Beispiel #1
0
    def setUp(self):  # pylint; disable-msg=C0103
        """test fixture"""
        self.search_distances = {'upstream': (-20, 150)}
        self.scan_distances = {'upstream': (-30, 250)}

        self.ratio_matrix = dm.create_from_csv('example_data/hal/halo_ratios5.tsv',
                                               filters=[dm.nochange_filter, dm.center_scale_filter])
        self.organism = testutil.make_halo(self.search_distances, self.scan_distances,
                                           self.ratio_matrix)
        self.config_params = {'memb.min_cluster_rows_allowed': 3,
                              'memb.max_cluster_rows_allowed': 70,
                              'multiprocessing': False,
                              'num_cores': None,
                              'memb.clusters_per_row': 2,
                              'memb.clusters_per_col': int(round(43 * 2.0 / 3.0)),
                              'num_clusters': 43,
                              'output_dir': 'out',
                              'remap_network_nodes': False,
                              'use_BSCM': False,
                              'num_iterations': 2000,
                              'debug': {},
                              'search_distances': {'upstream': (-20, 150)},
                              'Columns': {'schedule': lambda i: True },
                              'Rows': {'schedule': lambda i: True, 'scaling': ('scaling_const', 6.0) },
                              'Motifs': {'schedule': lambda i: True,
                                         'scaling': ('scaling_rvec', 'seq(0, 1, length=num_iterations*3/4)')},
                              'MEME': {'version': '4.3.0',
                                       'global_background': False,
                                       'schedule': lambda i: True,
                                       'nmotifs_rvec': 'c(rep(1, num_iterations/3), rep(2, num_iterations/3))',
                                       'max_width': 24, 'arg_mod': 'zoops',
                                       'background_order': 3, 'use_revcomp': 'True'},
                              'Networks': {'schedule': lambda i: True, 'scaling': ('scaling_rvec', 'seq(1e-5, 0.5, length=num_iterations*3/4)')}}
        self.membership = self.__read_members()  # relies on config_params
        self.iteration_result = { 'iteration': 51, 'score_means': {} }
    def test_motif_scoring(self):
        """tests the motif scoring in integration"""
        search_distances = {'upstream': (-20, 150)}
        scan_distances = {'upstream': (-30, 250)}

        matrix_factory = dm.DataMatrixFactory([dm.nochange_filter, dm.center_scale_filter])
        infile = util.read_dfile('example_data/hal/halo_ratios5.tsv',
                                 has_header=True, quote='\"')
        ratio_matrix = matrix_factory.create_from(infile)
        organism = testutil.make_halo(search_distances, scan_distances, ratio_matrix)
        membership = FakeMembership()
        config_params = {'memb.min_cluster_rows_allowed': 3,
                         'memb.max_cluster_rows_allowed': 70,
                         'multiprocessing': False,
                         'num_clusters': 1,
                         'output_dir': 'out',
                         'debug': {},
                         'search_distances': {'upstream': (-20, 150)},
                         'num_iterations': 2000,
                         'MEME': {'schedule': lambda i: True,
                                  'version': '4.3.0',
                                  'global_background': False,
                                  'arg_mod': 'zoops',
                                  'nmotifs_rvec': 'c(rep(1, num_iterations/3), rep(2, num_iterations/3))',
                                  'use_revcomp': 'True', 'max_width': 24, 'background_order': 3},
                         'Motifs': {'schedule': lambda i: True, 'scaling': ('scaling_const', 1.0)}}
        func = motif.MemeScoringFunction(organism, membership, ratio_matrix,
                                         config_params=config_params)
        iteration_result = { 'iteration': 100 }
        matrix = func.compute(iteration_result)
    def setUp(self):  # pylint; disable-msg=C0103
        """test fixture"""
        self.search_distances = {'upstream': (-20, 150)}
        self.scan_distances = {'upstream': (-30, 250)}

        matrix_factory = dm.DataMatrixFactory(
            [dm.nochange_filter, dm.center_scale_filter])
        infile = util.read_dfile('example_data/hal/halo_ratios5.tsv',
                                 has_header=True,
                                 quote='\"')
        self.ratio_matrix = matrix_factory.create_from(infile)
        self.organism = testutil.make_halo(self.search_distances,
                                           self.scan_distances,
                                           self.ratio_matrix)
        self.config_params = {
            'memb.min_cluster_rows_allowed': 3,
            'memb.max_cluster_rows_allowed': 70,
            'multiprocessing': False,
            'memb.clusters_per_row': 2,
            'memb.clusters_per_col': int(round(43 * 2.0 / 3.0)),
            'num_clusters': 43,
            'num_iterations': 2000
        }
        self.membership = self.__read_members()  # relies on config_params
        self.iteration_result = {'iteration': 51}
    def test_motif_scoring(self):
        """tests the motif scoring in integration"""
        search_distances = {"upstream": (-20, 150)}
        scan_distances = {"upstream": (-30, 250)}

