def test_profile_ct_bincounts(self): """ Test the ability of sortseq_tools.profile_ct to count frequencies """ print '\nIn test_profile_ct_bincounts...' library_files = glob.glob(self.input_dir+'library_*.txt') library_files += glob.glob(self.input_dir+'dataset_*.txt') good_bin_num = 2 bad_bin_num = 5 for file_name in library_files: print '\t%s ='%file_name, description = file_name.split('_')[-1].split('.')[0] executable = lambda:\ profile_ct.main(io.load_dataset(file_name),bin=good_bin_num) print '(bin=%d)'%good_bin_num, # If bad or library, then profile_ct.main should raise SortSeqError if ('_bad' in file_name) or ('library' in file_name): try: self.assertRaises(SortSeqError,executable) print 'badtype,', except: print 'good (ERROR).' raise # If good, then profile_ct.main should produce a valid df elif ('_good' in file_name) or ('dataset' in file_name): try: df = executable() qc.validate_profile_ct(df) out_file = self.output_dir+\ 'profile_ct_bin_%s.txt'%description io.write(df,out_file) io.load_profile_ct(out_file) print 'good,', except: print 'bad (ERROR).' raise # There are no other options else: raise SortSeqError('Unrecognized class of file_name.') # Should always raise an error if bin num is too large executable = lambda:\ profile_ct.main(io.load_dataset(file_name),bin=bad_bin_num) print '(bin=%d)'%bad_bin_num, try: self.assertRaises(SortSeqError,executable) print 'badtype.' except: print 'good (ERROR).' raise
def main(dataset_df, bin=None, start=0, end=None): """ Computes character frequencies (0.0 to 1.0) at each position Arguments: dataset_df (pd.DataFrame): A dataframe containing a valid dataset. bin (int): A bin number specifying which counts to use start (int): An integer specifying the sequence start position end (int): An integer specifying the sequence end position Returns: freq_df (pd.DataFrame): A dataframe containing counts for each nucleotide/amino acid character at each position. """ # Validate dataset_df qc.validate_dataset(dataset_df) # Compute counts counts_df = profile_ct.main(dataset_df, bin=bin, start=start, end=end) # Create columns for profile_freqs table ct_cols = [c for c in counts_df.columns if qc.is_col_type(c,'ct_')] freq_cols = ['freq_'+c.split('_')[1] for c in ct_cols] # Compute frequencies from counts freq_df = counts_df[ct_cols].div(counts_df['ct'], axis=0) freq_df.columns = freq_cols freq_df['pos'] = counts_df['pos'] # Validate as counts dataframe freq_df = qc.validate_profile_freq(freq_df,fix=True) return freq_df
def main(dataset_df, bin=None, start=0, end=None, err=False): """ Computes the mutation rate (0.0 to 1.0) at each position. Mutation rate is defined as 1.0 minus the maximum character frequency at a position. Errors are estimated using bionomial uncertainty Arguments: dataset_df (pd.DataFrame): A dataframe containing a valid dataset. bin (int): A bin number specifying which counts to use start (int): An integer specifying the sequence start position end (int): An integer specifying the sequence end position Returns: freq_df (pd.DataFrame): A dataframe containing results. """ # Validate dataset_df qc.validate_dataset(dataset_df) # Compute counts counts_df = profile_ct.main(dataset_df, bin=bin, start=start, end=end) # Create columns for profile_freqs table ct_cols = [c for c in counts_df.columns if qc.is_col_type(c,'ct_')] # Record positions in new dataframe mut_df = counts_df[['pos']].copy() # Compute mutation rate across counts max_ct = counts_df[ct_cols].max(axis=1) sum_ct = counts_df[ct_cols].sum(axis=1) mut = 1.0 - (max_ct/sum_ct) mut_df['mut'] = mut # Computation of error rate is optional if err: mut_err = np.sqrt(mut*(1.0-mut)/sum_ct) mut_df['mut_err'] = mut_err # Figure out which alphabet the cts dataframe specifies alphabet = ''.join([c.split('_')[1] for c in ct_cols]) seqtype = qc.alphabet_to_seqtype_dict[alphabet] wt_col = qc.seqtype_to_wtcolname_dict[seqtype] # Compute WT base at each position mut_df[wt_col] = 'X' for col in ct_cols: indices = (counts_df[col]==max_ct).values mut_df.loc[indices,wt_col] = col.split('_')[1] # Validate as counts dataframe mut_df = qc.validate_profile_mut(mut_df,fix=True) return mut_df
def test_profile_ct_totalcounts(self): """ Test the ability of sortseq_tools.profile_ct to count frequencies based on total count values """ print '\nIn test_profile_ct_totalcounts...' library_files = glob.glob(self.input_dir+'library_*.txt') library_files += glob.glob(self.input_dir+'dataset_*.txt') for file_name in library_files: print '\t%s ='%file_name, description = file_name.split('_')[-1].split('.')[0] executable = lambda: profile_ct.main(io.load_dataset(file_name)) # If good, then profile_ct.main should produce a valid df if '_good' in file_name: try: df = executable() qc.validate_profile_ct(df) out_file = self.output_dir+\ 'profile_ct_total_%s.txt'%description io.write(df,out_file) io.load_profile_ct(out_file) print 'good.' except: print 'bad (ERROR).' raise # If bad, then profile_ct.main should raise SortSeqError elif '_bad' in file_name: try: self.assertRaises(SortSeqError,executable) print 'badtype.' except: print 'good (ERROR).' raise # There are no other options else: raise SortSeqError('Unrecognized class of file_name.')
