def test_MotifsOrTracksIDs_with_motifs():
"""Check if a MotifOrTrackIDs object can be constructed from a list of motif IDs."""
motif_or_track_ids_instance = MotifOrTrackIDs(
motif_or_track_ids=['motif5', 'motif10', 'motif3', 'motif10'],
motifs_or_tracks_type=MotifsOrTracksType.MOTIFS)
assert motif_or_track_ids_instance.type == MotifsOrTracksType.MOTIFS
assert motif_or_track_ids_instance.ids == ('motif10', 'motif3', 'motif5')
assert eval(
motif_or_track_ids_instance.__repr__()) == motif_or_track_ids_instance
assert len(motif_or_track_ids_instance) == 3
def test_MotifsOrTracksIDs_with_tracks():
"""Check if a MotifOrTrackIDs object can be constructed from a set of track IDs."""
motif_or_track_ids_instance = MotifOrTrackIDs(
motif_or_track_ids={'track5', 'track10', 'track3', 'track10'},
motifs_or_tracks_type=MotifsOrTracksType.TRACKS)
assert motif_or_track_ids_instance.type == MotifsOrTracksType.TRACKS
assert motif_or_track_ids_instance.ids == ('track10', 'track3', 'track5')
assert eval(
motif_or_track_ids_instance.__repr__()) == motif_or_track_ids_instance
assert len(motif_or_track_ids_instance) == 3
def test_MotifsOrTracksIDs_with_motifs_or_tracks_type_str():
"""
Check if a MotifOrTrackIDs object can be constructed from a tuple of track IDs,
where motifs_or_tracks_type is given as a string.
"""
motif_or_track_ids_instance = MotifOrTrackIDs(
motif_or_track_ids=('track5', 'track10', 'track3', 'track10'),
motifs_or_tracks_type='tracks')
assert motif_or_track_ids_instance.type == MotifsOrTracksType.TRACKS
assert motif_or_track_ids_instance.ids == ('track10', 'track3', 'track5')
assert eval(
motif_or_track_ids_instance.__repr__()) == motif_or_track_ids_instance
assert len(motif_or_track_ids_instance) == 3
def ct_rankings_db_genes_vs_tracks(db_numpy_array_rankings_db_genes_vs_tracks):
# Create cisTarget RANKINGS_DB_GENES_VS_TRACKS database.
# Create zeroed cisTarget RANKINGS_DB_GENES_VS_TRACKS database.
region_or_gene_ids_instance = RegionOrGeneIDs(
region_or_gene_ids=[
'gene1', 'gene2', 'gene3', 'gene4', 'gene5', 'gene6', 'gene7'
],
regions_or_genes_type=RegionsOrGenesType.GENES)
motif_or_track_ids_instance = MotifOrTrackIDs(
motif_or_track_ids=['track1', 'track2', 'track3', 'track4'],
motifs_or_tracks_type=MotifsOrTracksType.TRACKS)
ct_rankings_db_genes_vs_tracks = CisTargetDatabase.create_db(
db_type=DatabaseTypes.RANKINGS_DB_GENES_VS_TRACKS,
region_or_gene_ids=region_or_gene_ids_instance,
motif_or_track_ids=motif_or_track_ids_instance,
db_numpy_array=db_numpy_array_rankings_db_genes_vs_tracks)
return ct_rankings_db_genes_vs_tracks
def ct_scores_db_motifs_vs_regions(db_numpy_array_scores_db_motifs_vs_regions):
