#from configurations.slurm_info import info
from configurations.ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(353, name = 'num_cpgs')),
("input", param('horvath_cpg', name = 'input')),
("algorithm", param('svc', name = 'algorithm')),
("min_score", param(0.9, name = 'min_score')),
("num_groups", param(4, name = 'num_groups')),
("id_part", param(value_be = 0, value_en = 29, num_ticks = 30, name = 'id_part')),
# ("thr_p", param(value_be = 0.1, value_en = 0.9, num_ticks = 9, name = 'threshold_p')),
("id_sample", param(value_be = 0, value_en = 728, num_ticks = 729, name = 'id_sample')),
("num_parts", param(2, name = 'num_parts')),
("num_workers", param(2, name = 'num_workers')),
("num_samples", param(729, name = 'num_samples')),
("young_mask", param([], name = 'young_mask')),
("old_mask", param([], name = 'old_mask')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
"cpgs": "cpg_beta.npz",
"cpgs_annotations": "cpgs_annotations.txt",
"cpgs_names": "cpgs_names.npz",
"patients_info": "GSE87571_samples.txt",
"name_genes": 'linreg_genes_mean_islands_shores',
#"ranged_genes": 'linreg_genes_mean_islands_shores.txt',
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_genes", param(14756, name='num_genes')), # 15024, 20270
("algorithm", param('kde', name='algorithm')), # svc, kde
("thr_p", param(0.88, name='thr_p')),
#("min_score", param(0.9, name = 'min_score')),
("num_genes", param(14756, name='num_genes')), # 15024, 20270
("id_part", param(value_be=0, value_en=29, num_ticks=30, name='id_part')),
("id_sample", param(value_be=0,
value_en=28,
num_ticks=29,
name='id_sample')),
("num_parts", param(30, name='num_parts')),
("num_workers", param(10, name='num_workers')),
("num_samples", param(29, name='num_samples')),
])
files = {
"g": 'graph',
"kdes": Path('kdes') / 'kdes',
"graphs": 'graphs',
"degrees": Path('degrees') / 'degrees',
"parenclitic": Path("parenclitics") / "parenclitic",
"degrees_boxplots": "degrees_boxplots",
"parenclitic_boxplots": "parenclitic_boxplots",
#from slurm_info import info
from configurations.ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(353, name = 'num_cpgs')),
("input", param('horvath_cpg', name = 'input')),
("kde_mask", param('normal_mask', name = 'kde_mask')),
("id_part", param(value_be = 0, value_en = 29, num_ticks = 30, name = 'id_part')),
("thr_p", param(value_be = 0.1, value_en = 0.9, num_ticks = 9, name = 'threshold_p')),
("id_sample", param(value_be = 0, value_en = 70, num_ticks = 71, name = 'id_sample')),
("num_parts", param(2, name = 'num_parts')),
("num_workers", param(2, name = 'num_workers')),
("num_samples", param(71, name = 'num_samples')),
("down_mask", param([], name = 'down_mask')),
("normal_mask", param([], name = 'normal_mask')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
"horvath_cpgs_beta": "horvath_cpgs_beta.txt",
"patients_info": "patients_info.txt",
"name_genes": 'linreg_genes_mean_islands_shores',
#"ranged_genes": 'linreg_genes_mean_islands_shores.txt',
"g": 'graph',
"kdes": Path('kdes') / 'kdes',
"graphs": 'graphs',
"degrees": Path('degrees') / 'degrees',
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_genes", param(14768, name = 'num_genes')), # 15024, 20270
("algorithm", param('pdf', name = 'algorithm')), # svc, kde
("thr_type", param('best', name = 'thr_type')), # best, one
("division_rule", param('non_control', name = 'division_rule')), # non_control, atypical
("thr_p", param(0.88, name = 'thr_p')),
#("by_group", param(True, name = 'by_group')),
("min_score", param(0.85, name = 'min_score')),
("age_delimiter", param(28, name = 'age_delimiter')),
# ("num_groups", param(4, name = 'num_groups')),
# ("age_group", param(value_be = 1, value_en = 4, num_ticks = 4, name = 'age_group')),
("by_group", param(True, name = 'by_group')),
("id_part", param(value_be = 0, value_en = 29, num_ticks = 30, name = 'id_part')),
("id_sample", param(value_be = 0, value_en = 728, num_ticks = 729, name = 'id_sample')),
("num_parts", param(30, name = 'num_parts')),
("num_workers", param(10, name = 'num_workers')),
("num_samples", param(729, name = 'num_samples')),
("young_mask", param([], name = 'young_mask')),
("old_mask", param([], name = 'old_mask'))
])
files = {
"gene_chromosome": 'gene_chr.txt',
"x": 'GSE87571_beta_qf.npz',
from configurations.slurm_info import info
