def count_graph_coverage_wrapper(fname_in, fname_out, CFG):
(genes, inserted) = cPickle.load(open(fname_in, 'r'))
if genes[0].segmentgraph is None or genes[0].segmentgraph.is_empty():
for g in genes:
g.segmentgraph = Segmentgraph(g)
cPickle.dump((genes, inserted), open(fname_in, 'w'), -1)
counts = dict()
counts['segments'] = []
counts['seg_pos'] = []
counts['gene_ids_segs'] = []
counts['edges'] = []
counts['gene_ids_edges'] = []
counts['seg_len'] = sp.hstack([
x.segmentgraph.segments[1, :] - x.segmentgraph.segments[0, :]
for x in genes
]).T
counts['gene_names'] = sp.array([x.name for x in genes], dtype='str')
if not CFG['rproc']:
for s_idx in range(CFG['strains'].shape[0]):
print '\n%i/%i' % (s_idx + 1, CFG['strains'].shape[0])
if s_idx == 0:
counts_tmp = count_graph_coverage(genes,
CFG['bam_fnames'][s_idx],
CFG)
else:
counts_tmp = sp.r_[
sp.atleast_2d(counts_tmp),
count_graph_coverage(genes, CFG['bam_fnames'][s_idx], CFG)]
for c in range(counts_tmp.shape[1]):
counts['segments'].append(
sp.hstack(
[sp.atleast_2d(x.segments).T for x in counts_tmp[:, c]]))
counts['seg_pos'].append(
sp.hstack(
[sp.atleast_2d(x.seg_pos).T for x in counts_tmp[:, c]]))
counts['gene_ids_segs'].append(
sp.ones((sp.atleast_2d(counts_tmp[0, c].seg_pos).shape[1], 1),
dtype='int') * c)
tmp = [
sp.atleast_2d(x.edges) for x in counts_tmp[:, c]
if x.edges.shape[0] > 0
]
if len(tmp) == 0:
continue
tmp = sp.hstack(tmp)
if tmp.shape[0] > 0:
counts['edges'].append(sp.c_[tmp[:, 0],
tmp[:,
range(1, tmp.shape[1], 2)]])
counts['gene_ids_edges'].append(
sp.ones((tmp.shape[0], 1), dtype='int') * c)
### write result data to hdf5
for key in counts:
counts[key] = sp.vstack(
counts[key]) if len(counts[key]) > 0 else counts[key]
counts['edge_idx'] = counts['edges'][:, 0] if len(
counts['edges']) > 0 else sp.array([])
counts['edges'] = counts['edges'][:, 1:] if len(
counts['edges']) > 0 else sp.array([])
h5fid = h5py.File(fname_out, 'w')
h5fid.create_dataset(name='strains', data=CFG['strains'])
for key in counts:
h5fid.create_dataset(name=key, data=counts[key])
h5fid.close()
else:
### have an adaptive chunk size, that takes into account the number of strains (take as many genes as it takes to have ~10K strains)
chunksize = int(max(1, math.floor(10000 / len(CFG['strains']))))
jobinfo = []
PAR = dict()
PAR['CFG'] = CFG
for c_idx in range(0, genes.shape[0], chunksize):
cc_idx = min(genes.shape[0], c_idx + chunksize)
fn = fname_out.replace('.pickle',
'.chunk_%i_%i.pickle' % (c_idx, cc_idx))
if os.path.exists(fn):
continue
else:
print 'submitting chunk %i to %i' % (c_idx, cc_idx)
PAR['genes'] = genes[c_idx:cc_idx]
PAR['fn_bam'] = CFG['bam_fnames']
PAR['fn_out'] = fn
PAR['CFG'] = CFG
jobinfo.append(
rp.rproc('count_graph_coverage', PAR, 6000,
CFG['options_rproc'], 60 * 48))
rp.rproc_wait(jobinfo, 30, 1.0, -1)
### merge results from count chunks
if 'verbose' in CFG and CFG['verbose']:
print '\nCollecting count data from chunks ...\n'
print 'writing data to %s' % fname_out
### write data to hdf5 continuously
h5fid = h5py.File(fname_out, 'w')
h5fid.create_dataset(name='gene_names', data=counts['gene_names'])
h5fid.create_dataset(name='seg_len', data=counts['seg_len'])
h5fid.create_dataset(name='strains', data=CFG['strains'])
for c_idx in range(0, genes.shape[0], chunksize):
cc_idx = min(genes.shape[0], c_idx + chunksize)
if 'verbose' in CFG and CFG['verbose']:
print 'collecting chunk %i-%i (%i)' % (c_idx, cc_idx,
genes.shape[0])
fn = fname_out.replace('.pickle',
'.chunk_%i_%i.pickle' % (c_idx, cc_idx))
if not os.path.exists(fn):
print >> sys.stderr, 'ERROR: Not all chunks in counting graph coverage completed!'
