def __init__(self,
email=None,
upload=None,
log_name='seqc.log',
terminate=True,
debug=False):
"""Execution context for on-server code execution with defined clean-up practices.
This is the cognate context manager to aws_setup, and when used with aws_setup(),
ensures that all errors are captured and, if desirable, instances can be properly
terminated.
usage:
------
with aws_execute(email=addr, upload=s3://my_bucket/my_key/, terminate=True):
<execute code here>
:param email: email address to send the log containing execution summary and any
errors
:param upload: s3 location to upload the log to
:param log_name: name of the log object
:param terminate: if True, terminate the instance upon completion, provided that
no errors occurred.
:param debug: if True, instance is not terminated when an error is raised
"""
self.email = email
self.log_name = log_name # changed mount point to ~, cwd is good.
self.terminate = terminate # only terminate if no errors occur
self.aws_upload_key = upload
self.err_status = False
self.mutt = verify.executables('mutt')[
0] # unpacking necessary for singleton
self.debug = debug
def run(args) -> None:
"""Run SEQC on the files provided in args, given specifications provided on the
command line
:param args: parsed argv, produced by seqc.parser(). This function is only called
when args.subprocess_name is "run".
"""
# import inside module for pickle functionality
# top 2 only needed for post-filtering
import os
import multiprocessing
from seqc import log, ec2, platforms, io
from seqc.sequence import fastq
from seqc.alignment import star
from seqc.email_ import email_user
from seqc.read_array import ReadArray
from seqc.core import verify, download
from seqc import filter
from seqc.sequence.gtf import GeneIntervals
from seqc.summary.summary import Section, Summary
import numpy as np
import scipy.io
from shutil import copyfile
from seqc.summary.summary import MiniSummary
from seqc.stats.mast import run_mast
import logging
logger = logging.getLogger('weasyprint')
logger.handlers = [] # Remove the default stderr handler
logger.setLevel(100)
logger.addHandler(logging.FileHandler('weasyprint.log'))
def determine_start_point(arguments) -> (bool, bool, bool):
"""
determine where seqc should start based on which parameters were passed.
:param arguments: Namespace object, result of ArgumentParser.parse_args()
:returns merge, align, process_bamfile: indicates whether merging, alignment, and
processing bamfiles should be executed.
"""
if arguments.read_array:
return False, False, False
if arguments.alignment_file:
return False, False, True
if arguments.merged_fastq:
return False, True, True
else:
return True, True, True
def download_input(dir_, arguments):
"""parse input arguments and download any necessary data
:param str dir_: directory to download data to
:param arguments: namespace object from argparse
:return args: updated namespace object reflecting local file paths of downloaded
files
"""
# download basespace data if necessary
if arguments.basespace:
arguments.barcode_fastq, arguments.genomic_fastq = io.BaseSpace.download(
arguments.platform, arguments.basespace, dir_, arguments.basespace_token)
# check for remote fastq file links
arguments.genomic_fastq = download.s3_data(
arguments.genomic_fastq, dir_ + '/genomic_fastq/')
arguments.barcode_fastq = download.s3_data(
arguments.barcode_fastq, dir_ + '/barcode_fastq/')
# get merged fastq file, unzip if necessary
arguments.merged_fastq = (
download.s3_data([arguments.merged_fastq], dir_ + '/')[0] if
arguments.merged_fastq is not None else None)
# check if the index must be downloaded
if any((arguments.alignment_file, arguments.read_array)):
index_link = arguments.index + 'annotations.gtf'
else:
index_link = arguments.index
download.s3_data([index_link], dir_ + '/index/')
arguments.index = dir_ + '/index/'
# check if barcode files must be downloaded
arguments.barcode_files = download.s3_data(
arguments.barcode_files, dir_ + '/barcodes/')
# check if alignment_file needs downloading
if arguments.alignment_file:
arguments.alignment_file = download.s3_data(
[arguments.alignment_file], dir_ + '/')[0]
# check if readarray needs downloading
if arguments.read_array:
arguments.read_array = download.s3_data([arguments.read_array], dir_ + '/')[0]
return arguments
def merge_fastq_files(
technology_platform, barcode_fastq: [str], output_stem: str,
genomic_fastq: [str]) -> (str, int):
"""annotates genomic fastq with barcode information; merging the two files.
:param technology_platform: class from platforms.py that defines the
characteristics of the data being processed
:param barcode_fastq: list of str names of fastq files containing barcode
information
:param output_stem: str, stem for output files
:param genomic_fastq: list of str names of fastq files containing genomic
information
:returns str merged_fastq: name of merged fastq file
"""
log.info('Merging genomic reads and barcode annotations.')
merged_fastq = fastq.merge_paired(
merge_function=technology_platform.merge_function,
fout=output_stem + '_merged.fastq',
genomic=genomic_fastq,
barcode=barcode_fastq)
# delete genomic/barcode fastq files after merged.fastq creation
log.info('Removing original fastq file for memory management.')
delete_fastq = ' '.join(['rm'] + genomic_fastq + barcode_fastq)
io.ProcessManager(delete_fastq).run_all()
return merged_fastq
def align_fastq_records(
merged_fastq, dir_, star_args, star_index, n_proc,
aws_upload_key) -> (str, str, io.ProcessManager):
"""
Align fastq records.