#from configurations.ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_genes", param(15022, name='num_genes')), # 15022
("kde_mask", param('normal_mask', name='kde_mask')),
("algorithm", param('svc', name='algorithm')),
("id_part", param(value_be=0, value_en=29, num_ticks=30, name='id_part')),
# ("thr_p", param(value_be = 0.1, value_en = 0.9, num_ticks = 9, name = 'threshold_p')),
("id_sample", param(value_be=0,
value_en=70,
num_ticks=71,
name='id_sample')),
("num_parts", param(30, name='num_parts')),
("num_workers", param(30, name='num_workers')),
("num_samples", param(71, name='num_samples')),
("down_mask", param([], name='down_mask')),
("normal_mask", param([], name='normal_mask')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
"x": 'gene_mean_islands_shores.txt',
"patients_info": "patients_info.txt",
"name_genes": 'linreg_genes_mean_islands_shores',
#"ranged_genes": 'linreg_genes_mean_islands_shores.txt',
"g": 'graph',
#from configurations.slurm_info import info
from configurations.ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(353, name='num_cpgs')),
("input", param('horvath_cpg', name='input')),
("kde_mask", param('young_mask', name='kde_mask')),
("algorithm", param('svc', name='algorithm')),
("delimiter_age", param(65, name='delimiter_age')),
("id_part", param(value_be=0, value_en=29, num_ticks=30, name='id_part')),
# ("thr_p", param(value_be = 0.1, value_en = 0.9, num_ticks = 9, name = 'threshold_p')),
("id_sample",
param(value_be=0, value_en=728, num_ticks=729, name='id_sample')),
("num_parts", param(2, name='num_parts')),
("num_workers", param(2, name='num_workers')),
("num_samples", param(729, name='num_samples')),
("young_mask", param([], name='young_mask')),
("old_mask", param([], name='old_mask')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
"horvath_cpgs_beta": "horvath_cpgs_beta.txt",
"patients_info": "GSE87571_samples.txt",
"name_genes": 'linreg_genes_mean_islands_shores',
#"ranged_genes": 'linreg_genes_mean_islands_shores.txt',
"g": 'graph',
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_genes", param(14756, name='num_genes')), # 15024, 20270
("kde_mask", param('mothers_mask', name='kde_mask')),
("algorithm", param('svc', name='algorithm')), # svc, kde
#("thr_p", param(0.88, name = 'thr_p')),
#("min_score", param(0.9, name = 'min_score')),
("num_genes", param(14756, name='num_genes')), # 15024, 20270
("id_part", param(value_be=0, value_en=29, num_ticks=30, name='id_part')),
("id_sample", param(value_be=0,
value_en=86,
num_ticks=87,
name='id_sample')),
("num_parts", param(30, name='num_parts')),
("num_workers", param(10, name='num_workers')),
("num_samples", param(87, name='num_samples')),
("mongoloids_mask", param(np.arange(0, 29), name='mongoloids_mask')),
("siblings_mask", param(np.arange(29, 58), name='siblings_mask')),
("mothers_mask", param(np.arange(58, 87), name='mothers_mask')),
])
files = {
"x": 'GSE52588_average_beta.txt',
"g": 'graph',
"kdes": Path('kdes') / 'kdes',
#from configurations.slurm_info import info
from configurations.ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(431906, name = 'num_cpgs')),
("algorithm", param('pdf', name = 'algorithm')), # svc, kde
("thr_type", param('best', name = 'thr_type')), # best, one
("division_rule", param('non_control', name = 'division_rule')), # non_control, atypical
("min_score", param(0.9, name = 'min_score')),
("max_score_1d", param(0.5, name = 'max_score_1d')),
("age_delimiter", param(38, name = 'age_delimiter')),
#("num_groups", param(4, name = 'num_groups')),
("id_part", param(value_be = 0, value_en = 29, num_ticks = 30, name = 'id_part')),
("id_sample", param(value_be = 0, value_en = 2710, num_ticks = 2711, name = 'id_sample')),
("num_parts", param(2, name = 'num_parts')),
("num_workers", param(2, name = 'num_workers')),
("num_samples", param(2711, name = 'num_samples')),
("young_mask", param([], name = 'young_mask')),
("old_mask", param([], name = 'old_mask')),
])
files = {
"cpgs": "GSE55763_betas.npz",
"cpgs_names": "cpgs_names.tsv",
"patients_info": "GSE55763_samples.csv",
"cpgs_annotations": Path("..") / "common" / "cpgs_annotations.txt",
"bad_cpgs": Path("..") / "common" / "bad_cpgs.txt",
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_genes", param(14756, name = 'num_genes')), # 15024, 20270
("kde_mask", param('age_mask', name = 'kde_mask')),
("algorithm", param('pdf', name = 'algorithm')), # svc, kde
