def main(args, outs):
martian.log_info(u'Logging à non-ascii character from unicode string.')
martian.log_info(u'Logging à non-ascii character from python string.'.encode('utf-8'))
martian.log_info({'hello': 'world'})
martian.update_progress(u'%s = %f' % (
u'+'.join([u'%f\u00b2' % v for v in args.values]),
args.sum))
def prepare_transcriptome_indexes(reference_path, vdj_reference_path):
""" Use ReadStates of R1/R2 to determine SC3Pv1 vs SC3Pv2 vs SC5P-R1 vs SC5P_auto/SCVDJ.
Returns (chemistry_name, report, metrics)
where report is a text report and metrics is a dict """
## Index the reference fasta
fa_path = os.path.join(reference_path, cr_constants.REFERENCE_FASTA_PATH)
new_fa_path = martian.make_path('ref.fa')
need_index = True
if os.path.exists(fa_path + '.fai'):
# Look for existing .fai file (won't exist for our standard ref packages)
martian.update_progress('Found genome FASTA index....')
new_fa_path = fa_path
need_index = False
else:
# Note: this will fail if user's fs doesn't support symlinks
martian.update_progress('Symlinking genome FASTA...')
os.symlink(fa_path, new_fa_path)
if need_index:
martian.update_progress('Indexing genome...')
run(['samtools', 'faidx', new_fa_path])
## Generate a transcriptome reference from a genome ref
martian.update_progress('Building transcriptome...')
gtf_path = os.path.join(reference_path,
cr_constants.REFERENCE_GENES_GTF_PATH)
out_fa_path = martian.make_path('transcriptome.fa')
# Only index the 1st encountered transcript per gene
run([
'detect_chemistry', 'get-transcripts', new_fa_path, gtf_path,
out_fa_path
])
## Build kmer index
martian.update_progress('Building kmer index...')
kmer_idx_path = martian.make_path('kmers.idx')
run(['detect_chemistry', 'index-transcripts', out_fa_path, kmer_idx_path])
# Build VDJ kmer index (optional)
vdj_idx_path = None
if vdj_reference_path is not None:
vdj_fa_path = vdj_ref.get_vdj_reference_fasta(vdj_reference_path)
vdj_idx_path = martian.make_path('vdj_kmers.idx')
run([
'detect_chemistry', 'index-transcripts', vdj_fa_path, vdj_idx_path
])
return (kmer_idx_path, vdj_idx_path)
def infer_sc3p_or_sc5p(chunks, kmer_idx_path, vdj_idx_path):
""" Use ReadStates of R1/R2 to determine SC3Pv1 vs SC3Pv2 vs SC5P-R1 vs SC5P_auto/SCVDJ.
Returns (chemistry_name, report, metrics)
where report is a text report and metrics is a dict """
## Map read kmers to each strand
martian.update_progress('Mapping reads...')
# Prepare fastq paths
fq_specs = {}
for read_type in READ_TYPES:
fq_specs[read_type] = martian.make_path('%s_in.json' % read_type)
with open(fq_specs[read_type], 'w') as f:
json.dump([c for c in chunks if c['read_type'] == read_type], f)
# Map reads to gene expression reference
metrics = {}
for read_type in READ_TYPES:
map_out_path = martian.make_path('%s_out.json' % read_type)
metrics[read_type] = map_reads(fq_specs[read_type], kmer_idx_path,
map_out_path)
# Map reads to VDJ reference (optional)
if vdj_idx_path is not None:
for read_type in READ_TYPES:
map_out_path = martian.make_path('vdj_%s_out.json' % read_type)
vdj_metrics = map_reads(fq_specs[read_type], vdj_idx_path,
map_out_path)
for k, v in vdj_metrics.iteritems():
metrics[read_type]['vdj_' + k] = v
# Verify total read counts
r1_total = metrics['R1']['total_reads']
r2_total = metrics['R2']['total_reads']
if r1_total != 0 and r2_total != 0 and r1_total != r2_total:
martian.exit(
'Total read counts for R1 and R2 must be identical if both are present. There were %d R1 reads and %d R2 reads. Check that all of the FASTQ files are present.'
