def annotation_workflow(args):
config = inpututils.load_config(args)
annotation_infiles = inpututils.load_yaml(args['input_yaml'])
lib = args["library_id"]
workflow = pypeliner.workflow.Workflow(ctx={
'docker_image':
config['annotation']['docker']['single_cell_pipeline']
}, )
annotation_dir = args["out_dir"]
input_yaml_blob = os.path.join(annotation_dir, 'input.yaml')
annotation_files = get_output_files(annotation_dir, lib)
annotation_meta = os.path.join(annotation_dir, 'metadata.yaml')
workflow.subworkflow(
name='annotation_workflow',
func=qc_annotation.create_qc_annotation_workflow,
args=(
mgd.InputFile(annotation_infiles['hmmcopy_metrics']),
mgd.InputFile(annotation_infiles['hmmcopy_reads']),
mgd.InputFile(annotation_infiles['alignment_metrics']),
mgd.InputFile(annotation_infiles['gc_metrics']),
mgd.InputFile(annotation_infiles['segs_pdf_tar']),
mgd.OutputFile(annotation_files['merged_metrics_csvs']),
mgd.OutputFile(annotation_files['qc_report']),
mgd.OutputFile(annotation_files['corrupt_tree_newick']),
mgd.OutputFile(annotation_files['consensus_tree_newick']),
mgd.OutputFile(annotation_files['phylo_csv']),
mgd.OutputFile(annotation_files['loci_rank_trees']),
mgd.OutputFile(annotation_files['filtered_data']),
mgd.OutputFile(annotation_files['corrupt_tree_pdf']),
mgd.OutputFile(annotation_files['segs_pass']),
mgd.OutputFile(annotation_files['segs_fail']),
mgd.OutputFile(annotation_files['corrupt_heatmap_pdf']),
mgd.OutputFile(annotation_files['heatmap_filt_pdf']),
config['annotation'],
lib,
),
kwargs={'no_corrupt_tree': args['no_corrupt_tree']})
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], annotation_dir, list(annotation_files.values()),
mgd.OutputFile(annotation_meta)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'library_id': lib,
'type': 'annotation'
}
})
return workflow
def count_haps_workflow(args):
config = inpututils.load_config(args)
config = config['count_haps']
baseimage = config['docker']['single_cell_pipeline']
ctx = dict(mem_retry_increment=2,
disk_retry_increment=50,
ncpus=1,
docker_image=baseimage)
workflow = pypeliner.workflow.Workflow(ctx=ctx)
allele_counts_filename = os.path.join(args["out_dir"],
"allele_counts.csv.gz")
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
haplotypes_filename, tumour_cells = inpututils.load_count_haps_input(
args['input_yaml'])
workflow.setobj(
obj=mgd.OutputChunks('tumour_cell_id'),
value=list(tumour_cells.keys()),
)
workflow.subworkflow(
name='extract_allele_readcounts',
func=
'single_cell.workflows.extract_allele_readcounts.extract_allele_readcounts',
args=(
mgd.InputFile(haplotypes_filename, extensions=['.yaml']),
mgd.InputFile('tumour_cells.bam',
'tumour_cell_id',
extensions=['.bai'],
axes_origin=[],
fnames=tumour_cells),
mgd.OutputFile(allele_counts_filename),
config,
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'], [allele_counts_filename],
mgd.OutputFile(meta_yaml)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'type': 'count_haps'
}
})
return workflow
def infer_haps_workflow(args):
config = inpututils.load_config(args)
config = config['infer_haps']
baseimage = config['docker']['single_cell_pipeline']
ctx = dict(mem_retry_increment=2,
disk_retry_increment=50,
ncpus=1,
docker_image=baseimage)
workflow = pypeliner.workflow.Workflow(ctx=ctx)
haplotypes_filename = os.path.join(args["out_dir"], "haplotypes.tsv")
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
normal_data = inpututils.load_infer_haps_input(args['input_yaml'])
if isinstance(normal_data, dict):
workflow.setobj(
obj=mgd.OutputChunks('normal_cell_id'),
value=list(normal_data.keys()),
)
bam_file = mgd.InputFile('normal.bam',
'normal_cell_id',
fnames=normal_data,
extensions=['.bai'])
else:
bam_file = mgd.InputFile(normal_data, extensions=['.bai'])
