def load_methylation(pids,
ref_names=None,
norm_method='swan',
ref_name_filter=None,
units='beta'):
"""
Load and prepare the Illumina methylation data
"""
# patient data
obj = loader.load_by_patient(pids, norm_method=norm_method)
anno = loader.load_illumina_methylationepic_annotation()
# reference data
if ref_names is not None:
ref_obj = loader.load_reference(ref_names, norm_method=norm_method)
if ref_name_filter is not None:
ref_obj.filter_by_sample_name(ref_name_filter, exact=True)
obj = loader.loader.MultipleBatchLoader([obj, ref_obj])
me_data = obj.data.dropna()
if units == 'm':
me_data = process.m_from_beta(me_data)
# reduce anno and data down to common probes
common_probes = anno.index.intersection(me_data.index)
anno = anno.loc[common_probes]
# dmr.add_merged_probe_classes(anno)
me_data = me_data.loc[common_probes]
obj.data = me_data
return obj, anno
'DURA054_IPSC_N3C_P11',
'DURA054_FB_P5',
'DURA061_NSC_N4_P2',
'DURA061_NSC_N6_P4',
'DURA061_NSC_N1_P3n4',
'DURA026_NSC_N31D_P5',
'DURA052_NSC_N4_P3',
'DURA052_NSC_N5_P2',
'GIBCONSC_P4',
# 'DURA052_NH16_2214_P6_14/04/2017',
# 'DURA026_NH16_270_P8_15/05/2017',
# 'DURA018_NH15_1877_P6_15/05/2017',
]
patient_obj = loader.load_by_patient(pids,
norm_method=norm_method,
samples=our_samples)
nazor_ldr = loader.load_reference('GSE31848', norm_method=norm_method)
ix = nazor_ldr.meta.index.str.contains(r'(ES__WA)|(iPS__HDF)')
ix = ix & (~nazor_ldr.meta.index.str.contains(r'HDF51IPS7')
) # this sample is an outlier, so remove it now
nazor_ldr.filter_samples(ix)
# Zhou et al.: lots of samples here, but we'll only keep 2 x ESC lines
zhou_ldr = loader.load_reference('GSE92462_450K', norm_method=norm_method)
ix = zhou_ldr.meta.index.str.contains(r'^H[19]ES')
zhou_ldr.filter_samples(ix)
hip_epic_ldr = loader.hipsci(norm_method=norm_method,
n_sample=n_hipsci,
return {'axs': axs, 'fig': fig}
if __name__ == "__main__":
pids = consts.PIDS
norm_method = 'swan'
dmr_params = consts.DMR_PARAMS
dmr_params['n_jobs'] = mp.cpu_count()
outdir = output.unique_output_dir()
DMR_LOAD_DIR = os.path.join(INTERMEDIATE_DIR, 'dmr')
# load our data
our_obj = loader.load_by_patient(pids,
norm_method=norm_method,
samples=consts.S1_METHYL_SAMPLES)
anno = loader.load_illumina_methylationepic_annotation()
our_obj.meta.insert(
0, 'patient_id',
our_obj.meta.index.str.replace(r'(GBM|DURA)(?P<pid>[0-9]{3}).*',
'\g<pid>'))
# load validation data
val_obj = loader.load_reference('GSE92462_450k', norm_method=norm_method)
# filter
val_obj.filter_samples(val_obj.meta.type.isin(['GBM (GSC)', 'NSC']))
# TODO: upload to the classifier and run (toggle this so it's only run once)
# combine and reduce probes
if __name__ == "__main__":
pids = consts.PIDS
norm_method = 'swan'
alpha = 0.05
pk_alpha = -np.log10(alpha)
dmr_params = consts.DMR_PARAMS
dmr_params['n_jobs'] = mp.cpu_count()
outdir = output.unique_output_dir()
DMR_LOAD_DIR = os.path.join(INTERMEDIATE_DIR, 'dmr')
# load our data
cc_obj = loader.load_by_patient(pids,
norm_method=norm_method,
samples=consts.S1_METHYL_SAMPLES)
ffpe_obj = loader.load_by_patient(pids,
norm_method=norm_method,
type='ffpe')
anno = loader.load_illumina_methylationepic_annotation()
# add patient ID column to metadata
cc_obj.meta.insert(
0, 'patient_id',
cc_obj.meta.index.str.replace(r'(GBM|DURA)(?P<pid>[0-9]{3}).*',
'\g<pid>'))
ffpe_obj.meta.insert(
0, 'patient_id',
[hgic_consts.NH_ID_TO_PATIENT_ID_MAP[t] for t in ffpe_obj.meta.index])
ffpe_obj.meta.insert(1, 'type', 'ffpe')
from utils import output, log, setops
from scripts.hgic_final import consts, two_strategies_grouped_dispersion as tsgd
from methylation import loader as methylation_loader, dmr, process
from rnaseq import loader as rnaseq_loader
from settings import INTERMEDIATE_DIR
from plotting import genomics
logger = log.get_console_logger()
if __name__ == '__main__':
outdir = output.unique_output_dir()
# load methylation and DMR data
meth_obj = methylation_loader.load_by_patient(consts.PIDS,
include_control=False)
meth_obj.filter_by_sample_name(consts.S1_METHYL_SAMPLES_GIC +
consts.S1_METHYL_SAMPLES_INSC)
meth_obj.meta.insert(
0, 'patient_id',
meth_obj.meta.index.str.replace(r'(GBM|DURA)(?P<pid>[0-9]{3}).*',
'\g<pid>'))
mdat = process.m_from_beta(meth_obj.data)
norm_method_s1 = 'swan'
dmr_params = consts.DMR_PARAMS
de_params = consts.DE_PARAMS
DMR_LOAD_DIR = os.path.join(INTERMEDIATE_DIR, 'dmr')
DE_LOAD_DIR = os.path.join(INTERMEDIATE_DIR, 'de')
if __name__ == "__main__":
"""
Here, we simply load the methylation data and export it in an efficient manner (restricting the floating point
bit depth to save space).