sys.exit(1)
else:
counts_tmp = cPickle.load(open(fn, 'r'))
for c in range(counts_tmp.shape[1]):
if 'segments' in h5fid:
appendToHDF5(
h5fid,
sp.hstack([
sp.atleast_2d(x.segments).T
for x in counts_tmp[:, c]
]), 'segments')
appendToHDF5(
h5fid,
sp.hstack([
sp.atleast_2d(x.seg_pos).T
for x in counts_tmp[:, c]
]), 'seg_pos')
appendToHDF5(
h5fid,
sp.ones((sp.atleast_2d(
counts_tmp[0, c].seg_pos).shape[1], 1),
dtype='int') * (c_idx + c),
'gene_ids_segs')
else:
h5fid.create_dataset(name='segments',
data=sp.hstack([
sp.atleast_2d(x.segments).T
for x in counts_tmp[:, c]
]),
chunks=True,
compression='gzip',
maxshape=(None,
len(CFG['strains'])))
h5fid.create_dataset(name='seg_pos',
data=sp.hstack([
sp.atleast_2d(x.seg_pos).T
for x in counts_tmp[:, c]
]),
chunks=True,
compression='gzip',
maxshape=(None,
len(CFG['strains'])))
h5fid.create_dataset(
name='gene_ids_segs',
data=sp.ones((sp.atleast_2d(
counts_tmp[0, c].seg_pos).shape[1], 1),
dtype='int') * (c_idx + c),
chunks=True,
compression='gzip',
maxshape=(None, 1))
#counts['segments'].append(sp.hstack([sp.atleast_2d(x.segments).T for x in counts_tmp[:, c]]))
#counts['seg_pos'].append(sp.hstack([sp.atleast_2d(x.seg_pos).T for x in counts_tmp[:, c]]))
#counts['gene_ids_segs'].append(sp.ones((sp.atleast_2d(counts_tmp[0, c].seg_pos).shape[1], 1), dtype='int') * (c_idx + c))
tmp = [
sp.atleast_2d(x.edges) for x in counts_tmp[:, c]
if x.edges.shape[0] > 0
]
if len(tmp) == 0:
continue
tmp = sp.hstack(tmp)
if tmp.shape[0] > 0:
if 'edges' in h5fid:
appendToHDF5(h5fid, tmp[:,
range(1, tmp.shape[1], 2)],
'edges')
appendToHDF5(h5fid, tmp[:, 0], 'edge_idx')
appendToHDF5(
h5fid,
sp.ones((tmp.shape[0], 1), dtype='int') *
(c_idx + c), 'gene_ids_edges')
else:
h5fid.create_dataset(
name='edges',
data=tmp[:, range(1, tmp.shape[1], 2)],
chunks=True,
compression='gzip',
maxshape=(None, tmp.shape[1] / 2))
h5fid.create_dataset(name='edge_idx',
data=tmp[:, 0],
chunks=True,
compression='gzip',
maxshape=(None, ))
h5fid.create_dataset(
name='gene_ids_edges',
data=sp.ones((tmp.shape[0], 1), dtype='int') *
(c_idx + c),
chunks=True,
compression='gzip',
maxshape=(None, 1))
#counts['edges'].append(sp.c_[tmp[:, 0], tmp[:, range(1, tmp.shape[1], 2)]])
#counts['gene_ids_edges'].append(sp.ones((tmp.shape[0], 1), dtype='int') * (c_idx + c))
del tmp, counts_tmp
h5fid.close()
def count_graph_coverage_wrapper(fname_in, fname_out, CFG, sample_idx=None):
(genes, inserted) = cPickle.load(open(fname_in, 'r'))
if genes[0].segmentgraph is None or genes[0].segmentgraph.is_empty():
for g in genes:
g.segmentgraph = Segmentgraph(g)
cPickle.dump((genes, inserted), open(fname_in, 'w'), -1)
counts = dict()
counts['segments'] = []
counts['seg_pos'] = []
counts['gene_ids_segs'] = []
counts['edges'] = []
counts['gene_ids_edges'] = []
counts['seg_len'] = sp.hstack([x.segmentgraph.segments[1, :] - x.segmentgraph.segments[0, :] for x in genes]).T
counts['gene_names'] = sp.array([x.name for x in genes], dtype='str')
if not CFG['rproc']:
if CFG['merge_strategy'] == 'single':