def test_profile_ct_seqslicing(self): """ Test the ability of sortseq_tools.profile_ct to slice sequences properly, and to raise the correct errors """ print '\nIn test_profile_ct_seqslicing...' library_files = glob.glob(self.input_dir+'library_*.txt') library_files += glob.glob(self.input_dir+'dataset_*.txt') for file_name in library_files: print '\t%s ='%file_name, description = file_name.split('_')[-1].split('.')[0] executable_good1 =\ lambda: profile_ct.main(io.load_dataset(file_name),\ start=2,end=10) executable_good2 =\ lambda: profile_ct.main(io.load_dataset(file_name),\ start=2) executable_good3 =\ lambda: profile_ct.main(io.load_dataset(file_name),\ end=2) executable_nopro =\ lambda: profile_ct.main(io.load_dataset(file_name),\ start=50,end=60) executable_bad1 =\ lambda: profile_ct.main(io.load_dataset(file_name),\ start=-1) executable_bad2 =\ lambda: profile_ct.main(io.load_dataset(file_name),\ end=100) executable_bad3 =\ lambda: profile_ct.main(io.load_dataset(file_name),\ start=20,end=10) # If good, then sequences will be valid if 'good' in file_name: try: df = executable_good1() io.write(df,self.output_dir+\ 'profile_ct_splice2-10_%s.txt'%description) executable_good2() executable_good3() self.assertRaises(SortSeqError,executable_bad1) self.assertRaises(SortSeqError,executable_bad2) self.assertRaises(SortSeqError,executable_bad3) if '_pro' in file_name: self.assertRaises(SortSeqError,executable_nopro) else: df = executable_nopro() print 'ok.' except: print 'ok (ERROR).' raise # If bad, then profile_ct.main should raise SortSeqError elif '_bad' in file_name: try: self.assertRaises(SortSeqError,executable_good1) self.assertRaises(SortSeqError,executable_good2) self.assertRaises(SortSeqError,executable_good3) self.assertRaises(SortSeqError,executable_nopro) self.assertRaises(SortSeqError,executable_bad1) self.assertRaises(SortSeqError,executable_bad2) self.assertRaises(SortSeqError,executable_bad3) print 'ok.' except: print 'not ok (ERROR).' raise # There are no other options else: raise SortSeqError('Unrecognized class of file_name.')
def main(dataset_df, err=False, method='naive',\ pseudocount=1.0, start=0, end=None): """ Computes the mutual information (in bits), at each position, between the character and the bin number. Arguments: dataset_df (pd.DataFrame): A dataframe containing a valid dataset. start (int): An integer specifying the sequence start position end (int): An integer specifying the sequence end position method (str): Which method to use to estimate mutual information Returns: info_df (pd.DataFrame): A dataframe containing results. """ # Validate dataset_df qc.validate_dataset(dataset_df) # Get number of bins bin_cols = [c for c in dataset_df.columns if qc.is_col_type(c,'ct_')] if not len(bin_cols) >= 2: raise SortSeqError('Information profile requires at least 2 bins.') bins = [int(c.split('_')[1]) for c in bin_cols] num_bins = len(bins) # Get number of characters seq_cols = [c for c in dataset_df.columns if qc.is_col_type(c,'seqs')] if not len(seq_cols)==1: raise SortSeqError('Must be only one seq column.') seq_col = seq_cols[0] seqtype = qc.colname_to_seqtype_dict[seq_col] alphabet = qc.seqtype_to_alphabet_dict[seqtype] ct_cols = ['ct_'+a for a in alphabet] num_chars = len(alphabet) # Get sequence length and check start, end numbers num_pos = len(dataset_df[seq_col][0]) if not (0 <= start < num_pos): raise SortSeqError('Invalid start==%d, num_pos==%d'%(start,num_pos)) if end is None: end = num_pos elif (end > num_pos): raise SortSeqError('Invalid end==%d, num_pos==%d'%(end,num_pos)) elif end <= start: raise SortSeqError('Invalid: start==%d >= end==%d'%(start,end)) # Record positions in new dataframe counts_df = profile_ct.main(dataset_df) info_df = counts_df.loc[start:(end-1),['pos']].copy() # rows from start:end info_df['info'] = 0.0 if err: info_df['info_err'] = 0.0 # Fill in 3D array of counts ct_3d_array = np.zeros([end-start, num_chars, num_bins]) for i, bin_num in enumerate(bins): # Compute counts counts_df = profile_ct.main(dataset_df, bin=bin_num) # Fill in counts table ct_3d_array[:,:,i] = counts_df.loc[start:(end-1),ct_cols].astype(float) # Compute mutual information for each position for i in range(end-start): # i only from start:end # Get 2D counts nxy = ct_3d_array[i,:,:] assert len(nxy.shape) == 2 # Compute mutual informaiton if err: mi, mi_err = info.estimate_mutualinfo(nxy,err=True,\ method=method,pseudocount=pseudocount) info_df.loc[i+start,'info'] = mi info_df.loc[i+start,'info_err'] = mi_err else: mi = info.estimate_mutualinfo(nxy,err=False,\ method=method,pseudocount=pseudocount) info_df.loc[i+start,'info'] = mi # Validate info dataframe info_df = qc.validate_profile_info(info_df,fix=True) return info_df