def concatenate_sparse_counts(*samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
umi_dir = os.path.join(work_dir, "umis")
out_file = os.path.join(umi_dir, "tagcounts.mtx")
if file_exists(out_file):
return out_file
files = [
dd.get_count_file(data) for data in dd.sample_data_iterator(samples)
if dd.get_count_file(data)
]
descriptions = [
dd.get_sample_name(data) for data in dd.sample_data_iterator(samples)
if dd.get_count_file(data)
]
if not files:
return samples
counts = SparseMatrix()
counts.read(filename=files.pop(), colprefix=descriptions.pop())
for filename, description in zip(files, descriptions):
newcounts = SparseMatrix()
newcounts.read(filename=filename, colprefix=description)
counts.cat(newcounts)
counts.write(out_file)
newsamples = []
for data in dd.sample_data_iterator(samples):
newsamples.append([dd.set_combined_counts(data, out_file)])
return newsamples
def _detect_rRNA(data):
gtf_file = dd.get_gtf_file(data)
count_file = dd.get_count_file(data)
rrna_features = gtf.get_rRNA(gtf_file)
genes = [x[0] for x in rrna_features if x]
count_table = pd.read_csv(count_file, sep="\t", names=["id", "counts"])
return {'rRNA': sum(count_table.ix[genes]["counts"])}
def _detect_rRNA(data):
gtf_file = dd.get_gtf_file(data)
count_file = dd.get_count_file(data)
rrna_features = gtf.get_rRNA(gtf_file)
genes = [x[0] for x in rrna_features if x]
count_table = pd.read_csv(count_file, sep="\t", names=["id", "counts"])
return {'rRNA': sum(count_table.ix[genes]["counts"])}
def combine_files(samples):
"""
after quantitation, combine the counts/FPKM/TPM/etc into a single table with
all samples
"""
gtf_file = dd.get_gtf_file(samples[0][0], None)
dexseq_gff = dd.get_dexseq_gff(samples[0][0])
# combine featureCount files
count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
combined = count.combine_count_files(count_files, ext=".counts")
annotated = count.annotate_combined_count_file(combined, gtf_file)
# combine eXpress files
express_counts_combined = combine_express(samples, combined)
# combine Cufflinks files
fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
if fpkm_files:
fpkm_combined = count.combine_count_files(fpkm_files, fpkm_combined_file)
else:
fpkm_combined = None
fpkm_isoform_combined_file = os.path.splitext(combined)[0] + ".isoform.fpkm"
isoform_files = filter_missing([dd.get_fpkm_isoform(x[0]) for x in samples])
if isoform_files:
fpkm_isoform_combined = count.combine_count_files(isoform_files,
fpkm_isoform_combined_file,
".isoform.fpkm")
else:
fpkm_isoform_combined = None
# combine DEXseq files
dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
to_combine_dexseq = filter_missing([dd.get_dexseq_counts(data[0]) for data in samples])
if to_combine_dexseq:
dexseq_combined = count.combine_count_files(to_combine_dexseq,
dexseq_combined_file, ".dexseq")
dexseq.create_dexseq_annotation(dexseq_gff, dexseq_combined)
else:
dexseq_combined = None
samples = spikein.combine_spikein(samples)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_combined_counts(data, combined)
if annotated:
data = dd.set_annotated_combined_counts(data, annotated)
if fpkm_combined:
data = dd.set_combined_fpkm(data, fpkm_combined)
if fpkm_isoform_combined:
data = dd.set_combined_fpkm_isoform(data, fpkm_isoform_combined)
if express_counts_combined:
data = dd.set_express_counts(data, express_counts_combined['counts'])
data = dd.set_express_tpm(data, express_counts_combined['tpm'])
data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
data = dd.set_isoform_to_gene(data, express_counts_combined['isoform_to_gene'])
if dexseq_combined:
data = dd.set_dexseq_counts(data, dexseq_combined_file)
updated_samples.append([data])
return updated_samples
def combine_files(samples):
"""
after quantitation, combine the counts/FPKM/TPM/etc into a single table with
all samples
"""
gtf_file = dd.get_gtf_file(samples[0][0], None)
dexseq_gff = dd.get_dexseq_gff(samples[0][0])
# combine featureCount files
count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