:param merged_fastq: str, path to merged .fastq file
:param dir_: str, stem for output files
:param star_args: dict, extra keyword arguments for STAR
:param star_index: str, file path to directory containing STAR index
:param n_proc: int, number of STAR processes to initiate
:param aws_upload_key: str, location to upload files, or None if seqc was
initiated from a merged fastq file.
:return bamfile, input_data, upload_manager: (str, str, io.ProcessManager)
name of .sam file containing aligned reads, indicator of which data was used as
input, and a ProcessManager for merged fastq files
"""
log.info('Aligning merged fastq records.')
alignment_directory = dir_ + '/alignments/'
os.makedirs(alignment_directory, exist_ok=True)
if star_args is not None:
star_kwargs = dict(a.strip().split('=') for a in star_args)
else:
star_kwargs = {}
bamfile = star.align(
merged_fastq, star_index, n_proc, alignment_directory,
**star_kwargs)
if aws_upload_key:
log.info('Gzipping merged fastq file.')
if pigz:
pigz_zip = "pigz --best -k -f {fname}".format(fname=merged_fastq)
else:
pigz_zip = "gzip -kf {fname}".format(fname=merged_fastq)
pigz_proc = io.ProcessManager(pigz_zip)
pigz_proc.run_all()
pigz_proc.wait_until_complete() # prevents slowing down STAR alignment
merged_fastq += '.gz' # reflect gzipped nature of file
log.info('Uploading gzipped merged fastq file to S3.')
merge_upload = 'aws s3 mv {fname} {s3link}'.format(
fname=merged_fastq, s3link=aws_upload_key)
upload_manager = io.ProcessManager(merge_upload)
upload_manager.run_all()
else:
log.info('Removing merged fastq file for memory management.')
rm_merged = 'rm %s' % merged_fastq
io.ProcessManager(rm_merged).run_all()
upload_manager = None
return bamfile, upload_manager
def create_read_array(bamfile, index, aws_upload_key, min_poly_t,
max_transcript_length):
"""Create or download a ReadArray object.
:param max_transcript_length:
:param str bamfile: filename of .bam file
:param str index: directory containing index files
:param str aws_upload_key: key where aws files should be uploaded
:param int min_poly_t: minimum number of poly_t nucleotides for a read to be valid
:returns ReadArray, UploadManager: ReadArray object, bamfile ProcessManager
"""
log.info('Filtering aligned records and constructing record database.')
# Construct translator
translator = GeneIntervals(
index + 'annotations.gtf', max_transcript_length=max_transcript_length)
read_array = ReadArray.from_alignment_file(
bamfile, translator, min_poly_t)
# converting sam to bam and uploading to S3, else removing bamfile
if aws_upload_key:
log.info('Uploading bam file to S3.')
upload_bam = 'aws s3 mv {fname} {s3link}{prefix}_Aligned.out.bam'.format(
fname=bamfile, s3link=aws_upload_key, prefix=args.output_prefix)
print(upload_bam)
upload_manager = io.ProcessManager(upload_bam)
upload_manager.run_all()
else:
log.info('Removing bamfile for memory management.')
rm_bamfile = 'rm %s' % bamfile
io.ProcessManager(rm_bamfile).run_all()
upload_manager = None
return read_array, upload_manager
# ######################## MAIN FUNCTION BEGINS HERE ################################
log.setup_logger(args.log_name)
with ec2.instance_clean_up(
email=args.email, upload=args.upload_prefix, log_name=args.log_name,
debug=args.debug, terminate=args.terminate
):
pigz, mutt = verify.executables('pigz', 'mutt')
if mutt:
log.notify('mutt executable identified, email will be sent when run '
'terminates. ')
else:
log.notify('mutt was not found on this machine; an email will not be sent to '
'the user upon termination of SEQC run.')
# turn off lower coverage filter for 10x
if (args.platform == "ten_x") or (args.platform == "ten_x_v2") or (args.platform == "ten_x_v3"):
args.filter_low_coverage = False
max_insert_size = args.max_insert_size
if args.filter_mode == "scRNA-seq":
# for scRNA-seq
if (args.platform == "ten_x") or (args.platform == "ten_x_v2") or (args.platform == "ten_x_v3"):
# set max_transcript_length (max_insert_size) = 10000
max_insert_size = 10000
log.notify("Full length transcripts are used for read mapping in 10x data.")
elif args.filter_mode == "snRNA-seq":
# for snRNA-seq
# e.g. 2304700 # hg38
# e.g. 4434881 # mm38
max_insert_size = args.max_insert_size
else:
# all others
max_insert_size = args.max_insert_size
log.notify("max_insert_size is set to {}".format(max_insert_size))
log.args(args)
output_dir, output_prefix = os.path.split(args.output_prefix)
if not output_dir:
output_dir = '.'
