def run_MutationSeq(config, normal_bam, tumour_bam, output_file):
workflow = pypeliner.workflow.Workflow()
workflow.setobj(obj=mgd.OutputChunks('interval',), value=list(map(str, range(1, 23) + ['X'])))
workflow.transform(
name='run_museq_paired',
ctx={'mem': 8, 'ncpus': 1, 'walltime': '24:00'},
axes=('interval',),
func=tasks.run_museq,
args=(
config,
mgd.InputFile(normal_bam),
mgd.InputFile(tumour_bam),
mgd.InputInstance('interval'),
mgd.TempOutputFile('museq.vcf', 'interval'),
mgd.TempOutputFile('museq.log', 'interval'),
)
)
workflow.transform(
name='merge_vcfs',
func=tasks.merge_vcfs,
args=(
mgd.TempInputFile('museq.vcf', 'interval', axes_origin=[]),
mgd.OutputFile(output_file),
mgd.TempSpace('merge_vcf'),
)
)
return workflow
def call_copynumber(
samples, config, tumours, normals, breakpoints,
titan_raw_dir, remixt_results,
remixt_raw_dir, titan_segments, titan_params, titan_markers
):
breakpoints = dict([(sampid, breakpoints[sampid])
for sampid in samples])
remixt_results = dict([(sampid, remixt_results[sampid])
for sampid in samples])
titan_segments = dict([(sampid, titan_segments[sampid])
for sampid in samples])
titan_params = dict([(sampid, titan_params[sampid])
for sampid in samples])
titan_markers = dict([(sampid, titan_markers[sampid])
for sampid in samples])
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('sample_id'),
value=samples)
workflow.subworkflow(
name='titan',
func=titan.create_titan_workflow,
axes=('sample_id',),
args=(
mgd.InputFile('tumour_bam', 'sample_id', fnames=tumours, extensions=['.bai']),
mgd.InputFile('normal_bam', 'sample_id', fnames=normals, extensions=['.bai']),
mgd.Template(titan_raw_dir, 'sample_id'),
mgd.OutputFile('titan_segments', 'sample_id', fnames=titan_segments),
mgd.OutputFile('titan_params', 'sample_id', fnames=titan_params),
mgd.OutputFile('titan_markers', 'sample_id', fnames=titan_markers),
config['globals'],
config['cna_calling'],
config['cna_calling']['titan_intervals'],
),
)
workflow.subworkflow(
name='remixt',
func=remixt.create_remixt_workflow,
axes=('sample_id',),
args=(
mgd.InputFile('tumour_bam', 'sample_id', fnames=tumours, extensions=['.bai']),
mgd.InputFile('normal_bam', 'sample_id', fnames=normals, extensions=['.bai']),
mgd.InputFile('breakpoints', 'sample_id', fnames=breakpoints),
mgd.InputInstance('sample_id'),
config['cna_calling']['remixt_refdata'],
mgd.OutputFile('remixt_results', 'sample_id', fnames=remixt_results),
mgd.Template(remixt_raw_dir, 'sample_id'),
config['cna_calling']['min_num_reads']
),
)
return workflow
def patient_workflow(config, patient_id, patient_input, output_file):
workflow = pypeliner.workflow.Workflow()
patient_bam_dir = config["bam_directory"] + patient_id
patient_result_dir = config["results_dir"] + patient_id
helpers.makedirs(patient_bam_dir)
helpers.makedirs(patient_result_dir)
input_args = helpers.create_input_args(patient_input, patient_bam_dir)
workflow.setobj(obj=mgd.OutputChunks('sample_id', ),
value=input_args['all_samples'])
workflow.subworkflow(name='align_samples',
func=alignment.align_sample,
axes=('sample_id', ),
args=(
config,
mgd.InputFile('fastq_1',
'sample_id',
fnames=input_args['fastqs_r1']),
mgd.InputFile('fastq_2',
'sample_id',
fnames=input_args['fastqs_r2']),
mgd.InputInstance('sample_id'),
mgd.OutputFile('sample.bam',
'sample_id',
fnames=input_args['all_bams']),
mgd.OutputFile('sample.bam.bai',
'sample_id',
fnames=input_args['all_bais']),
))
workflow.subworkflow(name='run_analyses',
func=analysis.partition_tumour,
args=(
config,
input_args,
patient_id,
patient_result_dir,
mgd.InputFile('sample.bam',
'sample_id',
fnames=input_args['all_bams'],
axes_origin=[]),
mgd.InputFile('sample.bam.bai',
'sample_id',
fnames=input_args['all_bais'],
axes_origin=[]),
mgd.OutputFile(output_file),
))
return workflow
def ctDNA_workflow(args):
pyp = pypeliner.app.Pypeline(config=args)
workflow = pypeliner.workflow.Workflow()
config = helpers.load_yaml(args['config'])
for arg, value in args.iteritems():
config[arg] = value
helpers.makedirs(config["bam_directory"])
helpers.makedirs(config["results_dir"])
inputs = helpers.load_yaml(args['input_yaml'])
patients = inputs.keys()
workflow.setobj(obj=mgd.OutputChunks('patient_id', ), value=patients)
workflow.transform(name='get_input_by_patient',
func=helpers.get_input_by_patient,
ret=mgd.TempOutputObj('patient_input', 'patient_id'),
axes=('patient_id', ),
args=(
inputs,
mgd.InputInstance('patient_id'),
))
workflow.subworkflow(name='patient_workflow',
func=patient_workflow,
axes=('patient_id', ),
args=(
config,
mgd.InputInstance('patient_id'),
mgd.TempInputObj('patient_input', 'patient_id'),
mgd.OutputFile(
os.path.join(config['results_dir'],
'{patient_id}.log'),
'patient_id'),
))
pyp.run(workflow)
def create_museq_workflow(
normal_bam, tumour_bam, ref_genome, snv_vcf,
config):
ctx = {'mem_retry_increment': 2, 'ncpus': 1}
docker_ctx = helpers.get_container_ctx(config['containers'], 'single_cell_pipeline')
ctx.update(docker_ctx)
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('region'),
value=normal_bam.keys(),
)
workflow.transform(
name='run_museq',
ctx=dict(mem=config["memory"]['med'],
pool_id=config['pools']['highmem'],
**ctx),
axes=('region',),
func='single_cell.workflows.mutationseq.tasks.run_museq',
args=(
mgd.InputFile('merged_bam', 'region', fnames=tumour_bam),
mgd.InputFile('normal.split.bam', 'region', fnames=normal_bam),
mgd.TempOutputFile('museq.vcf', 'region'),
mgd.TempOutputFile('museq.log', 'region'),
mgd.InputInstance('region'),
config,
),
kwargs={'docker_kwargs': helpers.get_container_ctx(config['containers'], 'mutationseq')}
)
workflow.transform(
name='merge_snvs',
ctx=dict(mem=config["memory"]['med'],
pool_id=config['pools']['standard'],
**ctx),
func='biowrappers.components.io.vcf.tasks.concatenate_vcf',
args=(
mgd.TempInputFile('museq.vcf', 'region'),
mgd.OutputFile(snv_vcf),
),
)
return workflow
def create_extract_seqdata_workflow(
bam_filename,
seqdata_filename,
config,
ref_data_dir,
):
chromosomes = remixt.config.get_chromosomes(config, ref_data_dir)
snp_positions_filename = remixt.config.get_filename(config, ref_data_dir, 'snp_positions')
bam_max_fragment_length = remixt.config.get_param(config, 'bam_max_fragment_length')
bam_max_soft_clipped = remixt.config.get_param(config, 'bam_max_soft_clipped')
bam_check_proper_pair = remixt.config.get_param(config, 'bam_check_proper_pair')
workflow = pypeliner.workflow.Workflow()
workflow.setobj(obj=mgd.OutputChunks('chromosome'), value=chromosomes)
workflow.transform(
name='create_chromosome_seqdata',
axes=('chromosome',),
ctx={'mem': 16},
func=remixt.seqdataio.create_chromosome_seqdata,
args=(
mgd.TempOutputFile('seqdata', 'chromosome'),
mgd.InputFile(bam_filename),
mgd.InputFile(snp_positions_filename),
mgd.InputInstance('chromosome'),
bam_max_fragment_length,
bam_max_soft_clipped,
bam_check_proper_pair,
),
)
workflow.transform(
name='merge_seqdata',
ctx={'mem': 16},
func=remixt.seqdataio.merge_seqdata,
args=(
mgd.OutputFile(seqdata_filename),
mgd.TempInputFile('seqdata', 'chromosome'),
),
)
return workflow
def align_samples(config, fastq1_inputs, fastq2_inputs, bam_outputs, outdir):
samples = bam_outputs.keys()
workflow = pypeliner.workflow.Workflow()
workflow.setobj(obj=mgd.OutputChunks('sample_id'), value=samples)
workflow.subworkflow(
name='align_samples',
func=align_sample,
axes=('sample_id', ),
args=(config,
mgd.InputFile('input.r1.fastq.gz',
'sample_id',
fnames=fastq1_inputs),
mgd.InputFile('input.r2.fastq.gz',
'sample_id',
fnames=fastq2_inputs),
mgd.OutputFile('output.bam', 'sample_id', fnames=bam_outputs),
mgd.InputInstance("sample_id"), outdir),
)
return workflow
def cohort_qc_pipeline(args):
"""Process maf, run classify copynumber, make plots.