workflow.subworkflow(
name='infer_haps',
func=infer_haps,
args=(
bam_file,
mgd.OutputFile(haplotypes_filename),
config,
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'], [haplotypes_filename],
mgd.OutputFile(meta_yaml)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'type': 'infer_haps'
}
})
return workflow
def make_meta(args):
workflow = pypeliner.workflow.Workflow()
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
filelist = []
for root, dirs, files in os.walk(args['out_dir']):
for file in files:
filelist.append(os.path.join(root, file))
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'], filelist,
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'
}
})
return workflow
def breakpoint_calling_workflow(args):
config = inpututils.load_config(args)
config = config['breakpoint_calling']
run_destruct = True if args['destruct'] else False
run_lumpy = True if args['lumpy'] else False
if not run_destruct and not run_lumpy:
run_destruct = True
run_lumpy = True
normal_data, tumour_cells = inpututils.load_breakpoint_calling_input(args['input_yaml'])
bkp_dir = os.path.join(args['out_dir'])
bkp_meta = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
out_files = get_output_files(bkp_dir, run_destruct, run_lumpy)
ref_data_directory = config['ref_data_directory']
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('tumour_cell_id'),
value=list(tumour_cells.keys()),
)
if isinstance(normal_data, dict):
workflow.setobj(
obj=mgd.OutputChunks('normal_cell_id'),
value=list(normal_data.keys()),
)
normal_bam = mgd.InputFile(
'normal_cells.bam', 'normal_cell_id',
extensions=['.bai'], fnames=normal_data
)
else:
normal_bam = mgd.InputFile(normal_data, extensions=['.bai'])
if run_destruct:
workflow.subworkflow(
name='destruct',
func="single_cell.workflows.destruct_singlecell.create_destruct_workflow",
args=(
normal_bam,
mgd.InputFile('tumour.bam', 'tumour_cell_id', fnames=tumour_cells),
config.get('destruct_config', {}),
config,
ref_data_directory,
mgd.OutputFile(out_files['destruct_breakpoints_filename'], extensions=['.yaml']),
mgd.OutputFile(out_files['destruct_breakpoints_lib_filename'], extensions=['.yaml']),
mgd.OutputFile(out_files['destruct_cell_counts_filename'], extensions=['.yaml']),
),
)
if run_lumpy:
workflow.subworkflow(
name='lumpy',
func="single_cell.workflows.lumpy.create_lumpy_workflow",
args=(
config,
normal_bam,
mgd.InputFile('tumour.bam', 'tumour_cell_id', fnames=tumour_cells, extensions=['.bai']),
mgd.OutputFile(out_files['lumpy_breakpoints_csv'], extensions=['.yaml']),
mgd.OutputFile(out_files['lumpy_breakpoints_evidence_csv'], extensions=['.yaml']),
mgd.OutputFile(out_files['lumpy_breakpoints_bed']),
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(
sys.argv[0:],
bkp_dir,
list(out_files.values()),
mgd.OutputFile(bkp_meta)
),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {'type': 'breakpoint_calling'}
}
)
return workflow
def variant_calling_workflow(args):
config = inpututils.load_config(args)
config = config['variant_calling']
normal_bams, tumour_bams = inpututils.load_variant_calling_input(
args['input_yaml'])
filepaths = get_file_paths(args['out_dir'], config)
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
ctx = {
'ncpus': 1,
'mem_retry_increment': 2,
'disk_retry_increment': 50,
'mem': config["memory"]['low'],
}
workflow = pypeliner.workflow.Workflow(ctx=ctx)
workflow.setobj(
obj=mgd.OutputChunks('region'),
value=list(normal_bams.keys()),
)
workflow.subworkflow(
name='museq',
func=mutationseq.create_museq_workflow,
args=(
mgd.InputFile('normal_regions.bam',
'region',
extensions=['.bai'],
fnames=normal_bams),
mgd.InputFile('tumour_regions.bam',
'region',
extensions=['.bai'],
fnames=tumour_bams),