("thr_type", param('best', name = 'thr_type')), # best, one
("division_rule", param('non_control', name = 'division_rule')), # non_control, atypical
#("thr_p", param(0.88, name = 'thr_p')),
#("by_group", param(True, name = 'by_group')),
("min_score", param(0.9, name = 'min_score')),
("id_part", param(value_be = 0, value_en = 29, num_ticks = 30, name = 'id_part')),
("id_sample", param(value_be = 0, value_en = 86, num_ticks = 87, name = 'id_sample')),
("num_parts", param(30, name = 'num_parts')),
("num_workers", param(10, name = 'num_workers')),
("num_samples", param(87, name = 'num_samples')),
("mongoloids_mask", param(np.arange(0, 29), name = 'mongoloids_mask')),
("siblings_mask", param(np.arange(29, 58), name = 'siblings_mask')),
("mothers_mask", param(np.arange(58, 87), name = 'mothers_mask')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
#"x": 'gene_mean_islands_shores.txt',
"x": 'GSE52588_average_beta.txt',
"horvath_cpgs_beta": "horvath_cpgs_beta.txt",
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_genes", param(14756, name = 'num_genes')), # 15024, 20270
("id_part", param(value_be = 0, value_en = 29, num_ticks = 30, name = 'id_part')),
("id_sample", param(value_be = 0, value_en = 86, num_ticks = 87, name = 'id_sample')),
("num_parts", param(30, name = 'num_parts')),
("num_workers", param(10, name = 'num_workers')),
("num_samples", param(87, name = 'num_samples')),
])
files = {
"g": 'graph',
"kdes": Path('kdes') / 'kdes',
"graphs": 'graphs',
"degrees": Path('degrees') / 'degrees',
"parenclitic": Path("parenclitics") / "parenclitic",
"degrees_boxplots": "degrees_boxplots",
"parenclitic_boxplots": "parenclitic_boxplots",
"degrees_all": 'degrees_all',
"parenclitic_all": "parenclitic_all",
"diff_graph": 'diff_graph',
"pair_genes": Path('pair_genes') / 'pair_genes',
"kdes_dist": Path('kdes_dist') / 'kdes_dist',
"parenclitic_boxplot": Path("parenclitic_boxplots") / "parenclitic_boxplot",
"down_phenotypes": "down_phenotypes",
from configurations.slurm_info import info
#from configurations.ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(150254, name = 'num_cpgs')), # 150254
("kde_mask", param('normal_mask', name = 'kde_mask')),
("algorithm", param('svc', name = 'algorithm')),
("geotypes", param(['Island'], name = 'geotypes')),
("id_part", param(value_be = 0, value_en = 29, num_ticks = 30, name = 'id_part')),
("id_sample", param(value_be = 0, value_en = 70, num_ticks = 71, name = 'id_sample')),
("num_parts", param(900, name = 'num_parts')),
("num_workers", param(10, name = 'num_workers')),
("num_samples", param(71, name = 'num_samples')),
("down_mask", param([], name = 'down_mask')),
("normal_mask", param([], name = 'normal_mask')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
"x": 'GSE63347_series_matrix.txt',
"cpgs": "cpgs_annotations.txt",
"patients_info": "patients_info.txt",
"g": 'graph',
"kdes": Path('kdes') / 'kdes',
"graphs": 'graphs',
"degrees": Path('degrees') / 'degrees',
"parenclitic": Path("parenclitics") / "parenclitic",
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(385730, name = 'num_cpgs')), # 150254
("kde_mask", param('siblings_mask', name = 'kde_mask')),
("algorithm", param('pdf', name = 'algorithm')), # svc, kde
("thr_type", param('best', name = 'thr_type')), # best, one
("division_rule", param('non_control', name = 'division_rule')), # non_control, atypical
#("thr_p", param(0.88, name = 'thr_p')),
#("by_group", param(True, name = 'by_group')),
("min_score", param(0.9, name = 'min_score')),
("id_part", param(value_be = 0, value_en = 29, num_ticks = 30, name = 'id_part')),
("id_sample", param(value_be = 0, value_en = 86, num_ticks = 87, name = 'id_sample')),
("num_parts", param(30, name = 'num_parts')),
("num_workers", param(10, name = 'num_workers')),
("num_samples", param(87, name = 'num_samples')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
#"x": 'gene_mean_islands_shores.txt',
"x": 'GSE131752_filtered_probes_TO_BE_USED.txt',
"beta_values": 'GSE52588_beta_fn.npz',
"horvath_cpgs_beta": "horvath_cpgs_beta.txt",
"name_genes": 'linreg_genes_mean_islands_shores',
"good_pairs": "good_pairs.npz",
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(422801, name='num_cpgs')), # 150254 114674 422801
("normalization", param('fn', name='normalization')), # qf fn
("geotypes", param(['Island'], name='geotypes')), # ONLY ISLANDS!!!