# Create cisTarget SCORES_DB_MOTIFS_VS_REGIONS database.
# Create zeroed cisTarget SCORES_DB_MOTIFS_VS_REGIONS database.
region_or_gene_ids_instance = RegionOrGeneIDs(
region_or_gene_ids=[
'reg1', 'reg2', 'reg3', 'reg4', 'reg5', 'reg6', 'reg7'
],
regions_or_genes_type=RegionsOrGenesType.REGIONS)
motif_or_track_ids_instance = MotifOrTrackIDs(
motif_or_track_ids=['motif1', 'motif2', 'motif3', 'motif4'],
motifs_or_tracks_type=MotifsOrTracksType.MOTIFS)
ct_scores_db_motifs_vs_regions = CisTargetDatabase.create_db(
db_type=DatabaseTypes.SCORES_DB_MOTIFS_VS_REGIONS,
region_or_gene_ids=region_or_gene_ids_instance,
motif_or_track_ids=motif_or_track_ids_instance,
db_numpy_array=db_numpy_array_scores_db_motifs_vs_regions)
return ct_scores_db_motifs_vs_regions
def main():
parser = argparse.ArgumentParser(
description='Create cisTarget motif databases.')
parser.add_argument(
'-f',
'--fasta',
dest='fasta_filename',
action='store',
type=str,
required=True,
help=
'FASTA filename which contains the regions/genes to score with Cluster-Buster for each motif. When '
'creating a cisTarget species database from regions/genes lifted over from a different species, provide '
'the original FASTA file for that species to -F.')
parser.add_argument(
'-F',
'--fasta-original-species',
dest='original_species_fasta_filename',
action='store',
type=str,
required=False,
help=
'FASTA filename which contains all the regions/genes of the original species. The fasta file provided to '
'-f can contain less regions (not all regions could be lifted over) than the one provided to -F, but to '
'create a cisTarget cross-species database later, all individual cisTarget species databases need to '
'contain the same amount of regions/genes.')
parser.add_argument('-M',
'--motifs_dir',
dest='motifs_dir',
action='store',
type=str,
required=True,
help='Path to directory with Cluster-Buster motifs.')
parser.add_argument(
'-m',
'--motifs',
dest='motifs_list_filename',
action='store',
type=str,
required=True,
help=
'Filename with list of motif IDs or motif MD5 names to be scored from directory specified by '
'"--motifs_dir".')
parser.add_argument(
'-5',
'--md5',
dest='motif_md5_to_motif_id_filename',
action='store',
type=str,
required=False,
help=
'Filename with motif MD5 to motif ID mappings to map Cluster-Buster motif MD5 filenames to motif IDs.'
)
parser.add_argument('-o',
'--output',
dest='db_prefix',
action='store',
type=str,
required=True,
help='Feather database prefix output filename.')
parser.add_argument(
'-c',
'--cbust',
dest='cluster_buster_path',
action='store',
type=str,
required=False,
default='cbust',
help=
'Path to Cluster-Buster (https://github.com/weng-lab/cluster-buster/). Default: "cbust".'
)
parser.add_argument(
'-t',
'--threads',
dest='nbr_threads',
action='store',
type=int,
required=False,
default=1,
help='Number of threads to use when scoring motifs. Default: 1.')
parser.add_argument(
'-p',
'--partial',
dest='partial',
nargs=2,
metavar=('CURRENT_PART', 'NBR_TOTAL_PARTS'),
action='store',
type=int,
required=False,
help=
'Divide the motif list in a number of total parts (of similar size) and score only the part defined by '
'current_part. This allows creating partial databases on machines which do not have enough RAM to score '
'all motifs in one iteration. This will only create a partial regions/genes vs motifs scoring database '
'({db_prefix}.part_000{current_part}_of_000{nbr_total_parts}.regions_vs_motifs.scores.feather or '
'{db_prefix}.part_000{current_part}_of_000{nbr_total_parts}.genes_vs_motifs.scores.feather).'
)
parser.add_argument(
'-g',
'--genes',
dest='extract_gene_id_from_region_id_regex_replace',
action='store',
type=str,
required=False,
default=None,
help=
'Take top CRM score for a gene by taking the maximum CRM score of multiple regions for that gene. '
'Define a regex which will remove the non-gene part of the region ID, so only the gene ID remains. '
'Examples: "gene_id#some_number": "#[0-9]+$" or "region_id@@gene_id": "^.+@@".'
