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 main(dataset_df, bin=None, start=0, end=None):
"""
Computes character counts 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:
counts_df (pd.DataFrame): A dataframe containing counts for each nucleotide/amino acid character at each position.
"""
# Validate dataset_df
qc.validate_dataset(dataset_df)
# Retrieve type of sequence
seq_cols = [c for c in dataset_df.columns if qc.is_col_type(c,'seqs')]
if not len(seq_cols)==1:
raise SortSeqError('Dataset dataframe must have only one seq colum.')
colname = seq_cols[0]
seqtype = qc.colname_to_seqtype_dict[colname]
alphabet = qc.seqtype_to_alphabet_dict[seqtype]
num_chars = len(alphabet)
# Retrieve sequence length
if not dataset_df.shape[0] > 1:
raise SortSeqError('Dataset dataframe must have at least one row.')
total_seq_length = len(dataset_df[colname].iloc[0])
# Validate start and end
if start<0:
raise SortSeqError('start=%d is negative.'%start)
elif start>=total_seq_length:
raise SortSeqError('start=%d >= total_seq_length=%d'%\
(start,total_seq_length))
if end is None:
end=total_seq_length
elif end<=start:
raise SortSeqError('end=%d <= start=%d.'%(end,start))
elif end>total_seq_length:
raise SortSeqError('end=%d > total_seq_length=%d'%\
(start,total_seq_length))
# Set positions
poss = pd.Series(range(start,end),name='pos')
num_poss = len(poss)
# Retrieve counts
if bin is None:
ct_col = 'ct'
else:
ct_col = 'ct_%d'%bin
if not ct_col in dataset_df.columns:
raise SortSeqError('Column "%s" is not in columns=%s'%\
(ct_col,str(dataset_df.columns)))
counts = dataset_df[ct_col]
# Compute counts profile
counts_array = np.zeros([num_poss,num_chars])
counts_cols = ['ct_'+a for a in alphabet]
for i,pos in enumerate(range(start,end)):
char_list = dataset_df[colname].str.slice(pos,pos+1)
counts_array[i,:] = [np.sum(counts[char_list==a]) for a in alphabet]
temp_df = pd.DataFrame(counts_array,columns=counts_cols)
counts_df = pd.concat([poss,temp_df],axis=1)
# Validate as counts dataframe
counts_df = qc.validate_profile_ct(counts_df,fix=True)
return counts_df
def merge_datasets(dataset_df_dict):
"""
Merges multiple datasets into one. Data from disparate files is merged via values in 'tag', seq', 'seq_rna', or 'seq_pro' columns (in order of preference, chosen according to availability). Each value in the 'ct' column of each dataset is recorded in the 'ct_[bin]' column of the final dataset. A total 'ct' column is then computed, and rows in the final dataset are sorted in descending order according to this.
Arguments:
dataset_df_dict (dict): Keys are bin numbers, values are dataset dataframes
Returns:
out_df (pd.DataFrame): A validated dataset dataframe
"""
# Make sure datasets were loaded
if not len(dataset_df_dict)>=1:
raise SortSeqError('No datasets were loaded')
# Determine index column. Must be same for all files
df = dataset_df_dict.values()[0]
if 'tag' in df.columns:
index_col = 'tag'
elif 'seq' in df.columns:
index_col = 'seq'
elif 'seq_rna' in df.columns:
index_col = 'seq_rna'
elif 'seq_pro' in df.columns:
index_col = 'seq_pro'
# Concatenate dataset dataframes
out_df = pd.DataFrame()
for b in dataset_df_dict.keys():
df = dataset_df_dict[b]
# Verify that dataframe has correct column
if not index_col in df.columns:
raise SortSeqError('\
Dataframe does not contain index_col="%s"'%index_col)
if not 'ct' in df.columns:
raise SortSeqError('\
Dataframe does not contain a "ct" column')
# Delete "ct_X" columns
for col in df.columns:
if qc.is_col_type(col,'ct_'):
del df[col]
# Add bin number to name of counts column.
df = df.rename(columns={'ct':'ct_%d'%b})
# Index dataset by index_col
df = df.groupby(index_col).sum()
# Concatenate
out_df = pd.concat([out_df,df],axis=1)
# Rename index as tag
out_df.reset_index(inplace=True)
out_df.rename(columns = {'index':index_col},inplace=True)
# Fill undefined counts with zero
out_df.fillna(value=0,inplace=True)
# Add 'ct' column, with proper counts
out_df['ct'] = 0
for col in out_df.columns:
if qc.is_col_type(col,'ct_'):
out_df['ct'] += out_df[col]
# Sort by 'ct' column
out_df.sort('ct',ascending=False,inplace=True)
out_df.reset_index(drop=True,inplace=True)
# Validate out_df as dataset and return it
out_df = qc.validate_dataset(out_df,fix=True)
return out_df
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
