def create_sample_data(input_seqs, sample_size):
"""
Takes a sample of size 'sample_size' from an input file
containing sequences and their associated expression levels,
and writes them to a separate file. The format of the first
2 lines of the resulting output file will be of the format:
"
number_of_seqs_in_file\t<###>
length_of_each_sequence\t<$$$>
"
where '<###>' is the number of sequences in the file, and
'<$$$>'is the length to which every sequence in the file is
padded.
Args:
-----
input_seqs (str) -- the absolute path of the input file
containing sequence and expression level data to sample.
sample_size (int) -- the number of samples to take from
the input file.
Returns:
-----
sample_data (str) -- the absolute path of the output file
containing the sample of sequence and expression level
data.
"""
# Assertions
assert isinstance(input_seqs, str), 'Input sequences file path must be\
passed as a string.'
assert os.path.exists(input_seqs), 'Input file does not exist.'
assert isinstance(sample_size, int), 'Number of sequences to sample must\
be passed as an integer.'
assert sample_size < get_seq_count(input_seqs), 'Sample size must be\
smaller than the number of sequences in the input file.'
# Functionality
# Define output file path
index = input_seqs.rfind('/') + 1
insert = str(sample_size) + '_from_'
sample_seqs = input_seqs[:index] + insert + input_seqs[index:]
# Pull sequences to create sample data
with smart_open(input_seqs, 'r') as inf:
inf.readline()
inf.readline() # skip the first 2 info lines
all_lines = inf.readlines()
for i in range(50):
lines = random.sample(all_lines, sample_size)
with smart_open(sample_seqs, 'w') as g:
for line in lines:
g.write(line)
# Write number and length of sequence info to top of resulting file
write_num_and_len_of_seqs_to_file(sample_seqs)
return sample_seqs
def write_num_and_len_of_seqs_to_file(input_seqs):
"""
Prepends the number of sequences and the length of the
sequences in an input file to the first 2 lines of the
file. Assumes sequences have been processed so that all
sequences have been padded to the same length. The first
2 lines of the input file will be in the following format
after writing the info to the file:
"
number_of_seqs_in_file\t<###>\n
length_of_each_sequence\t<$$$>\n
"
where '<###>' is the number of sequences in the file, and
'<$$$>'is the length to which every sequence in the file is
padded.
Args:
-----
input_seqs (str) -- the absolute path of the processed
input sequences to extract information from.
Returns:
-----
None
"""
# Assertions
assert isinstance(input_seqs, str), 'Absolute pathname must be passed\
as a string.'
assert os.path.exists(input_seqs), 'Input file does not exist.'
# Functionality
num_seqs = get_seq_count(input_seqs)
with smart_open(input_seqs, 'r') as f:
line = check_valid_line(f.readline())
if line == 'skip_line':
raise AssertionError('First line is not valid.')
seq, _ = separate_seq_and_el_data(line)
len_seqs = len(seq) # assumes all sequences padded to same length
with smart_open(input_seqs, 'r+') as f:
contents = f.read()
with smart_open(input_seqs, 'w+') as f:
line_to_append = 'number_of_seqs_in_file\t' + str(num_seqs) + '\n'
line_to_append += 'length_of_each_sequence\t' + str(len_seqs) + '\n'
if input_seqs.endswith('.gz'):
line_to_append = line_to_append.encode()
f.write(line_to_append + contents)
return
def get_max_min_mode_length_of_seqs(input_seqs):
"""
Returns the maximum, minimum, and modal length of the sequences
in a file containing input sequences.
Args:
-----
input_seqs (str) -- the absolute path of the file
containing the input sequences and their expression levels,
tab separated.
Returns:
-----
max_length (int) -- the length of the longest sequence in
the input file.
min_length (int) -- the length of the shortest sequence in
the input file.
modal_length (int) -- the most common sequence length of
the sequences in the input file.
"""
# Assertions
assert isinstance(input_seqs, str), 'Path name for input file must be \
passed as a string.'
assert os.path.exists(input_seqs), 'Input file does not exist.'
# Functionality
infile = smart_open(input_seqs, 'r')
seq_lengths = []
for line in infile:
line = check_valid_line(line)
if line == 'skip_line':
continue
seq, exp_level = separate_seq_and_el_data(line)
seq_lengths.append(len(seq))
max_length = max(seq_lengths)
min_length = min(seq_lengths)
modal_length = max(set(seq_lengths), key=seq_lengths.count)
# Close the input file.
infile.close()
return max_length, min_length, modal_length
def sort_by_exp_level(input_seqs):
"""
Given an input file of sequences tab separated with their
associated expression levels, sorts the lines of the file
by expression level, with the highest levels at the top of
the file.