Args:
args ([dict]): [pipeline arguments]
"""
config = inpututils.load_config(args)
config = config["cohort_qc"]
pyp = pypeliner.app.Pypeline(config=args)
workflow = pypeliner.workflow.Workflow()
out_dir = args["out_dir"]
api_key = args["API_key"]
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
# inputs
cohort, germline_mafs, vcfs, hmmcopy = inpututils.load_cohort_qc_inputs(
args["input_yaml"]
)
museq = {
label: data["museq"] for label, data in vcfs.items()
}
strelka_snv = {
label: data["strelka_snv"] for label, data in vcfs.items()
}
strelka_indel = {
label: data["strelka_indel"] for label, data in vcfs.items()
}
hmmcopy_files = {
label: data["hmmcopy"] for label, data in hmmcopy.items()
}
hmmcopy_metrics_files = {
label: data["hmmcopy_metrics"] for label, data in hmmcopy.items()
}
# outputs
cbiofile_paths = get_cbioportal_paths(os.path.join(out_dir, cohort))
maftools_filepaths = get_maftools_paths(os.path.join(out_dir, cohort))
workflow.setobj(
obj=mgd.OutputChunks('sample_label', 'library_label'),
value=list(museq.keys()),
)
workflow.subworkflow(
name="merge_somatic_mafs",
func="single_cell.workflows.cohort_qc.merge_somatic_mafs",
axes=('sample_label',),
args=(
mgd.InputInstance('sample_label'),
config,
mgd.InputFile(
'museq', 'sample_label', 'library_label',
fnames=museq, axes_origin=[]
),
mgd.InputFile(
'strelka_snv', 'sample_label', 'library_label',
fnames=strelka_snv, axes_origin=[]
),
mgd.InputFile(
'strelka_indel', 'sample_label', 'library_label',
fnames=strelka_indel, axes_origin=[]
),
mgd.TempOutputFile('somatic_maf', 'sample_label')
),
)
workflow.subworkflow(
name="classifycopynumber",
func="single_cell.workflows.cohort_qc.cna_annotation_workflow",
args=(
config,
mgd.InputFile(
'hmmcopy_dict', 'sample_label', 'library_label',
fnames=hmmcopy_files, axes_origin=[]
),
mgd.InputFile(
'hmmcopy_metrics_dict', 'sample_label', 'library_label',
fnames=hmmcopy_metrics_files, axes_origin=[]
),
mgd.OutputFile(cbiofile_paths["cna_table"]),
mgd.OutputFile(maftools_filepaths["maftools_cna"]),
mgd.OutputFile(cbiofile_paths["segments"]),
config["gtf"],
),
)
workflow.subworkflow(
name="maf_annotation_workflow",
func="single_cell.workflows.cohort_qc.preprocess_mafs_workflow",
args=(
config,
mgd.InputFile(
'germline_mafs_dict', 'sample_label',
fnames=germline_mafs, axes_origin=[]
),
mgd.TempInputFile(
'somatic_maf', 'sample_label',
axes_origin=[]
),
mgd.OutputFile(cbiofile_paths["filtered_germline_maf"]),
mgd.OutputFile(cbiofile_paths["annotated_somatic_maf"]),
api_key
),
)
workflow.subworkflow(
name="make_plots_and_report",
func="single_cell.workflows.cohort_qc.create_cohort_oncoplot",
args=(
config,
mgd.InputFile(cbiofile_paths["filtered_germline_maf"]),
mgd.InputFile(cbiofile_paths["annotated_somatic_maf"]),
mgd.InputFile(maftools_filepaths["maftools_cna"]),
mgd.OutputFile(maftools_filepaths["maftools_maf"]),
mgd.OutputFile(maftools_filepaths["cohort_oncoplot"]),
mgd.OutputFile(maftools_filepaths["report"]),
cohort
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(
sys.argv[0:],
args['out_dir'],
list(cbiofile_paths.values()) + list(maftools_filepaths.values()),
mgd.OutputFile(meta_yaml)
),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {'type': 'cohort_qc'}
}
)
pyp.run(workflow)
def process_cells_destruct(destruct_config,
cell_bam_files,
reads_1,
reads_2,
sample_1,
sample_2,
stats,
tag=False):
ctx = {
'mem_retry_increment': 2,
'disk_retry_increment': 50,
'ncpus': 1,
}
cells = list(cell_bam_files.keys())
workflow = pypeliner.workflow.Workflow(ctx=ctx)
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=cells,
)
workflow.transform(
name='bamdisc_and_numreads_cell',
func=
"single_cell.workflows.destruct_singlecell.tasks.destruct_bamdisc_and_numreads",
axes=('cell_id', ),
ctx={
'io': 1,
'mem': 8
},
ret=mgd.TempOutputObj("numreads", "cell_id"),
args=(
destruct_config,
mgd.InputFile('bam', 'cell_id', fnames=cell_bam_files),
mgd.TempOutputFile('cell_stats', 'cell_id'),
mgd.TempOutputFile('cell_reads_1.fastq.gz', 'cell_id'),
mgd.TempOutputFile('cell_reads_2.fastq.gz', 'cell_id'),
mgd.TempOutputFile('cell_sample_1.fastq.gz', 'cell_id'),
mgd.TempOutputFile('cell_sample_2.fastq.gz', 'cell_id'),
mgd.TempSpace('bamdisc_cell_tempspace', 'cell_id'),
),
)
workflow.transform(
name='merge_read_counts',
ret=mgd.TempOutputObj("readcounts"),
func=
"single_cell.workflows.destruct_singlecell.tasks.merge_read_counts",
ctx={
'io': 1,
'mem': 8
},
args=(mgd.TempInputObj('numreads', 'cell_id'), ))
workflow.transform(
name='reindex_reads',
func=
"single_cell.workflows.destruct_singlecell.tasks.re_index_reads_both",
ctx={
'io': 1,
'mem': 8
},
axes=('cell_id', ),
args=(
mgd.TempInputFile('cell_reads_1.fastq.gz', 'cell_id'),
mgd.TempOutputFile('cell_reads_1_reindex.fastq.gz', 'cell_id'),
mgd.TempInputFile('cell_reads_2.fastq.gz', 'cell_id'),
mgd.TempOutputFile('cell_reads_2_reindex.fastq.gz', 'cell_id'),
mgd.InputInstance('cell_id'),
cells,
mgd.TempInputObj('readcounts'),
),
kwargs={'tag': tag})
workflow.transform(
name='merge_reads_r1',
ctx={
'io': 1,
'mem': 8,
'disk': 100
},
func=
"single_cell.workflows.destruct_singlecell.tasks.merge_cell_fastqs",
args=(
mgd.TempInputFile('cell_reads_1_reindex.fastq.gz', 'cell_id'),
mgd.OutputFile(reads_1),
),
)
workflow.transform(
name='merge_reads_r2',
ctx={
'io': 1,
'mem': 8,
'disk': 100
},
func=
"single_cell.workflows.destruct_singlecell.tasks.merge_cell_fastqs",
args=(
mgd.TempInputFile('cell_reads_2_reindex.fastq.gz', 'cell_id'),
mgd.OutputFile(reads_2),
),
)
workflow.transform(
name='merge_sample',
ctx={
'io': 1,
'mem': 8,
'disk': 100
},
func="single_cell.workflows.destruct_singlecell.tasks.resample_fastqs",
args=(
mgd.TempInputFile('cell_sample_1.fastq.gz', 'cell_id'),
mgd.TempInputFile('cell_sample_2.fastq.gz', 'cell_id'),
mgd.OutputFile(sample_1),
mgd.OutputFile(sample_2),
destruct_config['num_read_samples'],
),
)
workflow.transform(
name='merge_stats',
ctx={
'io': 1,
'mem': 8
},
func="single_cell.workflows.destruct_singlecell.tasks.merge_stats",
args=(
mgd.TempInputFile('cell_stats', 'cell_id'),
mgd.OutputFile(stats),
),
)
return workflow
def create_split_workflow(normal_bam,
normal_split_bam,
regions,
config,
by_reads=False):
normal_split_bam = dict([(ival, normal_split_bam[ival])
for ival in regions])
one_split_job = config["one_split_job"]
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('region'),
value=regions,
)
# split by reads always runs no a single node
if by_reads:
workflow.transform(
name='split_normal_bam',
ctx={
'mem': config['memory']['low'],
'ncpus': config['max_cores']
},
func=
"single_cell.workflows.split_bams.tasks.split_bam_file_by_reads",
args=(
mgd.InputFile(normal_bam, extensions=['.bai']),
mgd.OutputFile("normal.split.bam",
"region",
fnames=normal_split_bam,
axes_origin=[],
extensions=['.bai']),
mgd.TempSpace("bam_split_by_reads"),
regions,
),
)
elif one_split_job:
workflow.transform(
name='split_normal_bam',
ctx={
'mem': config['memory']['low'],
'ncpus': config['max_cores']
},
func=
"single_cell.workflows.split_bams.tasks.split_bam_file_one_job",
args=(mgd.InputFile(normal_bam, extensions=['.bai']),
mgd.OutputFile(
"normal.split.bam",
"region",
fnames=normal_split_bam,
axes_origin=[],
extensions=['.bai'],
), regions, mgd.TempSpace("one_job_split_tempdir")),
kwargs={"ncores": config["max_cores"]})
else:
workflow.transform(
name='split_normal_bam',
ctx={
'mem': config['memory']['low'],
'ncpus': config['max_cores']
},
axes=('region', ),
func="single_cell.workflows.split_bams.tasks.split_bam_file",
args=(mgd.InputFile(normal_bam, extensions=['.bai']),
mgd.OutputFile("normal.split.bam",
"region",
fnames=normal_split_bam,
extensions=['.bai']),
mgd.InputInstance('region')))
return workflow
def create_split_workflow(normal_bam,
normal_bai,
normal_split_bam,
normal_split_bai,
regions,
config,
by_reads=False):
ctx = {'mem_retry_increment': 2}
docker_ctx = helpers.get_container_ctx(config['containers'],
'single_cell_pipeline')
ctx.update(docker_ctx)
normal_split_bam = dict([(ival, normal_split_bam[ival])
for ival in regions])
normal_split_bai = dict([(ival, normal_split_bai[ival])
for ival in regions])
one_split_job = config["one_split_job"]
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('region'),
value=regions,
)
# split by reads always runs no a single node
if by_reads:
workflow.transform(
name='split_normal_bam',
ctx=dict(mem=config['memory']['low'],
pool_id=config['pools']['multicore'],
ncpus=config['max_cores'],
**ctx),
func=
"single_cell.workflows.split_bams.tasks.split_bam_file_by_reads",
args=(mgd.InputFile(normal_bam), mgd.InputFile(normal_bai),
mgd.OutputFile("normal.split.bam",
"region",
fnames=normal_split_bam,
axes_origin=[]),
mgd.OutputFile("normal.split.bam.bai",
"region",
fnames=normal_split_bai,
axes_origin=[]),
mgd.TempSpace("bam_split_by_reads"), regions,
helpers.get_container_ctx(config['containers'], 'samtools')),
)
elif one_split_job:
workflow.transform(
name='split_normal_bam',
ctx=dict(mem=config['memory']['low'],
pool_id=config['pools']['multicore'],
ncpus=config['max_cores'],
**ctx),
func=
"single_cell.workflows.split_bams.tasks.split_bam_file_one_job",
args=(mgd.InputFile(normal_bam, extensions=['.bai']),
mgd.OutputFile(
"normal.split.bam",
"region",
fnames=normal_split_bam,
axes_origin=[],
extensions=['.bai'],
), regions,
helpers.get_container_ctx(config['containers'], 'samtools')),
kwargs={"ncores": config["max_cores"]})
else:
workflow.transform(
name='split_normal_bam',
ctx=dict(mem=config['memory']['low'],
pool_id=config['pools']['standard'],
ncpus=1,
**ctx),
axes=('region', ),
func="single_cell.workflows.split_bams.tasks.split_bam_file",
args=(mgd.InputFile(normal_bam), mgd.InputFile(normal_bai),
mgd.OutputFile("normal.split.bam",
"region",
fnames=normal_split_bam),
mgd.OutputFile("normal.split.bam.bai",
"region",