print '\nprocessing %s' % (CFG['samples'][sample_idx])
counts_tmp = count_graph_coverage(genes, CFG['bam_fnames'][sample_idx], CFG)
else:
for s_idx in range(CFG['strains'].shape[0]):
print '\n%i/%i' % (s_idx + 1, CFG['strains'].shape[0])
if s_idx == 0:
counts_tmp = count_graph_coverage(genes, CFG['bam_fnames'][s_idx], CFG)
else:
counts_tmp = sp.r_[sp.atleast_2d(counts_tmp), count_graph_coverage(genes, CFG['bam_fnames'][s_idx], CFG)]
for c in range(counts_tmp.shape[1]):
counts['segments'].append(sp.hstack([sp.atleast_2d(x.segments).T for x in counts_tmp[:, c]]))
counts['seg_pos'].append(sp.hstack([sp.atleast_2d(x.seg_pos).T for x in counts_tmp[:, c]]))
counts['gene_ids_segs'].append(sp.ones((sp.atleast_2d(counts_tmp[0, c].seg_pos).shape[1], 1), dtype='int') * c)
tmp = [sp.atleast_2d(x.edges) for x in counts_tmp[:, c] if x.edges.shape[0] > 0]
if len(tmp) == 0:
continue
tmp = sp.hstack(tmp)
if tmp.shape[0] > 0:
counts['edges'].append(sp.c_[tmp[:, 0], tmp[:, range(1, tmp.shape[1], 2)]])
counts['gene_ids_edges'].append(sp.ones((tmp.shape[0], 1), dtype='int') * c)
### write result data to hdf5
for key in counts:
counts[key] = sp.vstack(counts[key]) if len(counts[key]) > 0 else counts[key]
counts['edge_idx'] = counts['edges'][:, 0] if len(counts['edges']) > 0 else sp.array([])
counts['edges'] = counts['edges'][:, 1:] if len(counts['edges']) > 0 else sp.array([])
h5fid = h5py.File(fname_out, 'w')
h5fid.create_dataset(name='strains', data=CFG['strains'])
for key in counts:
h5fid.create_dataset(name=key, data=counts[key])
h5fid.close()
else:
### have an adaptive chunk size, that takes into account the number of strains (take as many genes as it takes to have ~10K strains)
chunksize = int(max(1, math.floor(10000 / len(CFG['strains']))))
jobinfo = []
PAR = dict()
PAR['CFG'] = CFG.copy()
if CFG['merge_strategy'] == 'single':
PAR['CFG']['bam_fnames'] = PAR['CFG']['bam_fnames'][sample_idx]
PAR['CFG']['samples'] = PAR['CFG']['samples'][sample_idx]
PAR['CFG']['strains'] = PAR['CFG']['strains'][sample_idx]
#s_idx = sp.argsort([x.chr for x in genes]) # TODO
s_idx = sp.arange(genes.shape[0])
for c_idx in range(0, s_idx.shape[0], chunksize):
cc_idx = min(s_idx.shape[0], c_idx + chunksize)
fn = re.sub(r'.hdf5$', '', fname_out) + '.chunk_%i_%i.pickle' % (c_idx, cc_idx)
if os.path.exists(fn):
continue
else:
print 'submitting chunk %i to %i (%i)' % (c_idx, cc_idx, s_idx.shape[0])
PAR['genes'] = genes[s_idx][c_idx:cc_idx]
PAR['fn_bam'] = CFG['bam_fnames']
PAR['fn_out'] = fn
PAR['CFG'] = CFG
jobinfo.append(rp.rproc('count_graph_coverage', PAR, 15000, CFG['options_rproc'], 60*12))
rp.rproc_wait(jobinfo, 30, 1.0, -1)
del genes
### merge results from count chunks
if 'verbose' in CFG and CFG['verbose']:
print '\nCollecting count data from chunks ...\n'
print 'writing data to %s' % fname_out
### write data to hdf5 continuously
h5fid = h5py.File(fname_out, 'w')
h5fid.create_dataset(name='gene_names', data=counts['gene_names'])
h5fid.create_dataset(name='seg_len', data=counts['seg_len'])
h5fid.create_dataset(name='strains', data=CFG['strains'])
