def dump_inflated_elements(self): dump_object( Dumps.GO_Inflated, (self.inflated_Laplacian, self.inflated_idx2lbl, self.inflated_lbl2idx, self.binding_intensity))
def dump_core(self): dump_object( Dumps.GO_dump, (self.UP2GO_Dict, self.GO2UP, self.SeedSet, self.Reachable_nodes_dict, self.GO_Names, self.GO_Legacy_IDs, self.rev_GO_IDs, self.All_GOs, self.GO2Num, self.Num2GO, self.UP_Names, self.UPs_without_GO))
def dump_statics(self): dump_object( Dumps.GO_builder_stat, (self.go_namespace_filter, self.InitSet, self.correction_factor, self.ultraspec_cleaned, self.ultraspec_lvl))
def dump_informativities(self): dump_object( Dumps.GO_Infos, (self.UP2GO_Reachable_nodes, self.GO2UP_Reachable_nodes, self.UP2GO_step_Reachable_nodes, self.GO2UP_step_Reachable_nodes, self.GO2_Pure_Inf, self.GO2_Weighted_Ent))
def dump_eigen(self): """ dumps self.adj_eigenvals and self.laplacian_matrix and writes them to csv """ write_to_csv(Dumps.eigen_VaMat, self.adj_eigenvals) write_to_csv(Dumps.eigen_ConMat, self.cond_eigenvals) dump_object(Dumps.val_eigen, (self.adj_eigenvals, self.adj_eigenvects)) dump_object(Dumps.cond_eigen, (self.cond_eigenvals, self.cond_eigenvects))
def dump_memoized(self): md5 = hashlib.md5( json.dumps( sorted( self.analytic_uniprots), sort_keys=True)).hexdigest() payload = { 'UP_hash': md5, 'sys_hash': self.md5_hash(), 'size': len( self.analytic_uniprots), 'UPs': pickle.dumps( self.analytic_uniprots), 'currents': pickle.dumps( (self.current_accumulator, self.node_current)), 'voltages': pickle.dumps( self.uniprots_2_voltage_and_circulation)} dump_object(Dumps.GO_Analysis_memoized, payload)
def run_analysis_suite( rna_source, no_of_experiments, experimental_groups, groups_to_compare, count_filter_level=5, false_discovery_rate=0.05): """ Imports counts table, runs test suite and stores the result of statistical analysis for further computation. returns stored values to the standard output. :param rna_source: the file from which the raw counts are to be read :param no_of_experiments: number of experiments :param experimental_groups: experiment groupings :param groups_to_compare: groups to be compared :param count_filter_level: minimum counts to run statistics :param false_discovery_rate: desired false discovery rate """ names, lengths, counts = load_rna_counts_table( rna_source, no_of_experiments) _, _, names[:, 1] = db_io_routines.look_up_annotation_set(names[ :, 0].tolist()) filter_mask = counts_filter( counts, experimental_groups, filter_level=count_filter_level) names = names[filter_mask, :] lengths = lengths[filter_mask, :] counts = counts[filter_mask, :] rpkms = convert_to_rpkm(lengths, counts) testres = significantly_different_genes( rpkms, experimental_groups, groups_to_compare, false_discovery_rate) filter_masks = [test_[1] for test_ in testres] dump_object(Dumps.RNA_seq_counts_compare, filter_masks) return filter_masks
def dump_maps(self): """ dumps all the elements required for the mapping between the types and ids of database entries and matrix columns """ log.debug("pre-dump e_p_u_b_i length: %s", len(self.entry_point_uniprots_bulbs_ids)) log.debug("dumping into: %s", Dumps.interactome_maps) dump_object( Dumps.interactome_maps, (self.bulbs_id_2_matrix_index, self.matrix_index_2_bulbs_id, self.bulbs_id_2_display_name, self.bulbs_id_2_legacy_id, self.bulbs_id2_node_type, self.bulbs_id_2_localization, self.reached_uniprots_bulbs_id_list, self.all_uniprots_bulbs_id_list, self.Uniprot_attachments, self.UP2Chrom, self.chromosomes_2_uniprot, self.uniprot_matrix_index_list, self.entry_point_uniprots_bulbs_ids )) # TODO: delete here and below entry point u_b_i
def run_analysis_suite(rna_source, no_of_experiments, experimental_groups, groups_to_compare, count_filter_level=5, false_discovery_rate=0.05): """ Imports counts table, runs test suite and stores the result of statistical analysis for further computation. returns stored values to the standard output. :param rna_source: the file from which the raw counts are to be read :param no_of_experiments: number of experiments :param experimental_groups: experiment groupings :param groups_to_compare: groups to be compared :param count_filter_level: minimum counts to run statistics :param false_discovery_rate: desired false discovery rate """ names, lengths, counts = load_rna_counts_table(rna_source, no_of_experiments) _, _, names[:, 1] = db_io_routines.look_up_annotation_set(names[:, 0].tolist()) filter_mask = counts_filter(counts, experimental_groups, filter_level=count_filter_level) names = names[filter_mask, :] lengths = lengths[filter_mask, :] counts = counts[filter_mask, :] rpkms = convert_to_rpkm(lengths, counts) testres = significantly_different_genes(rpkms, experimental_groups, groups_to_compare, false_discovery_rate) filter_masks = [test_[1] for test_ in testres] dump_object(Dumps.RNA_seq_counts_compare, filter_masks) return filter_masks
def dump_independent_linear_sets(self): dump_object(Dumps.GO_Indep_Linset, self.Indep_Lapl)
def dump_matrices(self): """ dumps self.adjacency_Matrix and self.laplacian_matrix """ dump_object(Dumps.interactome_adjacency_matrix, self.adjacency_Matrix) dump_object(Dumps.interactome_laplacian_matrix, self.laplacian_matrix)
def dump_matrices(self): dump_object( Dumps.GO_Mats, (self.adjacency_matrix, self.dir_adj_matrix, self.laplacian_matrix))