# check if the platform name provided is supported by seqc
# todo move into verify for run
platform_name = verify.platform_name(args.platform)
platform = platforms.AbstractPlatform.factory(platform_name) # returns platform
n_processes = multiprocessing.cpu_count() - 1 # get number of processors
merge, align, process_bamfile = determine_start_point(args)
args = download_input(output_dir, args)
if args.platform == "in_drop_v5":
platform = platform.build_cb2_barcodes(args.barcode_files)
log.notify("Built cb2 barcode hash for v5 barcodes.")
if merge:
if args.min_poly_t is None: # estimate min_poly_t if it was not provided
args.min_poly_t = filter.estimate_min_poly_t(
args.barcode_fastq, platform)
log.notify('Estimated min_poly_t={!s}'.format(args.min_poly_t))
args.merged_fastq = merge_fastq_files(
platform, args.barcode_fastq, args.output_prefix, args.genomic_fastq)
# SEQC was started from input other than fastq files
if args.min_poly_t is None:
args.min_poly_t = 0
log.notify('Warning: SEQC started from step other than unmerged fastq with '
'empty --min-poly-t parameter. Continuing with --min-poly-t 0.')
if align:
upload_merged = args.upload_prefix if merge else None
args.alignment_file, manage_merged = align_fastq_records(
args.merged_fastq, output_dir, args.star_args,
args.index, n_processes, upload_merged)
else:
manage_merged = None
if process_bamfile:
upload_bamfile = args.upload_prefix if align else None
ra, manage_bamfile, = create_read_array(
args.alignment_file, args.index, upload_bamfile, args.min_poly_t,
max_insert_size)
else:
manage_bamfile = None
ra = ReadArray.load(args.read_array)
# create the first summary section here
status_filters_section = Section.from_status_filters(ra, 'initial_filtering.html')
sections = [status_filters_section]
# Skip over the corrections if read array is specified by the user
if not args.read_array:
# Correct barcodes
log.info('Correcting barcodes and estimating error rates.')
error_rate = platform.apply_barcode_correction(ra, args.barcode_files)
# Resolve multimapping
log.info('Resolving ambiguous alignments.')
mm_results = ra.resolve_ambiguous_alignments()
# correct errors
log.info('Identifying RMT errors.')
platform.apply_rmt_correction(ra, error_rate)
# Apply low coverage filter
if platform.filter_lonely_triplets:
log.info('Filtering lonely triplet reads')
ra.filter_low_coverage(alpha=args.low_coverage_alpha)
log.info('Saving read array.')
ra.save(args.output_prefix + '.h5')
# Summary sections
# create the sections for the summary object
sections += [
Section.from_cell_barcode_correction(ra, 'cell_barcode_correction.html'),
Section.from_rmt_correction(ra, 'rmt_correction.html'),
Section.from_resolve_multiple_alignments(mm_results, 'multialignment.html')]
# create a dictionary to store output parameters
mini_summary_d = dict()
# filter non-cells
log.info('Creating counts matrix.')
sp_reads, sp_mols = ra.to_count_matrix(
sparse_frame=True, genes_to_symbols=args.index + 'annotations.gtf')
# Save sparse matrices
log.info('Saving sparse matrices')
scipy.io.mmwrite(args.output_prefix + '_sparse_read_counts.mtx', sp_reads.data)
scipy.io.mmwrite(args.output_prefix + '_sparse_molecule_counts.mtx', sp_mols.data)
# Indices
df = np.array([np.arange(sp_reads.shape[0]), sp_reads.index]).T
np.savetxt(
args.output_prefix + '_sparse_counts_barcodes.csv', df,
fmt='%d', delimiter=',')
# Columns
df = np.array([np.arange(sp_reads.shape[1]), sp_reads.columns]).T
np.savetxt(
args.output_prefix + '_sparse_counts_genes.csv', df,
fmt='%s', delimiter=',')
log.info('Creating filtered counts matrix.')
cell_filter_figure = args.output_prefix + '_cell_filters.png'
# By pass low count filter for mars seq
sp_csv, total_molecules, molecules_lost, cells_lost, cell_description = (
filter.create_filtered_dense_count_matrix(
sp_mols, sp_reads, mini_summary_d, plot=True, figname=cell_filter_figure,
filter_low_count=platform.filter_low_count,
filter_mitochondrial_rna=args.filter_mitochondrial_rna,
filter_low_coverage=args.filter_low_coverage,
filter_low_gene_abundance=args.filter_low_gene_abundance))
# Output files
files = [cell_filter_figure,
args.output_prefix + '.h5',
args.output_prefix + '_sparse_read_counts.mtx',
args.output_prefix + '_sparse_molecule_counts.mtx',
args.output_prefix + '_sparse_counts_barcodes.csv',
args.output_prefix + '_sparse_counts_genes.csv']
# Summary sections
# create the sections for the summary object
sections += [
Section.from_cell_filtering(cell_filter_figure, 'cell_filtering.html'),
Section.from_run_time(args.log_name, 'seqc_log.html')]
# get alignment summary
if os.path.isfile(output_dir + '/alignments/Log.final.out'):
os.rename(output_dir + '/alignments/Log.final.out',
output_dir + '/' + args.output_prefix + '_alignment_summary.txt')
# Upload files and summary sections
files += [output_dir + '/' + args.output_prefix + '_alignment_summary.txt']
sections.insert(