fnames=normal_split_bai),
mgd.InputInstance('region'),
helpers.get_container_ctx(config['containers'], 'samtools')))
return workflow
def create_titan_workflow(tumour_bam, normal_bam, targets, titan_raw_dir,
segments, params, markers, global_config, config,
intervals, sample_id):
titan_outdir = os.path.join(titan_raw_dir, 'clusters_{numclusters}',
'ploidy_{ploidy}')
igv_template = os.path.join(titan_outdir, 'igv_segs.txt')
outfile_template = os.path.join(titan_outdir, 'titan_markers.txt')
params_template = os.path.join(titan_outdir, 'titan_params.txt')
segs_template = os.path.join(titan_outdir, 'titan_segs.txt')
plots_template = os.path.join(titan_outdir, 'titan_plots.tar.gz')
parsed_template = os.path.join(titan_outdir, 'titan_parsed.csv')
museq_vcf = os.path.join(titan_raw_dir, 'museq.vcf')
chunks = [(v['num_clusters'], v['ploidy']) for v in intervals]
targets = mgd.InputFile(targets) if targets else None
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('numclusters', 'ploidy'),
value=chunks,
)
workflow.transform(name='generate_intervals',
func=tasks.generate_intervals,
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
ret=mgd.OutputChunks('interval'),
args=(config['reference_genome'],
config['chromosomes']))
workflow.transform(
name='run_museq',
ctx={
'mem': global_config['memory']['high'],
'ncpus': global_config['threads'],
'walltime': '02:00'
},
func=tasks.run_museq,
axes=('interval', ),
args=(mgd.InputFile(tumour_bam, extensions=['.bai']),
mgd.InputFile(normal_bam, extensions=['.bai']),
mgd.TempOutputFile('museq.vcf', 'interval'),
mgd.TempOutputFile('museq.log',
'interval'), config['reference_genome'],
mgd.InputInstance('interval'), config['museq_params']),
)
workflow.transform(name='merge_vcfs',
ctx={
'num_retry': 3,
'mem_retry_increment': 2,
'mem': global_config['memory']['high'],
'ncpus': 1
},
func=tasks.merge_vcfs,
args=(
mgd.TempInputFile('museq.vcf', 'interval'),
mgd.OutputFile(museq_vcf),
))
workflow.transform(
name='convert_museq_vcf2counts',
ctx={
'mem': global_config['memory']['high'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.convert_museq_vcf2counts,
args=(
mgd.InputFile(museq_vcf),
mgd.TempOutputFile('museq_postprocess.txt'),
config,
),
)
workflow.transform(
name='run_readcounter_tumour',
ctx={
'mem': global_config['memory']['high'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.run_readcounter,
args=(
mgd.InputFile(tumour_bam, extensions=['.bai']),
mgd.TempOutputFile('tumour.wig'),
config,
),
)
workflow.transform(
name='run_readcounter_normal',
ctx={
'mem': global_config['memory']['high'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.run_readcounter,
args=(
mgd.InputFile(normal_bam, extensions=['.bai']),
mgd.TempOutputFile('normal.wig'),
config,
),
)
workflow.transform(
name='calc_correctreads_wig',
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.calc_correctreads_wig,
args=(
mgd.TempInputFile('tumour.wig'),
mgd.TempInputFile('normal.wig'),
targets,
mgd.TempOutputFile('correct_reads.txt'),
config,
),
)
workflow.transform(name='run_titan',
axes=('numclusters', 'ploidy'),
ctx={
'mem': global_config['memory']['high'],
'ncpus': 1,
'walltime': '06:00'
},
func=tasks.run_titan,
args=(mgd.TempInputFile('museq_postprocess.txt'),
mgd.TempInputFile('correct_reads.txt'),
mgd.OutputFile('titan_outfile',
'numclusters',
'ploidy',
template=outfile_template),
mgd.TempOutputFile('titan.Rdata', 'numclusters',
'ploidy'),
mgd.OutputFile('titan_params',
'numclusters',
'ploidy',
template=params_template),
config['titan_params'],
mgd.InputInstance('numclusters'),
mgd.InputInstance('ploidy')))
workflow.transform(
name='plot_titan',
axes=('numclusters', 'ploidy'),
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.plot_titan,
args=(mgd.TempInputFile('titan.Rdata', 'numclusters', 'ploidy'),
mgd.InputFile('titan_params',
'numclusters',
'ploidy',
template=params_template),
mgd.OutputFile('titan_plots',
'numclusters',
'ploidy',
template=plots_template),
mgd.TempSpace("titan_plots_tempdir",
'numclusters', 'ploidy'), config,
mgd.InputInstance('numclusters'), mgd.InputInstance('ploidy')),
)
workflow.transform(
name='calc_cnsegments_titan',
axes=('numclusters', 'ploidy'),
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.calc_cnsegments_titan,
args=(
mgd.InputFile('titan_outfile',
'numclusters',
'ploidy',
template=outfile_template),
mgd.OutputFile('titan_igv',
'numclusters',
'ploidy',
template=igv_template),
mgd.TempOutputFile('segs.csv', 'numclusters', 'ploidy'),
),
)
workflow.transform(
name='annot_pygenes',
axes=('numclusters', 'ploidy'),
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.annot_pygenes,
args=(
mgd.TempInputFile('segs.csv', 'numclusters', 'ploidy'),
mgd.OutputFile('titan_segs.csv',
'numclusters',
'ploidy',
template=segs_template),
config,
),
)
workflow.transform(
name='parse_titan',
axes=('numclusters', 'ploidy'),
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.parse_titan,
args=(
mgd.InputFile('titan_segs.csv',
'numclusters',
'ploidy',
template=segs_template),
mgd.InputFile('titan_params',
'numclusters',
'ploidy',
template=params_template),
mgd.InputFile('titan_outfile',
'numclusters',
'ploidy',
template=outfile_template),
mgd.OutputFile('titan_parsed.csv',
'numclusters',
'ploidy',
template=parsed_template),
config['parse_titan'],
sample_id,
),
)
workflow.transform(
name='segments_h5',
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.merge_to_h5,
args=(mgd.InputFile('titan_segs.csv',
'numclusters',
'ploidy',
template=segs_template), mgd.OutputFile(segments),
intervals),
)
workflow.transform(
name='params_h5',
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.merge_to_h5,
args=(mgd.InputFile('titan_params',
'numclusters',
'ploidy',
template=params_template), mgd.OutputFile(params),
intervals),
)
workflow.transform(name='markers_h5',
ctx={
'mem': global_config['memory']['low'],
'ncpus': 1,
'walltime': '02:00'
},
func=tasks.merge_to_h5,
args=(mgd.InputFile('titan_outfile',
'numclusters',
'ploidy',
template=outfile_template),
mgd.OutputFile(markers), intervals),
kwargs={'dtype': {
'Chr': str
}})
return workflow
def infer_haps(
bam_file,
haplotypes_filename,
config,
from_tumour=False,
):
baseimage = {'docker_image': config['docker']['single_cell_pipeline']}
remixt_image = config['docker']['remixt']
remixt_config = config.get('extract_seqdata', {})
remixt_ref_data_dir = config['ref_data_dir']
chromosomes = config['chromosomes']
remixt_config['chromosomes'] = chromosomes
ctx = dict(mem_retry_increment=2, disk_retry_increment=50, ncpus=1, **baseimage)
workflow = pypeliner.workflow.Workflow(ctx=ctx)
if isinstance(bam_file, dict):
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=list(bam_file.keys()),
)
# dont parallelize over chromosomes for per cell bams
workflow.subworkflow(
name="extract_seqdata",
axes=('cell_id',),
func='remixt.workflow.create_extract_seqdata_workflow',
ctx={'docker_image': remixt_image},
args=(
mgd.InputFile(
'bam_markdups', 'cell_id', fnames=bam_file, extensions=['.bai']
),
mgd.TempOutputFile('seqdata_cell.h5', 'cell_id'),
remixt_config,
remixt_ref_data_dir,
),
kwargs={'no_parallelism': True}
)
workflow.transform(
name='merge_all_seqdata',
func="remixt.seqdataio.merge_overlapping_seqdata",
ctx={'docker_image': remixt_image},
args=(
mgd.TempOutputFile('seqdata_file.h5'),
mgd.TempInputFile("seqdata_cell.h5", "cell_id"),
config["chromosomes"]
),
)
else:
workflow.subworkflow(
name='extract_seqdata',
func='remixt.workflow.create_extract_seqdata_workflow',
ctx={'disk': 150, 'docker_image': remixt_image},
args=(
mgd.InputFile(bam_file, extensions=['.bai']),
mgd.TempOutputFile('seqdata_file.h5'),
remixt_config,
remixt_ref_data_dir,
),
)
workflow.setobj(
obj=mgd.OutputChunks('chromosome'),
value=chromosomes,
)
if from_tumour:
func = 'remixt.analysis.haplotype.infer_snp_genotype_from_tumour'
else:
func = 'remixt.analysis.haplotype.infer_snp_genotype_from_normal'
workflow.transform(
name='infer_snp_genotype',
axes=('chromosome',),
ctx={'mem': 16, 'docker_image': remixt_image},
func=func,
args=(
mgd.TempOutputFile('snp_genotype.tsv', 'chromosome'),
mgd.TempInputFile('seqdata_file.h5'),
mgd.InputInstance('chromosome'),
config,
),
)
workflow.transform(
name='infer_haps',
axes=('chromosome',),
ctx={'mem': 16, 'docker_image': remixt_image},
func='remixt.analysis.haplotype.infer_haps',
args=(
mgd.TempOutputFile('haplotypes.tsv', 'chromosome'),
mgd.TempInputFile('snp_genotype.tsv', 'chromosome'),
mgd.InputInstance('chromosome'),
mgd.TempSpace('haplotyping', 'chromosome'),
remixt_config,
remixt_ref_data_dir,
),
)
workflow.transform(
name='merge_haps',
ctx={'mem': 16, 'docker_image': remixt_image},
func='remixt.utils.merge_tables',
args=(
mgd.TempOutputFile('haplotypes_merged.tsv'),
mgd.TempInputFile('haplotypes.tsv', 'chromosome'),
)
)
workflow.transform(
name='finalize_csv',
ctx={'mem': 16},
func='single_cell.utils.csvutils.rewrite_csv_file',
args=(
mgd.TempInputFile('haplotypes_merged.tsv'),
mgd.OutputFile(haplotypes_filename, extensions=['.yaml']),
),
kwargs={
'write_header': True,
'dtypes': dtypes()['haplotypes']
},
)
return workflow
def create_titan_workflow(
tumour_bam, normal_bam, targets, outfile, params, segs, igv_segs,
parsed, plots, tar_outputs, museq_vcf,
sample_id, reference, chromosomes, het_positions, map_wig, gc_wig, pygenes_gtf,
single_node=None
):
cn_params = config.default_params('copynumber_calling')
chunks = [(v['num_clusters'], v['ploidy']) for v in cn_params['titan_intervals']]
targets = mgd.InputFile(targets) if targets else None
ctx = {'docker_image': config.containers('wgs')}
workflow = pypeliner.workflow.Workflow(ctx=ctx)
workflow.setobj(
obj=mgd.OutputChunks('numclusters', 'ploidy'),
value=chunks,
)
workflow.transform(
name='generate_intervals',
func='wgs.workflows.titan.tasks.generate_intervals',
ctx=helpers.get_default_ctx(
memory=5,