Args:
-----
input_seqs (str) -- the absolute path of the input file
containing sequences to be sorted by expression level.
Returns:
-----
sorted_df (pandas.DataFrame) -- a data frame where rows
are sorted in descending order based on expression level.
"""
# Assertions
assert isinstance(input_seqs, str), 'Path name for input file must be \
passed as a string.'
assert os.path.exists(input_seqs), 'Input file does not exist.'
# Functionality
with smart_open(input_seqs, 'r') as f:
line = check_valid_line(f.readline())
seq1, _ = separate_seq_and_el_data(line)
line = check_valid_line(f.readline())
seq2, _ = separate_seq_and_el_data(line)
exp_seq1 = 'number_of_seqs_in_file'
exp_seq2 = 'length_of_each_sequence'
if seq1 == exp_seq1 and seq2 == exp_seq2:
skip = 2
else:
skip = 0
# Import data into a pandas data frame
df = pd.read_csv(input_seqs, sep='\t', names=['seq', 'el'], skiprows=skip)
# Sort it based on expression level
sorted_df = df.sort_values('el', ascending=False)
sorted_df = sorted_df.reset_index()
sorted_df = sorted_df.drop(columns='index')
return sorted_df
def get_num_and_len_of_seqs_from_file(input_seqs):
"""
Returns the number of sequences and length of sequences in an
input file. Assumes sequences have been processed so that all
sequences have been padded to the same length, and that the
file containing the process sequences have the first 2 lines in
the following format:
"
number_of_seqs_in_file\t<###>
length_of_each_sequence\t<$$$>
"
where '<###>' is the number of sequences in the file, and
'<$$$>'is the length to which every sequence in the file is
padded.
Args:
-----
input_seqs (str) -- the absolute path of the processed
input sequences to extract information from.
Returns:
-----
num_seqs (int) -- the number of sequences in input_seqs.
len_seqs (int) -- the length of the all the padded
sequences in the input file.
"""
# Assertions
assert isinstance(input_seqs, str), 'Absolute pathname must be passed\
as a string.'
assert os.path.exists(input_seqs), 'Input file does not exist.'
# Functionality
with smart_open(input_seqs, 'r') as f:
# Parse first line of file containing info about num of seqs in file
first_line = check_valid_line(f.readline())
assert first_line != 'skip_line', 'Invalid first line of file. Must\
be of the form: "number_of_seqs_in_file\t<###>" where <###> is the\
number of sequences in the file.'
token, num_seqs = separate_seq_and_el_data(first_line)
if num_seqs % 1 != 0:
raise ValueError('Number of sequences on first line must be\
an integer.')
assert token == 'number_of_seqs_in_file', 'First line of the input\
file must be of the form: "number_of_seqs_in_file\t<###>" where\
<###> is the number of sequences in the file.'
# Parse 2nd line of file containing info about length of seqs in file
second_line = check_valid_line(f.readline())
assert second_line != 'skip_line', 'Invalid second line of file.\
Must be of the form: "length_of_each_sequence\t<###>" where <###> is\
the length of every sequence in the file.'
token, len_seqs = separate_seq_and_el_data(second_line)
if len_seqs % 1 != 0:
raise ValueError('Sequence length on second line must be an\
integer.')
assert token == 'length_of_each_sequence', 'Second line of the input\
file must be of the form: "length_of_each_sequence\t<###>" where\
<###> is the length of every sequence in the file. Assumes\
homogeneity and/or padding of sequences.'
return num_seqs, len_seqs
def check_oligonucleotide_flanks(seq_infile, scaffold_type):
"""
Checks that all the oligonucleotide sequences in an input file
consist of the same sequences that flank the variable 80-mer
sequence. i.e. all sequences in the input file should be of the
form:
TGCATTTTTTTCACATC-(variable region)-GTTACGGCTGTT
Whereas the input sequences measured in the Abf1TATA scaffold
will be of the form:
TCACGCAGTATAGTTC-(variable region)-GGTTTATTGTTTATAAAAA
These flanking sequences are for in-lab sequencing purposes only,
so can be discarded when the 80-mer variable sequences are
inserted into the a scaffold sequence.