combined = count.combine_count_files(count_files, ext=".counts")
annotated = count.annotate_combined_count_file(combined, gtf_file)
# combine eXpress files
express_counts_combined = combine_express(samples, combined)
# combine Cufflinks files
fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
if fpkm_files:
fpkm_combined = count.combine_count_files(fpkm_files, fpkm_combined_file)
else:
fpkm_combined = None
fpkm_isoform_combined_file = os.path.splitext(combined)[0] + ".isoform.fpkm"
isoform_files = filter_missing([dd.get_fpkm_isoform(x[0]) for x in samples])
if isoform_files:
fpkm_isoform_combined = count.combine_count_files(isoform_files,
fpkm_isoform_combined_file,
".isoform.fpkm")
else:
fpkm_isoform_combined = None
# combine DEXseq files
dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
to_combine_dexseq = filter_missing([dd.get_dexseq_counts(data[0]) for data in samples])
if to_combine_dexseq:
dexseq_combined = count.combine_count_files(to_combine_dexseq,
dexseq_combined_file, ".dexseq")
dexseq.create_dexseq_annotation(dexseq_gff, dexseq_combined)
else:
dexseq_combined = None
samples = spikein.combine_spikein(samples)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_combined_counts(data, combined)
if annotated:
data = dd.set_annotated_combined_counts(data, annotated)
if fpkm_combined:
data = dd.set_combined_fpkm(data, fpkm_combined)
if fpkm_isoform_combined:
data = dd.set_combined_fpkm_isoform(data, fpkm_isoform_combined)
if express_counts_combined:
data = dd.set_express_counts(data, express_counts_combined['counts'])
data = dd.set_express_tpm(data, express_counts_combined['tpm'])
data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
data = dd.set_isoform_to_gene(data, express_counts_combined['isoform_to_gene'])
if dexseq_combined:
data = dd.set_dexseq_counts(data, dexseq_combined_file)
updated_samples.append([data])
return updated_samples
def _maybe_add_scrnaseq(algorithm, sample, out):
count_file = dd.get_count_file(sample)
if not count_file:
return out
out.append({"path": count_file, "type": "mtx"})
out.append({"path": count_file + ".rownames", "type": "rownames"})
out.append({"path": count_file + ".colnames", "type": "colnames"})
return out
def _maybe_add_barcode_histogram(algorithm, sample, out):
if not dd.get_count_file(sample):
return out
count_file = sample["count_file"]
histogram_file = os.path.join(os.path.dirname(count_file),
"cb-histogram.txt")
out.append({"path": histogram_file, "type": "tsv", "ext": "barcodes"})
return out
def _maybe_add_barcode_histogram(algorithm, sample, out):
if not dd.get_count_file(sample):
return out
count_file = sample["count_file"]
histogram_file = os.path.join(os.path.dirname(count_file), "cb-histogram.txt")
out.append({"path": histogram_file,
"type": "tsv",
"ext": "barcodes"})
return out
def concatenate_sparse_matrices(samples, deduped=True):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
umi_dir = os.path.join(work_dir, "umis")
if deduped:
out_file = os.path.join(umi_dir, "tagcounts.mtx")
else:
out_file = os.path.join(umi_dir, "tagcounts-dupes.mtx")
if file_exists(out_file):
if deduped:
newsamples = []
for data in dd.sample_data_iterator(samples):
newsamples.append([dd.set_combined_counts(data, out_file)])
return newsamples
else:
return samples
files = [
dd.get_count_file(data) for data in dd.sample_data_iterator(samples)
if dd.get_count_file(data)
]
if not deduped:
files = [os.path.splitext(x)[0] + "-dupes.mtx" for x in files]
files = [fn for fn in files if file_exists(fn)]
descriptions = [
dd.get_sample_name(data) for data in dd.sample_data_iterator(samples)
if dd.get_count_file(data)
]
if not files:
return samples
counts = SparseMatrix()
counts.read(filename=files.pop(), colprefix=descriptions.pop())
for filename, description in zip(files, descriptions):
newcounts = SparseMatrix()
newcounts.read(filename=filename, colprefix=description)
counts.cat(newcounts)
counts.write(out_file)
newsamples = []
if deduped:
for data in dd.sample_data_iterator(samples):
newsamples.append([dd.set_combined_counts(data, out_file)])