walltime='2:00', ),
ret=mgd.OutputChunks('interval'),
args=(
reference,
chromosomes,
),
kwargs={'size': cn_params['split_size']}
)
if single_node:
workflow.transform(
name='run_museq',
ctx=helpers.get_default_ctx(
memory=15,
walltime='96:00',
ncpus=8),
func='wgs.utils.museq_utils.run_museq_one_job',
args=(
mgd.TempSpace("run_museq_temp"),
mgd.OutputFile(museq_vcf),
reference,
mgd.InputChunks('interval'),
cn_params['museq_params'],
),
kwargs={
'tumour_bam': mgd.InputFile(tumour_bam, extensions=['.bai']),
'normal_bam': mgd.InputFile(normal_bam, extensions=['.bai']),
'titan_mode': True,
'museq_docker_image': config.containers('mutationseq'),
'vcftools_docker_image': config.containers('vcftools')
}
)
else:
workflow.transform(
name='run_museq',
ctx=helpers.get_default_ctx(
memory=15,
walltime='24:00'),
axes=('interval',),
func='wgs.utils.museq_utils.run_museq',
args=(
mgd.TempOutputFile('museq.vcf', 'interval'),
mgd.TempOutputFile('museq.log', 'interval'),
reference,
mgd.InputInstance('interval'),
cn_params['museq_params']
),
kwargs={
'tumour_bam': mgd.InputFile(tumour_bam, extensions=['.bai']),
'normal_bam': mgd.InputFile(normal_bam, extensions=['.bai']),
'titan_mode': True,
'docker_image': config.containers('mutationseq')
}
)
workflow.transform(
name='merge_vcfs',
ctx=helpers.get_default_ctx(
memory=15,
walltime='4:00', ),
func='wgs.utils.museq_utils.merge_vcfs',
args=(
mgd.TempInputFile('museq.vcf', 'interval'),
mgd.OutputFile(museq_vcf),
mgd.TempSpace('merge_vcf'),
),
kwargs={'docker_image': config.containers('vcftools')}
)
workflow.transform(
name='convert_museq_vcf2counts',
ctx=helpers.get_default_ctx(
memory=10,
walltime='4:00', ),
func='wgs.workflows.titan.tasks.convert_museq_vcf2counts',
args=(
mgd.InputFile(museq_vcf),
mgd.TempOutputFile('museq_postprocess.txt'),
het_positions,
),
)
workflow.transform(
name='run_readcounter_tumour',
ctx=helpers.get_default_ctx(
memory=10,
walltime='16:00',
disk=200
),
func='wgs.workflows.titan.tasks.run_readcounter',
args=(
mgd.InputFile(tumour_bam, extensions=['.bai']),
mgd.TempOutputFile('tumour.wig'),
chromosomes,
cn_params['readcounter']
),
)
workflow.transform(
name='run_readcounter_normal',
ctx=helpers.get_default_ctx(
memory=10,
walltime='16:00',
disk=200
),
func='wgs.workflows.titan.tasks.run_readcounter',
args=(
mgd.InputFile(normal_bam, extensions=['.bai']),
mgd.TempOutputFile('normal.wig'),
chromosomes,
cn_params['readcounter']
),
)
workflow.transform(
name='calc_correctreads_wig',
ctx=helpers.get_default_ctx(
memory=10,
walltime='4:00', ),
func='wgs.workflows.titan.tasks.calc_correctreads_wig',
args=(
mgd.TempInputFile('tumour.wig'),
mgd.TempInputFile('normal.wig'),
targets,
mgd.TempOutputFile('correct_reads.txt'),
gc_wig,
map_wig,
cn_params['genome_type']
),
kwargs={'docker_image': config.containers('titan')}
)
workflow.transform(
name='run_titan',
axes=('numclusters', 'ploidy'),
ctx=helpers.get_default_ctx(
memory=15,
walltime='72:00',
ncpus='8'),
func='wgs.workflows.titan.tasks.run_titan',
args=(
mgd.TempInputFile('museq_postprocess.txt'),
mgd.TempInputFile('correct_reads.txt'),
mgd.TempOutputFile('titan_outfile', 'numclusters', 'ploidy'),
mgd.TempOutputFile('titan.Rdata', 'numclusters', 'ploidy'),
mgd.TempOutputFile('titan_params', 'numclusters', 'ploidy'),
mgd.InputInstance('numclusters'),
mgd.InputInstance('ploidy'),
sample_id,
map_wig,
cn_params['titan_params'],
cn_params['genome_type']
),
kwargs={'docker_image': config.containers('titan'), 'threads': '8'}
)
workflow.transform(
name='plot_titan',
axes=('numclusters', 'ploidy'),
ctx=helpers.get_default_ctx(
memory=10,
walltime='16:00', ),
func='wgs.workflows.titan.tasks.plot_titan',
args=(
mgd.TempInputFile('titan.Rdata', 'numclusters', 'ploidy'),
mgd.TempOutputFile('titan_plots', 'numclusters', 'ploidy'),
mgd.TempSpace("titan_plots_tempdir", 'numclusters', 'ploidy'),
mgd.InputInstance('numclusters'),
mgd.InputInstance('ploidy')
),
kwargs={
'chromosomes': chromosomes,
'docker_image': config.containers('titan'),
},
)
workflow.transform(
name='calc_cnsegments_titan',
axes=('numclusters', 'ploidy'),
ctx=helpers.get_default_ctx(
memory=5,
walltime='4:00', ),
func='wgs.workflows.titan.tasks.calc_cnsegments_titan',
args=(
mgd.TempInputFile('titan_outfile', 'numclusters', 'ploidy'),
mgd.TempOutputFile('titan_igv', 'numclusters', 'ploidy'),
mgd.TempOutputFile('segs.csv', 'numclusters', 'ploidy'),
sample_id,
),
kwargs={'docker_image': config.containers('titan')}
)
workflow.transform(
name='annot_pygenes',
axes=('numclusters', 'ploidy'),
ctx=helpers.get_default_ctx(
memory=10,
walltime='4:00', ),
func='wgs.workflows.titan.tasks.annot_pygenes',
args=(
mgd.TempInputFile('segs.csv', 'numclusters', 'ploidy'),
mgd.TempOutputFile('titan_segs.csv', 'numclusters', 'ploidy'),
pygenes_gtf,
),
)
workflow.transform(
name='parse_titan',
axes=('numclusters', 'ploidy'),
ctx=helpers.get_default_ctx(
memory=5,
walltime='4:00', ),
func='wgs.workflows.titan.tasks.parse_titan_data',
args=(
mgd.TempInputFile('titan_segs.csv', 'numclusters', 'ploidy'),
mgd.TempInputFile('titan_outfile', 'numclusters', 'ploidy'),
mgd.TempOutputFile('titan_parsed.csv', 'numclusters', 'ploidy'),
),
)
# select optimal solution
workflow.transform(
name="select_optimal_solution",
ctx=helpers.get_default_ctx(
memory=5,
walltime='4:00', ),
func="wgs.workflows.titan.tasks.select_optimal_solution",
args=(
chunks,
mgd.TempInputFile('titan_params', 'numclusters', 'ploidy', axes_origin=[]),
mgd.TempInputFile("titan_segs.csv", 'numclusters', 'ploidy', axes_origin=[]),
mgd.TempInputFile('titan_igv', 'numclusters', 'ploidy'),
mgd.TempInputFile("titan_outfile", 'numclusters', 'ploidy', axes_origin=[]),
mgd.TempInputFile("titan_parsed.csv", 'numclusters', 'ploidy', axes_origin=[]),
mgd.TempInputFile("titan_plots", 'numclusters', 'ploidy', axes_origin=[]),
mgd.OutputFile(segs, extensions=['.yaml']),
mgd.OutputFile(igv_segs, extensions=['.yaml']),
mgd.OutputFile(params, extensions=['.yaml']),
mgd.OutputFile(outfile, extensions=['.yaml']),
mgd.OutputFile(parsed, extensions=['.yaml']),
mgd.OutputFile(plots),
)
)
workflow.transform(
name='tar_all_data',
ctx=helpers.get_default_ctx(
memory=5,
walltime='4:00', ),
func="wgs.workflows.titan.tasks.tar_all_data",
args=(
mgd.TempInputFile('titan_params', 'numclusters', 'ploidy', axes_origin=[]),
mgd.TempInputFile("titan_segs.csv", 'numclusters', 'ploidy', axes_origin=[]),
mgd.TempInputFile('titan_igv', 'numclusters', 'ploidy'),
mgd.TempInputFile("titan_outfile", 'numclusters', 'ploidy', axes_origin=[]),
mgd.TempInputFile("titan_parsed.csv", 'numclusters', 'ploidy', axes_origin=[]),
mgd.TempInputFile("titan_plots", 'numclusters', 'ploidy', axes_origin=[]),
mgd.OutputFile(tar_outputs),
mgd.TempSpace("titan_all_parameters_data"),
chunks
)
)
return workflow
def cna_calling_workflow(args):
pyp = pypeliner.app.Pypeline(config=args)
workflow = pypeliner.workflow.Workflow()
config = helpers.load_yaml(args['config_file'])
inputs = helpers.load_yaml(args['input_yaml'])
tumours = helpers.get_values_from_input(inputs, 'tumour')
normals = helpers.get_values_from_input(inputs, 'normal')
targets = helpers.get_values_from_input(inputs, 'target_list')
breakpoints = helpers.get_values_from_input(inputs, 'breakpoints')
samples = tumours.keys()
cna_outdir = os.path.join(args['out_dir'], 'copynumber', '{sample_id}')
remixt_results_filename = os.path.join(cna_outdir, 'remixt', 'results.h5')
remixt_raw_dir = os.path.join(cna_outdir, 'remixt', 'raw_data')
titan_raw_dir = os.path.join(cna_outdir, 'titan')
titan_segments_filename = os.path.join(titan_raw_dir, 'segments.h5')
titan_markers_filename = os.path.join(titan_raw_dir, 'markers.h5')
titan_params_filename = os.path.join(titan_raw_dir, 'params.h5')
workflow.setobj(
obj=mgd.OutputChunks('sample_id'),
value=samples)
workflow.subworkflow(
name='titan',
func=titan.create_titan_workflow,
axes=('sample_id',),
args=(
mgd.InputFile("tumour.bam", 'sample_id', fnames=tumours,
extensions=['.bai'], axes_origin=[]),
mgd.InputFile("normal.bam", 'sample_id', fnames=normals,
extensions=['.bai'], axes_origin=[]),
mgd.InputFile("target_list", 'sample_id', fnames=targets,
axes_origin=[]),
mgd.Template(titan_raw_dir, 'sample_id'),
mgd.OutputFile('titan_segments_filename', 'sample_id',
axes_origin=[], template=titan_segments_filename),
mgd.OutputFile('titan_params_filename', 'sample_id',
axes_origin=[], template=titan_params_filename),
mgd.OutputFile('titan_markers_filename', 'sample_id',
axes_origin=[], template=titan_markers_filename),
config['globals'],
config['cna_calling'],
config['cna_calling']['titan_intervals'],
mgd.InputInstance('sample_id'),
),
)
workflow.subworkflow(
name='remixt',
func=remixt.create_remixt_workflow,
axes=('sample_id',),
args=(
mgd.InputFile('tumour_bam', 'sample_id',
fnames=tumours, extensions=['.bai']),
mgd.InputFile('normal_bam', 'sample_id',
fnames=normals, extensions=['.bai']),
mgd.InputFile('destruct_breakpoints', 'sample_id',
axes_origin=[], fnames=breakpoints),
mgd.InputInstance('sample_id'),
config['cna_calling']['remixt_refdata'],
mgd.OutputFile('remixt_results_filename', 'sample_id',
axes_origin=[], template=remixt_results_filename),
mgd.Template(remixt_raw_dir, 'sample_id'),
config['cna_calling']['min_num_reads']
),
)
pyp.run(workflow)
def create_museq_workflow(
normal_bam, tumour_bam, ref_genome, snv_vcf,
config):
museq_docker = {'docker_image': config['docker']['mutationseq']}
vcftools_docker = {'docker_image': config['docker']['vcftools']}
ctx = {'mem_retry_increment': 2, 'disk_retry_increment': 50, 'ncpus': 1, 'num_retry': 3,
'docker_image': config['docker']['single_cell_pipeline']}
workflow = pypeliner.workflow.Workflow(ctx=ctx)
workflow.setobj(
obj=mgd.OutputChunks('region'),
value=list(normal_bam.keys()),
)
workflow.transform(
name='run_museq',
ctx=dict(mem=config["memory"]['med']),