for c_idx in range(0, s_idx.shape[0], chunksize):
cc_idx = min(s_idx.shape[0], c_idx + chunksize)
if 'verbose' in CFG and CFG['verbose']:
print 'collecting chunk %i-%i (%i)' % (c_idx, cc_idx, s_idx.shape[0])
fn = re.sub(r'.hdf5$', '', fname_out) + '.chunk_%i_%i.pickle' % (c_idx, cc_idx)
if not os.path.exists(fn):
print >> sys.stderr, 'ERROR: Not all chunks in counting graph coverage completed!'
sys.exit(1)
else:
counts_tmp = cPickle.load(open(fn, 'r'))
for c in range(counts_tmp.shape[1]):
if 'segments' in h5fid:
appendToHDF5(h5fid, sp.hstack([sp.atleast_2d(x.segments).T for x in counts_tmp[:, c]]), 'segments')
appendToHDF5(h5fid, sp.hstack([sp.atleast_2d(x.seg_pos).T for x in counts_tmp[:, c]]), 'seg_pos')
appendToHDF5(h5fid, sp.ones((sp.atleast_2d(counts_tmp[0, c].seg_pos).shape[1], 1), dtype='int') * (s_idx[c_idx + c]), 'gene_ids_segs')
else:
h5fid.create_dataset(name='segments', data=sp.hstack([sp.atleast_2d(x.segments).T for x in counts_tmp[:, c]]), chunks=True, compression='gzip', maxshape=(None, len(CFG['strains'])))
h5fid.create_dataset(name='seg_pos', data=sp.hstack([sp.atleast_2d(x.seg_pos).T for x in counts_tmp[:, c]]), chunks=True, compression='gzip', maxshape=(None, len(CFG['strains'])))
h5fid.create_dataset(name='gene_ids_segs', data=sp.ones((sp.atleast_2d(counts_tmp[0, c].seg_pos).shape[1], 1), dtype='int') * (s_idx[c_idx + c]), chunks=True, compression='gzip', maxshape=(None, 1))
tmp = [sp.atleast_2d(x.edges) for x in counts_tmp[:, c] if x.edges.shape[0] > 0]
if len(tmp) == 0:
continue
tmp = sp.hstack(tmp)
if tmp.shape[0] > 0:
if 'edges' in h5fid:
appendToHDF5(h5fid, tmp[:, range(1, tmp.shape[1], 2)], 'edges')
appendToHDF5(h5fid, tmp[:, 0], 'edge_idx')
appendToHDF5(h5fid, sp.ones((tmp.shape[0], 1), dtype='int') * (s_idx[c_idx + c]), 'gene_ids_edges')
else:
h5fid.create_dataset(name='edges', data=tmp[:, range(1, tmp.shape[1], 2)], chunks=True, compression='gzip', maxshape=(None, tmp.shape[1] / 2))
h5fid.create_dataset(name='edge_idx', data=tmp[:, 0], chunks=True, compression='gzip', maxshape=(None,))
h5fid.create_dataset(name='gene_ids_edges', data=sp.ones((tmp.shape[0], 1), dtype='int') * (s_idx[c_idx + c]), chunks=True, compression='gzip', maxshape=(None, 1))
del tmp, counts_tmp
h5fid.close()
def run_merge(CFG):
merge_all = (CFG['merge_strategy'] == 'merge_all')
merge_all_tag = ''
if merge_all:
merge_all_tag = '_merged_bams'
prune_tag = ''
if CFG['do_prune']:
prune_tag = '_pruned'
chunksize = 50
fn_out = '%s/spladder/genes_graph_conf%i.%s%s.pickle' % (CFG['out_dirname'] , CFG['confidence_level'], CFG['merge_strategy'], prune_tag)
fn_out_val = '%s/spladder/genes_graph_conf%i.%s%s.validated.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'], prune_tag)
if CFG['validate_splicegraphs']:
fn_out_count = '%s/spladder/genes_graph_conf%i.%s%s.validated.count.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'] , prune_tag)
else:
fn_out_count = '%s/spladder/genes_graph_conf%i.%s%s.count.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'] , prune_tag)