mgd.OutputFile(filepaths['museq_vcf'], extensions=['.tbi',
'.csi']),
mgd.OutputFile(filepaths['museq_csv'], extensions=['.tbi',
'.csi']),
config,
),
)
workflow.subworkflow(name='strelka',
func=strelka.create_strelka_workflow,
args=(
mgd.InputFile('normal_regions.bam',
'region',
extensions=['.bai'],
fnames=normal_bams),
mgd.InputFile('tumour_regions.bam',
'region',
extensions=['.bai'],
fnames=tumour_bams),
config['ref_genome'],
mgd.OutputFile(filepaths['strelka_indel'],
extensions=['.tbi', '.csi']),
mgd.OutputFile(filepaths['strelka_snv'],
extensions=['.tbi', '.csi']),
mgd.OutputFile(filepaths['strelka_csv'],
extensions=['.yaml']),
),
kwargs={
"chromosomes": config["chromosomes"],
"use_depth_thresholds":
config['use_depth_thresholds']
})
workflow.subworkflow(
name='annotate_snv_vcf_files',
func=snv_annotate.create_snv_annotate_workflow,
args=(config,
mgd.InputFile(filepaths['museq_vcf'],
extensions=['.tbi', '.csi']),
mgd.InputFile(filepaths['strelka_snv'],
extensions=['.tbi', '.csi']),
mgd.OutputFile(filepaths['mappability_csv'],
extensions=['.yaml']),
mgd.OutputFile(filepaths['snpeff_csv'], extensions=['.yaml']),
mgd.OutputFile(filepaths['trinuc_csv'], extensions=['.yaml']), {
k: mgd.OutputFile(v)
for k, v in filepaths['additional_databases'].items()
}, config['memory']))
allfiles = [
filepaths[k] for k in filepaths if not k == 'additional_databases'
]
allfiles += filepaths['additional_databases'].values()
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'], allfiles,
mgd.OutputFile(meta_yaml)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'type': 'variant_calling'
}
})
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 germline_calling_workflow(args):
config = inpututils.load_config(args)
config = config['germline_calling']
normal_bams = inpututils.load_germline_data(args['input_yaml'])
varcalls_meta = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
out_files = get_output_files(args['out_dir'])
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('region'),
value=list(normal_bams.keys()),
)
workflow.subworkflow(
name='samtools_germline',
func=germline.create_samtools_germline_workflow,
args=(
mgd.InputFile("normal_split.bam",
"region",
extensions=['.bai'],
fnames=normal_bams),
config['ref_genome'],
mgd.OutputFile(out_files['samtools_germline_vcf'],
extensions=['.tbi']),
config,
),
)
workflow.subworkflow(
name='annotate_mappability',
func=
"biowrappers.components.variant_calling.mappability.create_vcf_mappability_annotation_workflow",
args=(
config['databases']['mappability']['local_path'],
mgd.InputFile(out_files['samtools_germline_vcf'],
extensions=['.tbi']),
mgd.OutputFile(out_files['mappability_filename']),
),
kwargs={'chromosomes': config['chromosomes']})
workflow.transform(
name='annotate_genotype',
func="single_cell.workflows.germline.tasks.annotate_normal_genotype",
args=(
mgd.InputFile(out_files['samtools_germline_vcf'],
extensions=['.tbi']),
mgd.OutputFile(out_files['normal_genotype_filename']),
config["chromosomes"],
),
)
workflow.subworkflow(
name='snpeff',
func=
"biowrappers.components.variant_calling.snpeff.create_snpeff_annotation_workflow",
args=(
config['databases']['snpeff']['db'],
config['databases']['snpeff']['data_dir'],
mgd.InputFile(out_files['samtools_germline_vcf'],
extensions=['.tbi']),
mgd.OutputFile(out_files['snpeff_vcf_filename']),
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'], list(out_files.values()),
mgd.OutputFile(varcalls_meta)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'type': 'germline_calling'
}
})
return workflow
def alignment_workflow(args):
config = inpututils.load_config(args)
config = config['alignment']
lib = args["library_id"]
alignment_dir = args["out_dir"]
bams_dir = args["bams_dir"]
trim = args['trim']
center = args['sequencing_center']