axes=('region',),
func='single_cell.workflows.mutationseq.tasks.run_museq',
args=(
mgd.InputFile('merged_bam', 'region', fnames=tumour_bam, extensions=['.bai']),
mgd.InputFile('normal.split.bam', 'region', fnames=normal_bam, extensions=['.bai']),
mgd.TempOutputFile('museq.vcf', 'region'),
mgd.TempOutputFile('museq.log', 'region'),
mgd.InputInstance('region'),
config,
),
kwargs={'docker_kwargs': museq_docker}
)
workflow.transform(
name='finalise_region_vcfs',
axes=('region',),
ctx=dict(mem=config["memory"]['med']),
func='biowrappers.components.io.vcf.tasks.finalise_vcf',
args=(
mgd.TempInputFile('museq.vcf', 'region'),
mgd.TempOutputFile('museq.vcf.gz', 'region', extensions=['.tbi', '.csi']),
),
kwargs={'docker_config': vcftools_docker}
)
workflow.transform(
name='merge_snvs',
ctx=dict(mem=config["memory"]['med']),
func='biowrappers.components.io.vcf.tasks.concatenate_vcf',
args=(
mgd.TempInputFile('museq.vcf.gz', 'region', extensions=['.tbi', '.csi']),
mgd.TempOutputFile('museq.vcf.gz', extensions=['.tbi', '.csi']),
),
kwargs={
'allow_overlap': True,
'docker_config': vcftools_docker
},
)
workflow.transform(
name='finalise_vcf',
func='biowrappers.components.io.vcf.tasks.finalise_vcf',
ctx=dict(mem=config["memory"]['med']),
args=(
mgd.TempInputFile('museq.vcf.gz', extensions=['.tbi', '.csi']),
mgd.OutputFile(snv_vcf, extensions=['.tbi', '.csi']),
),
kwargs={'docker_config': vcftools_docker}
)
return workflow
def extract_allele_readcounts(
haplotypes_filename,
cell_bams,
allele_counts_filename,
config,
):
baseimage = {'docker_image': config['docker']['single_cell_pipeline']}
remixt_image = config['docker']['remixt']
remixt_config = config.get('extract_seqdata', {})
remixt_ref_data_dir = config['ref_data_dir']
chromosomes = config['chromosomes']
remixt_config['chromosomes'] = chromosomes
workflow = pypeliner.workflow.Workflow(ctx=baseimage)
workflow.set_filenames('cell.bam', 'cell_id', fnames=cell_bams)
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=list(cell_bams.keys()),
)
workflow.subworkflow(
name='create_seqdata_readcounts',
axes=('cell_id', ),
func='remixt.workflow.create_extract_seqdata_workflow',
ctx={'docker_image': remixt_image},
args=(
mgd.InputFile('cell.bam', 'cell_id', extensions=['.bai']),
mgd.TempOutputFile('seqdata.h5', 'cell_id', axes_origin=[]),
remixt_config,
remixt_ref_data_dir,
),
kwargs={'no_parallelism': True})
# TODO Segments with bin width from single cell
workflow.transform(
name='create_segments',
func='remixt.analysis.segment.create_segments',
ctx={
'mem': 16,
'docker_image': remixt_image
},
args=(
mgd.TempOutputFile('segments.tsv'),
remixt_config,
remixt_ref_data_dir,
),
)
workflow.transform(
name='generate_haplotypes_tsv',
func=
'single_cell.workflows.extract_allele_readcounts.tasks.convert_csv_to_tsv',
args=(mgd.InputFile(haplotypes_filename, extensions=['.yaml']),
mgd.TempOutputFile('haplotypes.tsv')))
workflow.transform(
name='haplotype_allele_readcount',
axes=('cell_id', ),
ctx={
'mem': 16,
'docker_image': remixt_image
},
func='remixt.analysis.readcount.haplotype_allele_readcount',
args=(
mgd.TempOutputFile('allele_counts.tsv', 'cell_id', axes_origin=[]),
mgd.TempInputFile('segments.tsv'),
mgd.TempInputFile('seqdata.h5', 'cell_id'),
mgd.TempInputFile('haplotypes.tsv'),
remixt_config,
),
)
workflow.transform(
name='prep_readcount_csv',
axes=('cell_id', ),
func='single_cell.utils.csvutils.rewrite_csv_file',
args=(
mgd.TempInputFile('allele_counts.tsv', 'cell_id'),
mgd.TempOutputFile('allele_counts.csv.gz',
'cell_id',
extensions=['.yaml']),
),
kwargs={
'write_header': True,
'dtypes': dtypes()['readcount']
},
)
workflow.transform(
name='readcounts_cell_id_annotate',
axes=('cell_id', ),
func='single_cell.utils.csvutils.add_col_from_dict',
args=(
mgd.TempInputFile('allele_counts.csv.gz',
'cell_id',
extensions=['.yaml']),
{
'cell_id': mgd.InputInstance('cell_id')
},
mgd.TempOutputFile('allele_counts_annotate.csv.gz',
'cell_id',
extensions=['.yaml']),
dtypes()['readcount'],
),
)
workflow.transform(
name='merge_allele_readcount',
ctx={'mem': 16},
func='single_cell.utils.csvutils.concatenate_csv',
args=(
mgd.TempInputFile('allele_counts_annotate.csv.gz',
'cell_id',
extensions=['.yaml']),
mgd.OutputFile(allele_counts_filename, extensions=['.yaml']),
),
kwargs={'write_header': True},
)
return workflow
def create_infer_haps_workflow(
seqdata_filenames,
haps_filename,
config,
ref_data_dir,
normal_id=None,
):
chromosomes = remixt.config.get_chromosomes(config, ref_data_dir)
workflow = pypeliner.workflow.Workflow()
workflow.setobj(obj=mgd.OutputChunks('chromosome'), value=chromosomes)
if normal_id is not None:
normal_seqdata_filename = seqdata_filenames[normal_id]
workflow.transform(
name='infer_snp_genotype_from_normal',
axes=('chromosome',),
ctx={'mem': 16},
func=remixt.analysis.haplotype.infer_snp_genotype_from_normal,
args=(
mgd.TempOutputFile('snp_genotype.tsv', 'chromosome'),
mgd.InputFile(normal_seqdata_filename),
mgd.InputInstance('chromosome'),
config,
),
)
else:
workflow.setobj(
obj=mgd.OutputChunks('tumour_id'),
value=seqdata_filenames.keys(),
)
workflow.transform(
name='infer_snp_genotype_from_tumour',
axes=('chromosome',),
ctx={'mem': 16},
func=remixt.analysis.haplotype.infer_snp_genotype_from_tumour,
args=(
mgd.TempOutputFile('snp_genotype.tsv', 'chromosome'),
mgd.InputFile('tumour_seqdata', 'tumour_id', fnames=seqdata_filenames),
mgd.InputInstance('chromosome'),
config,
),
)
workflow.transform(
name='infer_haps',
axes=('chromosome',),
ctx={'mem': 16},
func=remixt.analysis.haplotype.infer_haps,
args=(
mgd.TempOutputFile('haps.tsv', 'chromosome'),
mgd.TempInputFile('snp_genotype.tsv', 'chromosome'),
mgd.InputInstance('chromosome'),
mgd.TempSpace('haplotyping', 'chromosome'),
config,
ref_data_dir,
)
)
workflow.transform(
name='merge_haps',
ctx={'mem': 16},
func=remixt.utils.merge_tables,
args=(
mgd.OutputFile(haps_filename),
mgd.TempInputFile('haps.tsv', 'chromosome'),
)
)
return workflow
def create_remixt_seqdata_workflow(
breakpoint_filename,
seqdata_filenames,
results_filenames,
raw_data_directory,
config,
ref_data_dir,
normal_id=None,
):
sample_ids = seqdata_filenames.keys()
tumour_ids = seqdata_filenames.keys()
if normal_id is not None:
tumour_ids.remove(normal_id)
results_filenames = dict([(tumour_id, results_filenames[tumour_id]) for tumour_id in tumour_ids])
segment_filename = os.path.join(raw_data_directory, 'segments.tsv')
haplotypes_filename = os.path.join(raw_data_directory, 'haplotypes.tsv')
counts_table_template = os.path.join(raw_data_directory, 'counts', 'sample_{tumour_id}.tsv')
experiment_template = os.path.join(raw_data_directory, 'experiment', 'sample_{tumour_id}.pickle')
ploidy_plots_template = os.path.join(raw_data_directory, 'ploidy_plots', 'sample_{tumour_id}.pdf')
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('sample_id'),
value=sample_ids,
)
workflow.setobj(
obj=mgd.OutputChunks('tumour_id'),
value=tumour_ids,
)
workflow.transform(
name='create_segments',
func=remixt.analysis.segment.create_segments,
args=(
mgd.OutputFile(segment_filename),
config,
ref_data_dir,
),
kwargs={
'breakpoint_filename': mgd.InputFile(breakpoint_filename),
},
)
workflow.subworkflow(
name='infer_haps_workflow',
func=remixt.workflow.create_infer_haps_workflow,
args=(
mgd.InputFile('seqdata', 'sample_id', fnames=seqdata_filenames),
mgd.OutputFile(haplotypes_filename),
config,
ref_data_dir,
),
kwargs={
'normal_id': normal_id,
}
)
workflow.subworkflow(
name='prepare_counts_workflow',
func=remixt.workflow.create_prepare_counts_workflow,
args=(
mgd.InputFile(segment_filename),
mgd.InputFile(haplotypes_filename),
mgd.InputFile('seqdata', 'tumour_id', fnames=seqdata_filenames),
mgd.TempOutputFile('rawcounts', 'tumour_id', axes_origin=[]),
config,
),
)
workflow.subworkflow(
name='calc_bias_workflow',
axes=('tumour_id',),
func=remixt.workflow.create_calc_bias_workflow,
args=(
mgd.InputFile('seqdata', 'tumour_id', fnames=seqdata_filenames),
mgd.TempInputFile('rawcounts', 'tumour_id'),
mgd.OutputFile('counts', 'tumour_id', template=counts_table_template),
config,
ref_data_dir,
),
)
workflow.transform(
name='create_experiment',
axes=('tumour_id',),
ctx={'mem': 8},
func=remixt.analysis.experiment.create_experiment,
args=(
mgd.InputFile('counts', 'tumour_id', template=counts_table_template),
mgd.InputFile(breakpoint_filename),
mgd.OutputFile('experiment', 'tumour_id', template=experiment_template),
),
)
workflow.transform(
name='ploidy_analysis_plots',
axes=('tumour_id',),
ctx={'mem': 8},
func=remixt.cn_plot.ploidy_analysis_plots,
args=(
mgd.InputFile('experiment', 'tumour_id', template=experiment_template),
mgd.OutputFile('plots', 'tumour_id', template=ploidy_plots_template),
),
)
workflow.subworkflow(
name='fit_model',
axes=('tumour_id',),
func=remixt.workflow.create_fit_model_workflow,
args=(
mgd.InputFile('experiment', 'tumour_id', template=experiment_template),
mgd.OutputFile('results', 'tumour_id', fnames=results_filenames),
config,
ref_data_dir,
),
kwargs={
'tumour_id': mgd.InputInstance('tumour_id'),
},
)
return workflow
def create_museq_workflow(snv_vcf,
museqportrait_pdf,
reference,
chromosomes,
thousand_genomes=None,
dbsnp=None,
germline_refdata=None,
tumour_bam=None,
normal_bam=None,
single_node=None):
name = 'run_museq'
if tumour_bam:
tumour_bam = mgd.InputFile(tumour_bam, extensions=['.bai'])
name += '_tumour'
if normal_bam:
normal_bam = mgd.InputFile(normal_bam, extensions=['.bai'])
name += '_normal'
single = False if name == 'run_museq_tumour_normal' else True
params = config.default_params('variant_calling')
workflow = pypeliner.workflow.Workflow(
ctx={'docker_image': config.containers('wgs')})
workflow.transform(
name='generate_intervals',
func='wgs.workflows.mutationseq.tasks.generate_intervals',
ctx=helpers.get_default_ctx(
memory=5,
walltime='1:00',
),
