def load_data(hmmcopy_filename, alignment_filename, colnames):
hmmcopy_data = csvutils.read_csv_and_yaml(hmmcopy_filename)
alignment_data = csvutils.read_csv_and_yaml(alignment_filename)
hmmcopy_data = hmmcopy_data.set_index('cell_id')
alignment_data = alignment_data.set_index('cell_id')
data = []
for colname in colnames:
if colname in hmmcopy_data:
coldata = hmmcopy_data[colname]
else:
coldata = alignment_data[colname]
if colname == 'scaled_halfiness':
# haploid poison adds inf, replace with big number since 0 is considered good
# and we want to score to decrease
coldata = coldata.replace(np.inf, 1e10)
data.append(coldata)
data = pd.concat(data, axis=1)
data = data.replace(-np.inf, np.nan)
data = data.fillna(0)
return data
def compare_annotation(annotation, refannotation):
annotation = csvutils.read_csv_and_yaml(annotation)
refannotation = csvutils.read_csv_and_yaml(refannotation)
common_cols = _check_for_missing_cols(annotation, refannotation)
for col in common_cols:
ann = annotation[col].dropna()
ref = refannotation[col].dropna()
assert set(ann) == set(ref)
def test_breakpoint_calling(args):
output_path = args[1]
ref_path = args[2]
ref_strelka, ref_museq, ref_snpeff = get_inputs(ref_path)
strelka, museq, snpeff = get_inputs(output_path)
compare.compare_variant_calls(ref_snpeff, snpeff)
ref_strelka = csvutils.read_csv_and_yaml(ref_strelka)
strelka = csvutils.read_csv_and_yaml(strelka)
assert ref_strelka.empty and strelka.empty
def read_data(filename, tablename, gzipped=True):
fileformat = single_cell.utils.helpers.get_file_format(filename)
if fileformat == 'h5':
data = read_from_h5(filename, tablename)
elif fileformat == 'csv':
data = csvutils.read_csv_and_yaml(filename)
elif fileformat == 'gzip':
data = csvutils.read_csv_and_yaml(filename)
else:
raise Exception("unknown file format")
return data
def cell_cycle_classifier(hmmcopy_reads,
hmmcopy_metrics,
alignment_metrics,
output,
tempdir,
docker_image=None):
helpers.makedirs(tempdir)
temp_output = os.path.join(tempdir, 'cell_cycle_output.csv')
cmd = [
'cell_cycle_classifier', 'train-classify', hmmcopy_reads,
hmmcopy_metrics, alignment_metrics, temp_output
]
pypeliner.commandline.execute(*cmd, docker_image=docker_image)
cell_cycle_df = pd.read_csv(temp_output)
hmm_metrics_df = csvutils.read_csv_and_yaml(hmmcopy_metrics)
hmm_metrics_df = hmm_metrics_df.merge(cell_cycle_df,
on=['cell_id'],
how='outer')
csvutils.write_dataframe_to_csv_and_yaml(hmm_metrics_df, output)
def add_corrupt_tree_order(corrupt_tree, metrics, output):
"""
adds corrupt tree order to metrics
"""
with open(corrupt_tree) as newickfile:
newickdata = newickfile.readline()
assert newickfile.readline() == ''
tree = Tree(newickdata, format=1)
leaves = [node.name for node in tree.traverse("levelorder")]
leaves = [val[len('cell_'):] for val in leaves if val.startswith("cell_")]
ordering = {val: i for i, val in enumerate(leaves)}
metrics = csvutils.read_csv_and_yaml(metrics)
cells = metrics.cell_id
for cellid in cells:
order = ordering.get(cellid, float('nan'))
metrics.loc[metrics["cell_id"] == cellid, "order_corrupt_tree"] = order
csvutils.write_dataframe_to_csv_and_yaml(metrics,
output,
write_header=True)
def add_contamination_status(infile,
outfile,
config,
reference='grch37',
threshold=0.05):
data = csvutils.read_csv_and_yaml(infile)
data = data.set_index('cell_id', drop=False)
organisms = [genome['name'] for genome in config['genomes']]
if reference not in organisms:
raise Exception("Could not find the fastq screen counts")
alts = [col for col in organisms if not col == reference]
data['is_contaminated'] = False
for altcol in alts:
perc_alt = _get_col_data(data, altcol) / data['total_reads']
data.loc[perc_alt > threshold, 'is_contaminated'] = True
col_type = dtypes()['metrics']['is_contaminated']
data['is_contaminated'] = data['is_contaminated'].astype(col_type)
csvutils.write_dataframe_to_csv_and_yaml(data, outfile,