We also export the annotation and metadata separately.
"""
norm_method = 'swan'
# the float format is used when exporting to Excel - it reduces the file size by restricting the precision
float_format = '%.2f'
outdir = output.unique_output_dir()
anno = loader.load_illumina_methylationepic_annotation()
obj_cc = loader.load_by_patient(consts.PIDS,
type='cell_culture',
norm_method=norm_method,
reduce_to_common_probes=False)
obj_ff = loader.load_by_patient(consts.PIDS,
type='ffpe',
norm_method=norm_method,
reduce_to_common_probes=False)
# add useful patient ID column to metadata
obj_ff.meta.insert(0, 'patient_id',
[NH_ID_TO_PATIENT_ID_MAP[k] for k in obj_ff.meta.index])
# export methylation data
obj_cc.data.to_excel(os.path.join(outdir,
"methylation_beta_cell_culture.xlsx"),
float_format=float_format)
obj_ff.data.to_excel(os.path.join(outdir, "methylation_beta_ffpe.xlsx"),
float_format=float_format)
'core_min_sample_overlap': 3, # 3 / 4 samples must match
'd_max': 400,
'n_min': 6,
'delta_m_min': 1.4,
'fdr': 0.01,
'dmr_test_method': 'mwu', # 'mwu', 'mwu_permute'
'test_kwargs': {},
'n_jobs': 4,
}
norm_method = 'swan'
intersecter = lambda x, y: set(x).intersection(y)
unioner = lambda x, y: set(x).union(y)
# Load DNA Methylation
me_obj = loader.load_by_patient(pids, norm_method=norm_method)
me_meta = me_obj.meta
# me_data, me_meta = methylation_array.load_by_patient(pids)
# me_data.dropna(inplace=True)
# me_data = process.m_from_beta(me_data)
me_data = process.m_from_beta(me_obj.data)
anno = loader.load_illumina_methylationepic_annotation()
# anno = methylation_array.load_illumina_methylationepic_annotation()
# reduce anno and data down to common probes
common_probes = anno.index.intersection(me_data.index)
anno = anno.loc[common_probes]
me_data = me_data.loc[common_probes]
'n_min': 6,
'delta_m_min': 1.4,
'alpha': 0.01,
'dmr_test_method': 'mwu', # 'mwu', 'mwu_permute'
'test_kwargs': {},
}
norm_method_s1 = 'swan'
############
# 1: FFPE #
############
# in this case, we want the median beta value over all probes that are associated with a given gene
# we'll exclude those associated with gene body only
ffpe_obj = loader.load_by_patient(pids, type='ffpe', norm_method=norm_method_s1)
anno = loader.load_illumina_methylationepic_annotation(split_genes=False)
# reduce anno to (probe ID, gene, relation)
probe_tups = set()
for i, row in anno.iterrows():
if pd.isnull(row.UCSC_RefGene_Name):
continue
genes = row.UCSC_RefGene_Name.split(';')
rels = row.UCSC_RefGene_Group.split(';')
for g, r in zip(genes, rels):
probe_tups.add(
(i, g, r)
)
probe_tups = list(probe_tups)
n_probe_to_show = 2000
clustering_metric = 'euclidean'
outdir = output.unique_output_dir()
norm_method = 'swan'
pdx_bulk_samples = ['SM18_108A_GBM019Luc_PDX1', 'SM18_119A_GBM019Luc_PDX2']
gic_late_samples = [
'GBM019Luc_P12',
'GBM019Luc_P3_PDX1',
'GBM019Luc_P2_PDX2',
]
# load all relevant data
our_gic_obj = loader.load_by_patient(consts.PIDS,
include_control=False,
samples=consts.S1_METHYL_SAMPLES_GIC,
norm_method=norm_method)
our_ffpe_obj = loader.load_by_patient(consts.PIDS,
type='ffpe',
include_control=False,
norm_method=norm_method)
pdx_bulk = loader.load_reference('2018-12-14',
norm_method=norm_method,
samples=pdx_bulk_samples)
gic_late = loader.load_reference('2018-12-06',
norm_method=norm_method,
samples=gic_late_samples)
# add patient ID to samples
our_gic_obj.meta.insert(
0, 'patient_id',