return newsamples
return samples
def _detect_rRNA(data):
gtf_file = dd.get_gtf_file(data)
count_file = dd.get_count_file(data)
rrna_features = gtf.get_rRNA(gtf_file)
genes = [x[0] for x in rrna_features if x]
if not genes:
return {'rRNA': "NA", "rRNA_rate": "NA"}
count_table = pd.read_csv(count_file, sep="\t", names=["id", "counts"])
rrna = sum(count_table[count_table["id"].isin(genes)]["counts"])
rrna_rate = float(rrna) / sum(count_table["counts"])
return {'rRNA': str(rrna), 'rRNA_rate': str(rrna_rate)}
def _maybe_add_scrnaseq(algorithm, sample, out):
count_file = dd.get_count_file(sample)
if not count_file:
return out
out.append({"path": count_file,
"type": "mtx"})
out.append({"path": count_file + ".rownames",
"type": "rownames"})
out.append({"path": count_file + ".colnames",
"type": "colnames"})
return out
def _maybe_add_counts(algorithm, sample, out):
if not dd.get_count_file(sample):
return out
out.append({"path": sample["count_file"],
"type": "counts",
"ext": "ready"})
stats_file = os.path.splitext(sample["count_file"])[0] + ".stats"
if utils.file_exists(stats_file):
out.append({"path": stats_file,
"type": "count_stats",
"ext": "ready"})
return out
def _maybe_add_counts(algorithm, sample, out):
if not dd.get_count_file(sample):
return out
out.append({
"path": sample["count_file"],
"type": "counts",
"ext": "ready"
})
stats_file = os.path.splitext(sample["count_file"])[0] + ".stats"
if utils.file_exists(stats_file):
out.append({"path": stats_file, "type": "count_stats", "ext": "ready"})
return out
def concatenate_sparse_matrices(samples, deduped=True):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
umi_dir = os.path.join(work_dir, "umis")
if deduped:
out_file = os.path.join(umi_dir, "tagcounts.mtx")
else:
out_file = os.path.join(umi_dir, "tagcounts-dupes.mtx")
if file_exists(out_file):
if deduped:
newsamples = []
for data in dd.sample_data_iterator(samples):
newsamples.append([dd.set_combined_counts(data, out_file)])
return newsamples
else:
return samples
files = [dd.get_count_file(data) for data in
dd.sample_data_iterator(samples)
if dd.get_count_file(data)]
if not deduped:
files = [os.path.splitext(x)[0] + "-dupes.mtx" for x in files]
files = [fn for fn in files if file_exists(fn)]
descriptions = [dd.get_sample_name(data) for data in
dd.sample_data_iterator(samples) if dd.get_count_file(data)]
if not files:
return samples
counts = SparseMatrix()
counts.read(filename=files.pop(), colprefix=descriptions.pop())
for filename, description in zip(files, descriptions):
newcounts = SparseMatrix()
newcounts.read(filename=filename, colprefix=description)
counts.cat(newcounts)
counts.write(out_file)
newsamples = []
if deduped:
for data in dd.sample_data_iterator(samples):
newsamples.append([dd.set_combined_counts(data, out_file)])
return newsamples
return samples
def _maybe_add_scrnaseq(algorithm, sample, out):
count_file = dd.get_count_file(sample)
if not count_file:
return out
else:
out.append({"path": count_file, "type": "mtx"})
out.append({"path": count_file + ".rownames", "type": "rownames"})
out.append({"path": count_file + ".colnames", "type": "colnames"})
umi_file = os.path.splitext(count_file)[0] + "-dupes.mtx"
if utils.file_exists(umi_file):
out.append({"path": umi_file, "type": "mtx"})
out.append({"path": umi_file + ".rownames", "type": "rownames"})
out.append({"path": umi_file + ".colnames", "type": "colnames"})
return out
def concatenate_sparse_counts(*samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
umi_dir = os.path.join(work_dir, "umis")
out_file = os.path.join(umi_dir, "tagcounts.mtx")
if file_exists(out_file):
return out_file
files = [dd.get_count_file(data) for data in
dd.sample_data_iterator(samples)
if dd.get_count_file(data)]
descriptions = [dd.get_sample_name(data) for data in
dd.sample_data_iterator(samples) if dd.get_count_file(data)]
if not files:
return samples
counts = SparseMatrix()
counts.read(filename=files.pop(), colprefix=descriptions.pop())
for filename, description in zip(files, descriptions):
