target = get_target(data.base)
data_params = get_data_params(data.base)
data_params['cells'] = ['CD8T', 'CD4T', 'NK', 'Bcell', 'Gran']
if data.base == 'GSE55763':
observables_list = [
{
'gender': 'any',
'is_duplicate': '0',
'age': (35, 100)
},
]
else:
observables_list = [
{
'gender': 'any'
},
]
for obs in observables_list:
observables = pdm.Observables(name='observables', types=obs)
attributes = pdm.Attributes(target=target,
observables=observables,
cells=cells)
pdm.residuals_table_linreg(data=data,
annotations=annotations,
attributes=attributes,
data_params=data_params)
'is_duplicate': '0',
'age': (35, 100)
},
]
else:
# observables_list = [
# {'sex': 'F'},
# {'sex': 'M'},
# ]
observables_list = [
{
'gender': 'any'
},
]
for obs in observables_list:
observables = pdm.Observables(
name='observables',
#name='observables_part(control)',
types=obs)
attributes = pdm.Attributes(target=target,
observables=observables,
cells=cells)
pdm.betas_table_linreg(data=data,
annotations=annotations,
attributes=attributes,
data_params=data_params)
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,
cells=cells
)
import pydnameth as pdm
from scripts.develop.routines import *
data = pdm.Data(path='', base='unn_epic')
annotations = pdm.Annotations(name='annotations',
type='850k',
exclude='bad_cpgs_from_ChAMP',
select_dict={'CHR': ['-X', '-Y']})
observables = pdm.Observables(name='observables_part(final)', types={})
cells = pdm.Cells(name='cell_counts', types='any')
target = 'Sample_Group'
attributes = pdm.Attributes(target=target,
observables=observables,
cells=cells)
#data_params = get_data_params(data.base)
data_params = {
'norm': 'fun',
'part': 'final',
}
pdm.betas_table_pbc(
data=data,
annotations=annotations,
attributes=attributes,
data_params=data_params,
)
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,
cells=cells_unn_epic
)
data_params_unn_epic = get_data_params(data_unn_epic.base)
# data_params_unn_epic = {
# 'norm': 'BMIQ',
# 'part': 'raw',
# }
data = pdm.Data(
path='',
#path='E:/YandexDisk/Work/pydnameth/tissues/brain(DLPFC)',
#base='GSE87571'
base='liver'
#base='GSE74193'
)
annotations = pdm.Annotations(name='annotations',
type='450k',
exclude='bad_cpgs',
select_dict={'CHR': ['-X', '-Y']})
observables = pdm.Observables(
name='observables',
#name='observables_part(control)',
types={}
#types={'group': 'Control'}
)
cells = pdm.Cells(
#name='cells_horvath_calculator',
name='',
types='any')
target = get_target(data.base)
attributes = pdm.Attributes(target=target,
observables=observables,
cells=cells)
method_params = {
'observables': ['age', 'gender'],
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,
import pydnameth as pdm
data = pdm.Data(path='', base='GSE55763')
data_params = {'file': 'cpgs_variance.txt'}
if data.base == 'GSE55763':
observables = pdm.Observables(
name='observables',
types={}
#types={'is_duplicate': '0', 'age': (35, 100)}
)
else:
observables = pdm.Observables(name='observables', types={})
pdm.betas_spec_create_regular(data, data_params, observables)