0, Section.from_alignment_summary(
output_dir + '/' + args.output_prefix + '_alignment_summary.txt',
'alignment_summary.html'))
cell_size_figure = 'cell_size_distribution.png'
index_section = Section.from_final_matrix(
sp_csv, cell_size_figure, 'cell_distribution.html')
seqc_summary = Summary(
output_dir + '/' + args.output_prefix + '_summary', sections, index_section)
seqc_summary.prepare_archive()
seqc_summary.import_image(cell_filter_figure)
seqc_summary.import_image(cell_size_figure)
seqc_summary.render()
summary_archive = seqc_summary.compress_archive()
files += [summary_archive]
# Create a mini summary section
alignment_summary_file = output_dir + '/' + args.output_prefix + '_alignment_summary.txt'
seqc_mini_summary = MiniSummary(
args.output_prefix, mini_summary_d, alignment_summary_file, cell_filter_figure,
cell_size_figure)
seqc_mini_summary.compute_summary_fields(ra, sp_csv)
seqc_mini_summary_json, seqc_mini_summary_pdf = seqc_mini_summary.render()
files += [seqc_mini_summary_json, seqc_mini_summary_pdf]
# Running MAST for differential analysis
# file storing the list of differentially expressed genes for each cluster
de_gene_list_file = run_mast(
seqc_mini_summary.get_counts_filtered(), seqc_mini_summary.get_clustering_result(),
args.output_prefix)
files += [de_gene_list_file]
# adding the cluster column and write down gene-cell count matrix
dense_csv = args.output_prefix + '_dense.csv'
sp_csv.insert(loc=0, column='CLUSTER', value=seqc_mini_summary.get_clustering_result())
sp_csv.to_csv(dense_csv)
files += [dense_csv]
if args.upload_prefix:
# Upload count matrices files, logs, and return
bucket, key = io.S3.split_link(args.upload_prefix)
for item in files:
try:
ec2.Retry(retries=5)(io.S3.upload_file)(item, bucket, key)
item_name = item.split('/')[-1]
log.info('Successfully uploaded %s to the specified S3 location '
'"%s%s".' % (item, args.upload_prefix, item_name))
except FileNotFoundError:
log.notify('Item %s was not found! Continuing with upload...' % item)
if manage_merged:
manage_merged.wait_until_complete()
log.info('Successfully uploaded %s to the specified S3 location "%s"' %
(args.merged_fastq, args.upload_prefix))
if manage_bamfile:
manage_bamfile.wait_until_complete()
log.info('Successfully uploaded %s to the specified S3 location "%s"'
% (args.alignment_file, args.upload_prefix))
log.info('SEQC run complete. Cluster will be terminated')
# upload logs
if args.upload_prefix:
# Upload count matrices files, logs, and return
bucket, key = io.S3.split_link(args.upload_prefix)
for item in [args.log_name, './nohup.log']:
try:
# Make a copy of the file with the output prefix
copyfile(item, args.output_prefix + '_' + item)
print(args.output_prefix + '_' + item)
ec2.Retry(retries=5)(io.S3.upload_file)(
args.output_prefix + '_' + item, bucket, key)
log.info('Successfully uploaded %s to the specified S3 location '
'"%s".' % (item, args.upload_prefix))
except FileNotFoundError:
log.notify('Item %s was not found! Continuing with upload...' % item)
# todo local test does not send this email
if mutt:
email_body = (
'<font face="Courier New, Courier, monospace">'
'SEQC RUN COMPLETE.\n\n'
'The run log has been attached to this email and '
'results are now available in the S3 location you specified: '
'"%s"\n\n' % args.upload_prefix)
email_body = email_body.replace('\n', '<br>').replace('\t', ' ')
email_user(summary_archive, email_body, args.email)
def run(args) -> None:
"""Run SEQC on the files provided in args, given specifications provided on the
command line
:param args: parsed argv, produced by seqc.parser(). This function is only called
when args.subprocess_name is "run".
"""
# import inside module for pickle functionality
# top 2 only needed for post-filtering
import os
import multiprocessing
from seqc import log, ec2, platforms, io, version
from seqc.sequence import fastq
from seqc.alignment import star
from seqc.alignment import sam
from seqc.email_ import email_user
from seqc.read_array import ReadArray
from seqc.core import verify, download
from seqc import filter
from seqc.sequence.gtf import GeneIntervals
from seqc.summary.summary import Section, Summary
import numpy as np
import scipy.io
from shutil import copyfile
from shutil import move as movefile
from seqc.summary.summary import MiniSummary
from seqc.stats.mast import run_mast
import logging
import pickle
import pendulum
# logger = logging.getLogger('weasyprint')
# logger.handlers = [] # Remove the default stderr handler
# logger.setLevel(100)
# logger.addHandler(logging.FileHandler('weasyprint.log'))
def determine_start_point(arguments) -> (bool, bool, bool):
"""
determine where seqc should start based on which parameters were passed.
:param arguments: Namespace object, result of ArgumentParser.parse_args()
:returns merge, align, process_bamfile: indicates whether merging, alignment, and
processing bamfiles should be executed.