Args:
-----
seq_infile (str) -- the absolute path of the input file
containing all of the oligonucleotide sequences to be
checked, and their expression level values (tab separated).
scaffold_type (str) -- the scaffold type (pTpA or Abf1TATA)
in which the expression levels for the sequences in the
input file were measured.
Returns:
-----
incorrect_lines (list) -- returns a list of line numbers for
for sequences that contain incorrect flank sequences.
"""
# Assertions
assert isinstance(seq_infile, str), 'Absolute pathname must be passed \
as a string.'
assert isinstance(scaffold_type, str), 'Scaffold type must be passed as a\
string.'
assert scaffold_type == 'pTpA' or scaffold_type == 'Abf1TATA', 'Scaffold \
type must be specified as either pTpA or Abf1TATA.'
# Functionality
if scaffold_type == 'pTpA':
flank_A = 'TGCATTTTTTTCACATC'
flank_B = 'GGTTACGGCTGTT'
elif scaffold_type == 'Abf1TATA':
flank_A = 'TCACGCAGTATAGTTC'
flank_B = 'GGTTTATTGTTTATAAAAA'
infile = smart_open(seq_infile, 'r')
line_number = 0
incorrect_lines = []
for line in infile:
line_number += 1
line = check_valid_line(line)
if line == 'skip_line':
continue
seq, exp_level = separate_seq_and_el_data(line)
if seq.startswith(flank_A) and seq.endswith(flank_B):
pass
else:
incorrect_lines.append(line_number)
return incorrect_lines
def pull_homogeneous_seqs(input_seqs, scaffold_type=None):
"""
Pulls all sequences of the modal length (i.e. 110 bp for pTpA-type
sequences and 115 bp for Abf1TATA-type) from an input file and
writes them into an output file.
Args:
-----
input_seqs (str) -- the absolute pathname of the input file
containing all of the raw oligonucleotide sequences and
their expression levels, tab separated.
scaffold_type (str) -- the scaffold type (pTpA or Abf1TATA
for which the modal length is known to be 110 and 115
respectively) in which the expression levels for the
sequences in the input file were measured. If None, the
modal length is calculated manually. Default: None.
Returns:
-----
absolute_path (str) -- the absolute pathname of the output
file containing the sequences of modal length.
"""
# Assertions
assert isinstance(input_seqs, str), ('Input file pathname must be a'
'string.')
assert os.path.isfile(input_seqs), 'Input file does not exist!'
assert isinstance(scaffold_type, (str, type(None))), 'Scaffold type must\
be passed as a string.'
if isinstance(scaffold_type, str):
assert scaffold_type == 'pTpA' or scaffold_type == 'Abf1TATA', 'Scaff\
type must be specified as either pTpA or Abf1TATA, or else\
unspecified (in which case it takes value of None).'
# Functionality
# Defining the path name of the output file.
relative_path = 'example/'
time_stamp = get_time_stamp()
if scaffold_type is None:
relative_path += ('other_scaffolds/' + time_stamp +
'_homogeneous_seqs.txt')
else:
relative_path += (scaffold_type + '_data/' + time_stamp + '_' +
scaffold_type + '_homogeneous_seqs.txt')
absolute_path = os.path.join(ROOT_DIR, relative_path)
# Open the input and output files.
infile = smart_open(input_seqs, 'r')
output_seqs = smart_open(absolute_path, 'w')
# Retrieve modal length for sequences in input file.
if scaffold_type == 'pTpA':
modal_length = 110
elif scaffold_type == 'Abf1TATA':
modal_length = 115
else:
_, _, modal_length = get_max_min_mode_length_of_seqs(input_seqs)
# Find seqs in input file w/ modal length and write them to output file
for line in infile:
line = check_valid_line(line)
if line == 'skip_line':
continue
seq, exp_level = separate_seq_and_el_data(line)
if len(seq) == modal_length:
output_seqs.write(seq + '\t' + str(exp_level) + '\n')
else:
continue
# Close the input and output files.
infile.close()
output_seqs.close()
return absolute_path
def encode_sequences_with_method(input_seqs,
method='One-Hot',
scale_els=True,
model_type='1DCNN',
binarized_els=False):
"""
A wrapper function that encodes all of the sequences in an
input file according to the specified method, and returns
them in a numpy array, as well as returning the associated
expression levels in a separate numpy array.