dtypes()['metrics'])
def cell_cycle_classifier(hmmcopy_reads, hmmcopy_metrics, alignment_metrics,
output, tempdir, genome_labels):
helpers.makedirs(tempdir)
temp_output = os.path.join(tempdir, 'cell_cycle_output.csv')
cmd = [
'cell_cycle_classifier', 'train-classify', hmmcopy_reads,
hmmcopy_metrics, alignment_metrics, temp_output
]
pypeliner.commandline.execute(*cmd)
cell_cycle_df = pd.read_csv(temp_output)
cols_cell_cycle = cell_cycle_df.columns.values
hmm_metrics_df = csvutils.read_csv_and_yaml(hmmcopy_metrics)
hmm_metrics_df = hmm_metrics_df.merge(cell_cycle_df,
on=['cell_id'],
how='outer')
out_dtypes = dtypes(genome_labels)
for colname in cols_cell_cycle:
hmm_metrics_df[colname] = hmm_metrics_df[colname].astype(
out_dtypes[colname])
csvutils.write_dataframe_to_csv_and_yaml(hmm_metrics_df, output,
out_dtypes)
def filter_plot_tar(metrics, src_tar, pass_tar, fail_tar, tempdir, filters):
allplots = os.path.join(tempdir, 'allplots')
helpers.makedirs(allplots)
helpers.extract_tar(src_tar, allplots)
metrics_data = csvutils.read_csv_and_yaml(metrics)
all_cells = metrics_data.cell_id.tolist()
metrics_data = helpers.filter_metrics(metrics_data, filters)
good_cells = metrics_data.cell_id.tolist()
bad_cells = [cell for cell in all_cells if cell not in good_cells]
plotdir = os.path.join(tempdir, 'segs_pass')
helpers.makedirs(plotdir)
for cell in good_cells:
src_path = os.path.join(allplots, 'segments',
'{}_{}.png'.format(cell, 'segments'))
dest_path = os.path.join(plotdir, '{}_{}.png'.format(cell, 'segments'))
shutil.copyfile(src_path, dest_path)
helpers.make_tarfile(pass_tar, plotdir)
plotdir = os.path.join(tempdir, 'segs_fail')
helpers.makedirs(plotdir)
for cell in bad_cells:
src_path = os.path.join(allplots, 'segments',
'{}_{}.png'.format(cell, 'segments'))
dest_path = os.path.join(plotdir, '{}_{}.png'.format(cell, 'segments'))
shutil.copyfile(src_path, dest_path)
helpers.make_tarfile(fail_tar, plotdir)
def test_breakpoint_calling(args):
output_path = args[1]
ref_path = args[2]
ref_must_exist, ref_lumpy, ref_destruct = get_inputs(ref_path)
must_exist, lumpy, destruct = get_inputs(output_path)
assert all(map(os.path.exists, ref_must_exist))
assert all(map(os.path.exists, must_exist))
compare.compare_breakpoint_calls(ref_lumpy, lumpy)
ref_destruct = csvutils.read_csv_and_yaml(ref_destruct)
destruct = csvutils.read_csv_and_yaml(destruct)
assert ref_destruct.empty and destruct.empty
def load_data(infile, gc=False):
df = csvutils.read_csv_and_yaml(infile)
if gc:
df.index = df.cell_id
del df['cell_id']
df.columns = map(int, df.columns.values)
return df
def _load(file, by, reindex=False):
loaded = csvutils.read_csv_and_yaml(file)
loaded = loaded.sort_values(by, ascending=[True] * len(by))
if reindex:
loaded = loaded.set_index(by)
return loaded
def get_good_cells(metrics, cell_filters):
metrics_data = csvutils.read_csv_and_yaml(metrics)
if not cell_filters:
return metrics_data.cell_id.tolist()
metrics_data = helpers.filter_metrics(metrics_data, cell_filters)
return metrics_data.cell_id.tolist()
def load_hmmcopy_reads_data(readsfile):
keepcols = ['ideal', 'valid', 'gc', 'map', 'state', 'cor_gc', 'copy']
reads = csvutils.read_csv_and_yaml(readsfile)
reads = reads.set_index(['cell_id', 'chr', 'start', 'end'])
reads = reads[keepcols]
return reads
def get_mappability_col(reads, annotated_reads):
reads = csvutils.read_csv_and_yaml(reads, chunksize=100)
alldata = []
for read_data in reads:
read_data['is_low_mappability'] = (read_data['map'] <= 0.9)
alldata.append(read_data)
alldata = pd.concat(alldata)
csvutils.write_dataframe_to_csv_and_yaml(
alldata, annotated_reads, dtypes()['reads'], write_header=True
)
def write_to_output(hmmcopy_filename, output, predictions):
data = csvutils.read_csv_and_yaml(hmmcopy_filename)
data['quality'] = data['cell_id'].map(predictions)
data.quality = data.quality.astype(float)