ret=mgd.OutputChunks('interval'),
args=(reference, chromosomes),
kwargs={'size': params['split_size']})
if single_node:
workflow.transform(name=name,
ctx=helpers.get_default_ctx(memory=15,
walltime='48:00',
ncpus='8',
disk=600),
func='wgs.utils.museq_utils.run_museq_one_job',
args=(
mgd.TempSpace("run_museq_temp"),
mgd.TempOutputFile('merged.vcf'),
reference,
mgd.InputChunks('interval'),
params['museq_params'],
),
kwargs={
'tumour_bam':
tumour_bam,
'normal_bam':
normal_bam,
'museq_docker_image':
config.containers('mutationseq'),
'vcftools_docker_image':
config.containers('vcftools')
})
else:
workflow.transform(name=name,
ctx=helpers.get_default_ctx(
memory=15,
walltime='24:00',
),
axes=('interval', ),
func='wgs.utils.museq_utils.run_museq',
args=(
mgd.TempOutputFile('museq.vcf', 'interval'),
mgd.TempOutputFile('museq.log', 'interval'),
reference,
mgd.InputInstance('interval'),
params['museq_params'],
),
kwargs={
'tumour_bam': tumour_bam,
'normal_bam': normal_bam,
'docker_image':
config.containers('mutationseq'),
})
workflow.transform(
name='merge_vcfs',
ctx=helpers.get_default_ctx(
memory=15,
walltime='8:00',
),
func='wgs.utils.museq_utils.merge_vcfs',
args=(
mgd.TempInputFile('museq.vcf', 'interval'),
mgd.TempOutputFile('merged.vcf'),
mgd.TempSpace('merge_vcf'),
),
kwargs={'docker_image': config.containers('vcftools')})
workflow.transform(name='finalise_snvs',
ctx=helpers.get_default_ctx(walltime='8:00', ),
func='wgs.utils.vcf_tasks.finalise_vcf',
args=(
mgd.TempInputFile('merged.vcf'),
mgd.OutputFile(snv_vcf, extensions=['.tbi',
'.csi']),
),
kwargs={'docker_image': config.containers('vcftools')})
workflow.transform(
name='run_museqportrait',
ctx=helpers.get_default_ctx(
memory=5,
walltime='8:00',
),
func='wgs.workflows.mutationseq.tasks.run_museqportrait',
args=(
mgd.InputFile(snv_vcf, extensions=['.tbi', '.csi']),
mgd.OutputFile(museqportrait_pdf),
mgd.TempOutputFile('museqportrait.txt'),
mgd.TempOutputFile('museqportrait.log'),
single,
),
kwargs={
'docker_image': config.containers('mutationseq'),
'thousand_genomes': thousand_genomes,
'dbsnp': dbsnp,
'germline_refdata': germline_refdata,
'germline_plot_threshold': params['germline_portrait_threshold']
})
return workflow
def create_mutect_workflow(normal_bam,
tumour_bam,
snv_vcf,
snv_maf,
reference,
reference_vep,
chromosomes,
normal_id,
tumour_id,
single_node=None):
params = config.default_params('variant_calling')
workflow = pypeliner.workflow.Workflow()
workflow.transform(name='generate_intervals',
func='wgs.workflows.mutect.tasks.generate_intervals',
ctx=helpers.get_default_ctx(
memory=5,
walltime='1:00',
),
ret=mgd.OutputChunks('interval'),
args=(reference, chromosomes),
kwargs={'size': params['split_size']})
if single_node:
workflow.transform(
name='mutect_one_node',
ctx=helpers.get_default_ctx(memory=15,
walltime='48:00',
ncpus=8,
disk=600),
func='wgs.workflows.mutect.tasks.run_mutect_one_job',
args=(mgd.TempSpace("run_mutect_temp"),
mgd.TempOutputFile('merged.vcf'), reference,
mgd.InputChunks('interval'), mgd.InputFile(normal_bam),
mgd.InputFile(tumour_bam)),
)
else:
workflow.transform(
name='mutect_caller',
ctx=helpers.get_default_ctx(
memory=15,
walltime='24:00',
),
axes=('interval', ),
func='wgs.workflows.mutect.tasks.run_mutect',
args=(mgd.TempOutputFile('mutect.vcf', 'interval'), reference,
mgd.InputInstance('interval'), mgd.InputFile(normal_bam),
mgd.InputFile(tumour_bam),
mgd.TempSpace('mutect_temp', 'interval')),
)
workflow.transform(
name='merge_vcfs',
ctx=helpers.get_default_ctx(
memory=15,
walltime='8:00',
),
func='wgs.workflows.mutect.tasks.merge_vcfs',
args=(
mgd.TempInputFile('mutect.vcf', 'interval'),
mgd.TempOutputFile('merged.vcf'),
mgd.TempSpace('merge_vcf'),
),
)
workflow.transform(name='bcftools_normalize',
ctx=helpers.get_default_ctx(walltime='8:00', ),
func='wgs.utils.vcfutils.bcftools_normalize',
args=(
mgd.TempInputFile('merged.vcf'),
mgd.TempOutputFile('normalized.vcf'),
reference,
))
workflow.transform(
name='finalise_snvs',
ctx=helpers.get_default_ctx(walltime='8:00', ),
func='wgs.utils.vcf_tasks.finalise_vcf',
args=(
mgd.TempInputFile('normalized.vcf'),
mgd.OutputFile(snv_vcf, extensions=['.tbi', '.csi']),
),
)
workflow.subworkflow(name="strelka_indel_maf",
func='wgs.workflows.vcf2maf.create_vcf2maf_workflow',
args=(
mgd.InputFile(snv_vcf,
extensions=['.tbi', '.csi']),
mgd.OutputFile(snv_maf),
reference_vep,
),
kwargs={
'tumour_id': tumour_id,
'normal_id': normal_id
})
return workflow
def infer_haps(
bam_file,
haplotypes_filename,
allele_counts_filename,
config,
normal=False,
):
baseimage = {'docker_image': config['docker']['single_cell_pipeline']}
remixt_config = config.get('extract_seqdata', {})
remixt_ref_data_dir = config['ref_data_dir']
chromosomes = config['chromosomes']
ctx = dict(mem_retry_increment=2,
disk_retry_increment=50,
ncpus=1,
**baseimage)
workflow = pypeliner.workflow.Workflow(ctx=ctx)
if isinstance(bam_file, dict):
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=list(bam_file.keys()),
)
# dont parallelize over chromosomes for per cell bams
workflow.subworkflow(
name="extract_seqdata",
axes=('cell_id', ),
func=
'single_cell.workflows.extract_seqdata.create_extract_seqdata_workflow',
args=(
mgd.InputFile('bam_markdups',
'cell_id',
fnames=bam_file,
extensions=['.bai']),
mgd.TempOutputFile('seqdata_cell.h5', 'cell_id'),
config.get('extract_seqdata', {}),
config['ref_data_dir'],
config,
))
workflow.transform(
name='merge_all_seqdata',
func="single_cell.workflows.titan.tasks.merge_overlapping_seqdata",
args=(mgd.TempOutputFile('seqdata_file.h5'),
mgd.TempInputFile("seqdata_cell.h5",
"cell_id"), config["chromosomes"]),
)
else:
# if its a single bam, then its probably whole genome
# so parallelize over chromosomes
workflow.subworkflow(
name='extract_seqdata',
func='remixt.workflow.create_extract_seqdata_workflow',
ctx={'disk': 150},
args=(
mgd.InputFile(bam_file, extensions=['.bai']),
mgd.TempOutputFile('seqdata_file.h5'),
remixt_config,
remixt_ref_data_dir,
),
)
workflow.setobj(
obj=mgd.OutputChunks('chromosome'),
value=chromosomes,
)
if normal:
func = 'remixt.analysis.haplotype.infer_snp_genotype_from_normal'
else:
func = 'remixt.analysis.haplotype.infer_snp_genotype_from_tumour'
workflow.transform(
name='infer_snp_genotype',
axes=('chromosome', ),
ctx=dict(mem=16, **ctx),
func=func,
args=(
mgd.TempOutputFile('snp_genotype.tsv', 'chromosome'),
mgd.TempInputFile('seqdata_file.h5'),
mgd.InputInstance('chromosome'),
config,
),
)
workflow.transform(
name='infer_haps',
axes=('chromosome', ),
ctx=dict(mem=16, **ctx),
func='remixt.analysis.haplotype.infer_haps',
args=(
mgd.TempOutputFile('haplotypes.tsv', 'chromosome'),
mgd.TempInputFile('snp_genotype.tsv', 'chromosome'),
mgd.InputInstance('chromosome'),
mgd.TempSpace('haplotyping', 'chromosome'),
remixt_config,
remixt_ref_data_dir,
),
)
workflow.transform(name='merge_haps',
ctx=dict(mem=16, **ctx),
func='remixt.utils.merge_tables',
args=(
mgd.OutputFile(haplotypes_filename),
mgd.TempInputFile('haplotypes.tsv', 'chromosome'),
))
workflow.transform(
name='create_segments',
ctx=dict(mem=16, **ctx),
func='remixt.analysis.segment.create_segments',
args=(
mgd.TempOutputFile('segments.tsv'),
remixt_config,
config['ref_data_dir'],
),
)
workflow.transform(
name='haplotype_allele_readcount',
ctx=dict(mem=16, **ctx),
func='remixt.analysis.readcount.haplotype_allele_readcount',
args=(mgd.OutputFile(allele_counts_filename),
mgd.TempInputFile('segments.tsv'),
mgd.TempInputFile('seqdata_file.h5'),
mgd.InputFile(haplotypes_filename), remixt_config),
)
return workflow
def partition_tumour(config, input_args, patient_id, results_dir, input_bams,
input_bais, output_file):
workflow = pypeliner.workflow.Workflow()
workflow.setobj(obj=mgd.OutputChunks('tumour_id', ),
value=input_args['tumour_samples'])
workflow.setobj(obj=mgd.OutputChunks('normal_id', ),
value=input_args['normal_samples'])
workflow.transform(name='merge_normal',
func=tasks.merge_normal,
args=(config,
mgd.InputFile('normal.bam',
'normal_id',
fnames=input_args['normal_bams'],
axes_origin=[]),
mgd.OutputFile(
os.path.join(input_args['patient_bam_dir'],
'merged_normal.bam')),
mgd.OutputFile(
os.path.join(input_args['patient_bam_dir'],
'merged_normal.bam.bai'))))
workflow.subworkflow(
name='analyze_tumour',
func=analyze_tumour_normal,
axes=('tumour_id', ),
args=(
config,
input_args,
results_dir,
mgd.InputFile(
os.path.join(input_args['patient_bam_dir'],
'merged_normal.bam')),
mgd.InputInstance('tumour_id'),
mgd.InputFile('tumour.bam', 'tumour_id', fnames=input_bams),
mgd.OutputFile(
os.path.join(results_dir, patient_id + '_{tumour_id}.snv.tsv'),
'tumour_id'),
mgd.OutputFile(
os.path.join(results_dir,
patient_id + '_{tumour_id}.indel.tsv'),
'tumour_id'),
mgd.TempOutputFile('snv.vcf', 'tumour_id'),
mgd.TempOutputFile('indel.vcf', 'tumour_id'),
))
workflow.transform(name='annotate_snvs',
func=tasks.annotate_outputs,
axes=('tumour_id', ),
args=(
config,
mgd.TempSpace('snv_annotation_space', 'tumour_id'),
mgd.TempInputFile('snv.vcf', 'tumour_id'),
mgd.OutputFile(
os.path.join(
results_dir,
patient_id + '_{tumour_id}.snv.txt'),
'tumour_id'),
))
workflow.transform(name='annotate_indels',
func=tasks.annotate_outputs,
axes=('tumour_id', ),
args=(
config,
mgd.TempSpace('indel_annotation_space',
'tumour_id'),
mgd.TempInputFile('indel.vcf', 'tumour_id'),
mgd.OutputFile(
os.path.join(
results_dir,
patient_id + '_{tumour_id}.indel.txt'),
'tumour_id'),
))
workflow.transform(name='vcf_annotate_indels',
func=tasks.vcf_annotate_outputs,
axes=('tumour_id', ),
args=(
config,
mgd.TempSpace('indel_vcf_annotation_space',
'tumour_id'),
mgd.TempInputFile('indel.vcf', 'tumour_id'),
mgd.OutputFile(
os.path.join(
results_dir,
patient_id + '_{tumour_id}.indel.vcf'),
'tumour_id'),
))
workflow.transform(
name='vcf_annotate_snvs',
func=tasks.vcf_annotate_outputs,