)
parser.add_argument(
'-b',
'--bgpadding',
dest='bg_padding',
action='store',
type=int,
required=False,
default=0,
help=
'Background padding in bp that was added for each sequence in FASTA file. Default: 0.'
)
parser.add_argument(
'--min',
dest='min_nbr_motifs',
action='store',
type=int,
required=False,
default=1,
help=
'Minimum number of motifs needed per Cluster-Buster motif file to be considered for scoring '
'(filters motifs list). Default: 1.')
parser.add_argument(
'--max',
dest='max_nbr_motifs',
action='store',
type=int,
required=False,
default=None,
help=
'Maximum number of motifs needed per Cluster-Buster motif file to be considered for scoring '
'(filters motifs list). Default: None.')
parser.add_argument('-l',
'--mask',
dest='mask',
action='store_true',
help='Consider masked (lowercase) nucleotides as Ns.')
parser.add_argument(
'-s',
'--seed',
dest='seed',
action='store',
type=int,
required=False,
help=
'Random seed used for breaking ties when creating rankings for a range of tied scores. '
'When setting this seed to a specific value and running this script with the same input, will result in '
'the same rankings databases as output.')
parser.add_argument(
'-r',
'--ssh',
dest='ssh_command',
action='store',
type=str,
required=False,
help=
'If defined, run Cluster-Buster over ssh by running the provided command to make the connection before '
'running Cluster-Buster itself. '
"Example: 'ssh -o ControlMaster=auto -o ControlPath=/tmp/ssh-control-path-%%l-%%h-%%p-%%r -o ControlPersist=600 <hostname>'"
)
args = parser.parse_args()
if not os.path.exists(args.fasta_filename):
print(f'Error: FASTA filename "{args.fasta_filename}" does not exist.',
file=sys.stderr)
sys.exit(1)
if args.original_species_fasta_filename and not os.path.exists(
args.original_species_fasta_filename):
print(
f'Error: Original species FASTA filename "{args.original_species_fasta_filename}" does not exist.',
file=sys.stderr)
sys.exit(1)
if not os.path.exists(args.motifs_dir):
print(f'Error: Motif directory "{args.motifs_dir}" does not exist.',
file=sys.stderr)
sys.exit(1)
if args.motif_md5_to_motif_id_filename and not os.path.exists(
args.motif_md5_to_motif_id_filename):
print(
f'Error: Motif MD5 to motif ID mappings filename "{args.motif_md5_to_motif_id_filename}" does not exist.',
file=sys.stderr)
sys.exit(1)
if not os.path.exists(args.motifs_list_filename):
print(
f'Error: Motifs list filename "{args.motifs_list_filename}" does not exist.',
file=sys.stderr)
sys.exit(1)
if os.path.dirname(args.db_prefix) and not os.path.exists(
os.path.dirname(args.db_prefix)):
print(
f'Error: Parent directory "{os.path.dirname(args.db_prefix)}" for Feather database prefix output filename '
'does not exist.',
file=sys.stderr)
sys.exit(1)
if args.partial:
current_part, nbr_total_parts = args.partial
if current_part < 1 or current_part > nbr_total_parts:
print(
f'Error: Current part ({current_part}) should be between 1 and the number of total parts '
f'({nbr_total_parts}).',
file=sys.stderr)
sys.exit(1)
# Add info about which part of the database this wil be.
db_prefix = f'{args.db_prefix}.part_{current_part:04d}_of_{nbr_total_parts:04d}'
else:
db_prefix = args.db_prefix
if not (args.min_nbr_motifs == 1 and not args.max_nbr_motifs):
if not args.partial:
# Add ".part_0001_of_0001" if partial was not specified but if min and max number of motifs per
# Cluster-Buster motif file is specified, so it is easier to combine those databases with different subsets
# of motifs in a later step.
db_prefix = f'{args.db_prefix}.part_0001_of_0001'
# Add info about number of motifs in Cluster-Buster motif file which will be scored (if min or max is set to a
# non-default value).
if args.max_nbr_motifs:
db_prefix = f'{db_prefix}.min_{args.min_nbr_motifs:d}_to_max_{args.max_nbr_motifs:d}_motifs'
else:
db_prefix = f'{db_prefix}.min_{args.min_nbr_motifs:d}_to_max_motifs'
# Get absolute path to Cluster-Buster binary and see if it can be executed.
cluster_buster_path = shutil.which(args.cluster_buster_path)
if not cluster_buster_path:
print(
f'Error: Cluster-Buster binary ("{args.cluster_buster_path}") could not be found or is not executable.'
)
sys.exit(1)
# Set random seed to provided input value or a random integer.
seed = args.seed if args.seed else random.randint(0, 2**64)
if args.original_species_fasta_filename:
# When creating cisTarget databases for a species with lifted over regions, check if the regions/genes in the
# current species FASTA file are available in the original species FASTA file. Due to liftover, regions might
# be lost in the current species, but the cisTarget database needs to contain all regions/genes from the
# original species to create the cisTarget cross-species database later.
# Get all region or gene IDs from current species FASTA sequence names as a RegionOrGeneIDs object.
region_or_gene_ids_current_species = RegionOrGeneIDs.get_region_or_gene_ids_from_fasta(
fasta_filename=args.fasta_filename,
extract_gene_id_from_region_id_regex_replace=args.
extract_gene_id_from_region_id_regex_replace)
# Get all region or gene IDs from the original FASTA sequence names as a RegionOrGeneIDs object.
region_or_gene_ids = RegionOrGeneIDs.get_region_or_gene_ids_from_fasta(
fasta_filename=args.original_species_fasta_filename,
extract_gene_id_from_region_id_regex_replace=args.
extract_gene_id_from_region_id_regex_replace)
if not region_or_gene_ids_current_species.issubset(region_or_gene_ids):
print(
f'Error: Region IDs/gene IDs in "{args.fasta_filename}" are not all present in '
f'"{args.original_species_fasta_filename}".',
file=sys.stderr)
sys.exit(1)
else:
# Get all region or gene IDs from the FASTA sequence names as a RegionOrGeneIDs object.
region_or_gene_ids = RegionOrGeneIDs.get_region_or_gene_ids_from_fasta(
fasta_filename=args.fasta_filename,
extract_gene_id_from_region_id_regex_replace=args.
extract_gene_id_from_region_id_regex_replace)
# Get absolute path name for FASTA filename so in case Cluster-Buster is ran over ssh, the FASTA file can be found.
fasta_filename = os.path.abspath(args.fasta_filename)
# Get motif ID to motif file name mapping and motif ID to number of motifs per motif file mapping for
# and (optionally) filtered list of motif IDs:
# - if partial is set
# - if min_nbr_motifs is set
# - if max_nbr_motifs is set
motif_id_to_filename_dict, motif_id_to_nbr_motifs_dict = get_motif_id_to_filename_and_nbr_motifs_dict(
motifs_dir=os.path.abspath(args.motifs_dir),
motifs_list_filename=args.motifs_list_filename,
motif_md5_to_motif_id_filename=args.motif_md5_to_motif_id_filename,
partial=(current_part, nbr_total_parts) if args.partial else None,
min_nbr_motifs=args.min_nbr_motifs,
max_nbr_motifs=args.max_nbr_motifs)