Args:
-----
input_seqs (str) -- absolute path of the file containing
all of the input sequences to be encoded, tab-separated
withtheir associated expression levels. The first line of
the file must be the number of sequences in the file, of
the format: "number_of_seqs_in_file\t<###>" where <###> is
the number of sequences in the file. The second line in the
file must be the length to which all sequences are padded,
of the format: "length_of_each_sequence\t<###>" where <###>
is the length of every sequence in the file. Assumes
homogeneity and/or padding of sequences.
method (str) -- the method by which the sequence should be
encoded. Must choose from: 'One-Hot'. Default: 'One-Hot'
scale_els (bool) -- if True (default), scales all of the
expression levels in the output list exp_levels to between
-1 and 1, corresponding to the min and max values
respectively.
model_type (str) -- the type of model being used. Controls
the shape of the returned list that contains the encoded
sequences. Must be one of: '1DCNN' (for 1D-convolutional
net), '1DLOCCON' (for 1D-locally connected net), or 'LSTM'
(for Long-Short-Term-Memory net).
Returns:
-----
encoded_seqs (numpy.ndarray) -- a list of all the sequences
in the input file, encoded with the specified method. Each
element (i.e. each encoded sequence) is of type list. Shape
of this array depends on 'model_type'. For example, for an
input file containing 10000 sequences, each of length 257,
where the length of each base vector is 5 (corresponding to
bases A,T,G,C,N), the output shapes of encoded_seqs for each
model is as follows:
'1DCONV' ===> (10000, 257, 5)
'1DLOCCON' ===> (10000, 257, 5)
'LSTM' ===> (10000, 1, 1285) where 1285=257*5
exp_levels (numpy.ndarray) -- a list of all the expression
levels associated with the sequences. Each element (i.e.
each EL) is of type float. Values scaled to between -1 and
1 if argument 'scale_els=True'.
abs_max_el (float) -- the maximum expression level value in the
input file. Returned only if 'scale_els=True'.
"""
# Assertions
assert isinstance(input_seqs, str), 'TypeError: Input file path must be \
passed as a string.'
assert isinstance(method, str), 'TypeError: Specified method must be a \
a string.'
assert method in METHODS, 'Must specify one the method of encoding the \
sequence. Choose one of: %s' % (METHODS)
assert isinstance(scale_els, bool), 'scale_els argument must be passed\
as a bool.'
assert isinstance(model_type, str), 'model_type argument must be passed\
as a string.'
assert model_type in MODELS, 'Must specify model_type as one of the\
following: %s' % (MODELS)
# Functionality
# Open input file
infile = smart_open(input_seqs, 'r')
# Initialize output lists, preallocating dimensions for speed.
num_seqs, len_seq = organize.get_num_and_len_of_seqs_from_file(input_seqs)
encoded_seqs = np.zeros((int(num_seqs), int(len_seq), 5)).astype(int)
exp_levels = np.zeros(int(num_seqs))
# Encode sequences
line_number = -3
for line in infile:
line_number += 1
if line_number < 0:
continue # skip first 2 lines of the file
line = check_valid_line(line)
if line == 'skip_line':
continue # skip line if not a valid line
seq, exp_level = separate_seq_and_el_data(line)
# Encode with One-Hot method
if method == 'One-Hot':
try:
encoded_seq = one_hot_encode_sequence(seq)
except Exception:
raise AssertionError('Error on line %s' % (line_number))
# Encode with another method, i.e. embedding
else:
# Another encoding method will go here
# encoded_seq = another_encoding_method(seq)
pass
# Assign encoded sequences and expression levels to output arrays
encoded_seqs[line_number] = encoded_seq
exp_levels[line_number] = exp_level
# Close the input file
infile.close()
# Reshape array if needed as input to LSTM model
if model_type == 'LSTM':
encoded_seqs = encoded_seqs.reshape(int(num_seqs), -1)
encoded_seqs = encoded_seqs.reshape(int(num_seqs), 1,
(int(len_seq) * 5))
# Scale expression level values to between -1 and 1
if scale_els:
abs_max_el = abs(max(exp_levels, key=abs)) # the absolute max value
# numpy allows easy division of all elements at once
exp_levels = exp_levels / abs_max_el
else: # If no scaling required
abs_max_el = None
# If expression levels are binarized, convert them from float ---> int
if binarized_els:
exp_levels = exp_levels.astype(int)
return encoded_seqs, exp_levels, abs_max_el
def remove_flanks_from_all_seqs(input_seqs, scaffold_type='pTpA'):
"""
Removes all of the flanking sequences from an input file of
sequences and their expression levels (tab separated).
