107 0.037 -0.001 0.075 -0.054 5 | 11 -0.057 -0.020 -0.825 1.000 -0.164 -0.084 -0.020 -0.037 -0.046 0.109 0.082 0.149 0.007 0.074 -0.086 -0.025 0.133 0.034 0.023 -0.059 -0.098 0.073 0.004 0.058 0.017 0.073 0.051 -0.022 0.003 -0.006 -0.026 0.082 -0.078 -0.085 0.060 0.036 0.024 -0.041 -0.014 -0.005 0.052 -0.023 0.018 0.077 -0.030 -0.140 -0.079 -0.063 -0.042 0.078 6 | 12 0.053 0.051 0.187 -0.164 1.000 0.518 0.029 0.031 -0.121 -0.189 -0.036 -0.045 0.028 0.067 0.006 -0.118 -0.099 -0.027 -0.075 0.007 -0.067 -0.041 0.003 0.052 0.088 0.044 -0.200 0.068 0.006 0.023 0.012 -0.035 -0.079 -0.047 -0.017 0.009 -0.128 -0.044 0.106 0.019 -0.139 0.148 0.085 -0.071 0.026 0.036 -0.019 -0.033 0.001 -0.042 7 | 13 0.052 0.028 0.100 -0.084 0.518 1.000 0.034 0.104 -0.121 -0.049 0.087 -0.029 -0.026 0.052 -0.064 0.004 0.059 -0.028 0.049 -0.047 -0.102 0.026 -0.015 -0.018 0.069 0.035 -0.134 -0.023 0.087 -0.084 0.042 0.026 -0.153 -0.104 0.017 -0.011 -0.124 -0.125 0.079 0.011 0.020 0.186 0.117 -0.003 0.140 -0.009 0.005 -0.002 -0.031 -0.025 8 | 14 0.007 0.019 0.069 -0.020 0.029 0.034 1.000 -0.431 -0.119 -0.017 -0.051 -0.065 -0.014 -0.005 0.112 -0.032 0.063 -0.000 -0.139 -0.074 -0.095 0.009 0.069 -0.008 0.036 -0.009 -0.061 -0.056 -0.026 -0.052 -0.070 0.058 0.022 -0.080 -0.065 0.049 0.019 -0.089 0.025 -0.095 -0.001 -0.011 -0.033 -0.016 0.037 -0.075 -0.093 0.032 0.005 0.076 9 | 15 0.005 -0.040 0.005 -0.037 0.031 0.104 -0.431 1.000 -0.103 -0.046 0.050 0.020 -0.037 -0.039 -0.034 0.217 -0.034 -0.065 0.113 0.054 -0.033 -0.040 -0.046 0.069 0.197 0.050 -0.029 0.060 -0.020 0.008 0.007 -0.067 0.063 0.133 0.006 -0.070 -0.072 0.019 -0.018 0.018 0.051 0.072 0.122 -0.007 -0.072 0.100 0.080 -0.089 0.049 -0.030 10 | 16 -0.080 -0.048 -0.048 -0.046 -0.121 -0.121 -0.119 -0.103 1.000 -0.051 -0.050 0.027 -0.062 0.007 -0.111 -0.016 0.058 -0.063 -0.055 0.087 0.054 -0.114 0.077 0.065 -0.005 0.014 0.215 -0.084 -0.096 -0.082 -0.003 0.023 0.110 -0.037 0.005 -0.046 0.091 0.085 0.011 0.092 -0.079 -0.167 0.002 0.046 0.008 -0.026 0.124 -0.027 -0.070 0.009 11 | 17 -0.063 -0.089 -0.083 0.109 -0.189 -0.049 -0.017 -0.046 -0.051 1.000 -0.069 0.045 0.120 0.014 -0.060 -0.037 -0.070 -0.004 0.001 0.044 0.048 0.020 -0.130 -0.073 0.021 0.167 0.067 0.065 -0.045 -0.042 -0.054 0.046 0.000 0.076 0.123 0.106 0.175 0.063 -0.026 -0.015 0.133 -0.020 -0.024 0.023 -0.114 -0.109 -0.026 -0.039 -0.076 0.062 12 | 18 0.034 0.082 -0.033 0.082 -0.036 0.087 -0.051 0.050 -0.050 -0.069 1.000 0.053 -0.104 -0.071 -0.016 -0.067 0.140 0.101 0.031 0.058 -0.051 0.056 -0.004 -0.054 -0.008 -0.016 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-0.088 -0.073 -0.082 -0.010 -0.037 0.069 -0.020 -0.033 -0.064 -0.107 -0.011 0.055 -0.094 0.000 -0.069 0.065 0.023 0.050 0.147 0.018 0.022 0.021 -0.043 0.008 0.128 19 | 24 -0.071 -0.047 -0.039 0.034 -0.027 -0.028 -0.000 -0.065 -0.063 -0.004 0.101 -0.014 -0.106 