# Create MotifOrTracksIDs object from plain motif IDs.
motif_ids = MotifOrTrackIDs(
motif_or_track_ids=set(motif_id_to_filename_dict),
motifs_or_tracks_type=MotifsOrTracksType.MOTIFS)
nbr_region_or_gene_ids = len(region_or_gene_ids)
nbr_motifs = len(motif_id_to_filename_dict)
if nbr_region_or_gene_ids == 0:
print(f'Error: No {region_or_gene_ids.type.value} provided.',
file=sys.stderr)
sys.exit(1)
if nbr_motifs == 0:
print('Error: No motifs provided.', file=sys.stderr)
sys.exit(1)
print(
f'Initialize dataframe ({nbr_region_or_gene_ids} {region_or_gene_ids.type.value} '
f'x {nbr_motifs} motifs) for storing CRM scores for each {region_or_gene_ids.type.value} per motif.',
file=sys.stderr)
ct_scores_db_motifs_vs_regions_or_genes = CisTargetDatabase.create_db(
db_type=DatabaseTypes.from_strings(
scores_or_rankings='scores',
column_kind='motifs',
row_kind=region_or_gene_ids.type.value),
region_or_gene_ids=region_or_gene_ids,
motif_or_track_ids=motif_ids,
order='F')
def write_crm_scores_for_motif_to_ct_scores_db(
df_motif_id_and_crm_scores: Tuple[str, pd.DataFrame]) -> None:
if 'nbr_of_scored_motifs' not in write_crm_scores_for_motif_to_ct_scores_db.__dict__:
write_crm_scores_for_motif_to_ct_scores_db.nbr_of_scored_motifs = 0
motif_id, df_crm_scores = df_motif_id_and_crm_scores
start_time = time.monotonic()
ct_scores_db_motifs_vs_regions_or_genes.update_scores_for_motif_or_track(
motif_or_track_id=motif_id,
df_scores_for_motif_or_track=df_crm_scores['crm_score'])
elapsed_time = time.monotonic() - start_time
write_crm_scores_for_motif_to_ct_scores_db.nbr_of_scored_motifs += 1
print(
f'Adding Cluster-Buster CRM scores ({write_crm_scores_for_motif_to_ct_scores_db.nbr_of_scored_motifs:d} of '
f'{nbr_motifs:d}) for motif "{motif_id:s}" took {elapsed_time:0.6f} seconds.',
file=sys.stderr)
def report_error(exception: BaseException) -> None:
print(exception, file=sys.stderr)
start_time = time.monotonic()
with mp.Pool(processes=args.nbr_threads) as pool:
# Motif IDs are sorted by number of motifs in motif ID Cluster-Buster file (high to low) and then by motif ID
# name (in get_motif_id_to_filename_and_nbr_motifs_dict()), so motif IDs which have a lot of motifs in their
# Cluster-Buster motif file are scored first, before singletons are scored to prevent that a few Cluster-Buster
# motifs files with a huge number of motifs got sheduled near the end of the motif scoring and underutilizing
# the compute node.
for motif_id, motif_filename in motif_id_to_filename_dict.items():
# Score all regions/genes in the FASTA file for the current motif and write the result in the
# ct_scores_db_motifs_vs_regions_or_genes CisTargetDatabase object.
pool.apply_async(
func=run_cluster_buster_for_motif,
args=[
cluster_buster_path, fasta_filename, motif_filename,
motif_id,
args.extract_gene_id_from_region_id_regex_replace,
args.bg_padding, args.mask, args.ssh_command
],
callback=write_crm_scores_for_motif_to_ct_scores_db,
error_callback=report_error)
# Prevents any more task from being submitted to the pool.
pool.close()
# Wait for worker processes to exit.
pool.join()
elapsed_time = time.monotonic() - start_time
print(
f'\nScoring {nbr_motifs} motifs with Cluster-Buster took: {elapsed_time:.06f} seconds\n'
)
if 'nbr_of_scored_motifs' not in write_crm_scores_for_motif_to_ct_scores_db.__dict__:
print(
f'Error: None of {nbr_motifs:d} motifs were scored successfully.',
file=sys.stderr)
sys.exit(1)
elif write_crm_scores_for_motif_to_ct_scores_db.nbr_of_scored_motifs != nbr_motifs:
print(
f'Error: Only {write_crm_scores_for_motif_to_ct_scores_db.nbr_of_scored_motifs:d} out of {nbr_motifs:d} '
f'motifs were scored successfully.',
file=sys.stderr)
sys.exit(1)
print('', file=sys.stderr)
def write_db(ct_db: CisTargetDatabase, db_prefix: str):
"""
Write cisTarget database to a Feather file and print database location and elapsed time.