% (r1_total, r2_total))
## Infer chemistry
report = '\n'
for read_type, m in metrics.iteritems():
report += '%s Total Reads: %s\n' % (read_type, str(
m['total_reads']).rjust(20))
report += '%s Sense Reads: %s\n' % (read_type, str(
m['sense_reads']).rjust(20))
report += '%s Antisense Reads: %s\n' % (
read_type, str(m['antisense_reads']).rjust(20))
if vdj_idx_path is not None:
for read_type, m in metrics.iteritems():
report += '%s Sense V(D)J Reads: %s\n' % (
read_type, str(m['vdj_sense_reads']).rjust(20))
report += '%s Antisense V(D)J Reads: %s\n' % (
read_type, str(m['vdj_antisense_reads']).rjust(20))
r1_state = getReadState(metrics['R1']['sense_reads'],
metrics['R1']['antisense_reads'],
metrics['R1']['mapped_reads'],
metrics['R1']['total_reads'])
r2_state = getReadState(metrics['R2']['sense_reads'],
metrics['R2']['antisense_reads'],
metrics['R2']['mapped_reads'],
metrics['R2']['total_reads'])
report += "\n"
chemistry_name = None
if (r1_state == ReadState.SENSE_MAPPED) and (r2_state
== ReadState.UNMAPPED):
chemistry_name = 'SC3Pv1'
report += "This library is likely to be a Single Cell 3' gene expression library (v1)."
elif r2_state == ReadState.SENSE_MAPPED:
chemistry_name = 'SC3Pv2'
report += "This library is likely to be a Single Cell 3' gene expression library (v2)."
elif (r1_state == ReadState.SENSE_MAPPED) and (r2_state
== ReadState.ABSENT):
chemistry_name = 'SC5P-R1'
report += "This library is likely to be a Single Cell 5' gene expression library (R1)."
elif (r2_state == ReadState.ANTISENSE_MAPPED):
r1_gex_sense_count = metrics['R1'].get('sense_reads', 0)
r2_gex_anti_count = metrics['R2'].get('antisense_reads', 0)
r1_vdj_sense_count = metrics['R1'].get('vdj_sense_reads', 0)
r2_vdj_anti_count = metrics['R2'].get('vdj_antisense_reads', 0)
if vdj_idx_path is None:
report += "This library is likely to be a Single Cell V(D)J or Single Cell 5' gene expression library."
chemistry_name = 'SC5P_auto'
else:
if (is_vdj(r1_gex_sense_count, r1_vdj_sense_count)
or is_vdj(r2_gex_anti_count, r2_vdj_anti_count)):
report += "This library is likely to be a Single Cell V(D)J library."
chemistry_name = 'SCVDJ'
else:
report += "This library is likely to be a Single Cell 5' gene expression library."
chemistry_name = 'SC5P_auto'
else:
report += "There was not enough information to determine the nature of the library."
chemistry_name = None
return chemistry_name, report, metrics
def prepare_transcriptome_indexes(reference_path, vdj_reference_path):
""" Use ReadStates of R1/R2 to determine SC3Pv1 vs SC3Pv2 vs SC5P-R1 vs SC5P_auto/SCVDJ.
Returns (chemistry_name, report, metrics)
where report is a text report and metrics is a dict """
## Index the reference fasta
fa_path = os.path.join(reference_path, cr_constants.REFERENCE_FASTA_PATH)
new_fa_path = martian.make_path('ref.fa')
need_index = True
if os.path.exists(fa_path + '.fai'):
# Look for existing .fai file (won't exist for our standard ref packages)
martian.update_progress('Found genome FASTA index....')
new_fa_path = fa_path
need_index = False
else:
# Note: this will fail if user's fs doesn't support symlinks
martian.update_progress('Symlinking genome FASTA...')
os.symlink(fa_path, new_fa_path)
if need_index:
martian.update_progress('Indexing genome...')
run(['samtools', 'faidx', new_fa_path])
## Generate a transcriptome reference from a genome ref
martian.update_progress('Building transcriptome...')
gtf_path = os.path.join(reference_path,
cr_constants.REFERENCE_GENES_GTF_PATH)
out_fa_path = martian.make_path('transcriptome.fa')
# Only index the 1st encountered transcript per gene
run([
'detect_chemistry', 'get-transcripts', new_fa_path, gtf_path,
out_fa_path
])
## Build kmer index
martian.update_progress('Building kmer index...')
kmer_idx_path = martian.make_path('kmers.idx')
## Use a larger step size as the reference grows.
## This ensure the index size stays sane.
## Should get to a step of <10 for the whole genome, which
## is still 3x overlap w/ 32-mers
fa_size = os.path.getsize(os.path.realpath(out_fa_path))
step = fa_size / 400000000
skip = step - 1
index_args = ['detect_chemistry', 'index-transcripts']
if skip > 0:
index_args.append('--skip=%d' % skip)
index_args.extend([out_fa_path, kmer_idx_path])
run(index_args)
# Build VDJ kmer index (optional)
vdj_idx_path = None
if vdj_reference_path is not None:
vdj_fa_path = vdj_ref.get_vdj_reference_fasta(vdj_reference_path)
vdj_idx_path = martian.make_path('vdj_kmers.idx')
run([
'detect_chemistry', 'index-transcripts', vdj_fa_path, vdj_idx_path
])
return (kmer_idx_path, vdj_idx_path)
def main(args, outs):
if not args.sum is None:
martian.update_progress('%s = %f' %
('+'.join(['%f^2' % v
for v in args.values]), args.sum))
