def __init__(self,
organism,
membership,
ratios,
meme_suite,
seqtype,
sequence_filters=[],
scaling_func=None,
num_motif_func=None,
update_in_iteration=lambda iteration: True,
motif_in_iteration=lambda iteration: True,
config_params=None):
"""creates a ScoringFunction"""
# run_in_iteration does not apply here, since we actually have
# two schedules, motif_in_iteration and update_in_iteration here
scoring.ScoringFunctionBase.__init__(self,
organism,
membership,
ratios,
scaling_func,
schedule=None,
config_params=config_params)
# attributes accessible by subclasses
self.meme_suite = meme_suite
self.seqtype = seqtype
self.update_in_iteration = update_in_iteration
self.motif_in_iteration = motif_in_iteration
self.num_motif_func = num_motif_func
self.__sequence_filters = sequence_filters
self.__last_motif_infos = None
self.__last_iteration_result = {}
self.all_pvalues = None
self.last_result = None
self.update_log = scoring.RunLog("motif-score-" + seqtype,
config_params)
self.motif_log = scoring.RunLog("motif-motif-" + seqtype,
config_params)
used_genes = sorted(ratios.row_names)
self.used_seqs = organism.sequences_for_genes_scan(
used_genes, seqtype=self.seqtype)
logging.info("building reverse map...")
start_time = util.current_millis()
self.reverse_map = self.__build_reverse_map(ratios)
logging.info("reverse map built in %d ms.",
util.current_millis() - start_time)
def __init__(self, organism, membership, ratios, config_params=None):
"""Create scoring function instance"""
scoring.ScoringFunctionBase.__init__(self, "SetEnrichment", organism,
membership, ratios, config_params)
self.__set_types = read_set_types(config_params, organism.thesaurus(),
ratios.row_names)
self.run_log = scoring.RunLog('set_enrichment', config_params)
def __init__(self, organism, membership, ratios, scaling_func, schedule,
config_params):
"""Create scoring function instance"""
scoring.ScoringFunctionBase.__init__(self, organism, membership,
ratios, scaling_func, schedule,
config_params)
self.run_log = scoring.RunLog("row_scoring", config_params)
def __init__(self,
id,
organism,
membership,
ratios,
seqtype,
config_params=None):
"""creates a ScoringFunction"""
scoring.ScoringFunctionBase.__init__(self,
id,
organism,
membership,
ratios,
config_params=config_params)
# attributes accessible by subclasses
self.seqtype = seqtype
self.__setup_meme_suite(config_params)
self.num_motif_func = util.get_iter_fun(
config_params['MEME'], "nmotifs", config_params['num_iterations'])
self.__last_motif_infos = None
self.__last_iteration_result = {}
self.all_pvalues = None
self.last_result = None
self.update_log = scoring.RunLog("motif-score-" + seqtype,
config_params)
self.motif_log = scoring.RunLog("motif-motif-" + seqtype,
config_params)
used_genes = sorted(ratios.row_names)
self.used_seqs = organism.sequences_for_genes_scan(
used_genes, seqtype=self.seqtype)
logging.debug("building reverse map...")
start_time = util.current_millis()
self.reverse_map = self.__build_reverse_map(ratios)
logging.debug("reverse map built in %d ms.",
util.current_millis() - start_time)
self.__last_results = None # caches the results of the previous meme run
def __init__(self,
organism,
membership,
matrix,
set_types,
scaling_func=None,
schedule=lambda iteration: True,
config_params=None):
"""Create scoring function instance"""
scoring.ScoringFunctionBase.__init__(self, organism, membership,
matrix, scaling_func, schedule,
config_params)
self.__set_types = set_types
# stores (min_set, pvalue) pairs for each cluster and set type
# for the last run of the function
self.__last_min_enriched_set = {}
for set_type in set_types:
self.__last_min_enriched_set[set_type] = {}
self.run_log = scoring.RunLog('set_enrichment', config_params)
def __init__(self, organism, membership, ratios, config_params):
"""Create scoring function instance"""
scoring.ScoringFunctionBase.__init__(self, "Networks", organism,
membership, ratios, config_params)
self.__networks = None
self.run_log = scoring.RunLog("network", config_params)