        matrix_factory = dm.DataMatrixFactory([dm.nochange_filter, dm.center_scale_filter])
        infile = util.read_dfile("example_data/hal/halo_ratios5.tsv", has_header=True, quote='"')
        ratio_matrix = matrix_factory.create_from(infile)
        organism = testutil.make_halo(search_distances, scan_distances, ratio_matrix)
        membership = FakeMembership()
        config_params = {
            "memb.min_cluster_rows_allowed": 3,
            "memb.max_cluster_rows_allowed": 70,
            "multiprocessing": False,
            "num_clusters": 1,
            "output_dir": "out",
            "debug": {},
            "search_distances": {"upstream": (-20, 150)},
            "num_iterations": 2000,
            "MEME": {
                "schedule": lambda i: True,
                "version": "4.3.0",
                "global_background": False,
                "arg_mod": "zoops",
                "nmotifs_rvec": "c(rep(1, num_iterations/3), rep(2, num_iterations/3))",
                "use_revcomp": "True",
                "max_width": 24,
                "background_order": 3,
            },
            "Motifs": {"schedule": lambda i: True, "scaling": ("scaling_const", 1.0)},
        }
        func = motif.MemeScoringFunction(organism, membership, ratio_matrix, config_params=config_params)
        iteration_result = {"iteration": 100}
        matrix = func.compute(iteration_result)
Beispiel #5
0
    def test_motif_scoring(self):
        """tests the motif scoring in integration"""
        search_distances = {'upstream': (-20, 150)}
        scan_distances = {'upstream': (-30, 250)}

        matrix_factory = dm.DataMatrixFactory(
            [dm.nochange_filter, dm.center_scale_filter])
        infile = util.read_dfile('example_data/hal/halo_ratios5.tsv',
                                 has_header=True,
                                 quote='\"')
        ratio_matrix = matrix_factory.create_from(infile)
        organism = testutil.make_halo(search_distances, scan_distances,
                                      ratio_matrix)
        membership = FakeMembership()
        config_params = {
            'memb.min_cluster_rows_allowed': 3,
            'memb.max_cluster_rows_allowed': 70,
            'multiprocessing': False,
            'num_clusters': 1,
            'output_dir': 'out',
            'debug': {},
            'search_distances': {
                'upstream': (-20, 150)
            },
            'num_iterations': 2000,
            'MEME': {
                'schedule': lambda i: True,
                'version': '4.3.0',
                'global_background': False,
                'arg_mod': 'zoops',
                'nmotifs_rvec':
                'c(rep(1, num_iterations/3), rep(2, num_iterations/3))',
                'use_revcomp': 'True',
                'max_width': 24,
                'background_order': 3
            },
            'Motifs': {
                'schedule': lambda i: True,
                'scaling': ('scaling_const', 1.0)
            }
        }
        func = motif.MemeScoringFunction(organism,
                                         membership,
                                         ratio_matrix,
                                         config_params=config_params)
        iteration_result = {'iteration': 100}
        matrix = func.compute(iteration_result)
Beispiel #6
0
 def setUp(self):  # pylint; disable-msg=C0103
     """test fixture"""
     self.search_distances = {'upstream': (-20, 150)}
     self.scan_distances = {'upstream': (-30, 250)}
     self.ratio_matrix = dm.create_from_csv('example_data/hal/halo_ratios5.tsv',
                                            filters=[dm.nochange_filter, dm.center_scale_filter])
     self.organism = testutil.make_halo(self.search_distances, self.scan_distances,
                                        self.ratio_matrix)
     self.config_params = {'memb.min_cluster_rows_allowed': 3,
                           'memb.max_cluster_rows_allowed': 70,
                           'multiprocessing': False,
                           'memb.clusters_per_row': 2,
                           'memb.clusters_per_col': int(round(43 * 2.0 / 3.0)),
                           'num_clusters': 43,
                           'num_iterations': 2000}
     self.membership = self.__read_members()  # relies on config_params
     self.iteration_result = { 'iteration': 51 }
Beispiel #7
0
 def test_get_one_sequence(self):
     """get one simple sequence"""
     search_distances = {'upstream': (-20, 150)}
     scan_distances = {'upstream': (-30, 250)}
     halo = testutil.make_halo(search_distances, scan_distances)
     print("------")
     print("VNG1551G: ", halo.features_for_genes(['VNG1551G']))
     print("VNG1550G: ", halo.features_for_genes(['VNG1550G']))
     print("VNG1561C: ", halo.features_for_genes(['VNG1561C']))
     print("------")
     seq = halo.sequences_for_genes_search(['VNG1551G'], 'upstream')
     self.assertTrue('NP_280354.1' in seq)
     print("FINAL LOC: ", seq['NP_280354.1'][0])
     """
     operon_map = halo.operon_map()
     for key, value in operon_map.items():
         print("'%s' -> '%s'" % (key, value))
     print("OPERON MAPPED TO: ", halo.operon_map()['NP_280354.1'])
     """
     self.assertEquals('GTGATTCGACCATTACTGCAAGTTCAGACGACCCCAATTCAAGTAGTTTTGTGTAACCGCCGGCGTCGGGGGCGCTCGCGCCCATCTAAGAAAGCTCACTTTCCCTAATACAATCAAAATTGTTTTGGGTGCTTCTGACGTTGTGCCACCGATGGCACAGACACAGCTCC',
                       seq['NP_280354.1'][1])
 def test_get_one_sequence(self):
     """get one simple sequence"""
     search_distances = {'upstream': (-20, 150)}
     scan_distances = {'upstream': (-30, 250)}
     halo = testutil.make_halo(search_distances, scan_distances)
     print "------"
     print "VNG1551G: ", halo.features_for_genes(['VNG1551G'])
     print "VNG1550G: ", halo.features_for_genes(['VNG1550G'])
     print "VNG1561C: ", halo.features_for_genes(['VNG1561C'])
     print "------"
     seq = halo.sequences_for_genes_search(['VNG1551G'], 'upstream')
     self.assertTrue('NP_280354.1' in seq)
     print "FINAL LOC: ", seq['NP_280354.1'][0]
     """
     operon_map = halo.operon_map()
     for key, value in operon_map.items():
         print "'%s' -> '%s'" % (key, value)
     print "OPERON MAPPED TO: ", halo.operon_map()['NP_280354.1']
     """
     self.assertEquals(
         'GTGATTCGACCATTACTGCAAGTTCAGACGACCCCAATTCAAGTAGTTTTGTGTAACCGCCGGCGTCGGGGGCGCTCGCGCCCATCTAAGAAAGCTCACTTTCCCTAATACAATCAAAATTGTTTTGGGTGCTTCTGACGTTGTGCCACCGATGGCACAGACACAGCTCC',
         seq['NP_280354.1'][1])
    def setUp(self):  # pylint; disable-msg=C0103
        """test fixture"""
        self.search_distances = {'upstream': (-20, 150)}
        self.scan_distances = {'upstream': (-30, 250)}