if not os.path.exists(fn_out):
if not CFG['rproc']:
merge_genes_by_splicegraph(CFG)
else:
jobinfo = []
PAR = dict()
PAR['CFG'] = CFG
if chunksize > 0:
merge_list_len = len(CFG['samples'])
if merge_all:
merge_list_len += 1
for c_idx in range(0, merge_list_len, chunksize):
fn = '%s/spladder/genes_graph_conf%i.%s%s_chunk%i_%i.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'], prune_tag, c_idx, min(merge_list_len, c_idx + chunksize))
if os.path.exists(fn):
continue
else:
print 'submitting chunk %i to %i' % (c_idx, min(merge_list_len, c_idx + chunksize))
PAR['chunk_idx'] = range(c_idx, min(merge_list_len, c_idx + chunksize))
jobinfo.append(rp.rproc('merge_genes_by_splicegraph', PAR, 50000, CFG['options_rproc'], 40*60))
else:
jobinfo.append(rp.rproc('merge_genes_by_splicegraph', PAR, 10000, CFG['options_rproc'], 40*60))
rp.rproc_wait(jobinfo, 30, 1.0, -1)
### merge chunks
if chunksize > 0:
PAR['chunksize'] = chunksize
merge_chunks_by_splicegraph(PAR)
else:
print 'File %s already exists!' % fn_out
### generate validated version of splice graph
if CFG['validate_splicegraphs'] and not os.path.exists(fn_out_val):
(genes, inserted) = cPickle.load(open(fn_out, 'r'))
genes = filter_by_edgecount(genes, CFG)
cPickle.dump((genes, inserted), open(fn_out_val, 'w'), -1)
del genes
### count segment graph
if CFG['validate_splicegraphs']:
count_graph_coverage_wrapper(fn_out_val, fn_out_count, CFG)
else:
count_graph_coverage_wrapper(fn_out, fn_out_count, CFG)
if CFG['do_gen_isoforms']:
fn_out = '%s/spladder/genes_graph_conf%i.%s%s_isoforms.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'], prune_tag)
if not os.path.exists(fn_out):
if not CFG['rproc']:
merge_genes_by_isoform(CFG['out_dirname'], CFG['confidence_level'], merge_all, experiment)
else:
jobinfo = [rp.rproc('merge_genes_by_isoform', PAR, 10000, CFG['options_rproc'], 40*60)]
rp.rproc_wait(jobinfo, 30, 1.0, 1)
else:
print 'File %s already exists!' % fn_out
def run_merge(CFG):
merge_all = (CFG['merge_strategy'] == 'merge_all')
merge_all_tag = ''
if merge_all:
merge_all_tag = '_merged_bams'
prune_tag = ''
if CFG['do_prune']:
prune_tag = '_pruned'
chunksize = 10
fn_out = '%s/spladder/genes_graph_conf%i.%s%s.pickle' % (CFG['out_dirname'] , CFG['confidence_level'], CFG['merge_strategy'], prune_tag)
#fn_out_val = '%s/spladder/genes_graph_conf%i.%s%s.validated.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'], prune_tag)
if CFG['validate_splicegraphs']:
fn_out_count = '%s/spladder/genes_graph_conf%i.%s%s.validated.count.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'] , prune_tag)
else:
fn_out_count = '%s/spladder/genes_graph_conf%i.%s%s.count.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'] , prune_tag)
if not os.path.exists(fn_out):
if not CFG['rproc']:
merge_list = sp.array(['%s/spladder/genes_graph_conf%i.%s%s.pickle' % (CFG['out_dirname'], CFG['confidence_level'], x, prune_tag) for x in CFG['samples']])
merge_genes_by_splicegraph(CFG, merge_list=merge_list, fn_out=fn_out)
else:
jobinfo = []
PAR = dict()