fileformat = single_cell.utils.helpers.get_file_format(output)
if fileformat == 'csv':
write_to_csv(output, data)
elif fileformat == "gzip":
write_to_csv(output, data, gzipped=True)
else:
raise Exception("unknown file format")
def annotate_metrics(metrics, output, sample_info, cells):
"""
adds sample information to metrics in place
"""
metrics = csvutils.read_csv_and_yaml(metrics)
for cellid in cells:
cellinfo = sample_info[cellid]
for colname, value in cellinfo.items():
metrics.loc[metrics["cell_id"] == cellid, colname] = value
csvutils.write_dataframe_to_csv_and_yaml(metrics, output)
def get_hierarchical_clustering_order(
reads_filename, chromosomes=None):
data = []
chunksize = 10 ** 5
for chunk in csvutils.read_csv_and_yaml(
reads_filename, chunksize=chunksize):
chunk["bin"] = list(zip(chunk.chr, chunk.start, chunk.end))
# for some reason pivot doesnt like an Int64 state col
chunk['state'] = chunk['state'].astype('float')
chunk = chunk.pivot(index='cell_id', columns='bin', values='state')
data.append(chunk)
# merge chunks, sum cells that get split across chunks
table = pd.concat(data)
table = table.groupby(table.index).sum()
bins = pd.DataFrame(
table.columns.values.tolist(),
columns=[
'chr',
'start',
'end'])
bins['chr'] = bins['chr'].astype(str)
bins = sort_bins(bins, chromosomes)
table = table.sort_values(bins, axis=0)
data_mat = np.array(table.values)
data_mat[np.isnan(data_mat)] = -1
row_linkage = hc.linkage(sp.distance.pdist(data_mat, 'cityblock'),
method='ward')
order = hc.leaves_list(row_linkage)
samps = table.index
order = [samps[i] for i in order]
order = {v: i for i, v in enumerate(order)}
return order
def test_contamination(tmpdir):
data = {}
cols = [
'fastqscreen_nohit',
'fastqscreen_grch37',
'fastqscreen_grch37_multihit',
'fastqscreen_mm10',
'fastqscreen_mm10_multihit',
'fastqscreen_salmon',
'fastqscreen_salmon_multihit'
]
for i in range(5):
data[i] = {'cell_id': 'SA123_A123_R{0}_C{0}'.format(i)}
for col in cols:
data[i][col] = i * 10
data[i]['fastqscreen_grch37'] = i * 1000
data[i]['fastqscreen_mm10'] = i * 100
for i in range(5, 10):
data[i] = {'cell_id': 'SA123_A123_R{0}_C{0}'.format(i)}
for col in cols:
data[i][col] = (i * 10)
data[i]['fastqscreen_grch37'] = i * 1000
data = pd.DataFrame.from_dict(data, orient='index')
data['total_reads'] = data[cols].sum(axis=1)
dtypes = {col: 'int' for col in cols}
dtypes['cell_id'] = 'str'
dtypes['total_reads'] = 'int'
infile = os.path.join(tmpdir, 'input.csv.gz')
outfile = os.path.join(tmpdir, 'output.csv.gz')
csvutils.write_dataframe_to_csv_and_yaml(data, infile, dtypes)
config = {'genomes': [{'name': 'grch37'}, {'name': 'mm10'}, {'name': 'salmon'}]}
tasks.add_contamination_status(infile, outfile, config)
output = csvutils.read_csv_and_yaml(outfile)
assert output['is_contaminated'].tolist() == [False] + [True] * 4 + [False] * 5
def read_input_data(self, infile, tablename):
fileformat = helpers.get_file_format(infile)
if fileformat == "csv" or fileformat == 'gzip':
metrics = csvutils.read_csv_and_yaml(infile)
else:
with pd.HDFStore(infile, 'r') as metrics_store:
metrics = metrics_store[tablename]
metrics = metrics.reset_index()
if 'cell_call' in metrics.columns.values:
# plotting code doesnt work with nan
# tenx data will have nan for cell call, experimental condition
# row, col
metrics['cell_call'] = metrics["cell_call"].fillna("nan")
metrics['experimental_condition'] = metrics["experimental_condition"].fillna("nan")
return metrics
def load(self, fname):
'''
load tsv file into a pandas data frame
'''
extension = os.path.splitext(fname)[-1]
if extension in [".h5", ".hdf5"]:
with pandas.HDFStore(self.input, 'r') as metrics_store:
data = metrics_store[self.tablename]
data = data.reset_index()
else:
data = csvutils.read_csv_and_yaml(fname)
# data['chromosome'] = data['chromosome'].astype(str)
return data
def add_contamination_status(infile,
outfile,
reference='grch37',
