Python Cells示例

示例#1

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'
}

示例#2

文件： linreg.py 项目： GillianGrayson/dna-methylation

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'
        },
    ]

示例#3

文件： pbc.py 项目： GillianGrayson/dna-methylation

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,
)

示例#4

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
)

示例#5

文件： aggregator_variance.py 项目： GillianGrayson/dna-methylation

    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)
        }]

示例#6

文件： dataset_specific.py 项目： GillianGrayson/dna-methylation

)
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,

示例#7

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,