0.098 0.012 -0.158 0.054 1.000 0.108 0.070 -0.010 -0.050 0.014 0.041 -0.025 -0.005 -0.054 -0.014 -0.009 -0.117 0.035 0.095 -0.000 0.071 0.004 0.048 -0.078 0.061 0.024 -0.091 -0.093 -0.113 -0.019 0.067 0.033 0.085 0.057 -0.035 -0.125 0.162 20 | 25 -0.120 -0.123 -0.106 0.023 -0.075 0.049 -0.139 0.113 -0.055 0.001 0.031 -0.020 0.038 -0.106 -0.089 -0.099 0.039 0.108 1.000 0.025 -0.018 -0.072 0.056 -0.004 0.064 -0.153 0.028 -0.027 -0.041 -0.229 -0.033 0.053 -0.040 0.054 -0.048 -0.084 -0.099 0.099 0.049 0.083 0.144 -0.010 -0.054 -0.001 -0.030 0.050 0.062 -0.131 -0.007 0.106 21 | 26 -0.083 -0.017 0.056 -0.059 0.007 -0.047 -0.074 0.054 0.087 0.044 0.058 -0.137 -0.076 -0.021 0.007 -0.028 0.021 0.070 0.025 1.000 0.040 -0.040 -0.040 -0.038 -0.003 -0.040 0.032 -0.032 0.043 0.051 0.038 -0.036 -0.024 -0.017 -0.068 0.013 0.032 0.093 0.034 0.047 -0.086 0.012 -0.037 -0.114 -0.027 -0.025 0.025 -0.133 0.010 -0.045 22 | 27 0.091 0.105 0.123 -0.098 -0.067 -0.102 -0.095 -0.033 0.054 0.048 -0.051 -0.030 0.057 -0.056 -0.043 0.005 0.117 -0.010 -0.018 0.040 1.000 -0.085 -0.052 -0.120 -0.118 -0.000 -0.000 -0.128 0.010 -0.132 0.043 -0.143 0.127 -0.008 0.041 -0.002 0.123 0.059 -0.033 0.114 0.023 -0.066 -0.040 0.004 -0.154 -0.056 0.085 0.045 0.036 0.011 23 | 28 0.061 0.044 -0.040 0.073 -0.041 0.026 0.009 -0.040 -0.114 0.020 0.056 0.118 -0.032 -0.049 -0.060 0.032 0.014 -0.050 -0.072 -0.040 -0.085 1.000 -0.048 -0.037 -0.019 0.001 -0.124 0.046 0.030 0.094 -0.102 -0.064 -0.015 -0.027 -0.038 -0.064 -0.056 -0.121 0.024 0.017 0.104 0.006 -0.091 -0.008 -0.032 0.027 -0.047 -0.019 -0.094 -0.043 24 | 29 -0.015 0.017 0.014 0.004 0.003 -0.015 0.069 -0.046 0.077 -0.130 -0.004 -0.065 0.013 -0.053 -0.071 0.039 -0.105 0.014 0.056 -0.040 -0.052 -0.048 1.000 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0.010 0.030 0.124 -0.054 -0.126 -0.004 -0.062 -0.034 1.000 -0.171 -0.009 -0.119 -0.047 0.123 0.009 0.021 -0.067 -0.143 -0.033 -0.097 0.047 0.039 0.059 -0.007 -0.013 0.085 0.023 0.072 -0.013 -0.006 31 | 35 0.045 0.051 0.007 -0.006 0.023 -0.084 -0.052 0.008 -0.082 -0.042 -0.000 -0.025 0.016 -0.037 0.066 0.032 -0.037 -0.117 -0.229 0.051 -0.132 0.094 -0.128 0.041 0.008 0.001 -0.013 0.234 -0.171 1.000 0.042 -0.046 -0.089 0.059 -0.081 -0.070 -0.002 0.062 0.026 -0.045 -0.080 0.060 -0.035 -0.000 0.156 0.038 0.028 0.058 0.036 -0.129 32 | 36 -0.004 0.012 0.034 -0.026 0.012 0.042 -0.070 0.007 -0.003 -0.054 0.011 -0.020 -0.099 0.037 -0.024 -0.009 0.069 0.035 -0.033 0.038 0.043 -0.102 -0.050 -0.001 0.003 0.014 -0.023 -0.002 -0.009 0.042 1.000 0.073 -0.011 -0.114 0.010 0.028 -0.012 -0.062 0.016 -0.019 0.006 -0.014 0.097 0.021 -0.018 0.052 0.139 0.181 -0.082 0.069 33 | 37 -0.026 -0.061 -0.054 0.082 -0.035 0.026 0.058 -0.067 0.023 0.046 0.033 -0.041 -0.021 -0.015 0.006 -0.089 -0.020 0.095 0.053 -0.036 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