PAR['CFG'] = CFG
if chunksize > 0:
levels = int(math.ceil(math.log(len(CFG['samples']), chunksize)))
level_files = dict()
for level in range(1, levels + 1):
print 'merging files on level %i' % level
if level == 1:
merge_list = sp.array(['%s/spladder/genes_graph_conf%i.%s%s.pickle' % (CFG['out_dirname'], CFG['confidence_level'], x, prune_tag) for x in CFG['samples']])
else:
merge_list = sp.array(level_files[level - 1])
level_files[level] = []
for c_idx in range(0, len(merge_list), chunksize):
if level == levels:
assert(len(merge_list) <= chunksize)
fn = fn_out
else:
fn = '%s/spladder/genes_graph_conf%i.%s%s_level%i_chunk%i_%i.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'], prune_tag, level, c_idx, min(len(merge_list), c_idx + chunksize))
level_files[level].append(fn)
if os.path.exists(fn):
continue
else:
print 'submitting level %i chunk %i to %i' % (level, c_idx, min(len(merge_list), c_idx + chunksize))
chunk_idx = range(c_idx, min(len(merge_list), c_idx + chunksize))
PAR['merge_list'] = merge_list[chunk_idx]
PAR['fn_out'] = fn
jobinfo.append(rp.rproc('merge_genes_by_splicegraph', PAR, 20000*level, CFG['options_rproc'], 40*60))
rp.rproc_wait(jobinfo, 30, 1.0, -1)
else:
PAR['merge_list'] = CFG['samples']
PAR['fn_out'] = fn_out
jobinfo.append(rp.rproc('merge_genes_by_splicegraph', PAR, 10000, CFG['options_rproc'], 40*60))
rp.rproc_wait(jobinfo, 30, 1.0, -1)
else:
print 'File %s already exists!' % fn_out
### generate validated version of splice graph
#if CFG['validate_splicegraphs'] and not os.path.exists(fn_out_val):
# (genes, inserted) = cPickle.load(open(fn_out, 'r'))
# genes = filter_by_edgecount(genes, CFG)
# cPickle.dump((genes, inserted), open(fn_out_val, 'w'), -1)
# del genes
### count segment graph
#if CFG['validate_splicegraphs']:
# count_graph_coverage_wrapper(fn_out_val, fn_out_count, CFG)
#else:
# count_graph_coverage_wrapper(fn_out, fn_out_count, CFG)
if CFG['do_gen_isoforms']:
fn_out = '%s/spladder/genes_graph_conf%i.%s%s_isoforms.pickle' % (CFG['out_dirname'], CFG['confidence_level'], CFG['merge_strategy'], prune_tag)
if not os.path.exists(fn_out):
if not CFG['rproc']:
merge_genes_by_isoform(CFG['out_dirname'], CFG['confidence_level'], merge_all, experiment)
else:
jobinfo = [rp.rproc('merge_genes_by_isoform', PAR, 10000, CFG['options_rproc'], 40*60)]
rp.rproc_wait(jobinfo, 30, 1.0, 1)
else:
print 'File %s already exists!' % fn_out
def count_graph_coverage_wrapper(fname_in, fname_out, CFG):
(genes, inserted) = cPickle.load(open(fname_in, 'r'))
if genes[0].segmentgraph is None:
for g in genes:
g.segmentgraph = Segmentgraph(g)
cPickle.dump((genes, inserted), open(fname_in, 'w'), -1)
counts = dict()
counts['segments'] = []
counts['seg_pos'] = []
counts['gene_ids_segs'] = []
counts['edges'] = []
counts['gene_ids_edges'] = []
if not CFG['rproc']:
for s_idx in range(CFG['strains'].shape[0]):
print '\n%i/%i' % (s_idx + 1, CFG['strains'].shape[0])
if s_idx == 0:
counts_tmp = count_graph_coverage(genes, CFG['bam_fnames'][s_idx], CFG)
else:
