"--fo",
help=
"name output figure. Extension indiciates figure type. Data files saved with same name but different extension"
)
#parser.add_argument("--stagesPlot")
parser.add_argument("--vmin", type=float)
parser.add_argument("--vmax", type=float)
parser.add_argument("--linkage_regTargets", default="complete")
parser.add_argument("--linkage_TFs", default="complete")
args = parser.parse_args()
###########################################################################################################################
## LOAD DATA
corrDF = IOutils.loadDF(args.geneCorr)
genes = list(
set().union(*[IOutils.readListFromFile(x) for x in args.genes.split(",")]))
genes_missing = [x for x in genes if not (x in corrDF.index)]
if len(genes_missing) > 0:
print("WARNING the following genes are not in correlation data {}".format(
genes_missing))
genes = [x for x in genes if x in corrDF.index]
TFs = list(
set().union(*[IOutils.readListFromFile(x) for x in args.TFs.split(",")]))
TFs_missing = [x for x in TFs if not (x in corrDF.index)]
if len(TFs_missing) > 0:
print("WARNING the following TFs are not in correlation data {}".format(
TFs_missing))
TFs = [x for x in TFs if x in corrDF.index]
#############################################################################################################################
## COMPUTE SIGNED ADJACENCY
superstage_dict = OrderedDict([
(k, [int(re.search(r'(\d+)$', x).group(1)) for x in v.split(",")])
for k, v in superstage_dict.items()
])
############################################################################################################
## Load data
cellData = IOutils.loadCellData(
OrderedDict([("expr", args.fi_expr), ("pcComps", args.fi_stageIDs)]))
cellData = pd.concat([
cellData.loc[:,
("expr", slice(None))], cellData.loc[:,
("rowData", "clust_ID")]
],
axis=1) ## Remove unnecessary columns
cells_allowed = IOutils.readListFromFile(args.cells)
print("Restricting to {:d} allowed cells".format(len(cells_allowed)))
cellData = cellData.loc[cells_allowed, :].copy()
############################################################################################################
## COMPUTE CORRELATION BY STAGE
if not args.noSingleStageCorr:
stages = sorted(cellData["rowData"].loc[:, "clust_ID"].unique(),
key=lambda x: int(x))
corrDict_stages = OrderedDict([])
for stage in stages:
print("\tCalculating correlation for stage {}".format(stage))
exprDF = cellData["expr"].loc[cellData["rowData"].loc[:, "clust_ID"] ==
stage, :].copy()
corrDict_stages[stage] = cf.pearsonCorrel_log10tpm(exprDF)
##Write results