newcounts = SparseMatrix()
newcounts.read(filename=filename, colprefix=description)
counts.cat(newcounts)
counts.write(out_file)
newsamples = []
for data in dd.sample_data_iterator(samples):
newsamples.append([dd.set_combined_counts(data, out_file)])
return newsamples
def estimate_expression(samples, run_parallel):
samples = run_parallel("generate_transcript_counts", samples)
count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
combined = count.combine_count_files(count_files)
gtf_file = dd.get_gtf_file(samples[0][0], None)
annotated = count.annotate_combined_count_file(combined, gtf_file)
samples = run_parallel("run_express", samples)
express_counts_combined = combine_express(samples, combined)
samples = run_parallel("run_cufflinks", samples)
#gene
fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
fpkm_combined = count.combine_count_files(fpkm_files, fpkm_combined_file)
#isoform
fpkm_isoform_combined_file = os.path.splitext(combined)[0] + ".isoform.fpkm"
isoform_files = filter_missing([dd.get_fpkm_isoform(x[0]) for x in samples])
fpkm_isoform_combined = count.combine_count_files(isoform_files,
fpkm_isoform_combined_file,
".isoform.fpkm")
dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
to_combine_dexseq = filter_missing([dd.get_dexseq_counts(data[0]) for data in samples])
if to_combine_dexseq:
dexseq_combined = count.combine_count_files(to_combine_dexseq,
dexseq_combined_file, ".dexseq")
else:
dexseq_combined = None
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_combined_counts(data, combined)
if annotated:
data = dd.set_annotated_combined_counts(data, annotated)
if fpkm_combined:
data = dd.set_combined_fpkm(data, fpkm_combined)
if fpkm_isoform_combined:
data = dd.set_combined_fpkm_isoform(data, fpkm_combined)
if express_counts_combined:
data = dd.set_express_counts(data, express_counts_combined['counts'])
data = dd.set_express_tpm(data, express_counts_combined['tpm'])
data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
if dexseq_combined:
data = dd.set_dexseq_counts(data, dexseq_combined_file)
updated_samples.append([data])
return updated_samples
def _maybe_add_scrnaseq(algorithm, sample, out):
count_file = dd.get_count_file(sample)
if not count_file:
return out
else:
out.append({"path": count_file,
"type": "mtx"})
out.append({"path": count_file + ".rownames",
"type": "rownames"})
out.append({"path": count_file + ".colnames",
"type": "colnames"})
umi_file = os.path.splitext(count_file)[0] + "-dupes.mtx"
if utils.file_exists(umi_file):
out.append({"path": umi_file,
"type": "mtx"})
out.append({"path": umi_file + ".rownames",
"type": "rownames"})
out.append({"path": umi_file + ".colnames",
"type": "colnames"})
return out
def combine_files(samples):
"""
after quantitation, combine the counts/FPKM/TPM/etc into a single table with
all samples
"""
data = samples[0][0]
# prefer the supplied transcriptome gtf file
gtf_file = dd.get_transcriptome_gtf(data, None)
if not gtf_file:
gtf_file = dd.get_gtf_file(data, None)
dexseq_gff = dd.get_dexseq_gff(data)
# combine featureCount files
count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
combined = count.combine_count_files(count_files, ext=".counts")
annotated = count.annotate_combined_count_file(combined, gtf_file)
# add tx2gene file
tx2gene_file = os.path.join(dd.get_work_dir(data), "annotation", "tx2gene.csv")
if gtf_file:
tx2gene_file = sailfish.create_combined_tx2gene(data)
# combine eXpress files
express_counts_combined = combine_express(samples, combined)
# combine Cufflinks files
fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
if fpkm_files and combined:
fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
fpkm_combined = count.combine_count_files(fpkm_files, fpkm_combined_file)
else:
fpkm_combined = None
isoform_files = filter_missing([dd.get_fpkm_isoform(x[0]) for x in samples])
if isoform_files and combined:
fpkm_isoform_combined_file = os.path.splitext(combined)[0] + ".isoform.fpkm"
fpkm_isoform_combined = count.combine_count_files(isoform_files,