Example input file:
GSE104878_20160609_average_promoter_ELs_per_seq_pTpA_ALL.
shuffled.txt.gz from
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104878
Args:
-----
input_seqs (str) -- the absolute pathname of the file
containing all of the input sequences and their expression
levels (tab separated).
scaffold_type (str) -- the scaffold type (pTpA or Abf1TATA)
that the input sequences had their expression levels
measured in.
Returns:
-----
out_abs_path (str) -- the absolute path for the output file
containing all of the sequences with their flanks removed,
along with their expression levels (tab separated).
"""
# Assertions
assert isinstance(input_seqs, str), 'Input file pathname must be \
passed as a string.'
assert os.path.exists(input_seqs), 'Input file does not exist.'
assert isinstance(scaffold_type, str), 'Scaffold type must be passed \
as a string.'
assert scaffold_type == 'pTpA' or scaffold_type == 'Abf1TATA', 'Input \
scaffold type must be either pTpA or Abf1TATA.'
# Check that all of the flank sequences are the same in all
# sequences in the input file.
incorrect = organize.check_oligonucleotide_flanks(input_seqs,
scaffold_type)
assert len(incorrect) == 0, 'Not all sequences in input file have same \
flanking sequences. Error on line %s' % str(incorrect)
# Functionality
# Defining the pathname for the output file.
time_stamp = get_time_stamp() # Get unique time stamp for file naming
relative_path = ('example/' + scaffold_type + '_data/' + time_stamp + '_' +
scaffold_type + '_seqs_flanks_removed.txt')
absolute_path = os.path.join(ROOT_DIR, relative_path)
# Opening the input and output files.
infile = smart_open(input_seqs, 'r')
outfile = smart_open(absolute_path, 'w')
# Remove flanks and write data to output file.
for line in infile:
line = check_valid_line(line)
if line == 'skip_line':
continue
seq, exp_level = separate_seq_and_el_data(line)
deflanked_seq = remove_flanks_from_seq(seq, scaffold_type)
outfile.write(deflanked_seq + '\t' + str(exp_level) + '\n')
# Close the input and output files.
infile.close()
outfile.close()
return absolute_path
def pad_sequences(input_seqs, pad_front=False, extra_padding=0):
"""
Pads sequences in an input file to the length of the longest
sequence in the file, plus any extra padding if specified.
Pads the sequences at either the front or the back, with 'N'
characters.
Args:
-----
input_seqs (str) -- the absolute path of the input file
containing the sequences to be padded and their associated
expression levels, tab separated.
pad_front (bool) -- If True, will add padding to the front
of the sequences. If False (default) pads sequences at the
end (i.e. the RHS of the sequences).
extra_padding (int) -- The number of extra null bases to
add onto the front/back of the sequence
Returns:
-----
absolute_path (str) -- the absolute path of the output file
containing all of the padded sequences and their associated
expression levels, tab separated.
"""
# Assertions
assert isinstance(input_seqs, str), 'Pathname of input file must be \
passed as a string.'
assert os.path.exists(input_seqs), 'File does not exist.'
assert isinstance(pad_front, bool), 'The pad_front variable must be \
passed as a bool.'
assert isinstance(extra_padding, int), 'The amount of extra padding must \
be passed as an integer.'
assert extra_padding >= 0, 'The amount of extra padding must be passed as \
a non-negative integer.'
# Functionality
# Define and open the output file
absolute_path = input_seqs.replace('.txt', '_padded.txt')
outfile = smart_open(absolute_path, 'w')
# Retrieve input sequences, pad them, and write them to output file
max_length, _, _ = organize.get_max_min_mode_length_of_seqs(input_seqs)
pad_length = max_length + extra_padding
with smart_open(input_seqs) as f:
for line in f:
line = check_valid_line(line)
if line == 'skip_line':
continue
seq, exp_level = separate_seq_and_el_data(line)
difference = pad_length - len(seq)
if difference == 0: # No need for padding
padded_seq = seq
else: # Need to pad
padding_seq = 'P' * difference
if pad_front:
padded_seq = padding_seq + seq
else: # pad the end of the sequence
padded_seq = seq + padding_seq
outfile.write(padded_seq + '\t' + str(exp_level) + '\n')
# Close the output file
outfile.close()
return absolute_path
def insert_all_seq_into_one_scaffold(input_seqs, scaffold_type='pTpA'):
"""
Takes an input file containing N sequences and inserts them into
a single scaffold sequence, outputting the N unique promoter
sequences to an output file along with their expression levels
(tab separated).