        matrix_factory = dm.DataMatrixFactory([dm.nochange_filter, dm.center_scale_filter])
        infile = util.read_dfile('example_data/hal/halo_ratios5.tsv',
                                 has_header=True, quote='\"')
        self.ratio_matrix = matrix_factory.create_from(infile)
        self.organism = testutil.make_halo(self.search_distances, self.scan_distances,
                                           self.ratio_matrix)
        self.config_params = {'memb.min_cluster_rows_allowed': 3,
                              'memb.max_cluster_rows_allowed': 70,
                              'multiprocessing': False,
                              'num_cores': None,
                              'memb.clusters_per_row': 2,
                              'memb.clusters_per_col': int(round(43 * 2.0 / 3.0)),
                              'num_clusters': 43,
                              'output_dir': 'out',
                              'remap_network_nodes': False,
                              'use_BSCM': False,
                              'num_iterations': 2000,
                              'debug': {},
                              'search_distances': {'upstream': (-20, 150)},
                              'Columns': {'schedule': lambda i: True },
                              'Rows': {'schedule': lambda i: True, 'scaling': ('scaling_const', 6.0) },
                              'Motifs': {'schedule': lambda i: True,
                                         'scaling': ('scaling_rvec', 'seq(0, 1, length=num_iterations*3/4)')},
                              'MEME': {'version': '4.3.0',
                                       'global_background': False,
                                       'schedule': lambda i: True,
                                       'nmotifs_rvec': 'c(rep(1, num_iterations/3), rep(2, num_iterations/3))',
                                       'max_width': 24, 'arg_mod': 'zoops',
                                       'background_order': 3, 'use_revcomp': 'True'},
                              'Networks': {'schedule': lambda i: True, 'scaling': ('scaling_rvec', 'seq(1e-5, 0.5, length=num_iterations*3/4)')}}
        self.membership = self.__read_members()  # relies on config_params
        self.iteration_result = { 'iteration': 51, 'score_means': {} }