ch = args.ch
cell = args.cell
resolution_polarity = args.resolution
resolution = args.resolution
exp_factor = 4
percentile = args.percentile
fork_speed = args.fspeed
kon = 0.005
ndiff = args.ndiff
nsim = args.nsim
if args.signal == "peak":
x, d3p = detect_peaks(start,
end,
ch,
resolution_polarity=resolution_polarity,
exp_factor=exp_factor,
percentile=percentile,
cell=cell,
nanpolate=True)
if args.correct:
x, DNaseI = replication_data(cell,
"DNaseI",
chromosome=ch,
start=start,
end=end,
resolution=resolution,
raw=False)
x, CNV = replication_data(cell,
"CNV",
chromosome=ch,
name_w = args.name + "_%i_%i_%i" % (ch, start, end)
if args.signal in ["peak", "peakRFDonly"]:
print("Percentile", percentile, args.recomp, args.gsmooth, args.dec,
args.smoothpeak)
rfd_only = False
if args.signal == "peakRFDonly":
rfd_only = True
x, d3p = detect_peaks(start,
end,
ch,
resolution_polarity=resolution_polarity,
exp_factor=exp_factor,
percentile=percentile,
cell=cell,
cellMRT=comp,
cellRFD=cellseq,
nanpolate=True,
recomp=args.recomp,
gsmooth=args.gsmooth,
dec=args.dec,
fsmooth=args.smoothpeak,
expRFD=expRFD,
rfd_only=rfd_only)
if resolution != resolution_polarity:
x, d3p0 = detect_peaks(start,
end,
ch,
resolution_polarity=resolution,
exp_factor=exp_factor,
percentile=percentile,
248956422, 242193529, 198295559, 190214555, 181538259, 170805979,
159345973, 145138636, 138394717, 133797422, 135086622, 133275309,
114364328, 107043718, 101991189, 90338345, 83257441, 80373285, 58617616,
64444167, 46709983, 50818468
]
data = []
for chrom, length in enumerate(chromlength, 1):
data.append(
detect_peaks(0,
length // 1000,
chrom,
resolution_polarity=resolution_polarity,
exp_factor=exp_factor,
fsmooth=args.smoothpeak,
percentile=percentile,
cell=cell,
cellMRT=args.cellMRT,
cellRFD=args.cellRFD,
recomp=args.recomp,
dec=args.dec)[1])
data = np.concatenate(data)
data[data == 0] = np.nan
data[~np.isnan(data)] = 1
pd.DataFrame({"signalValue": data}).to_csv("ARS.csv", index=False)
with open(args.name, "wb") as f:
pickle.dump(data, f)