Args:
-----
input_seqs (str) -- the absolute path for the input file
containing all the oligonucleotide sequences to be inserted
into the single scaffold sequence. All sequences must be of
the same length as the scaffold variable region.
scaffold_type (str) -- the scaffold type (pTpA or Abf1TATA)
that the input sequences had their expression levels
measured in. Default: 'pTpA'.
Returns:
-----
absolute_path (str) -- the absolute path for the output file
containing all of the complete promoter sequences (where each
input sequence has been inserted into the scaffold sequence).
"""
# Assertions
assert isinstance(input_seqs, str), 'TypeError: pathname for input file \
must be a string.'
assert isinstance(scaffold_type, str), 'Scaffold type must be passed as \
a string.'
assert scaffold_type == 'pTpA' or scaffold_type == 'Abf1TATA', 'Scaffold \
type must either be passed as "pTpA" or "Abf1TATA".'
# Functionality
time_stamp = get_time_stamp() # get time stamp for unique file naming
relative_path = ('example/' + scaffold_type + '_data/' + time_stamp + '_' +
scaffold_type + '_seqs_inserted_into_scaffold.txt')
absolute_path = os.path.join(ROOT_DIR, relative_path)
# Open input and output files
infile = smart_open(input_seqs, 'r')
outfile = smart_open(absolute_path, 'w')
# Retrieve the scaffold sequence
scaff_directory = 'example/' + scaffold_type + '_data/'
scaff_rel_path = scaff_directory + scaffold_type + '_scaffold.txt'
scaff_abs_path = os.path.join(ROOT_DIR, scaff_rel_path)
scaff_file = smart_open(scaff_abs_path, 'r')
scaffold = scaff_file.readline().replace('\n', '')
# Insert sequences into scaffold and write data to output file
for line in infile:
line = check_valid_line(line)
if line == 'skip_line':
continue
seq, exp_level = separate_seq_and_el_data(line)
complete_seq = insert_seq_into_scaffold(seq, scaffold)
outfile.write(complete_seq + '\t' + str(exp_level) + '\n')
# Close the input, output, and scaffold files.
infile.close()
outfile.close()
scaff_file.close()
return absolute_path
def process_raw_data(input_seqs,
scaffold_type=None,
percentile=None,
binarize_els=True,
homogeneous=False,
deflank=True,
insert_into_scaffold=True,
extra_padding=0,
pad_front=False,
report_loss=True,
report_times=True,
remove_files=True,
create_sample_of_size=None):
"""
A wrapper function that:
Takes raw data as retrieved from Carl de Boer's publication
at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104878,
and processes the sequences according to the custom arguments,
pads them to same length, and writes them to an output file
along with their expression levels (tab separated). The end of
the file contains comments specifying the number of sequences
in the file and the lengths of the padded sequences.
Args:
-----
input_seqs (str) -- the absolute pathname of the file that
contains all of the input sequences and their expression
levels (tab separated).
scaffold_type (str) -- the scaffold type (pTpA or Abf1TATA)
that the input sequences had their expression levels
measured in.
percentile (float) -- the proportion of the raw input data
to extract from the sequences with the highest and lowest
expression levels. i.e if 'percentile=0.1' then the top
10 % of sequences with highest expression levels, and the
bottom 10 % of sequences with lowest expression levels will
be extracted from the raw input data. The resulting data
file will contain ~ 20 % of the data as the raw input data.
binarize_els (bool) -- if (and only if) a 'percentile'
value is passed, this argument determines whether the
expression level values (Els) will be binarized or not. If
True (defualt), sequences with ELs in the top percentile
will have their ELs binarized to 1, and sequences with ELs
in the bottom percentile will have their ELs binarized
to 0.
homogeneous (bool) -- if True, only sequences of modal
length will be processed. If False, all sequences will be
processed regardless of length. Default: False.
deflank (bool) -- if True, removes the constant flanking
regions of the input sequences. Default: True.
insert_into_scaffold (bool) -- if True inserts the input
sequences into the appropriate scaffold. If False, the
sequences are encoded as they are. Default: True.
extra_padding (int) -- the number of 'P' characters greater
than the maximum sequence length to pad each sequence to.
