'method': 'GLM'
}
subgroups = {
'RTK I': ['019', '030', '031'],
'RTK II': ['017', '050', '054'],
}
intersecter = lambda x, y: set(x).intersection(y)
unioner = lambda x, y: set(x).union(y)
# Load RNA-Seq from STAR
rnaseq_obj = rnaseq_data.load_by_patient(pids, annotate_by='Ensembl Gene ID')
# load additional references if required
h9_obj = rnaseq_data.gse61794(annotate_by='Ensembl Gene ID')
h1_obj = rnaseq_data.gse38993(annotate_by='Ensembl Gene ID')
rnaseq_obj = rnaseq_data.MultipleBatchLoader([rnaseq_obj, h1_obj, h9_obj])
# discard unmapped, etc
rnaseq_obj.data = rnaseq_obj.data.loc[rnaseq_obj.data.index.str.contains('ENSG')]
rnaseq_obj.meta = rnaseq_obj.meta.loc[~rnaseq_obj.meta.index.str.contains('PSC')]
rnaseq_obj.meta = rnaseq_obj.meta.loc[~rnaseq_obj.meta.index.str.contains('fibroblast')]
rnaseq_obj.data = rnaseq_obj.data.loc[:, rnaseq_obj.meta.index]
# load RNA-Seq from Salmon (for normalised comparison)
# disabled for now
if False:
salmon_dat = rnaseq_data.load_salmon_by_patient_id(pids)
idx = salmon_dat.index.str.replace(r'.[0-9]+$', '')
salmon_dat.index = idx
import os
from load_data import rnaseq_data
import pandas as pd
from rnaseq import filter
from matplotlib import pyplot as plt
from plotting import clustering
from stats.transformations import median_absolute_deviation, variance_stabilizing_transform
import numpy as np
from utils.output import unique_output_dir
if __name__ == "__main__":
outdir = unique_output_dir("compare_gibco_h9")
loader_hgic = rnaseq_data.all_hgic_loader(annotate_by='Ensembl Gene ID')
loader_h9 = rnaseq_data.gse61794(annotate_by='Ensembl Gene ID')
genes = loader_hgic.data.index.intersection(loader_h9.data.index)
genes = genes[genes.str.contains('ENSG')]
# collapse H9 replicates
h9_data = loader_h9.data.sum(axis=1).loc[genes]
h9_data.name = 'H9_NSC'
h9_meta = pd.Series(
{
'type': 'NSC',
'read_count': sum(loader_h9.meta.read_count),
'sample': 'H9_NSC',
'disease_subgroup': 'control',
},
name='H9_NSC')
data = pd.concat((loader_hgic.data.loc[genes], h9_data), axis=1)
'SLC1A3': 4170,
}
OUTDIR = unique_output_dir("jb.marker_levels", reuse_empty=True)
# GSE73721 (reference astrocytes, oligos, ...)
obj73721 = rnaseq_data.gse73721(source='star',
annotate_by='Ensembl Gene ID')
# remove unneeded samples
to_keep73721 = (obj73721.data.columns.str.contains('yo ctx astro')
| obj73721.data.columns.str.contains('Hippocampus astro')
| obj73721.data.columns.str.contains('oligo'))
# GSE61794 (H9-derived NSC x 2)
obj61794 = rnaseq_data.gse61794(source='star',
annotate_by='Ensembl Gene ID')
# combining replicates
rc = obj61794.meta.read_count.sum()
obj61794.meta = pd.DataFrame(data={
'cell_type': 'NSC',
'srr': 'SRR1586371-2',
'read_count': rc,
'sample': 'H9 NSC',
},
index=['SRR1586371-2'])
obj61794.data = pd.DataFrame(obj61794.data.sum(axis=1), columns=['H9 NSC'])
# WTCHG ALL samples
objwtchg_all = rnaseq_data.all_hgic_loader(annotate_by='Ensembl Gene ID',
include_derived=True)
to_keep_wtchg = (
}
intersecter = lambda x, y: set(x).intersection(y)
unioner = lambda x, y: set(x).union(y)
if njob != 1:
pool = mp.Pool(njob)
# Load RNA-Seq from STAR
rnaseq_obj = rnaseq_data.load_by_patient(pids,
annotate_by='Ensembl Gene ID')
# load additional references if required
refs = [('H1', rnaseq_data.gse38993(annotate_by='Ensembl Gene ID')),
('H9',
rnaseq_data.gse61794(annotate_by='Ensembl Gene ID',
collapse_replicates=False))]
all_refs = [t[0] for t in refs] + ['GIBCO']
rnaseq_obj = rnaseq_data.MultipleBatchLoader([rnaseq_obj] +
[t[1] for t in refs])
# only keep gene counts
rnaseq_obj.data = rnaseq_obj.data.loc[rnaseq_obj.data.index.str.contains(
'ENSG')]
# discard iPSC
rnaseq_obj.meta = rnaseq_obj.meta.loc[~rnaseq_obj.meta.index.str.
contains('PSC')]
rnaseq_obj.meta = rnaseq_obj.meta.loc[~rnaseq_obj.meta.index.str.
contains('NHF1-hTERT')]
rnaseq_obj.meta = rnaseq_obj.meta.loc[~rnaseq_obj.meta.index.str.