axes=('tumour_id', ),
args=(
config,
mgd.TempSpace('snv_vcf_annotation_space', 'tumour_id'),
mgd.TempInputFile('snv.vcf', 'tumour_id'),
mgd.OutputFile(
os.path.join(results_dir, patient_id + '_{tumour_id}.snv.vcf'),
'tumour_id'),
))
workflow.transform(
name='log_patient_analysis',
func=tasks.log_patient_analysis,
args=(
mgd.InputFile(os.path.join(results_dir,
patient_id + '_{tumour_id}.snv.tsv'),
'tumour_id',
axes_origin=[]),
mgd.InputFile(os.path.join(results_dir,
patient_id + '_{tumour_id}.indel.tsv'),
'tumour_id',
axes_origin=[]),
mgd.InputFile(os.path.join(results_dir,
patient_id + '_{tumour_id}.snv.txt'),
'tumour_id',
axes_origin=[]),
mgd.InputFile(os.path.join(results_dir,
patient_id + '_{tumour_id}.indel.txt'),
'tumour_id',
axes_origin=[]),
mgd.InputFile(os.path.join(results_dir,
patient_id + '_{tumour_id}.snv.vcf'),
'tumour_id',
axes_origin=[]),
mgd.InputFile(os.path.join(results_dir,
patient_id + '_{tumour_id}.indel.vcf'),
'tumour_id',
axes_origin=[]),
mgd.OutputFile(output_file),
))
return workflow
def run_LoLoPicker(config, args, normal_bam, tumour_bam, output_file):
workflow = pypeliner.workflow.Workflow()
workflow.setobj(obj=mgd.OutputChunks('region', ),
value=list(map(str,
range(1, 23) + ['X'])))
workflow.transform(name='create_axes_beds',
axes=('region', ),
func=tasks.create_axes_beds,
args=(mgd.InputFile(config["bed_file"]),
mgd.InputInstance('region'),
mgd.TempOutputFile('region.bed', 'region')))
workflow.transform(name='LoLoPicker_somatic',
axes=('region', ),
func=tasks.LoLoPicker_somatic,
args=(config, mgd.InputFile(tumour_bam),
mgd.InputFile(normal_bam),
mgd.TempInputFile('region.bed', 'region'),
mgd.TempSpace('LoLoPicker_somatic_temp',
'region'),
mgd.TempOutputFile("raw_somatic_varants.txt",
'region')))
workflow.transform(name='make_sample_list',
func=tasks.make_sample_list,
args=(
args,
mgd.TempOutputFile('samplelist.txt'),
))
workflow.transform(name='LoLoPicker_control',
axes=('region', ),
func=tasks.LoLoPicker_control,
args=(config, mgd.TempInputFile('samplelist.txt'),
mgd.TempSpace('LoLoPicker_control_temp',
'region'),
mgd.TempInputFile("raw_somatic_varants.txt",
'region'),
mgd.TempOutputFile("control_stats.txt",
'region')))
workflow.transform(name='LoLoPicker_stats',
axes=('region', ),
func=tasks.LoLoPicker_stats,
args=(
mgd.TempSpace('LoLoPicker_stats_temp', 'region'),
mgd.TempInputFile("raw_somatic_varants.txt",
'region'),
mgd.TempInputFile("control_stats.txt", 'region'),
mgd.TempOutputFile("stats_calls.txt", 'region'),
))
workflow.transform(name='merge_LoLoPicker',
func=tasks.merge_LoLoPicker,
args=(mgd.TempSpace("merge_LoLo"),
mgd.TempInputFile("stats_calls.txt",
'region',
axes_origin=[]),
mgd.OutputFile(output_file)))
return workflow
def breakpoint_calling_workflow(args):
pyp = pypeliner.app.Pypeline(config=args)
inputs = helpers.load_yaml(args['input_yaml'])
meta_yaml = os.path.join(args["out_dir"], 'metadata.yaml')
input_yaml_blob = os.path.join(args["out_dir"], 'input.yaml')
tumours = helpers.get_values_from_input(inputs, 'tumour')
normals = helpers.get_values_from_input(inputs, 'normal')
samples = list(tumours.keys())
sv_outdir = os.path.join(args['out_dir'], 'breakpoints', '{sample_id}')
destruct_breakpoints = os.path.join(
sv_outdir, '{sample_id}_destruct_breakpoints.csv.gz')
destruct_library = os.path.join(sv_outdir,
'{sample_id}_destruct_library.csv.gz')
destruct_raw_breakpoints = os.path.join(
sv_outdir, '{sample_id}_destruct_raw_breakpoints.csv.gz')
destruct_raw_library = os.path.join(
sv_outdir, '{sample_id}_destruct_raw_library.csv.gz')
destruct_reads = os.path.join(sv_outdir,
'{sample_id}_destruct_reads.csv.gz')
lumpy_vcf = os.path.join(sv_outdir, '{sample_id}_lumpy.vcf')
parsed_csv = os.path.join(sv_outdir,
'{sample_id}_filtered_consensus_calls.csv.gz')
svaba_vcf = os.path.join(sv_outdir, '{sample_id}_svaba.vcf')
single_node = args['single_node']
refdir_paths = config.refdir_data(args['refdir'])['paths']
chromosomes = config.refdir_data(args['refdir'])['params']['chromosomes']
workflow = pypeliner.workflow.Workflow()
workflow.setobj(obj=mgd.OutputChunks('sample_id'), value=samples)
workflow.subworkflow(
name='destruct',
func=destruct_wgs.create_destruct_wgs_workflow,
axes=('sample_id', ),
args=(mgd.InputFile("tumour.bam",
'sample_id',
fnames=tumours,
extensions=['.bai'],
axes_origin=[]),
mgd.InputFile("normal.bam",
'sample_id',
fnames=normals,
extensions=['.bai'],
axes_origin=[]),
mgd.OutputFile('destruct_raw_breakpoints',
'sample_id',
template=destruct_raw_breakpoints),
mgd.OutputFile('destruct_raw_library',
'sample_id',
template=destruct_raw_library),
mgd.OutputFile('destruct_breakpoints',
'sample_id',
template=destruct_breakpoints),
mgd.OutputFile('destruct_library',
'sample_id',
template=destruct_library),
mgd.OutputFile('destruct_reads',
'sample_id',
template=destruct_reads),
mgd.InputInstance('sample_id'), refdir_paths['reference'],
refdir_paths['refdata_destruct'], refdir_paths['gtf'],
refdir_paths['blacklist_destruct']),
kwargs={'single_node': single_node})
workflow.subworkflow(
name='lumpy',
func=lumpy.create_lumpy_workflow,
axes=('sample_id', ),
args=(mgd.OutputFile('lumpy_vcf', 'sample_id', template=lumpy_vcf), ),
kwargs={
'tumour_bam':
mgd.InputFile("tumour.bam",
'sample_id',
fnames=tumours,
extensions=['.bai'],
axes_origin=[]),
'normal_bam':
mgd.InputFile("normal.bam",
'sample_id',
fnames=normals,
extensions=['.bai'],
axes_origin=[]),
'single_node':
single_node
},
)
if args['svaba']:
workflow.subworkflow(
name='svaba',
func=svaba.create_svaba_workflow,
axes=('sample_id', ),
args=(
mgd.InputFile("tumour.bam",
'sample_id',
fnames=tumours,
extensions=['.bai'],
axes_origin=[]),
mgd.InputFile("normal.bam",
'sample_id',
fnames=normals,
extensions=['.bai'],
axes_origin=[]),
mgd.OutputFile('svaba_vcf', 'sample_id', template=svaba_vcf),
refdir_paths['reference'],
),
)
workflow.subworkflow(
name="consensus_calling",
func=breakpoint_calling_consensus.create_consensus_workflow,
axes=('sample_id', ),
args=(mgd.InputFile('destruct_breakpoints',
'sample_id',
template=destruct_breakpoints),
mgd.InputFile('lumpy_vcf', 'sample_id', template=lumpy_vcf),
mgd.OutputFile('consensus_calls',
'sample_id',
template=parsed_csv,
extensions=['.yaml']), chromosomes),
)
filenames = [
destruct_breakpoints, destruct_library, destruct_raw_breakpoints,
destruct_raw_library, destruct_reads, lumpy_vcf, parsed_csv
]
if args['svaba']:
filenames.append(svaba_vcf)
outputted_filenames = helpers.expand_list(filenames, samples, "sample_id")
workflow.transform(name='generate_meta_files_results',
func=helpers.generate_and_upload_metadata,
args=(sys.argv[0:], args["out_dir"],
outputted_filenames, mgd.OutputFile(meta_yaml)),
kwargs={
'input_yaml_data':
helpers.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'type': 'breakpoint_calling'
}
})
pyp.run(workflow)
def lumpy_preprocess_cells(config, bam_files, merged_discordants,
merged_splitters, hist_csv, mean_stdev_obj):
ctx = {
'mem_retry_increment': 2,
'disk_retry_increment': 50,
'ncpus': 1,
}
histogram_settings = dict(N=10000,
skip=0,
min_elements=100,
mads=10,
X=4,
read_length=101)
workflow = pypeliner.workflow.Workflow(ctx=ctx)
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=list(bam_files.keys()),
)
workflow.transform(
name='process_tumour_cells',
axes=('cell_id', ),
ctx={
'mem': 8,
'ncpus': 1
},
func='single_cell.workflows.lumpy.tasks.process_bam',
args=(
mgd.InputFile('tumour_bam',
'cell_id',
fnames=bam_files,
extensions=['.bai']),
mgd.TempOutputFile('tumour.discordants.sorted.bam', 'cell_id'),
mgd.TempOutputFile('tumour.splitters.sorted.bam', 'cell_id'),
mgd.TempOutputFile('hist.csv', 'cell_id'),
mgd.TempSpace("lumpy_tumour_processing", "cell_id"),
),
kwargs=dict(tag=mgd.InputInstance('cell_id'), **histogram_settings),
)
workflow.transform(
name='merge_disc',
ctx={
'mem': 8,
'ncpus': 1
},
func='single_cell.workflows.lumpy.tasks.merge_bams',
args=(mgd.TempInputFile('tumour.discordants.sorted.bam',
'cell_id'), mgd.OutputFile(merged_discordants),
mgd.TempSpace("merge_disc_temp")),
)
workflow.transform(
name='merge_split',
ctx={
'mem': 8,
'ncpus': 1
},
func='single_cell.workflows.lumpy.tasks.merge_bams',
args=(mgd.TempInputFile('tumour.splitters.sorted.bam',
'cell_id'), mgd.OutputFile(merged_splitters),
mgd.TempSpace("merge_split_temp")),
)
workflow.transform(
name='merge_histo',
ctx={
'mem': 8,
'ncpus': 1
},
func='single_cell.workflows.lumpy.merge_histograms.merge_histograms',
args=(mgd.TempInputFile('hist.csv',
'cell_id'), mgd.OutputFile(hist_csv),
mgd.OutputFile(mean_stdev_obj)),
)
return workflow
def alignment_workflow(args):
inputs = helpers.load_yaml(args['input_yaml'])
outdir = args['out_dir']
outputs = os.path.join(outdir, '{sample_id}', '{sample_id}.bam')
metrics_output = os.path.join(outdir, '{sample_id}',
'{sample_id}_metrics.csv.gz')
prealignment_tar = os.path.join(outdir, '{sample_id}',
'{sample_id}_fastqc.tar.gz')
postalignment_tar = os.path.join(outdir, '{sample_id}',
'{sample_id}_metrics.tar.gz')
samples = list(inputs.keys())
fastqs_r1, fastqs_r2 = helpers.get_fastqs(inputs, samples, None)
sample_info = helpers.get_sample_info(inputs)
pyp = pypeliner.app.Pypeline(config=args)
workflow = pypeliner.workflow.Workflow(ctx=helpers.get_default_ctx(
docker_image=config.containers('alignment')))
workflow.setobj(
obj=mgd.OutputChunks('sample_id', 'lane_id'),
value=list(fastqs_r1.keys()),
)
workflow.subworkflow(name="prealign",
func=pre_alignment.pre_alignment,
axes=('sample_id', 'lane_id'),
args=(
mgd.InputFile('input.r1.fastq.gz',
'sample_id',
'lane_id',
fnames=fastqs_r1),
mgd.InputFile('input.r2.fastq.gz',
'sample_id',
'lane_id',
fnames=fastqs_r2),
mgd.Template('prealignment.tar',
'sample_id',
template=prealignment_tar),
))
workflow.subworkflow(
name="align",
func=alignment.alignment,
args=(
mgd.InputFile('input.r1.fastq.gz',
