def normalize(data, wigList=[], annotationPath=""):
"""Returns the normalized data, using the empirical hist method.
Arguments:
wigList (list): List of paths to wig formatted datasets.
annotationPath (str): Path to annotation in .prot_table or GFF3 format.
Returns:
numpy array: Array with the normalization factors for the emphist method.
:Example:
>>> import pytransit.norm_tools as norm_tools
>>> import pytransit.tnseq_tools as tnseq_tools
>>> (data, position) = tnseq_tools.get_data(["transit/data/glycerol_H37Rv_rep1.wig", "transit/data/glycerol_H37Rv_rep2.wig"])
>>> print(data)
array([[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.]])
>>> factors = norm_tools.emphist_factors(["transit/data/glycerol_H37Rv_rep1.wig", "transit/data/glycerol_H37Rv_rep2.wig"], "transit/genomes/H37Rv.prot_table")
>>> print(factors)
array([[ 1. ],
[ 0.63464722]])
.. seealso:: :class:`normalize_data`
"""
from pytransit import tnseq_tools
G = tnseq_tools.Genes(wigList, annotationPath)
K = len(wigList)
temp = []
for j in range(K):
reads_per_gene = []
for gene in G:
tempdata = numpy.array(gene.reads)
if len(tempdata[0]) > 0:
reads_per_gene.append(numpy.sum(tempdata[j,:]))
temp.append(reads_per_gene)
temp = numpy.array(temp)
factors = numpy.ones((K,1))
for j in range(1, K):
ii_good = numpy.logical_and(temp[0,:] > 0, temp[j,:] > 0)
logFC = numpy.log(temp[j,ii_good]/temp[0,ii_good])
mean = numpy.mean(logFC)
std = numpy.sqrt(numpy.var(logFC))
X = numpy.linspace(mean - (5*std), mean + (std*5), 50000)
R = scipy.stats.gaussian_kde(logFC)
Y = R(X)
peakLogFC = X[Y.argmax()]
if peakLogFC < 0:
factors[j,0] = numpy.exp(abs(peakLogFC))
else:
factors[j,0] = 1.0/numpy.exp(abs(peakLogFC))
data = factors * data
return (data, factors)
def Run(self):
self.transit_message("Starting Gene Mean Counts Export")
start_time = time.time()
#Get orf data
self.transit_message("Getting Data")
(fulldata, position) = tnseq_tools.get_data(self.ctrldata)
(fulldata, factors) = norm_tools.normalize_data(fulldata, self.normalization,
self.ctrldata, self.annotation_path)
position = position.astype(int)
hash = transit_tools.get_pos_hash(self.annotation_path)
rv2info = transit_tools.get_gene_info(self.annotation_path)
self.transit_message("Normalizing")
self.output.write("#Summarized to Mean Gene Counts with TRANSIT.\n")
if self.normalization != "nonorm":
self.output.write("#Reads normalized using '%s'\n" % self.normalization)
if type(factors[0]) == type(0.0):
self.output.write("#Normalization Factors: %s\n" % "\t".join(["%s" % f for f in factors.flatten()]))
else:
self.output.write("#Normalization Factors: %s\n" % " ".join([",".join(["%s" % bx for bx in b]) for b in factors]))
self.output.write("#Files:\n")
for f in self.ctrldata:
self.output.write("#%s\n" % f)
K,Nsites = fulldata.shape
# Get Gene objects
G = tnseq_tools.Genes(self.ctrldata, self.annotation_path, norm=self.normalization)
N = len(G)
self.progress_range(N)
dataset_header = "\t".join([transit_tools.fetch_name(D) for D in self.ctrldata])
self.output.write("#Orf\tName\tNumber of TA sites\t%s\n" % dataset_header)
for i,gene in enumerate(G):
if gene.n > 0:
data_str = "\t".join(["%1.2f" % (M) for M in numpy.mean(gene.reads, 1)])
else:
data_str = "\t".join(["%1.2f" % (Z) for Z in numpy.zeros(K)])
self.output.write("%s\t%s\t%s\t%s\n" % (gene.orf, gene.name, gene.n, data_str))
# Update progress
text = "Running Export Method... %5.1f%%" % (100.0*i/N)
self.progress_update(text, i)
self.output.close()
self.transit_message("") # Printing empty line to flush stdout
self.finish()
self.transit_message("Finished Export")
def post_process_genes(self, data, position, states, output_path):
output = open(output_path, "w")
pos2state = dict([(position[t], states[t])
for t in range(len(states))])
theta = numpy.mean(data > 0)
G = tnseq_tools.Genes(self.ctrldata,
self.annotation_path,
