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)