"""
if arguments.read_array:
return False, False, False
if arguments.alignment_file:
return False, False, True
if arguments.merged_fastq:
return False, True, True
else:
return True, True, True
def download_input(dir_, arguments):
"""parse input arguments and download any necessary data
:param str dir_: directory to download data to
:param arguments: namespace object from argparse
:return args: updated namespace object reflecting local file paths of downloaded
files
"""
# download basespace data if necessary
if arguments.basespace:
arguments.barcode_fastq, arguments.genomic_fastq = io.BaseSpace.download(
arguments.platform, arguments.basespace, dir_,
arguments.basespace_token)
# get a list of input FASTQ files
# download from AWS S3 if the URI is prefixed with s3://
arguments.genomic_fastq = download.s3_data(arguments.genomic_fastq,
dir_ + "/genomic_fastq/")
arguments.barcode_fastq = download.s3_data(arguments.barcode_fastq,
dir_ + "/barcode_fastq/")
# get merged fastq file, unzip if necessary
arguments.merged_fastq = (download.s3_data(
[arguments.merged_fastq], dir_ +
"/")[0] if arguments.merged_fastq is not None else None)
# get a path to the STAR index files
# download from AWS S3 if the URI is prefixed with s3://
if any((arguments.alignment_file, arguments.read_array)):
index_link = arguments.index + "annotations.gtf"
else:
index_link = arguments.index
index_files = download.s3_data([index_link], dir_ + "/index/")
# use the first filename in the list to get the index directory
# add a trailing slash to make the rest of the code not break;;
# e.g. test-data/index/chrStart.txt --> test-data/index/
arguments.index = os.path.dirname(index_files[0]) + "/"
# get a list of whitelisted barcodes files
# download from AWS S3 if the URI is prefixed with s3://
arguments.barcode_files = download.s3_data(arguments.barcode_files,
dir_ + "/barcodes/")
# check if `alignment_file` is specified
if arguments.alignment_file:
# get the alignment filename (*.bam)
# download from AWS S3 if the URI is prefixed with s3://
arguments.alignment_file = download.s3_data(
[arguments.alignment_file], dir_ + "/")[0]
# check if `read_array` is specified
if arguments.read_array:
# get the readarray fileanem (*.h5)
# download from AWS S3 if the URI is prefixed with s3://
arguments.read_array = download.s3_data([arguments.read_array],
dir_ + "/")[0]
return arguments
def merge_fastq_files(
technology_platform,
barcode_fastq: [str],
output_stem: str,
genomic_fastq: [str],
) -> (str, int):
"""annotates genomic fastq with barcode information; merging the two files.
:param technology_platform: class from platforms.py that defines the
characteristics of the data being processed
:param barcode_fastq: list of str names of fastq files containing barcode
information
:param output_stem: str, stem for output files
:param genomic_fastq: list of str names of fastq files containing genomic
information
:returns str merged_fastq: name of merged fastq file
"""
# hack:
# Due to the non-platform agnostic glob behavior,
# it is possible that L001_R1 is merged with L002_R2 (not L001_R2).
# to avoid this problem, we first sort.
# this is a temporary hacky solution
barcode_fastq = sorted(barcode_fastq)
genomic_fastq = sorted(genomic_fastq)
log.info("Merging genomic reads and barcode annotations.")
for bar_fq, gen_fq in zip(barcode_fastq, genomic_fastq):
log.info("Merge {} with {}".format(os.path.basename(bar_fq),
os.path.basename(gen_fq)))
merged_fastq = fastq.merge_paired(
merge_function=technology_platform.merge_function,
fout=output_stem + "_merged.fastq",
genomic=genomic_fastq,
barcode=barcode_fastq,
)
# delete genomic/barcode fastq files after merged.fastq creation
# log.info('Removing original fastq file for memory management.')
# delete_fastq = ' '.join(['rm'] + genomic_fastq + barcode_fastq)
# io.ProcessManager(delete_fastq).run_all()
return merged_fastq
def align_fastq_records(merged_fastq, dir_, star_args, star_index, n_proc,
aws_upload_key) -> (str, str, io.ProcessManager):
"""
Align fastq records.
:param merged_fastq: str, path to merged .fastq file
:param dir_: str, stem for output files
:param star_args: dict, extra keyword arguments for STAR
:param star_index: str, file path to directory containing STAR index
:param n_proc: int, number of STAR processes to initiate
:param aws_upload_key: str, location to upload files, or None if seqc was
initiated from a merged fastq file.
:return bamfile, input_data, upload_manager: (str, str, io.ProcessManager)
name of .sam file containing aligned reads, indicator of which data was used as
input, and a ProcessManager for merged fastq files
"""
log.info("Aligning merged fastq records.")
alignment_directory = dir_ + "/alignments/"
os.makedirs(alignment_directory, exist_ok=True)
if star_args is not None:
star_kwargs = dict(a.strip().split("=") for a in star_args)
else:
star_kwargs = {}
bamfile = star.align(merged_fastq, star_index, n_proc,
alignment_directory, **star_kwargs)
log.info("Gzipping merged fastq file.")
if pigz:
pigz_zip = "pigz --best -f {fname}".format(fname=merged_fastq)
else:
pigz_zip = "gzip -f {fname}".format(fname=merged_fastq)
pigz_proc = io.ProcessManager(pigz_zip)
pigz_proc.run_all()
pigz_proc.wait_until_complete() # prevents slowing down STAR alignment
merged_fastq += ".gz" # reflect gzipped nature of file
if aws_upload_key:
log.info("Uploading gzipped merged fastq file to S3.")