counts_tmp = sp.r_[sp.atleast_2d(counts_tmp), count_graph_coverage(genes, CFG['bam_fnames'][s_idx], CFG)]
for c in range(counts_tmp.shape[1]):
counts['segments'].append(sp.hstack([sp.atleast_2d(x.segments).T for x in counts_tmp[:, c]]))
counts['seg_pos'].append(sp.hstack([sp.atleast_2d(x.seg_pos).T for x in counts_tmp[:, c]]))
counts['gene_ids_segs'].append(sp.ones((sp.atleast_2d(counts_tmp[0, c].seg_pos).shape[1], 1), dtype='int') * c)
tmp = sp.hstack([sp.atleast_2d(x.edges) for x in counts_tmp[:, c]])
if tmp.shape[0] > 0:
counts['edges'].append(sp.c_[tmp[:, 0], tmp[:, range(1, tmp.shape[1], 2)]])
counts['gene_ids_edges'].append(sp.ones((tmp.shape[0], 1), dtype='int') * c)
else:
### have an adaptive chunk size, that takes into account the number of strains (take as many genes as it takes to have ~10K strains)
chunksize = int(max(1, math.floor(10000 / len(CFG['strains']))))
jobinfo = []
PAR = dict()
PAR['CFG'] = CFG
for c_idx in range(0, genes.shape[0], chunksize):
cc_idx = min(genes.shape[0], c_idx + chunksize)
fn = fname_out.replace('.pickle', '.chunk_%i_%i.pickle' % (c_idx, cc_idx))
if os.path.exists(fn):
continue
else:
print 'submitting chunk %i to %i' % (c_idx, cc_idx)
PAR['genes'] = genes[c_idx:cc_idx]
PAR['fn_bam'] = CFG['bam_fnames']
PAR['fn_out'] = fn
PAR['CFG'] = CFG
jobinfo.append(rp.rproc('count_graph_coverage', PAR, 30000, CFG['options_rproc'], 60))
rp.rproc_wait(jobinfo, 30, 1.0, -1)
### merge results
for c_idx in range(0, genes.shape[0], chunksize):
cc_idx = min(genes.shape[0], c_idx + chunksize)
fn = fname_out.replace('.pickle', '.chunk_%i_%i.pickle' % (c_idx, cc_idx))
if not os.path.exists(fn):
print >> sys.stderr, 'ERROR: Not all chunks in counting graph coverage completed!'
sys.exit(1)
else:
counts_tmp = cPickle.load(open(fn, 'r'))
for c in range(counts_tmp.shape[1]):
counts['segments'].append(sp.hstack([sp.atleast_2d(x.segments).T for x in counts_tmp[:, c]]))
counts['seg_pos'].append(sp.hstack([sp.atleast_2d(x.seg_pos).T for x in counts_tmp[:, c]]))
counts['gene_ids_segs'].append(sp.ones((sp.atleast_2d(counts_tmp[0, c].seg_pos).shape[1], 1), dtype='int') * (c_idx + c))
tmp = sp.hstack([sp.atleast_2d(x.edges) for x in counts_tmp[:, c]])
if tmp.shape[0] > 0:
counts['edges'].append(sp.c_[tmp[:, 0], tmp[:, range(1, tmp.shape[1], 2)]])
counts['gene_ids_edges'].append(sp.ones((tmp.shape[0], 1), dtype='int') * (c_idx + c))
for key in counts:
if len(counts[key]) > 0:
counts[key] = sp.vstack(counts[key])
if len(counts['edges']) > 0:
counts['edge_idx'] = counts['edges'][:, 0]
counts['edges'] = counts['edges'][:, 1:]
else:
counts['edge_idx'] = sp.array([])
counts['edges'] = sp.array([])
counts['seg_len'] = sp.hstack([x.segmentgraph.segments[1, :] - x.segmentgraph.segments[0, :] for x in genes]).T
### write result data to hdf5
h5fid = h5py.File(fname_out, 'w')
h5fid.create_dataset(name='gene_names', data=sp.array([x.name for x in genes], dtype='str'))
h5fid.create_dataset(name='strains', data=CFG['strains'])
for key in counts:
h5fid.create_dataset(name=key, data=counts[key])
h5fid.close()