fpkm_isoform_combined_file,
".isoform.fpkm")
else:
fpkm_isoform_combined = None
# combine DEXseq files
to_combine_dexseq = filter_missing([dd.get_dexseq_counts(data[0]) for data
in samples])
if to_combine_dexseq and combined:
dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
dexseq_combined = count.combine_count_files(to_combine_dexseq,
dexseq_combined_file, ".dexseq")
if dexseq_combined:
dexseq.create_dexseq_annotation(dexseq_gff, dexseq_combined)
else:
dexseq_combined = None
samples = spikein.combine_spikein(samples)
updated_samples = []
for data in dd.sample_data_iterator(samples):
if combined:
data = dd.set_combined_counts(data, combined)
if annotated:
data = dd.set_annotated_combined_counts(data, annotated)
if fpkm_combined:
data = dd.set_combined_fpkm(data, fpkm_combined)
if fpkm_isoform_combined:
data = dd.set_combined_fpkm_isoform(data, fpkm_isoform_combined)
if express_counts_combined:
data = dd.set_express_counts(data, express_counts_combined['counts'])
data = dd.set_express_tpm(data, express_counts_combined['tpm'])
data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
data = dd.set_isoform_to_gene(data, express_counts_combined['isoform_to_gene'])
if dexseq_combined:
data = dd.set_dexseq_counts(data, dexseq_combined_file)
if gtf_file:
data = dd.set_tx2gene(data, tx2gene_file)
updated_samples.append([data])
return updated_samples
def combine_files(samples):
"""
after quantitation, combine the counts/FPKM/TPM/etc into a single table with
all samples
"""
data = samples[0][0]
# prefer the supplied transcriptome gtf file
gtf_file = dd.get_transcriptome_gtf(data, None)
if not gtf_file:
gtf_file = dd.get_gtf_file(data, None)
dexseq_gff = dd.get_dexseq_gff(data)
# combine featureCount files
count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
combined = count.combine_count_files(count_files, ext=".counts")
annotated = count.annotate_combined_count_file(combined, gtf_file)
# add tx2gene file
tx2gene_file = os.path.join(dd.get_work_dir(data), "annotation",
"tx2gene.csv")
if gtf_file:
tx2gene_file = sailfish.create_combined_tx2gene(data)
# combine eXpress files
express_counts_combined = combine_express(samples, combined)
# combine Cufflinks files
fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
if fpkm_files:
fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
fpkm_combined = count.combine_count_files(fpkm_files,
fpkm_combined_file)
else:
fpkm_combined = None
isoform_files = filter_missing(
[dd.get_fpkm_isoform(x[0]) for x in samples])
if isoform_files:
fpkm_isoform_combined_file = os.path.splitext(
combined)[0] + ".isoform.fpkm"
fpkm_isoform_combined = count.combine_count_files(
isoform_files, fpkm_isoform_combined_file, ".isoform.fpkm")
else:
fpkm_isoform_combined = None
# combine DEXseq files
to_combine_dexseq = filter_missing(
[dd.get_dexseq_counts(data[0]) for data in samples])
if to_combine_dexseq:
dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
dexseq_combined = count.combine_count_files(to_combine_dexseq,
dexseq_combined_file,
".dexseq")
dexseq.create_dexseq_annotation(dexseq_gff, dexseq_combined)
else:
dexseq_combined = None
samples = spikein.combine_spikein(samples)
updated_samples = []
for data in dd.sample_data_iterator(samples):
if combined:
data = dd.set_combined_counts(data, combined)
if annotated:
data = dd.set_annotated_combined_counts(data, annotated)
if fpkm_combined:
data = dd.set_combined_fpkm(data, fpkm_combined)
if fpkm_isoform_combined:
data = dd.set_combined_fpkm_isoform(data, fpkm_isoform_combined)
if express_counts_combined:
data = dd.set_express_counts(data,
express_counts_combined['counts'])
data = dd.set_express_tpm(data, express_counts_combined['tpm'])
data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
data = dd.set_isoform_to_gene(
data, express_counts_combined['isoform_to_gene'])
if dexseq_combined:
data = dd.set_dexseq_counts(data, dexseq_combined_file)
if gtf_file:
data = dd.set_tx2gene(data, tx2gene_file)
updated_samples.append([data])
return updated_samples