merge_upload = "aws s3 mv {fname} {s3link}".format(
fname=merged_fastq, s3link=aws_upload_key)
upload_manager = io.ProcessManager(merge_upload)
upload_manager.run_all()
else:
# log.info('Removing merged fastq file for memory management.')
# rm_merged = 'rm %s' % merged_fastq
# io.ProcessManager(rm_merged).run_all()
upload_manager = None
return bamfile, upload_manager
def create_read_array(bamfile, index, aws_upload_key, min_poly_t,
max_transcript_length):
"""Create or download a ReadArray object.
:param max_transcript_length:
:param str bamfile: filename of .bam file
:param str index: directory containing index files
:param str aws_upload_key: key where aws files should be uploaded
:param int min_poly_t: minimum number of poly_t nucleotides for a read to be valid
:returns ReadArray, UploadManager: ReadArray object, bamfile ProcessManager
"""
log.info("Filtering aligned records and constructing record database.")
# Construct translator
translator = GeneIntervals(index + "annotations.gtf",
max_transcript_length=max_transcript_length)
read_array, read_names = ReadArray.from_alignment_file(
bamfile, translator, min_poly_t)
# converting sam to bam and uploading to S3, else removing bamfile
if aws_upload_key:
log.info("Uploading bam file to S3.")
upload_bam = "aws s3 mv {fname} {s3link}{prefix}_Aligned.out.bam".format(
fname=bamfile,
s3link=aws_upload_key,
prefix=args.output_prefix)
print(upload_bam)
upload_manager = io.ProcessManager(upload_bam)
upload_manager.run_all()
else:
if os.path.exists(bamfile):
movefile(bamfile, args.output_prefix + "_Aligned.out.bam")
# log.info('Removing bamfile for memory management.')
# rm_bamfile = 'rm %s' % bamfile
# io.ProcessManager(rm_bamfile).run_all()
upload_manager = None
return read_array, upload_manager, read_names
# ######################## MAIN FUNCTION BEGINS HERE ################################
log.setup_logger(args.log_name, args.debug)
with ec2.instance_clean_up(
email=args.email,
upload=args.upload_prefix,
log_name=args.log_name,
debug=args.debug,
terminate=args.terminate,
running_remote=args.remote,
):
start_run_time = pendulum.now()
log.notify("SEQC=v{}".format(version.__version__))
log.notify("STAR=v{}".format(star.get_version()))
log.notify("samtools=v{}".format(sam.get_version()))
pigz, mutt = verify.executables("pigz", "mutt")
if mutt:
log.notify(
"mutt executable identified, email will be sent when run "
"terminates. ")
else:
log.notify(
"mutt was not found on this machine; an email will not be sent to "
"the user upon termination of SEQC run.")
# turn off lower coverage filter for 10x
if ((args.platform == "ten_x") or (args.platform == "ten_x_v2")
or (args.platform == "ten_x_v3")):
args.filter_low_coverage = False
if args.platform == "ten_x_v2" or args.platform == "ten_x_v3":
log.notify("Setting min_poly_t=0 for 10x v2 & v3")
args.min_poly_t = 0
max_insert_size = args.max_insert_size
if args.filter_mode == "scRNA-seq":
# for scRNA-seq
if ((args.platform == "ten_x") or (args.platform == "ten_x_v2")
or (args.platform == "ten_x_v3")):
# set max_transcript_length (max_insert_size) = 10000
max_insert_size = 10000
log.notify(
"Full length transcripts are used for read mapping in 10x data."
)
elif args.filter_mode == "snRNA-seq":
# for snRNA-seq
# e.g. 2304700 # hg38
# e.g. 4434881 # mm38
max_insert_size = args.max_insert_size
else:
# all others
max_insert_size = args.max_insert_size
log.notify("max_insert_size is set to {}".format(max_insert_size))
log.args(args)
# e.g.
# --output-prefix=test-data/_outs/test
# output_dir=test-data
# output_prefix=test
output_dir, output_prefix = os.path.split(args.output_prefix)
if not output_dir:
output_dir = "."
else:
os.makedirs(output_dir, exist_ok=True)
# check if the platform name provided is supported by seqc
# todo move into verify for run
platform_name = verify.platform_name(args.platform)
platform = platforms.AbstractPlatform.factory(
platform_name) # returns platform
n_processes = multiprocessing.cpu_count(
) - 1 # get number of processors
merge, align, process_bamfile = determine_start_point(args)
args = download_input(output_dir, args)
if args.platform == "in_drop_v5":
platform = platform.build_cb2_barcodes(args.barcode_files)
log.notify("Built cb2 barcode hash for v5 barcodes.")
if merge:
if args.min_poly_t is None: # estimate min_poly_t if it was not provided
args.min_poly_t = filter.estimate_min_poly_t(
args.barcode_fastq, platform)
log.notify("Estimated min_poly_t={!s}".format(args.min_poly_t))
args.merged_fastq = merge_fastq_files(platform, args.barcode_fastq,
args.output_prefix,
args.genomic_fastq)
# SEQC was started from input other than fastq files
if args.min_poly_t is None:
args.min_poly_t = 0
log.warn(
"Warning: SEQC started from step other than unmerged fastq with "
"empty --min-poly-t parameter. Continuing with --min-poly-t 0."