:param ct_db: cisTarget database object.
:param db_prefix: Feather database file prefix.
:return:
"""
db_filename = ct_db.create_db_filename_from_db_prefix(
db_prefix=db_prefix, extension='feather')
print(
f'Writing cisTarget {ct_db.db_type.row_kind} vs {ct_db.db_type.column_kind} '
f'{ct_db.db_type.scores_or_rankings} db: "{db_filename}"')
start_time = time.monotonic()
ct_db.write_db(db_prefix=db_prefix, version=1)
elapsed_time = time.monotonic() - start_time
print(
f'Writing cisTarget {ct_db.db_type.row_kind} vs {ct_db.db_type.column_kind} '
f'{ct_db.db_type.scores_or_rankings} db took: {elapsed_time:.06f} seconds\n'
)
# Write cisTarget scores database (motifs vs regions or genes) to Feather file.
write_db(ct_db=ct_scores_db_motifs_vs_regions_or_genes,
db_prefix=db_prefix)
if not args.partial:
# Create cisTarget scores database (regions or genes vs motifs) from (motifs vs regions or genes) version.
ct_scores_db_regions_or_genes_vs_motifs = ct_scores_db_motifs_vs_regions_or_genes.transpose(
)
# Write cisTarget scores database (regions or genes vs motifs) to Feather file.
write_db(ct_db=ct_scores_db_regions_or_genes_vs_motifs,
db_prefix=db_prefix)
# Create cisTarget rankings database (motifs vs regions or genes) from cisTarget scores database filename
# (motifs vs regions or genes).
print(f'''Create rankings from "{
ct_scores_db_motifs_vs_regions_or_genes.create_db_filename_from_db_prefix(
db_prefix=db_prefix,
extension='feather'
)
}" with random seed set to {seed}.''',
file=sys.stderr)
start_time = time.monotonic()
ct_rankings_db_motifs_vs_regions_or_genes = \
ct_scores_db_motifs_vs_regions_or_genes.convert_scores_db_to_rankings_db(seed=seed)
elapsed_time = time.monotonic() - start_time
print(f'Creating cisTarget rankings db from cisTarget scores db took: '
f'{elapsed_time:.06f} seconds\n')
# Reclaim memory occupied by cisTarget scores databases.
del ct_scores_db_motifs_vs_regions_or_genes
del ct_scores_db_regions_or_genes_vs_motifs
# Do not write cisTarget rankings database (motifs vs regions or genes) to Feather file
# as it can take a very long time to write it (1.5 hours for 1 milion regions) as the
# rankings database numpy array is in "C" order and writing a Feather database requires
# travering the numpy array in column order.
#write_db(ct_db=ct_rankings_db_motifs_vs_regions_or_genes, db_prefix=db_prefix)
# Create cisTarget rankings database (regions or genes vs motifs) from (motifs vs regions or genes) version.
ct_rankings_db_regions_or_genes_vs_motifs = ct_rankings_db_motifs_vs_regions_or_genes.transpose(
)
# Write cisTarget rankings database (regions or genes vs motifs) to Feather file.
write_db(ct_db=ct_rankings_db_regions_or_genes_vs_motifs,
db_prefix=db_prefix)
def test_cistargetdatabase_basic(db_numpy_array_scores_db_motifs_vs_regions,
db_numpy_array_rankings_db_genes_vs_tracks):