sampleinfo = inpututils.get_sample_info(args['input_yaml'])
cellids = inpututils.get_samples(args['input_yaml'])
fastq1_files, fastq2_files = inpututils.get_fastqs(args['input_yaml'])
alignment_files = get_output_files(alignment_dir, lib)
alignment_meta = os.path.join(alignment_dir, 'metadata.yaml')
bam_files_template = os.path.join(bams_dir, '{cell_id}.bam')
mt_bam_files_template = os.path.join(bams_dir, '{cell_id}_MT.bam')
bams_meta = os.path.join(bams_dir, 'metadata.yaml')
lanes = sorted(set([v[1] for v in fastq1_files.keys()]))
cells = sorted(set([v[0] for v in fastq1_files.keys()]))
input_yaml_blob = os.path.join(alignment_dir, 'input.yaml')
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('cell_id', 'lane'),
value=list(fastq1_files.keys()),
)
workflow.subworkflow(
name='alignment_workflow',
func=align.create_alignment_workflow,
args=(
mgd.InputFile('fastq_1',
'cell_id',
'lane',
fnames=fastq1_files,
axes_origin=[]),
mgd.InputFile('fastq_2',
'cell_id',
'lane',
fnames=fastq2_files,
axes_origin=[]),
mgd.OutputFile('bam_markdups',
'cell_id',
template=bam_files_template,
axes_origin=[],
extensions=['.bai']),
mgd.OutputFile('mt_bam_markdups',
'cell_id',
template=mt_bam_files_template,
axes_origin=[],
extensions=['.bai']),
mgd.OutputFile(alignment_files['alignment_metrics_csv']),
mgd.OutputFile(alignment_files['gc_metrics_csv']),
mgd.OutputFile(alignment_files['fastqc_metrics_csv']),
mgd.OutputFile(alignment_files['plot_metrics_output']),
config['ref_genome'],
config,
sampleinfo,
cellids,
mgd.OutputFile(alignment_files['alignment_metrics_tar']),
lib,
trim,
center,
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], alignment_dir, list(alignment_files.values()),
mgd.OutputFile(alignment_meta)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'library_id': lib,
'cell_ids': cells,
'lane_ids': lanes,
'type': 'alignment'
}
})
workflow.transform(
name='generate_meta_files_bams',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], bams_dir,
mgd.Template('aligned.bam',
'cell_id',
template=bam_files_template),
mgd.OutputFile(bams_meta)),
kwargs={
'metadata': {
'library_id': lib,
'cell_ids': cells,
'lane_ids': lanes,
'type': 'cellbams'
},
'template':
(mgd.InputChunks('cell_id'), bam_files_template, 'cell_id'),
})
return workflow
def variant_calling_workflow(args):
config = inpututils.load_config(args)
config = config['variant_calling']
normal_bams, tumour_bams = inpututils.load_variant_calling_input(
args['input_yaml'])
filepaths = get_file_paths(args['out_dir'])
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
basedocker = {'docker_image': config['docker']['single_cell_pipeline']}
vcftools_docker = {'docker_image': config['docker']['vcftools']}
baseimage = config['docker']['single_cell_pipeline']
ctx = {
'mem_retry_increment': 2,
'disk_retry_increment': 50,
'ncpus': 1,
'mem': config["memory"]['low'],
'docker_image': baseimage
}
workflow = pypeliner.workflow.Workflow(ctx=ctx)
workflow.setobj(
obj=mgd.OutputChunks('region'),
value=list(normal_bams.keys()),
)
workflow.subworkflow(
name='museq',
func=mutationseq.create_museq_workflow,
args=(
mgd.InputFile('normal_regions.bam',
'region',
extensions=['.bai'],
fnames=normal_bams),
mgd.InputFile('tumour_regions.bam',
'region',
extensions=['.bai'],
fnames=tumour_bams),
config['ref_genome'],
mgd.OutputFile(filepaths['museq_vcf'], extensions=['.tbi',
'.csi']),
config,
),
)
workflow.subworkflow(name='strelka',
func=strelka.create_strelka_workflow,
args=(
mgd.InputFile('normal_regions.bam',
'region',
extensions=['.bai'],
fnames=normal_bams),
mgd.InputFile('tumour_regions.bam',
'region',
extensions=['.bai'],
fnames=tumour_bams),
config['ref_genome'],
mgd.OutputFile(filepaths['strelka_indel'],
extensions=['.tbi', '.csi']),