)
if align:
upload_merged = args.upload_prefix if merge else None
args.alignment_file, manage_merged = align_fastq_records(
args.merged_fastq,
output_dir,
args.star_args,
args.index,
n_processes,
upload_merged,
)
else:
manage_merged = None
if process_bamfile:
# if the starting point was a BAM file (i.e. args.alignment_file=*.bam & align=False)
# do not upload by setting this to None
upload_bamfile = args.upload_prefix if align else None
ra, manage_bamfile, read_names = create_read_array(
args.alignment_file,
args.index,
upload_bamfile,
args.min_poly_t,
max_insert_size,
)
else:
manage_bamfile = None
ra = ReadArray.load(args.read_array)
# fixme: the old read_array doesn't have read_names
read_names = None
# create the first summary section here
status_filters_section = Section.from_status_filters(
ra, "initial_filtering.html")
sections = [status_filters_section]
# Skip over the corrections if read array is specified by the user
if not args.read_array:
# Correct barcodes
log.info("Correcting barcodes and estimating error rates.")
error_rate, df_cb_correction = platform.apply_barcode_correction(
ra, args.barcode_files)
if df_cb_correction is not None and len(df_cb_correction) > 0:
df_cb_correction.to_csv(
args.output_prefix + "_cb-correction.csv.gz",
index=False,
compression="gzip",
)
# Resolve multimapping
log.info("Resolving ambiguous alignments.")
mm_results = ra.resolve_ambiguous_alignments()
# 121319782799149 / 614086965 / pos=49492038 / AAACATAACG
# 121319782799149 / 512866590 / pos=49490848 / TCAATTAATC (1 hemming dist away from TCAATTAATT)
# ra.data["rmt"][91490] = 512866590
# ra.positions[91490] = 49492038
# correct errors
log.info("Identifying RMT errors.")
df_umi_correction = platform.apply_rmt_correction(ra, error_rate)
if df_umi_correction is not None and len(df_umi_correction) > 0:
df_umi_correction.to_csv(
args.output_prefix + "_umi-correction.csv.gz",
index=False,
compression="gzip",
)
# Apply low coverage filter
if platform.filter_lonely_triplets:
log.info("Filtering lonely triplet reads")
ra.filter_low_coverage(alpha=args.low_coverage_alpha)
log.info("Saving read array.")
ra.save(args.output_prefix + ".h5")
# generate a file with read_name, corrected cb, corrected umi
# read_name already has pre-corrected cb & umi
# log.info("Saving correction information.")
# ra.create_readname_cb_umi_mapping(
# read_names, args.output_prefix + "_correction.csv.gz"
# )
# Summary sections
# create the sections for the summary object
sections += [
Section.from_cell_barcode_correction(
ra, "cell_barcode_correction.html"),
Section.from_rmt_correction(ra, "rmt_correction.html"),
Section.from_resolve_multiple_alignments(
mm_results, "multialignment.html"),
]
# create a dictionary to store output parameters
mini_summary_d = dict()
# filter non-cells
log.info("Creating counts matrix.")
sp_reads, sp_mols = ra.to_count_matrix(sparse_frame=True,
genes_to_symbols=args.index +
"annotations.gtf")
# generate 10x compatible count matrix
log.info("Creating 10x compatible counts matrix.")
ra.to_10x_count_matrix(genes_to_symbols=args.index + "annotations.gtf")
# Save sparse matrices
log.info("Saving sparse matrices")
scipy.io.mmwrite(args.output_prefix + "_sparse_read_counts.mtx",
sp_reads.data)
scipy.io.mmwrite(args.output_prefix + "_sparse_molecule_counts.mtx",
sp_mols.data)
# Indices
df = np.array([np.arange(sp_reads.shape[0]), sp_reads.index]).T
np.savetxt(
args.output_prefix + "_sparse_counts_barcodes.csv",
df,
fmt="%d",
delimiter=",",
)
# Columns
df = np.array([np.arange(sp_reads.shape[1]), sp_reads.columns]).T
np.savetxt(args.output_prefix + "_sparse_counts_genes.csv",
df,
fmt="%s",
delimiter=",")
log.info("Creating filtered counts matrix.")
