table_dict[key] = list(curr_dict.values())
items = table_dict['i']
x_ranges = [[5, 105]] * len(items)
y_ranges = []
for index in range(0, len(items)):
y_ranges.append([table_dict['begin'][index], table_dict['end'][index]])
data_bases = ['GSE87571_TEST']
for data_base in data_bases:
data = pdm.Data(path='', base='GSE43414')
annotations = pdm.Annotations(name='annotations',
type='450k',
exclude='bad_cpgs',
select_dict={'CHR': ['-X', '-Y']})
observables = pdm.Observables(name='observables', types={})
cells = pdm.Cells(name='cells', types='any')
target = get_target(data.base)
observables_list = get_observables_list(data.base)
data_params = get_data_params(data.base)
attributes = pdm.Attributes(target=target,
observables=observables,
cells=cells)
pdm.betas_plot_scatter(data=data,
import pydnameth as pdm
data = pdm.Data(path='', base='GSE87571')
annotations = pdm.Annotations(name='annotations',
exclude='bad_cpgs',
cross_reactive='any',
snp='any',
chr='NS',
gene_region='any',
geo='any',
probe_class='any')
cells = pdm.Cells(name='cells', types='any')
if data.base == 'GSE55763':
observables_list = [
{
'gender': 'any',
'is_duplicate': '0',
'age': (35, 100)
},
]
else:
observables_list = [
{
'gender': 'any'
},
]
method_params = {
from tqdm import tqdm
import numpy as np
from scipy.stats import pearsonr, pointbiserialr
from statsmodels.stats.multitest import multipletests
from paper.routines.infrastructure.save.table import save_table_dict_xlsx
save_path = 'E:/YandexDisk/Work/pydnameth/unn_epic/comparison'
data_unn_epic = pdm.Data(
path='',
base='unn_epic'
)
annotations_unn_epic = pdm.Annotations(
name='annotations',
type='850k',
exclude='bad_cpgs_from_ChAMP',
select_dict={
'CHR': ['-X', '-Y']
}
)
target_unn_epic = 'Age'
observables_unn_epic = pdm.Observables(
name='observables',
types={}
)
cells_unn_epic = pdm.Cells(
name='cell_counts_horvath_filtered_normalized',
types='any'
)
attributes_unn_epic = pdm.Attributes(
target=target_unn_epic,
observables=observables_unn_epic,
import pydnameth as pdm
from scripts.develop.routines import *
data = pdm.Data(
path='',
base='unn_epic'
)
annotations = pdm.Annotations(
name='annotations',
type='850k',
exclude='none',
select_dict={}
)
observables = pdm.Observables(
name='observables_part(v1)',
types={'COVID': ['no', 'before'],
'Sample_Chronology': [0, 1]}
)
cells = pdm.Cells(
name='cell_counts_part(v1)',
types='any'
)
target = 'Group'
attributes = pdm.Attributes(
target=target,
observables=observables,
import pydnameth as pdm
from scripts.develop.routines import *
data = pdm.Data(path='', base='unn_epic')
annotations = pdm.Annotations(name='annotations',
type='850k',
exclude='none',
select_dict={'CHR': ['-X', '-Y']})
observables = pdm.Observables(name='observables_part(wo_noIntensity_detP)',
types={})
cells = pdm.Cells(name='cell_counts_part(wo_noIntensity_detP)', types='any')
target = 'Sample_Group'
attributes = pdm.Attributes(target=target,
observables=observables,
cells=cells)
data_params = {
'norm': 'fun',
'part': 'wo_noIntensity_detP',
}
method_params = {
'formula': 'cpg ~ Sample_Group + Sex*Age + Bcell + CD4T + CD8T + Neu + NK',
}
pdm.betas_table_formula_new(
data=data,