("kde_mask", param('age_mask', name='kde_mask')),
("algorithm", param('pdf', name='algorithm')), # svc, kde
("thr_type", param('best', name='thr_type')), # best, one
("division_rule", param('non_control',
name='division_rule')), # non_control, atypical
("LOO", param(value_be=0, value_en=28, num_ticks=29, name='LOO')),
#("thr_p", param(0.88, name = 'thr_p')),
#("by_group", param(True, name = 'by_group')),
("min_score", param(0.9, name='min_score')),
("max_score_1d", param(0.75, name='max_score_1d')),
("id_part", param(value_be=0, value_en=899, num_ticks=900,
name='id_part')),
("id_sample", param(value_be=0,
value_en=86,
num_ticks=87,
name='id_sample')),
("num_parts", param(900, name='num_parts')),
("num_workers", param(10, name='num_workers')),
("num_samples", param(87, name='num_samples')),
("mongoloids_mask", param(np.arange(0, 29), name='mongoloids_mask')),
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_genes", param(14756, name='num_genes')), # 15024, 20270
("algorithm", param('pdf', name='algorithm')), # svc, kde
("thr_type", param('best', name='thr_type')), # best, one
("division_rule", param('non_control',
name='division_rule')), # non_control, atypical
#("thr_p", param(0.88, name = 'thr_p')),
#("by_group", param(True, name = 'by_group')),
("min_score", param(0.9, name='min_score')),
("id_sample", param(value_be=0,
value_en=86,
num_ticks=87,
name='id_sample')),
("num_workers", param(10, name='num_workers')),
("num_samples", param(87, name='num_samples')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
#"x": 'gene_mean_islands_shores.txt',
"x": 'GSE52588_average_beta.txt',
"horvath_cpgs_beta": "horvath_cpgs_beta.txt",
"name_genes": 'linreg_genes_mean_islands_shores',
"good_pairs": "good_pairs.npz",
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(353, name='num_cpgs')), # 15024, 20270
("input", param('horvath_cpg', name='input')),
("kde_mask", param('siblings_mask', name='kde_mask')),
("algorithm", param('svc', name='algorithm')),
("id_part", param(value_be=0, value_en=1, num_ticks=2, name='id_part')),
# ("thr_p", param(value_be = 0.1, value_en = 0.9, num_ticks = 9, name = 'threshold_p')),
("id_sample", param(value_be=0,
value_en=86,
num_ticks=87,
name='id_sample')),
("num_parts", param(2, name='num_parts')),
("num_workers", param(30, name='num_workers')),
("num_samples", param(87, name='num_samples')),
("mongoloids_mask", param(np.arange(0, 29), name='mongoloids_mask')),
("siblings_mask", param(np.arange(29, 58), name='siblings_mask')),
("mothers_mask", param(np.arange(58, 87), name='mothers_mask')),
])
files = {
"gene_chromosome": 'gene_chr.txt',
"horvath_cpgs_beta": "horvath_cpgs_beta.txt",
"g": 'graph',
"kdes": Path('kdes') / 'kdes',
#from slurm_info import info
from .ws_info import info
from infrastructure.configuration import configuration, param
import collections
import numpy as np
from pathlib2 import Path
params = collections.OrderedDict([
("num_cpgs", param(422801, name = 'num_cpgs')), # 150254 114674 422801
#("normalization", param('fn', name = 'normalization')), # qf fn
#("geotypes", param(['Island'], name = 'geotypes')), # ONLY ISLANDS!!!
("kde_mask", param('control_mask', name = 'kde_mask')),
("algorithm", param('pdf', name = 'algorithm')), # svc, kde
("thr_type", param('best', name = 'thr_type')), # best, one
("division_rule", param('non_control', name = 'division_rule')), # non_control, atypical
#("LOO", param(value_be = 0, value_en = 28, num_ticks = 29, name = 'LOO')),
#("thr_p", param(0.88, name = 'thr_p')),
#("by_group", param(True, name = 'by_group')),
("is_full", param(True, name = 'is_full')),
("min_score", param(0.6, name = 'min_score')),
("max_score_1d", param(0.65, name = 'max_score_1d')),
("id_part", param(value_be = 0, value_en = 899, num_ticks = 900, name = 'id_part')),
("id_sample", param(value_be = 0, value_en = 3007, num_ticks = 3008, name = 'id_sample')),
("num_parts", param(900, name = 'num_parts')),
("num_workers", param(10, name = 'num_workers')),
("num_samples", param(3008, name = 'num_samples')),
("num_train", param(1522, name = 'num_samples')),
("control_mask", param([], name = 'control_mask')),
("schizophrenia_mask", param([], name = 'schizoprenia_mask')),
])
