hic的ice命令的问题
把ice的代码更新为下面的代码就可以了
#!/home/s312/miniconda3/envs/rna/bin/python
import sys
import argparse
import numpy as np
from scipy import sparse
import iced
from iced.io import loadtxt, savetxt
parser = argparse.ArgumentParser(“ICE normalization”)
parser.add_argument(‘filename’,
metavar=‘File to load’,
type=str,
help=‘Path to file of contact counts to load’)
parser.add_argument("–results_filename",
“-r”,
type=str,
default=None,
help=“results_filename”)
parser.add_argument("–filtering_perc", “-f”,
type=float,
default=None,
help=“Percentage of reads to filter out”)
parser.add_argument("–filter_low_counts_perc",
type=float,
default=0.02,
help=“Percentage of reads to filter out”)
parser.add_argument("–filter_high_counts_perc",
type=float,
default=0,
help=“Percentage of reads to filter out”)
parser.add_argument("–remove-all-zeros-loci", default=False,
action=“store_true”,
help=“If provided, all non-interacting loci will be "
“removed prior to the filtering strategy chosen.”)
parser.add_argument(”–max_iter", “-m”, default=100, type=int,
help=“Maximum number of iterations”)
parser.add_argument("–eps", “-e”, default=0.1, type=float,
help=“Precision”)
parser.add_argument("–dense", “-d”, default=False, action=“store_true”)
parser.add_argument("–output-bias", “-b”, default=False, help=“Output the bias vector”)
parser.add_argument("–verbose", “-v”, default=False)
args = parser.parse_args()
filename = args.filename
Deprecating filtering_perc option
filter_low_counts = None
if “–filtering_perc” in sys.argv:
DeprecationWarning(
"Option ‘–filtering_perc’ is deprecated. Please use "
“’–filter_low_counts_perc’ instead.’”)
# And print it again because deprecation warnings are not displayed for
# recent versions of python
print “–filtering_perc is deprecated. Please use filter_low_counts_perc”
print “instead. This option will be removed in ice 0.3”
filter_low_counts = args.filtering_perc
if “–filter_low_counts_perc” in sys.argv and “–filtering_perc” in sys.argv:
raise Warning(“This two options are incompatible”)
if “–filtering_perc” is None and “–filter_low_counts_perc” not in sys.argv:
filter_low_counts_perc = 0.02
elif args.filter_low_counts_perc is not None:
filter_low_counts_perc = args.filter_low_counts_perc
if args.verbose:
print(“Using iced version %s” % iced.version)
print “Loading files…”
Loads file as i, j, counts
i, j, data = loadtxt(filename).T
Detecting whether the file is 0 or 1 based.
if min(i.min(), j.min()) == 0:
index_base = 0
N = max(i.max(), j.max()) + 1
counts = sparse.coo_matrix((data, (i, j)), shape=(N, N), dtype=float)
else:
index_base = 1
N = max(i.max(), j.max())
counts = sparse.coo_matrix((data, (i - 1, j - 1)), shape=(N, N), dtype=float)
if args.dense:
counts = np.array(counts.todense())
else:
counts = sparse.csr_matrix(counts)
if args.verbose:
print “Normalizing…”
if filter_low_counts_perc != 0:
counts = iced.filter.filter_low_counts(counts,
percentage=filter_low_counts_perc,
remove_all_zeros_loci=args.remove_all_zeros_loci,
copy=False, sparsity=False, verbose=args.verbose)
if args.filter_high_counts_perc != 0:
counts = iced.filter.filter_high_counts(
counts,
percentage=args.filter_high_counts_perc,
copy=False)
counts, bias = iced.normalization.ICE_normalization(
counts, max_iter=args.max_iter, copy=False,
verbose=args.verbose, eps=args.eps, output_bias=True)
if args.results_filename is None:
results_filename = “.”.join(
filename.split(".")[:-1]) + “_normalized.” + filename.split(".")[-1]
else:
results_filename = args.results_filename
counts = sparse.coo_matrix(counts)
if args.verbose:
print “Writing results…”
savetxt(
