LOG = logging.getLogger("Crispy")
DPATH = pkg_resources.resource_filename("crispy", "data/")
RPATH = pkg_resources.resource_filename("reports", "eg/")
if __name__ == "__main__":
# Data-sets
#
wes_obj = WES()
mobem_obj = Mobem()
cn_obj = CopyNumber()
prot_obj = Proteomics()
gexp_obj = GeneExpression()
crispr_obj = CRISPR()
drug_obj = DrugResponse()
# Samples
#
samples = set.intersection(set(prot_obj.get_data()))
LOG.info(f"Samples: {len(samples)}")
# Filter data-sets
#
prot = prot_obj.filter(subset=samples)
LOG.info(f"Proteomics: {prot.shape}")
gexp = gexp_obj.filter(subset=samples)
# Gene information
#
ginfo = pd.read_csv(f"{TPATH}/mart_export.txt", sep="\t")
ginfo["mean_pos"] = ginfo[["Gene end (bp)", "Gene start (bp)"]].mean(1)
ginfo = ginfo[ginfo["Chromosome/scaffold name"].isin(Utils.CHR_ORDER)]
ginfo_pos = pd.concat([
ginfo.groupby("Gene name")["Chromosome/scaffold name"].first().rename(
"chr"),
ginfo.groupby("Gene name")["mean_pos"].mean().rename("chr_pos"),
],
axis=1)
# Y matrices
#
gexp_obj = GeneExpression()
gexp = gexp_obj.filter()
LOG.info(f"Gexp: {gexp.shape}")
prot_obj = Proteomics()
prot = prot_obj.filter()
prot = prot[prot.count(1) > 300]
LOG.info(f"Prot: {prot.shape}")
# X matrices
#
crispr_obj = CRISPR()
crispr = crispr_obj.filter(dtype="merged")
LOG.info(f"CRISPR: {crispr.shape}")
drespo_obj = DrugResponse()
LOG = logging.getLogger("Crispy")
DPATH = pkg_resources.resource_filename("crispy", "data/")
RPATH = pkg_resources.resource_filename("reports", "eg/")
TPATH = pkg_resources.resource_filename("tables", "/")
if __name__ == "__main__":
# Data-sets
#
prot_obj = Proteomics()
prot = prot_obj.filter()
LOG.info(f"Proteomics: {prot.shape}")
gexp_obj = GeneExpression()
gexp = gexp_obj.filter(subset=list(prot))
LOG.info(f"Transcriptomics: {gexp.shape}")
crispr_obj = CRISPR()
crispr = crispr_obj.filter(subset=list(prot))
LOG.info(f"CRISPR: {crispr.shape}")
drespo_obj = DrugResponse()
drespo = drespo_obj.filter()
drespo.index = [";".join(map(str, i)) for i in drespo.index]
dmax = drespo_obj.drugresponse.groupby(["drug_id", "drug_name", "dataset"])[
"max_screening_conc"
].first()