# Test cisTarget SCORES_DB_MOTIFS_VS_REGIONS databases.
region_or_gene_ids_instance = RegionOrGeneIDs(
region_or_gene_ids=[
'reg1', 'reg2', 'reg3', 'reg4', 'reg5', 'reg6', 'reg7'
],
regions_or_genes_type=RegionsOrGenesType.REGIONS)
motif_or_track_ids_instance = MotifOrTrackIDs(
motif_or_track_ids=['motif1', 'motif2', 'motif3', 'motif4'],
motifs_or_tracks_type=MotifsOrTracksType.MOTIFS)
def check_ct_scores_db_motifs_vs_regions(ct_scores_db_motifs_vs_regions,
db_numpy_array, order):
# Check if creation of cisTarget SCORES_DB_MOTIFS_VS_REGIONS database succeeded (float32 datatype).
assert np.all(
ct_scores_db_motifs_vs_regions.df.to_numpy() == db_numpy_array)
assert ct_scores_db_motifs_vs_regions.shape == (7, 4)
assert ct_scores_db_motifs_vs_regions.nbr_rows == 7
assert ct_scores_db_motifs_vs_regions.nbr_columns == 4
assert ct_scores_db_motifs_vs_regions.dtype == np.float32
if order == 'C':
assert ct_scores_db_motifs_vs_regions.df.to_numpy(
).flags.c_contiguous is True
elif order == 'F':
assert ct_scores_db_motifs_vs_regions.df.to_numpy(
).flags.f_contiguous is True
# Check if region or gene IDs and motif and track IDs are properly set.
assert ct_scores_db_motifs_vs_regions.region_or_gene_ids == region_or_gene_ids_instance
assert ct_scores_db_motifs_vs_regions.motif_or_track_ids == motif_or_track_ids_instance
# Columns contain motifs.
assert ct_scores_db_motifs_vs_regions.df.columns.to_list() == list(
motif_or_track_ids_instance.ids)
# Rows contain regions.
assert ct_scores_db_motifs_vs_regions.df.index.to_list() == list(
region_or_gene_ids_instance.ids)
# Create zeroed cisTarget SCORES_DB_MOTIFS_VS_REGIONS database in C order.
check_ct_scores_db_motifs_vs_regions(
ct_scores_db_motifs_vs_regions=CisTargetDatabase.create_db(
db_type=DatabaseTypes.SCORES_DB_MOTIFS_VS_REGIONS,
region_or_gene_ids=region_or_gene_ids_instance,
motif_or_track_ids=motif_or_track_ids_instance),
db_numpy_array=np.zeros((7, 4), dtype=np.float32),
order='C')
# Create zeroed cisTarget SCORES_DB_MOTIFS_VS_REGIONS database in Fortran order.
check_ct_scores_db_motifs_vs_regions(
ct_scores_db_motifs_vs_regions=CisTargetDatabase.create_db(
db_type=DatabaseTypes.SCORES_DB_MOTIFS_VS_REGIONS,
region_or_gene_ids=region_or_gene_ids_instance,
motif_or_track_ids=motif_or_track_ids_instance,
order='F'),
db_numpy_array=np.zeros((7, 4), dtype=np.float32),
order='F')
# Create cisTarget SCORES_DB_MOTIFS_VS_REGIONS database from numpy array
# (db_numpy_array_scores_db_motifs_vs_regions is in C order).
check_ct_scores_db_motifs_vs_regions(
ct_scores_db_motifs_vs_regions=CisTargetDatabase.create_db(
db_type=DatabaseTypes.SCORES_DB_MOTIFS_VS_REGIONS,
region_or_gene_ids=region_or_gene_ids_instance,
motif_or_track_ids=motif_or_track_ids_instance,
db_numpy_array=db_numpy_array_scores_db_motifs_vs_regions),
db_numpy_array=db_numpy_array_scores_db_motifs_vs_regions,
order='C')