results_filename, counts.col + index_base, counts.row + index_base, counts.data)
if args.output_bias:
np.savetxt(results_filename + “.biases”, bias)
旧代码
另外一个版本的代码运行失败
#!/data/zhangyong/miniconda3/envs/rna/bin/python
from future import print_function
import sys
import argparse
import numpy as np
from scipy import sparse
import iced
from iced.io import load_counts, savetxt, write_counts
parser = argparse.ArgumentParser(“ICE normalization”)
parser.add_argument(‘filename’,
metavar=‘File to load’,
type=str,
help=‘Path to file of contact counts to load’)
parser.add_argument("–results_filename",
“-r”,
type=str,
default=None,
help=“results_filename”)
parser.add_argument("–filtering_perc", “-f”,
type=float,
default=None,
help=“Percentage of reads to filter out”)
parser.add_argument("–filter_low_counts_perc",
type=float,
default=0.02,
help=“Percentage of reads to filter out”)
parser.add_argument("–filter_high_counts_perc",
type=float,
default=0,
help=“Percentage of reads to filter out”)
parser.add_argument("–remove-all-zeros-loci", default=False,
action=“store_true”,
help=“If provided, all non-interacting loci will be "
“removed prior to the filtering strategy chosen.”)
parser.add_argument(”–max_iter", “-m”, default=100, type=int,
help=“Maximum number of iterations”)
parser.add_argument("–eps", “-e”, default=0.1, type=float,
help=“Precision”)
parser.add_argument("–dense", “-d”, default=False, action=“store_true”)
parser.add_argument("–output-bias", “-b”, default=False, help=“Output the bias vector”)
parser.add_argument("–verbose", “-v”, default=False, type=bool)
args = parser.parse_args()
filename = args.filename
Deprecating filtering_perc option
filter_low_counts = None
if “–filtering_perc” in sys.argv:
DeprecationWarning(
“Option ‘–filtering_perc’ is deprecated. Please use "
“’–filter_low_counts_perc’ instead.’”)
# And print it again because deprecation warnings are not displayed for
# recent versions of python
print(”–filtering_perc is deprecated. Please use filter_low_counts_perc")
print(“instead. This option will be removed in ice 0.3”)
filter_low_counts = args.filtering_perc
if “–filter_low_counts_perc” in sys.argv and “–filtering_perc” in sys.argv:
raise Warning(“This two options are incompatible”)
if “–filtering_perc” is None and “–filter_low_counts_perc” not in sys.argv:
filter_low_counts_perc = 0.02
elif args.filter_low_counts_perc is not None:
filter_low_counts_perc = args.filter_low_counts_perc
if args.verbose:
print(“Using iced version %s” % iced.version)
print(“Loading files…”)
Loads file as i, j, counts
counts = load_counts(filename, base=0)
if args.dense:
counts = np.array(counts.todense())
else:
counts = sparse.csr_matrix(counts)
if args.verbose:
print(“Normalizing…”)
if filter_low_counts_perc != 0:
counts = iced.filter.filter_low_counts(counts,
percentage=filter_low_counts_perc,
remove_all_zeros_loci=args.remove_all_zeros_loci,
copy=False, sparsity=False, verbose=args.verbose)
if args.filter_high_counts_perc != 0:
counts = iced.filter.filter_high_counts(
counts,
percentage=args.filter_high_counts_perc,
copy=False)
counts, bias = iced.normalization.ICE_normalization(
counts, max_iter=args.max_iter, copy=False,
verbose=args.verbose, eps=args.eps, output_bias=True)
if args.results_filename is None:
results_filename = “.”.join(
filename.split(".")[:-1]) + “_normalized.” + filename.split(".")[-1]
else:
results_filename = args.results_filename
counts = sparse.coo_matrix(counts)
if args.verbose:
print(“Writing results…”)
#write_counts(results_filename, counts)
write_counts(
results_filename,
counts)
if args.output_bias:
np.savetxt(results_filename + “.biases”, bias)