Default: 0.
pad_front (bool) -- whether to pad out the front (left hand
side) or end (right hand side) of the sequences. If True,
pads the front. Default: False (will pad the end).
report_loss (bool) -- if True, reports the number of lines
of data lost at each step in the process. Default: False.
report_times (bool) -- if True, reports the time each step
in the cleaning process takes. Default: False.
remove_files (bool) -- if True, will remove intermediate
files created in the process of processing raw data.
Default: False (i.e. intermediary files will be kept).
create_sample_of_size (int) -- if a number is passed, a
sample of this size will be taken by pseudo-random from
the file containing processed data, and written to a
separate file.
Returns:
-----
processed_data (str) -- the absolute path for the file
containing processed sequences along with their expression
levels.
"""
# Assertions
assert isinstance(input_seqs, str), ('Input file path name must be '
'passed as a string.')
assert os.path.exists(input_seqs), 'Input file does not exist.'
assert isinstance(scaffold_type, str), ('Scaffold type must be passed as '
'a string if specified.')
assert scaffold_type == 'pTpA' or scaffold_type == 'Abf1TATA', 'Scaffold \
type must be specified as either "pTpA" or "Abf1TATA".'
assert isinstance(percentile, (float, type(None))), ('The "percentile" '
'argument must be '
'passed as a float.')
if percentile is not None:
assert percentile < 0.5, '"percentile" must be less that 0.5'
assert isinstance(homogeneous, bool), ('The homogeneous argument must be '
'passed as a bool.')
assert isinstance(deflank, bool), ('The deflank argument must be passed '
'as a bool.')
assert isinstance(insert_into_scaffold, bool), ('insert_into_scaffold '
'argument must be passed '
'as a bool.')
assert isinstance(extra_padding, int), ('The number of extra vectors to '
'pad each sequence by should be '
'passed as an integer.')
assert extra_padding >= 0, ('extra_padding must be passed as a non-'
'negative integer.')
assert isinstance(pad_front, bool), ('The pad_front argument must be '
'passed as a bool.')
assert isinstance(report_loss, bool), ('The report_loss argument must be '
'passed as a bool.')
assert isinstance(report_times, bool), ('The report_times argument must '
'be passed as a bool.')
assert isinstance(remove_files, bool), ('The remove_files argument must '
'be passed as a bool.')
if create_sample_of_size is not None:
assert isinstance(create_sample_of_size, int), ('Sample size must be '
'passed as an int')
# Functionality
print('Starting processing of raw data...')
raw_data = input_seqs
# Define final output file path
time_stamp = get_time_stamp()
relative_path = 'example/processed_data/' + time_stamp
processed_data = os.path.join(ROOT_DIR, relative_path)
# Create log file to write reports to
if report_loss or report_times:
report = smart_open(processed_data + '_process_report' + '.txt', 'w')
# Initialize custom operations if specified (i.e loss + timing reports)
if report_loss:
loss_report = {}
loss_report['Raw Data'] = get_seq_count(input_seqs)
if report_times:
t_init = t.time()
t0 = t_init
if remove_files:
created_files = [] # keep track of the intermediate files created.
# Pull out the top and bottom percentiles of data
if percentile is not None:
print('Pulling out the top and bottom percentiles...')
df = organize.sort_by_exp_level(input_seqs)
df = organize.discard_mid_data(df, percentile=percentile)
processed_data += '_percentiles'
if binarize_els:
print('Binarizing expression levels...')
df = organize.binarize_data(df)
processed_data += '_els_binarized'
input_seqs = organize.write_df_to_file(df)
if report_loss:
loss_report['Percentile Seqs'] = get_seq_count(input_seqs)
if report_times:
t1 = t.time()
text = '\tFile created in %s s' % (t1 - t0)
print(text)
report.write('Top & bottom percentiles pulled...\n' + text + '\n')
t0 = t1
if remove_files:
created_files.append(input_seqs)
# Create new file of only homogeneous (same length) seqs
if homogeneous:
print('Pulling homogeneous sequences from input file...')
input_seqs = organize.pull_homogeneous_seqs(input_seqs, scaffold_type)
processed_data += '_homogeneous'
if report_loss:
loss_report['Homogeneous Seqs'] = get_seq_count(input_seqs)
if report_times:
t1 = t.time()
text = '\tFile created in %s s' % (t1 - t0)
print(text)
report.write('Homogeneous sequences pulled...\n' + text + '\n')
t0 = t1
if remove_files:
created_files.append(input_seqs)
# Remove all of the flanking regions from the input sequences
if deflank:
print('Removing flank regions from sequences...')