cell_filter_figure = args.output_prefix + "_cell_filters.png"
# By pass low count filter for mars seq
(
sp_csv,
total_molecules,
molecules_lost,
cells_lost,
cell_description,
) = filter.create_filtered_dense_count_matrix(
sp_mols,
sp_reads,
mini_summary_d,
plot=True,
figname=cell_filter_figure,
filter_low_count=platform.filter_low_count,
filter_mitochondrial_rna=args.filter_mitochondrial_rna,
filter_low_coverage=args.filter_low_coverage,
filter_low_gene_abundance=args.filter_low_gene_abundance,
)
# Output files
files = [
cell_filter_figure,
args.output_prefix + ".h5",
args.output_prefix + "_sparse_read_counts.mtx",
args.output_prefix + "_sparse_molecule_counts.mtx",
args.output_prefix + "_sparse_counts_barcodes.csv",
args.output_prefix + "_sparse_counts_genes.csv",
"raw_feature_bc_matrix/matrix.mtx.gz",
"raw_feature_bc_matrix/barcodes.tsv.gz",
"raw_feature_bc_matrix/features.tsv.gz",
]
if os.path.exists(args.output_prefix + "_cb-correction.csv.gz"):
files.append(args.output_prefix + "_cb-correction.csv.gz")
if os.path.exists(args.output_prefix + "_umi-correction.csv.gz"):
files.append(args.output_prefix + "_umi-correction.csv.gz")
# Summary sections
# create the sections for the summary object
sections += [
Section.from_cell_filtering(cell_filter_figure,
"cell_filtering.html"),
Section.from_run_time(args.log_name, "seqc_log.html"),
]
# get alignment summary
if os.path.isfile(output_dir + "/alignments/Log.final.out"):
os.rename(
output_dir + "/alignments/Log.final.out",
args.output_prefix + "_alignment_summary.txt",
)
# Upload files and summary sections
files += [args.output_prefix + "_alignment_summary.txt"]
sections.insert(
0,
Section.from_alignment_summary(
args.output_prefix + "_alignment_summary.txt",
"alignment_summary.html",
),
)
cell_size_figure = args.output_prefix + "_cell_size_distribution.png"
index_section = Section.from_final_matrix(sp_csv, cell_size_figure,
"cell_distribution.html")
seqc_summary = Summary(args.output_prefix + "_summary", sections,
index_section)
seqc_summary.prepare_archive()
seqc_summary.import_image(cell_filter_figure)
seqc_summary.import_image(cell_size_figure)
seqc_summary.render()
# create a .tar.gz with `test_summary/*`
summary_archive = seqc_summary.compress_archive()
files += [summary_archive]
# Create a mini summary section
alignment_summary_file = args.output_prefix + "_alignment_summary.txt"
seqc_mini_summary = MiniSummary(
output_dir,
output_prefix,
mini_summary_d,
alignment_summary_file,
cell_filter_figure,
cell_size_figure,
)
seqc_mini_summary.compute_summary_fields(ra, sp_csv)
seqc_mini_summary_json, seqc_mini_summary_pdf = seqc_mini_summary.render(
)
files += [seqc_mini_summary_json, seqc_mini_summary_pdf]
# Running MAST for differential analysis
# file storing the list of differentially expressed genes for each cluster
de_gene_list_file = run_mast(
seqc_mini_summary.get_counts_filtered(),
seqc_mini_summary.get_clustering_result(),
args.output_prefix,
)
files += [de_gene_list_file]
# adding the cluster column and write down gene-cell count matrix
dense_csv = args.output_prefix + "_dense.csv"
sp_csv.insert(loc=0,
column="CLUSTER",
value=seqc_mini_summary.get_clustering_result())
sp_csv.to_csv(dense_csv)
files += [dense_csv]
if args.upload_prefix:
# Upload count matrices files, logs, and return
bucket, key = io.S3.split_link(args.upload_prefix)
for item in files:
try:
ec2.Retry(retries=5)(io.S3.upload_file)(item, bucket, key)
item_name = item.split("/")[-1]
log.info('Successfully uploaded %s to "%s%s".' %
(item, args.upload_prefix, item_name))
except FileNotFoundError:
log.notify(
"Item %s was not found! Continuing with upload..." %
item)
if manage_merged:
manage_merged.wait_until_complete()
log.info('Successfully uploaded %s to "%s"' %
(args.merged_fastq, args.upload_prefix))
if manage_bamfile:
manage_bamfile.wait_until_complete()
log.info('Successfully uploaded %s to "%s"' %
(args.alignment_file, args.upload_prefix))
log.info("SEQC run complete.")
end_run_time = pendulum.now()
running_time = end_run_time - start_run_time
log.info("Running Time={}".format(running_time.in_words()))
# upload logs
if args.upload_prefix:
# upload logs (seqc_log.txt, nohup.log)
bucket, key = io.S3.split_link(args.upload_prefix)
for item in [args.log_name, "./nohup.log"]:
try:
# Make a copy of the file with the output prefix
copyfile(item, args.output_prefix + "_" + item)
print(args.output_prefix + "_" + item)
ec2.Retry(retries=5)(io.S3.upload_file)(
args.output_prefix + "_" + item, bucket, key)
log.info('Successfully uploaded %s to "%s".' %
(item, args.upload_prefix))
except FileNotFoundError:
log.notify(
"Item %s was not found! Continuing with upload..." %
item)
else:
# move the log to output directory
movefile(args.log_name, args.output_prefix + "_" + args.log_name)
# todo local test does not send this email
if mutt:
email_body = (
'<font face="Courier New, Courier, monospace">'
"SEQC RUN COMPLETE.\n\n"
"The run log has been attached to this email and "
"results are now available in the S3 location you specified: "
'"%s"\n\n' % args.upload_prefix)
email_body = email_body.replace("\n",
"<br>").replace("\t", " ")
email_user(summary_archive, email_body, args.email)