mgd.OutputFile(filepaths['strelka_snv'],
extensions=['.tbi', '.csi']),
config,
),
kwargs={"chromosomes": config["chromosomes"]})
workflow.transform(
name='merge_snvs',
func='biowrappers.components.io.vcf.tasks.merge_vcfs',
ctx=ctx,
args=([
mgd.InputFile(filepaths['museq_vcf'], extensions=['.tbi', '.csi']),
mgd.InputFile(filepaths['strelka_snv'],
extensions=['.tbi', '.csi']),
], mgd.TempOutputFile('all.snv.vcf')),
)
workflow.transform(name='finalise_snvs',
func="biowrappers.components.io.vcf.tasks.finalise_vcf",
ctx=ctx,
args=(mgd.TempInputFile('all.snv.vcf'),
mgd.TempOutputFile('all.snv.vcf.gz',
extensions=['.tbi', '.csi'])),
kwargs={'docker_config': vcftools_docker})
workflow.subworkflow(
name='annotate_snvs',
axes=(),
ctx=ctx,
func=
"biowrappers.pipelines.snv_call_and_annotate.create_annotation_workflow",
args=(
config,
mgd.TempInputFile('all.snv.vcf.gz', extensions=['.tbi', '.csi']),
mgd.TempOutputFile('snv_annotations.h5'),
mgd.TempSpace('raw_data_dir_annotate'),
),
kwargs={
'variant_type': 'snv',
'docker_config': basedocker,
'snpeff_docker': vcftools_docker,
'vcftools_docker': vcftools_docker
})
workflow.transform(
name='convert_museq_to_csv',
func="biowrappers.components.io.vcf.tasks.convert_vcf_to_csv",
ctx=ctx,
args=(
mgd.InputFile(filepaths['museq_vcf']),
mgd.TempOutputFile('museq.csv'),
),
kwargs={
'score_callback': museq_callback,
})
workflow.transform(name='prep_museq_csv',
func='single_cell.utils.csvutils.prep_csv_files',
args=(mgd.TempInputFile('museq.csv'),
mgd.OutputFile(filepaths['museq_csv'],
extensions=['.yaml'])),
kwargs={'header': True})
workflow.transform(
name='convert_strelka_to_csv',
func="biowrappers.components.io.vcf.tasks.convert_vcf_to_csv",
ctx=ctx,
args=(
mgd.InputFile(filepaths['strelka_snv']),
mgd.TempOutputFile('strelka_snv.csv'),
),
kwargs={
'score_callback': strelka_snv_callback,
})
workflow.transform(name='prep_strelka_csv',
func='single_cell.utils.csvutils.prep_csv_files',
args=(mgd.TempInputFile('strelka_snv.csv'),
mgd.OutputFile(filepaths['strelka_csv'],
extensions=['.yaml'])),
kwargs={'header': True})
workflow.transform(name='convert_h5_to_csv',
func='single_cell.utils.hdfutils.convert_hdf_to_csv',
args=(mgd.TempInputFile('snv_annotations.h5'), {
'/snv/cosmic_status':
mgd.OutputFile(filepaths['cosmic_csv'],
extensions=['.yaml']),
'/snv/dbsnp_status':
mgd.OutputFile(filepaths['dbsnp_csv'],
extensions=['.yaml']),
'/snv/mappability':
mgd.OutputFile(filepaths['mappability_csv'],
extensions=['.yaml']),
'/snv/snpeff':
mgd.OutputFile(filepaths['snpeff_csv'],
extensions=['.yaml']),
'/snv/tri_nucleotide_context':
mgd.OutputFile(filepaths['trinuc_csv'],
extensions=['.yaml']),
}))
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'], list(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': 'variant_calling'
}
})
return workflow
def merge_bams_workflow(args):
config = inpututils.load_config(args)
config = config['merge_bams']
ctx = {
'mem_retry_increment': 2,
'disk_retry_increment': 50,
'ncpus': 1,
'mem': config["memory"]['low']
}
workflow = pypeliner.workflow.Workflow(ctx=ctx)
bam_files = inpututils.load_merge_cell_bams(args['input_yaml'])
merge_out_template = os.path.join(args['out_dir'], '{region}.bam')
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=list(bam_files.keys()),
)
workflow.transform(
name="get_regions",
func="single_cell.utils.pysamutils.get_regions_from_reference",
ret=pypeliner.managed.OutputChunks('region'),
args=(
config["ref_genome"],
config["split_size"],
config["chromosomes"],
))
workflow.transform(
name="remove_softclipped_reads",
func="single_cell.utils.pysamutils.remove_softclipped_reads",
axes=('cell_id', ),
args=(mgd.InputFile('bam_markdups',
'cell_id',
fnames=bam_files,
extensions=['.bai']),
mgd.TempOutputFile('bam_rm_softclipped.bam',