input_seqs = build.remove_flanks_from_all_seqs(input_seqs,
scaffold_type)
processed_data += '_deflanked'
if report_loss:
loss_report['Deflanked Seqs'] = get_seq_count(input_seqs)
if report_times:
t1 = t.time()
text = '\tFile created in %s s' % (t1 - t0)
print(text)
report.write('Sequences deflanked...\n' + text + '\n')
t0 = t1
if remove_files:
created_files.append(input_seqs)
processed_data += '_sequences'
# Insert sequences into appropriate scaffold
if insert_into_scaffold:
print('Inserting sequences into %s scaffold...' % (scaffold_type))
input_seqs = build.insert_all_seq_into_one_scaffold(
input_seqs, scaffold_type)
processed_data += '_inserted_into_%s_scaffold' % (scaffold_type)
if report_loss:
loss_report['Scaffold-Inserted Seqs'] = get_seq_count(input_seqs)
if report_times:
t1 = t.time()
text = '\tFile created in %s s' % (t1 - t0)
print(text)
report.write('Seqs inserted into ' + scaffold_type +
'scaffold...\n')
report.write(text + '\n')
t0 = t1
if remove_files:
created_files.append(input_seqs)
# Pad sequences
if homogeneous and extra_padding == 0:
pass
else:
print('Padding sequences...')
input_seqs = build.pad_sequences(input_seqs,
pad_front=pad_front,
extra_padding=extra_padding)
if not homogeneous: # then they will have been padded
processed_data += '_padded_at'
if pad_front:
processed_data += '_front'
else:
processed_data += '_back'
if extra_padding != 0:
processed_data += '_%s_extra' % (extra_padding)
if report_loss:
loss_report['Padded Seqs'] = get_seq_count(input_seqs)
if report_times:
t1 = t.time()
text = '\tFile created in %s s' % (t1 - t0)
print(text)
report.write('Padded sequences...\n')
report.write(text + '\n')
t0 = t1
# Remove intermediate files created in the process
if remove_files:
created_files.append(input_seqs)
# Rename the final output file to reflect how data has been cleaned.
processed_data += '_with_exp_levels.txt'
# Report end of process and print final output file locations.
if input_seqs != raw_data: # i.e. if data has been processed in some way
os.rename(input_seqs, processed_data)
# Report end of process and print absolute path of processed data.
text = ('\nRaw data successfully processed.\nLocation: %s\n' %
(processed_data))
print(text)
if report_loss or report_times:
report.write(text)
else: # If no processing was performed.
text = '\nNo processing performed.\n'
text += 'Change processing specifications and try again.'
print(text)
report.write(text + '\n')
text = 'Raw data remains unchanged.'
print(text)
report.write(text + '\n')
text = 'Location : %s' % (raw_data)
print(text)
if report_loss or report_times:
report.write(text + '\n')
# Write the number of seqs and length of seqs to the start of file
organize.write_num_and_len_of_seqs_to_file(processed_data)
# Report loss
if report_loss:
report.write('\nLine counts at each step of the process:\n')
for category in loss_report.keys():
curr_count = loss_report[category]
if category == 'Raw Data':
report.write('\t%s : %s\n' % (category, curr_count))
prev_count = curr_count
else:
report.write('\t%s : %s (%s lines lost since last step)\n' %
(category, curr_count, (prev_count - curr_count)))
prev_count = curr_count
# Remove intermediate files
if remove_files:
print('\nRemoving intermediate files...')
organize.remove_file_list(created_files)
print('Files successfully removed.')
print('Process complete.')
# Report total time taken
if report_times:
t_final = t.time()
text = '\nTotal processing time : %s s' % (t_final - t_init)
print(text)
report.write(text)
print('Please find the process report in the same directory as the'
' output file for reports of data losses and timings.')
if report_times or report_loss:
report.close()
# Create sample data
if create_sample_of_size is not None:
size = create_sample_of_size
print('\n\nCreating sample of size %s ...' % str(size))
sample_seqs = organize.create_sample_data(processed_data, size)
print('\nSample data successfully created.')
print('\nLocation: %s \n' % (sample_seqs))
return processed_data
