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
)
data_params = {
'part': 'v1',
'config': '0.01_0.10_0.10',
'norm': 'fun'
}
import pydnameth as pdm
from scripts.develop.routines import *
data = pdm.Data(path='', base='GSE87571')
annotations = pdm.Annotations(name='annotations',
type='450k',
exclude='bad_cpgs',
select_dict={'CHR': ['-X', '-Y']})
cells = pdm.Cells(name='cells_horvath_calculator', types='any')
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'
},
]
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,
)
annotations = pdm.Annotations(
name='annotations',
type='850k',
exclude='bad_cpgs_from_ChAMP',
select_dict={
'CHR': ['-X', '-Y']
}
)
observables = pdm.Observables(
name='observables',
types={}
)
cells = pdm.Cells(
name='',
types='any'
)
target = get_target(data.base)
#observables_list = get_observables_list(data.base)
observables_list = [
{'Sample_Group': 'C'},
{'Sample_Group': 'T'}
]
data_params = get_data_params(data.base)
attributes = pdm.Attributes(
target=target,
observables=observables,
cells=cells
)
cell_name = 'cells_horvath_calculator'
data = pdm.Data(path='', base=data_set)
annotations = pdm.Annotations(name='annotations',
exclude='bad_cpgs',
cross_reactive='any',
snp='any',
chr='NS',
gene_region='any',
geo='any',
probe_class='any')
observables = pdm.Observables(name='observables', types={})
cells = pdm.Cells(name=cell_name, types=cell_types)
attributes = pdm.Attributes(target='age',
observables=observables,
cells=cells)
if data.base == 'GSE55763':
observables_list = [{
'gender': 'F',
'is_duplicate': '0',
'age': (35, 100)
}, {
'gender': 'M',
'is_duplicate': '0',
'age': (35, 100)
}]
)
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',
# }
config_unn = pdm.load_beta_config(
data_unn_epic,
annotations_unn_epic,
attributes_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={'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,