'cell_id',
extensions=['.bai']),
args['softclipped_reads_threshold']))
workflow.subworkflow(name="wgs_merge_workflow",
func=merge_bams.create_merge_bams_workflow,
args=(
mgd.TempInputFile('bam_rm_softclipped.bam',
'cell_id',
extensions=['.bai']),
mgd.OutputFile("merged.bam",
"region",
axes_origin=[],
extensions=['.bai'],
template=merge_out_template),
mgd.InputChunks("region"),
config,
))
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'],
mgd.Template('bam_filenames',
'region',
template=merge_out_template),
mgd.OutputFile(meta_yaml)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'template':
(mgd.InputChunks('region'), merge_out_template, 'region'),
'metadata': {
'type': 'pseudowgs_regionbams',
'cell_ids': list(bam_files.keys())
}
})
return workflow
def hmmcopy_workflow(args):
config = inpututils.load_config(args)
config = config['hmmcopy']
sampleinfo = inpututils.get_sample_info(args['input_yaml'])
cellids = inpututils.get_samples(args['input_yaml'])
bam_files = inpututils.get_bams(args['input_yaml'])
lib = args["library_id"]
workflow = pypeliner.workflow.Workflow(
ctx={'docker_image': config['docker']['single_cell_pipeline']}, )
hmmcopy_dir = args["out_dir"]
hmmcopy_files = get_output_files(hmmcopy_dir, lib)
hmmcopy_meta = os.path.join(hmmcopy_dir, 'metadata.yaml')
input_yaml_blob = os.path.join(hmmcopy_dir, 'input.yaml')
workflow.setobj(
obj=mgd.OutputChunks('cell_id'),
value=list(bam_files.keys()),
)
workflow.subworkflow(
name='hmmcopy_workflow',
func=hmmcopy.create_hmmcopy_workflow,
args=(mgd.InputFile('bam_markdups',
'cell_id',
fnames=bam_files,
extensions=['.bai']),
mgd.OutputFile(hmmcopy_files['reads_csvs']),
mgd.OutputFile(hmmcopy_files['segs_csvs']),
mgd.OutputFile(hmmcopy_files['metrics_csvs']),
mgd.OutputFile(hmmcopy_files['params_csvs']),
mgd.OutputFile(hmmcopy_files['igv_csvs']),
mgd.OutputFile(hmmcopy_files['segs_pdf']),
mgd.OutputFile(hmmcopy_files['bias_pdf']),
mgd.OutputFile(hmmcopy_files['heatmap_pdf']),
mgd.OutputFile(hmmcopy_files['metrics_pdf']),
mgd.OutputFile(hmmcopy_files['kernel_density_pdf']),
mgd.OutputFile(hmmcopy_files['hmmcopy_data_tar']), cellids,
config, sampleinfo),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], hmmcopy_dir, list(hmmcopy_files.values()),
mgd.OutputFile(hmmcopy_meta)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'library_id': lib,
'cell_ids': list(bam_files.keys()),
'type': 'hmmcopy',
}
})
return workflow
def create_variant_counting_workflow(args):
""" Count variant reads for multiple sets of variants across cells.
"""
strelka_vcf, museq_vcf, tumour_cell_bams = inpututils.load_variant_counting_input(
args['input_yaml'])
counts_output = os.path.join(args['out_dir'], "counts.csv.gz")
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
config = inpututils.load_config(args)
config = config['variant_calling']
workflow = pypeliner.workflow.Workflow(
ctx={'docker_image': config['docker']['single_cell_pipeline']})
workflow.setobj(
obj=mgd.OutputChunks('sample_id', 'library_id', 'cell_id'),
value=list(tumour_cell_bams.keys()),
)
workflow.transform(name='merge_snvs_museq',
func='single_cell.utils.vcfutils.merge_vcf',
args=([
mgd.InputFile('museq.vcf',
'sample_id',
'library_id',
fnames=museq_vcf,
extensions=['.tbi', '.csi'],
axes_origin=[]),
mgd.InputFile('strelka.vcf',
'sample_id',
'library_id',
fnames=strelka_vcf,
extensions=['.tbi', '.csi'],
axes_origin=[]),
],
mgd.TempOutputFile('all.snv.vcf.gz',
extensions=['.tbi', '.csi']),
mgd.TempSpace("merge_vcf_temp")),
kwargs={'docker_image': config['docker']['vcftools']})
workflow.subworkflow(
name='count_alleles',
func=create_snv_allele_counts_for_vcf_targets_workflow,
args=(
mgd.InputFile('tumour_cells.bam',
'sample_id',
'library_id',
'cell_id',
