def opsm(E, l=2, **kwargs):
with TemporaryDirectory() as tmpdir:
pd.DataFrame(standardize(E)).T.to_csv(tmpdir + "E.csv", index=0, header=0, sep=" ")
output_location = os.path.abspath(tmpdir + "/output.txt")
# PERSOFTWARELOCATION is the location in which the software is installed
binary = "java -XX:ParallelGCThreads=1 -Xmx1G -jar " + os.environ["PERSOFTWARELOCATION"] + "/OPSM/OPSM.jar"
command = "{binary} {E_location} {nG} {nC} {output_location} {l}".format(
binary=binary,
E_location = os.path.abspath(tmpdir + "E.csv"),
nG = str(len(E.columns)),
nC = str(len(E.index)),
output_location = output_location,
l = str(l)
)
print(command)
sp.call(command, shell=True)
bics = []
with open(os.path.abspath(output_location) , "r") as infile:
lines = infile.readlines()
for line1, line2, _ in zip(lines[::3], lines[1::3], lines[2::3]):
if len(line1) > 0 and len(line2) > 0:
gids = [int(gid) for gid in line1.strip().split(" ")]
cids = [int(cid) for cid in line2.strip().split(" ")]
bics.append(Bicluster(E.columns[gids].tolist(), E.index[cids].tolist()))
return bics
def msbe(E,
alpha=0.4,
beta=0.5,
gamma=1.2,
refgene="random 500",
refcond="random 20",
**kwargs):
with TemporaryDirectory() as tmpdir:
standardize(E).to_csv(tmpdir + "/E.csv", sep="\t")
binary = "sh " + os.environ[
"PERSOFTWARELOCATION"] + "/MSBE_linux_1.0.5/additiveBi"
command = "{binary} {tmpdir}/E.csv {alpha} {beta} {gamma} {refgene} {refcond} {tmpdir}/results.txt".format(
**locals())
sp.call(command, shell=True)
bics = []
with open(tmpdir + "/results.txt", "r") as infile:
lines = infile.readlines()
for _, line1, line2 in zip(lines[::3], lines[1::3], lines[2::3]):
if len(line1) > 0 and len(line2) > 0:
print(line1, line2)
cids = [int(cid) - 1 for cid in line1.strip().split(" ")]
gids = [int(gid) - 1 for gid in line2.strip().split(" ")]
bics.append(
Bicluster(E.columns[gids].tolist(),
E.index[cids].tolist()))
return bics
def convert_biclustermatrix2biclusters(rows, columns, E):
bics = []
for column, row in zip(columns, rows):
print(column)
print(row)
bic = Bicluster(E.columns[column != 0], E.index[row != 0])
if bic.size() > 0:
bics.append(bic)
return bics
def spectral_biclust(E, ngenes=3, nconditions=1, spectral_method="bistochastic", n=6, n_best_ratio=0.5, **kwargs):
n_best = max([int(n*n_best_ratio), 1])
spectral = SpectralBiclustering(n_clusters=(nconditions,ngenes), method=spectral_method, n_components=n, n_best=n_best)
spectral.fit(standardize(E))
bics = []
for columns, rows in zip(spectral.columns_, spectral.rows_):
genes = E.columns[columns]
conditions = E.index[rows]
bics.append(Bicluster(genes, conditions))
return bics
def fabia(E,
n=13,
alpha=0.01,
cyc=500,
spl=0.,
spz=0.5,
non_negative=0,
random=1,
center=2,
norm=1,
scale=0,
lap=1,
nL=0,
lL=0,
bL=0,
thresZ=0.5,
thresL=None,
**kwargs):
importr("fabia")
rfabia = ro.r["fabia"]
if thresL is None or thresL == "None":
thresL = ro.NULL
rresults = rfabia(
standardize(E).as_matrix().T, n, alpha, cyc, spl, spz, non_negative,
random, center, norm, scale, lap, nL, lL, bL)
rresults_extracted = ro.r["extractBic"](rresults, thresZ,
thresL).rx2("bic")
bics = []
for i in range(1, rresults_extracted.nrow):
if np.min(rresults_extracted.rx2(
i, "binp")) > 0: # number of rows and columns > 0
gids = [
int(g[len("gene"):]) - 1
for g in rresults_extracted.rx2(i, "bixn")
]
cids = [
int(g[len("sample"):]) - 1
for g in rresults_extracted.rx2(i, "biypn")
]
bics.append(
Bicluster(E.columns[gids].tolist(), E.index[cids].tolist()))
return bics
def biforce(E, m="o", t=0, **kwargs):
with TemporaryDirectory() as tmpdir:
E_location = tmpdir + "/E.csv"
output_location = tmpdir + "/output.txt"
standardize(E).T.to_csv(tmpdir + "/E.csv",
index=False,
header=False,
sep="\t")
binary = "java -XX:ParallelGCThreads=1 -Xmx12G -jar mbiforce.jar"
command = "{binary} -i={i} -o={o} -m={m} -h=false -t={t}".format(
binary=binary,
i=os.path.abspath(E_location),
o=os.path.abspath(output_location),
m=m,
t=str(t))
original_wd = os.getcwd()
try:
os.chdir(
os.environ["PERSOFTWARELOCATION"] + "/BiForceV2/"
) # change working directory because biclue only looks for the parameter.ini file in the current working directory (...)
sp.call(command, shell=True)
except BaseException as e:
raise e
finally:
os.chdir(original_wd)
bics = []
with open(output_location) as infile:
for line in infile.readlines()[1:-1]:
line = line.strip().split(",")
genes = []
conditions = []
for rowcol in line:
if rowcol[0] == "R":
genes.append(E.columns[int(rowcol[1:]) - 1])
elif rowcol[0] == "C":
conditions.append(E.index[int(rowcol[1:]) - 1])
bics.append(Bicluster(genes, conditions))
return bics