ref_threshold=0.6,
alt_threshold=0.2,
strict_validation=True):
data = csvutils.read_csv_and_yaml(infile)
data = data.set_index('cell_id', drop=False)
fastqscreen_cols = [
col for col in data.columns.values if col.startswith('fastqscreen_')
]
reference = "fastqscreen_{}".format(reference)
if reference not in fastqscreen_cols:
raise Exception("Could not find the fastq screen counts")
alts = [col for col in fastqscreen_cols if not col == reference]
data['is_contaminated'] = False
perc_ref = data[reference] / data['total_reads']
data.loc[perc_ref <= ref_threshold, 'is_contaminated'] = True
for altcol in alts:
perc_alt = data[altcol] / data['total_reads']
data.loc[perc_alt > alt_threshold, 'is_contaminated'] = True
col_type = dtypes()['metrics']['is_contaminated']
data['is_contaminated'] = data['is_contaminated'].astype(col_type)
csvutils.write_dataframe_to_csv_and_yaml(data,
outfile,
write_header=True,
dtypes=dtypes()['metrics'])
# get cells that are contaminated and have enopugh human reads
check_df = data.loc[data['is_contaminated'] == True]
check_df['perc_ref'] = data[reference] / data['total_reads']
check_df = check_df[check_df['perc_ref'] > ref_threshold]
if strict_validation and (len(check_df) / len(data) > 0.2):
logging.error("over 20% of cells are contaminated")
def read_metrics(self):
"""
read metrics and get cell to mad mapping
"""
metrics = csvutils.read_csv_and_yaml(self.metrics)
metrics = metrics.set_index("cell_id")
cell_order = metrics.order.sort_values().index
# assume all cells are good, dont filter
if 'quality' not in metrics.columns.values:
logging.getLogger("single_cell.hmmcopy.igv_seg").warn(
"quality column missing in data")
metrics['quality'] = 1
qual_cell_map = {
cell: mad
for cell, mad in zip(metrics.index, metrics["quality"])
}
return qual_cell_map, cell_order
def classify_fastqscreen(training_data_path, metrics_path, metrics_output, dtypes):
df = csvutils.read_csv_and_yaml(metrics_path)
features_train, feature_transformer, model = train(training_data_path)
features = ["fastqscreen_nohit_ratio", "fastqscreen_grch37_ratio", "fastqscreen_mm10_ratio",
"fastqscreen_salmon_ratio"]
label_to_species = {0: "grch37", 1: "mm10", 2: "salmon"}
# check if all the features exists, if yes, make predictions, else create an empty species column.
exist = all([feature[:-6] in df for feature in features])
if exist:
# make the feature columns
for feature in features:
df[feature] = df[feature[:-6]].divide(df["total_reads"])
# check if there's any missing value
feature_test = df[features]
feature_test = feature_test.replace([np.inf, -np.inf], np.nan)
feature_test.fillna(features_train.mean(), inplace=True)
# scale the features
scaled_features = feature_transformer.transform(feature_test)
df["species"] = model.predict(scaled_features)
df["species"].replace(label_to_species, inplace=True)
csvutils.write_dataframe_to_csv_and_yaml(df, metrics_output, dtypes)
def add_contamination_status(infile,
outfile,
genome_labels,
reference='grch37',
threshold=0.05):
data = csvutils.read_csv_and_yaml(infile)
data = data.set_index('cell_id', drop=False)
if reference not in genome_labels:
raise Exception("Could not find the fastq screen counts")
alts = [col for col in genome_labels if not col == reference]
data['is_contaminated'] = False
for altcol in alts:
perc_alt = _get_col_data(data, altcol) / data['total_reads']
data.loc[perc_alt > threshold, 'is_contaminated'] = True
data['is_contaminated'] = data['is_contaminated'].astype('bool')
csvutils.write_dataframe_to_csv_and_yaml(data, outfile,
dtypes(genome_labels))
def load_metrics_data(filename):
reads = csvutils.read_csv_and_yaml(filename)
reads = reads.set_index(['cell_id'])
return reads
def read_csv(self, infile):
return csvutils.read_csv_and_yaml(infile)
def get_max_cn(reads):
df = csvutils.read_csv_and_yaml(reads)
max_cn = np.nanpercentile(df['copy'], 99)
return max_cn