'sample_id',
'lane_id',
fnames=fastqs_r1,
axes_origin=[]),
mgd.InputFile('input.r2.fastq.gz',
'sample_id',
'lane_id',
fnames=fastqs_r2,
axes_origin=[]),
mgd.OutputFile('output.bam',
'sample_id',
template=outputs,
axes_origin=[]),
args['refdir'],
sample_info,
),
)
workflow.subworkflow(
name="postalign",
func=post_alignment.post_alignment,
axes=('sample_id', ),
args=(
mgd.InputFile('output.bam', 'sample_id', template=outputs),
mgd.OutputFile('metrics.csv.gz',
'sample_id',
template=metrics_output,
extensions=['.yaml']),
mgd.OutputFile('metrics.tar.gz',
'sample_id',
template=postalignment_tar),
mgd.InputInstance('sample_id'),
args['refdir'],
),
)
pyp.run(workflow)
def get_coverage_data(
input_bam, output, refdir, chromosomes,
mapping_qual, bins, single_node=False
):
reference = config.refdir_data(refdir)['paths']['reference']
workflow = pypeliner.workflow.Workflow()
if single_node:
workflow.transform(
name='generate_coverage_bed',
func='wgs.workflows.sample_qc.tasks.generate_coverage_bed',
ctx=helpers.get_default_ctx(
memory=5
),
args=(
reference,
mgd.TempOutputFile('coverage_bed.bed'),
chromosomes,
bins,
)
)
workflow.transform(
name='samtools_coverage',
func='wgs.workflows.sample_qc.tasks.samtools_coverage',
ctx=helpers.get_default_ctx(
memory=5
),
args=(
mgd.InputFile(input_bam),
mgd.TempInputFile('coverage_bed.bed'),
mgd.TempOutputFile('per_interval.txt', 'chromosome'),
mapping_qual,
),
)
else:
workflow.setobj(
obj=mgd.OutputChunks('chromosome'),
value=chromosomes
)
workflow.transform(
name='generate_coverage_bed',
func='wgs.workflows.sample_qc.tasks.generate_coverage_bed',
ctx=helpers.get_default_ctx(
memory=5
),
axes=('chromosome',),
args=(
reference,
mgd.TempOutputFile('coverage_bed.bed', 'chromosome'),
mgd.InputInstance('chromosome'),
bins,
)
)
workflow.transform(
name='samtools_coverage',
func='wgs.workflows.sample_qc.tasks.samtools_coverage',
ctx=helpers.get_default_ctx(
memory=5
),
axes=('chromosome',),
args=(
mgd.InputFile(input_bam),
mgd.TempInputFile('coverage_bed.bed', 'chromosome'),
mgd.TempOutputFile('per_interval.txt', 'chromosome'),
mapping_qual,
),
)
workflow.transform(
name='merge_data',
func='wgs.utils.csvutils.concatenate_csv',
ctx=helpers.get_default_ctx(
memory=5
),
args=(
mgd.TempInputFile('per_interval.txt', 'chromosome', axes_origin=[]),
mgd.OutputFile(output),
)
)
return workflow
def create_hmmcopy_workflow(
bam_file, reads, segs, metrics, params, igv_seg_filename,
segs_pdf, bias_pdf, plot_heatmap_ec_output,
plot_metrics_output,
plot_kernel_density_output, hmmcopy_data_tar,
cell_ids, hmmparams, sample_info
):
chromosomes = hmmparams["chromosomes"]
baseimage = hmmparams['docker']['single_cell_pipeline']
hmmcopy_docker = hmmparams['docker']['hmmcopy']
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=cell_ids,
)
workflow.setobj(
obj=mgd.TempOutputObj('sampleinfo', 'cell_id', axes_origin=[]),
value=sample_info)
workflow.transform(
name='run_hmmcopy',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.run_hmmcopy",
axes=('cell_id',),
args=(
mgd.InputFile('bam_markdups', 'cell_id', fnames=bam_file, extensions=['.bai']),
mgd.TempOutputFile('reads.csv.gz', 'cell_id', extensions=['.yaml']),
mgd.TempOutputFile('segs.csv.gz', 'cell_id', extensions=['.yaml']),
mgd.TempOutputFile('params.csv.gz', 'cell_id', extensions=['.yaml']),
mgd.TempOutputFile('hmm_metrics.csv.gz', 'cell_id', extensions=['.yaml']),
mgd.TempOutputFile('hmm_data.tar.gz', 'cell_id'),
mgd.InputInstance('cell_id'),
hmmparams,
mgd.TempSpace('hmmcopy_temp', 'cell_id'),
hmmcopy_docker
),
)
workflow.transform(
name='merge_reads',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.concatenate_csv",
args=(
mgd.TempInputFile('reads.csv.gz', 'cell_id', axes_origin=[], extensions=['.yaml']),
mgd.TempOutputFile('reads_merged.csv.gz', extensions=['.yaml']),
),
kwargs={'low_memory': True}
)
workflow.transform(
name='add_mappability_bool',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.get_mappability_col",
args=(
mgd.TempInputFile('reads_merged.csv.gz', extensions=['.yaml']),
mgd.OutputFile(reads, extensions=['.yaml']),
),
)
workflow.transform(
name='merge_segs',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.concatenate_csv",
args=(
mgd.TempInputFile('segs.csv.gz', 'cell_id', axes_origin=[], extensions=['.yaml']),
mgd.OutputFile(segs, extensions=['.yaml']),
),
kwargs={'low_memory': True}
)
workflow.transform(
name='merge_metrics',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.concatenate_csv",
args=(
mgd.TempInputFile('hmm_metrics.csv.gz', 'cell_id', axes_origin=[], extensions=['.yaml']),
mgd.TempOutputFile("hmm_metrics.csv.gz", extensions=['.yaml']),
),
)
workflow.transform(
name='merge_params',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.concatenate_csv",
args=(
mgd.TempInputFile('params.csv.gz', 'cell_id', axes_origin=[], extensions=['.yaml']),
mgd.OutputFile(params, extensions=['.yaml']),
),
)
workflow.transform(
name='get_max_cn',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.get_max_cn",
ret=mgd.TempOutputObj('max_cn'),
args=(
mgd.InputFile(reads, extensions=['.yaml']),
)
)
workflow.transform(
name='hmmcopy_plots',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.plot_hmmcopy",
axes=('cell_id',),
args=(
mgd.TempInputFile('reads.csv.gz', 'cell_id', axes_origin=[], extensions=['.yaml']),
mgd.TempInputFile('segs.csv.gz', 'cell_id', axes_origin=[], extensions=['.yaml']),
mgd.TempInputFile('params.csv.gz', 'cell_id', axes_origin=[], extensions=['.yaml']),
mgd.TempInputFile('hmm_metrics.csv.gz', 'cell_id', axes_origin=[], extensions=['.yaml']),
hmmparams['ref_genome'],
mgd.TempOutputFile('segments.png', 'cell_id', axes_origin=[]),
mgd.TempOutputFile('bias.png', 'cell_id', axes_origin=[]),
mgd.InputInstance('cell_id'),
),
kwargs={
'num_states': hmmparams['num_states'],
'sample_info': mgd.TempInputObj('sampleinfo', 'cell_id'),
'max_cn': mgd.TempInputObj("max_cn")
}
)
workflow.transform(
name='annotate_metrics_with_info_and_clustering',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.add_clustering_order",
args=(
mgd.InputFile(reads, extensions=['.yaml']),
mgd.TempInputFile("hmm_metrics.csv.gz", extensions=['.yaml']),
mgd.OutputFile(metrics, extensions=['.yaml']),
),
kwargs={
'chromosomes': hmmparams["chromosomes"],
'sample_info': sample_info
}
)
workflow.transform(
name='merge_hmm_copy_plots',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.merge_pdf",
args=(
[
mgd.TempInputFile('segments.png', 'cell_id'),
mgd.TempInputFile('bias.png', 'cell_id'),
],
[
mgd.OutputFile(segs_pdf),
mgd.OutputFile(bias_pdf),
],
mgd.InputFile(metrics, extensions=['.yaml']),
None,
mgd.TempSpace("hmmcopy_plot_merge_temp"),
['segments', 'bias']
)
)
workflow.transform(
name='create_igv_seg',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.create_igv_seg",
args=(
mgd.InputFile(segs, extensions=['.yaml']),
mgd.InputFile(metrics, extensions=['.yaml']),
mgd.OutputFile(igv_seg_filename),
hmmparams,
)
)
workflow.transform(
name='plot_metrics',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.plot_metrics",
args=(
mgd.InputFile(metrics, extensions=['.yaml']),
mgd.OutputFile(plot_metrics_output),
'QC pipeline metrics',
)
)
workflow.transform(
name='plot_kernel_density',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.plot_kernel_density",
args=(
mgd.InputFile(metrics, extensions=['.yaml']),
mgd.OutputFile(plot_kernel_density_output),
',',
'mad_neutral_state',
'QC pipeline metrics',
)
)
workflow.transform(
name='plot_heatmap_ec',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.workflows.hmmcopy.tasks.plot_pcolor",
args=(
mgd.InputFile(reads, extensions=['.yaml']),
mgd.InputFile(metrics, extensions=['.yaml']),
mgd.OutputFile(plot_heatmap_ec_output),
),
kwargs={
'plot_title': 'QC pipeline metrics',
'column_name': 'state',
'plot_by_col': 'experimental_condition',
'color_by_col': 'cell_call',
'chromosomes': chromosomes,
'max_cn': hmmparams['num_states'],
'scale_by_cells': False,
'mappability_threshold': hmmparams["map_cutoff"]
}
)
workflow.transform(
name='merge_hmmcopy_data_tars',
ctx={'mem': hmmparams['memory']['med'], 'ncpus': 1, 'docker_image': baseimage},
func="single_cell.utils.helpers.tar_files",
args=(
mgd.TempInputFile('hmm_data.tar.gz', 'cell_id', axes_origin=[]),
mgd.OutputFile(hmmcopy_data_tar),
mgd.TempSpace("merge_tarballs")
),
)
return workflow
def create_aneufinder_workflow(
bam_file,
cell_ids,
config,
aneufinder_results_filename,
aneufinder_pdf_filename,
):
baseimage = config['docker']['single_cell_pipeline']
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=cell_ids,
)
workflow.transform(
name='run_aneufinder_on_individual_cells',
ctx={
'mem': config['memory']['med'],
'ncpus': 1,
'docker_image': baseimage
},
func="single_cell.workflows.aneufinder.tasks.run_aneufinder",
axes=('cell_id', ),
args=(
mgd.InputFile('bam_file', 'cell_id', fnames=bam_file),
mgd.TempSpace('working_dir', 'cell_id', fnames=bam_file),
mgd.InputInstance('cell_id'),
mgd.TempOutputFile('segments.csv', 'cell_id'),
mgd.TempOutputFile('reads.csv', 'cell_id'),
mgd.TempOutputFile('dnacopy.pdf', 'cell_id'),
),
kwargs={'docker_image': config['docker']['aneufinder']})
workflow.transform(
name='merge_outputs',
ctx={
'mem': config['memory']['med'],
'ncpus': 1,
'docker_image': baseimage
},
func="single_cell.workflows.aneufinder.tasks.merge_outputs_to_hdf",
args=(
mgd.TempInputFile('reads.csv', 'cell_id'),
mgd.TempInputFile('segments.csv', 'cell_id'),
mgd.OutputFile(aneufinder_results_filename),
mgd.TempSpace("aneufinder_merge"),
))
workflow.transform(name='merge_aneufinder_pdfs',
ctx={
'mem': config['memory']['med'],
'ncpus': 1,
'docker_image': baseimage
},
func="single_cell.workflows.aneufinder.tasks.merge_pdf",
args=(
[mgd.TempInputFile('dnacopy.pdf', 'cell_id')],
[mgd.OutputFile(aneufinder_pdf_filename)],
))
return workflow