extensions=['.bai'],
fnames=tumour_cell_bams,
axes_origin=[]),
mgd.TempInputFile('all.snv.vcf.gz', extensions=['.tbi', '.csi']),
mgd.OutputFile(counts_output),
config['memory'],
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'], [counts_output],
mgd.OutputFile(meta_yaml)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'type': 'snv_genotyping'
}
})
return workflow
def split_bam_workflow(args):
config = inpututils.load_config(args)
config = config['split_bam']
bam_file = inpututils.load_split_wgs_input(args['input_yaml'])
baseimage = config['docker']['single_cell_pipeline']
split_bam_template = os.path.join(args['out_dir'], '{region}.bam')
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
workflow = pypeliner.workflow.Workflow(ctx={'docker_image': baseimage})
workflow.transform(
name="get_regions",
ctx={
'mem': config['memory']['low'],
'ncpus': 1,
'docker_image': baseimage
},
func="single_cell.utils.pysamutils.get_regions_from_reference",
ret=pypeliner.managed.TempOutputObj('region'),
args=(
config["ref_genome"],
config["split_size"],
config["chromosomes"],
))
workflow.subworkflow(
name="split_normal",
func=split_bams.create_split_workflow,
ctx={
'mem': config['memory']['low'],
'ncpus': 1
},
args=(
mgd.InputFile(bam_file),
mgd.OutputFile("normal.split.bam",
'region',
template=split_bam_template,
axes_origin=[]),
pypeliner.managed.TempInputObj('region'),
config,
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'],
mgd.Template('bam_filenames',
'region',
template=split_bam_template),
mgd.OutputFile(meta_yaml)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'type': 'wgs_regionbams'
},
'template':
(mgd.TempInputObj('region'), split_bam_template, 'region'),
})
return workflow
def create_variant_counting_workflow(args):
""" Count variant reads for multiple sets of variants across cells.
"""
vcf_files, tumour_cell_bams, sample_library = inpututils.load_variant_counting_input(
args['input_yaml'])
counts_template = '{sample_id}_{library_id}_counts.csv.gz'
counts_output_template = os.path.join(args['out_dir'], counts_template)
meta_yaml = os.path.join(args['out_dir'], 'metadata.yaml')
input_yaml_blob = os.path.join(args['out_dir'], 'input.yaml')
config = inpututils.load_config(args)
config = config['variant_calling']
workflow = pypeliner.workflow.Workflow()
workflow.setobj(
obj=mgd.OutputChunks('sample_id', 'library_id', 'cell_id'),
value=list(tumour_cell_bams.keys()),
)
workflow.transform(
name='merge_snvs_museq',
func='single_cell.utils.vcfutils.merge_vcf',
args=([mgd.InputFile(vcf_file) for vcf_file in vcf_files],
mgd.TempOutputFile('all.snv.vcf.gz', extensions=['.tbi',
'.csi']),
mgd.TempSpace("merge_vcf_temp")),
)
workflow.subworkflow(
name='count_alleles',
axes=('sample_id', 'library_id'),
func=
'single_cell.workflows.snv_allele_counts.create_snv_allele_counts_for_vcf_targets_workflow',
args=(
mgd.InputFile('tumour_cells.bam',
'sample_id',
'library_id',
'cell_id',
extensions=['.bai'],
fnames=tumour_cell_bams,
axes_origin=[]),
mgd.TempInputFile('all.snv.vcf.gz', extensions=['.tbi', '.csi']),
mgd.OutputFile('counts.csv.gz',
'sample_id',
'library_id',
template=counts_output_template),
mgd.Instance('sample_id'),
mgd.Instance('library_id'),
config['memory'],
),
)
workflow.transform(
name='generate_meta_files_results',
func='single_cell.utils.helpers.generate_and_upload_metadata',
args=(sys.argv[0:], args['out_dir'],
mgd.Template('counts.csv.gz',
'sample_id',
'library_id',
template=counts_output_template),
mgd.OutputFile(meta_yaml)),
kwargs={
'input_yaml_data': inpututils.load_yaml(args['input_yaml']),
'input_yaml': mgd.OutputFile(input_yaml_blob),
'metadata': {
'type': 'snv_genotyping',
'counts': {
'template': counts_template,
'instances': sample_library,
}
}
})
return workflow