# Delete some objects so we don't accidentally reuse them in the next section.
del region_or_gene_ids_instance
del motif_or_track_ids_instance
# Test cisTarget RANKINGS_DB_GENES_VS_TRACKS databases.
region_or_gene_ids_instance = RegionOrGeneIDs(
region_or_gene_ids=[
'gene1', 'gene2', 'gene3', 'gene4', 'gene5', 'gene6', 'gene7'
],
regions_or_genes_type=RegionsOrGenesType.GENES)
motif_or_track_ids_instance = MotifOrTrackIDs(
motif_or_track_ids=['track1', 'track2', 'track3', 'track4'],
motifs_or_tracks_type=MotifsOrTracksType.TRACKS)
def check_ct_rankings_db_genes_vs_tracks(ct_rankings_db_genes_vs_tracks,
db_numpy_array, order):
# Check if creation of zeroed cisTarget RANKINGS_DB_GENES_VS_TRACKS database succeeded (int16 datatype).
assert np.all(
ct_rankings_db_genes_vs_tracks.df.to_numpy() == db_numpy_array)
assert ct_rankings_db_genes_vs_tracks.shape == (4, 7)
assert ct_rankings_db_genes_vs_tracks.nbr_rows == 4
assert ct_rankings_db_genes_vs_tracks.nbr_columns == 7
assert ct_rankings_db_genes_vs_tracks.dtype == np.int16
if order == 'C':
assert ct_rankings_db_genes_vs_tracks.df.to_numpy(
).flags.c_contiguous is True
elif order == 'F':
assert ct_rankings_db_genes_vs_tracks.df.to_numpy(
).flags.f_contiguous is True
# Check if region or gene IDs and motif and track IDs are properly set.
assert ct_rankings_db_genes_vs_tracks.region_or_gene_ids == region_or_gene_ids_instance
assert ct_rankings_db_genes_vs_tracks.motif_or_track_ids == motif_or_track_ids_instance
# Columns contain genes.
assert ct_rankings_db_genes_vs_tracks.df.columns.to_list() == list(
region_or_gene_ids_instance.ids)
# Rows contain tracks.
assert ct_rankings_db_genes_vs_tracks.df.index.to_list() == list(
motif_or_track_ids_instance.ids)
# Create zeroed cisTarget RANKINGS_DB_GENES_VS_TRACKS database in C order.
check_ct_rankings_db_genes_vs_tracks(
ct_rankings_db_genes_vs_tracks=CisTargetDatabase.create_db(
db_type=DatabaseTypes.RANKINGS_DB_GENES_VS_TRACKS,
region_or_gene_ids=region_or_gene_ids_instance,
motif_or_track_ids=motif_or_track_ids_instance),
db_numpy_array=np.zeros((4, 7), dtype=np.int16),
order='C')
# Create zeroed cisTarget RANKINGS_DB_GENES_VS_TRACKS database in Fortran order.
check_ct_rankings_db_genes_vs_tracks(
ct_rankings_db_genes_vs_tracks=CisTargetDatabase.create_db(
db_type=DatabaseTypes.RANKINGS_DB_GENES_VS_TRACKS,
region_or_gene_ids=region_or_gene_ids_instance,
motif_or_track_ids=motif_or_track_ids_instance,
order='F'),
db_numpy_array=np.zeros((4, 7), dtype=np.int16),
order='F')
# Create cisTarget RANKINGS_DB_GENES_VS_TRACKS database from numpy array
# (db_numpy_array_rankings_db_genes_vs_tracks is in Fortran order).
check_ct_rankings_db_genes_vs_tracks(
ct_rankings_db_genes_vs_tracks=CisTargetDatabase.create_db(
db_type=DatabaseTypes.RANKINGS_DB_GENES_VS_TRACKS,
region_or_gene_ids=region_or_gene_ids_instance,
motif_or_track_ids=motif_or_track_ids_instance,
db_numpy_array=db_numpy_array_rankings_db_genes_vs_tracks),
db_numpy_array=db_numpy_array_rankings_db_genes_vs_tracks,
order='F')
# Delete some objects so we don't accidentally reuse them in the next section.
del region_or_gene_ids_instance
del motif_or_track_ids_instance