data=data,
position=position,
ignoreCodon=False)
num2label = {0: "ES", 1: "GD", 2: "NE", 3: "GA"}
output.write("#HMM - Genes\n")
for gene in G:
reads_nz = [c for c in gene.reads.flatten() if c > 0]
avg_read_nz = 0
if len(reads_nz) > 0:
avg_read_nz = numpy.average(reads_nz)
# State
genestates = [pos2state[p] for p in gene.position]
statedist = {}
for st in genestates:
if st not in statedist: statedist[st] = 0
statedist[st] += 1
# State counts
n0 = statedist.get(0, 0)
n1 = statedist.get(1, 0)
n2 = statedist.get(2, 0)
n3 = statedist.get(3, 0)
if gene.n > 0:
E = tnseq_tools.ExpectedRuns(gene.n, 1.0 - theta)
V = tnseq_tools.VarR(gene.n, 1.0 - theta)
if n0 == gene.n: S = "ES"
elif n0 >= int(E + (3 * math.sqrt(V))): S = "ES"
else:
temp = max([(statedist.get(s, 0), s)
for s in [0, 1, 2, 3]])[1]
S = num2label[temp]
else:
E = 0.0
V = 0.0
S = "N/A"
output.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%1.4f\t%1.2f\t%s\n" %
(gene.orf, gene.name, gene.desc, gene.n, n0, n1, n2,
n3, gene.theta(), avg_read_nz, S))
output.close()
def convertToGeneCountSummary(dataset_list,
annotationPath,
outputPath,
normchoice="nonorm"):
"""Normalizes the input datasets and outputs the result in CombinedWig format.
Arguments:
dataset_list (list): List of paths to datasets in .wig format
annotationPath (str): Path to annotation in .prot_table or GFF3 format.
outputPath (str): Desired output path.
normchoice (str): Choice for normalization method.
"""
(fulldata, position) = tnseq_tools.get_data(dataset_list)
(fulldata, factors) = norm_tools.normalize_data(fulldata, normchoice,
dataset_list,
annotationPath)
output = open(outputPath, "w")
output.write("#Summarized to Mean Gene Counts with TRANSIT.\n")
if normchoice != "nonorm":
output.write("#Reads normalized using '%s'\n" % normchoice)
if type(factors[0]) == type(0.0):
output.write("#Normalization Factors: %s\n" %
"\t".join(["%s" % f for f in factors.flatten()]))
else:
output.write(
"#Normalization Factors: %s\n" %
" ".join([",".join(["%s" % bx for bx in b]) for b in factors]))
(K, N) = fulldata.shape
output.write("#Files:\n")
for f in dataset_list:
output.write("#%s\n" % f)
# Get Gene objects
G = tnseq_tools.Genes(dataset_list, annotationPath, norm=normchoice)
dataset_header = "\t".join([os.path.basename(D) for D in dataset_list])
output.write("#Orf\tName\tNumber of TA sites\t%s\n" % dataset_header)
for i, gene in enumerate(G):
if gene.n > 0:
data_str = "\t".join(
["%1.2f" % (M) for M in numpy.mean(gene.reads, 1)])
else:
data_str = "\t".join(["%1.2f" % (Z) for Z in numpy.zeros(K)])
output.write("%s\t%s\t%s\t%s\n" %
(gene.orf, gene.name, gene.n, data_str))
output.close()
def test_genes_creation_fromwig(self):
G = tnseq_tools.Genes(all_data_list, annotation)
N = len(G)
test_orf = "Rv0001"
test_name = "dnaA"
#Test list creation
self.assertGreater(N, 3000)
#Test dictionary lookup + data
self.assertEqual(G[test_orf].orf, test_orf)
self.assertEqual(G[test_orf].name, test_name)
#Test list lookup + data
self.assertEqual(G[0].orf, test_orf)
self.assertEqual(G[0].name, test_name)
def test_genes_creation_fromdata(self):
data, position = tnseq_tools.get_data(all_data_list)
Kreps, Nsites = data.shape
G = tnseq_tools.Genes([], annotation, data=data, position=position)
N = len(G)
test_orf = "Rv0001"
test_name = "dnaA"
#Test list creation
self.assertGreater(N, 3000)
#Test dictionary lookup + data
self.assertEqual(G[test_orf].orf, test_orf)
self.assertEqual(G[test_orf].name, test_name)
#Test list lookup + data
self.assertEqual(G[0].orf, test_orf)
self.assertEqual(G[0].name, test_name)
def Run(self):
self.transit_message("Starting rankproduct Method")
start_time = time.time()
Kctrl = len(self.ctrldata)
Kexp = len(self.expdata)
#Get orf data
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(self.ctrldata +
self.expdata,
wxobj=self.wxobj)
if self.normalization != "none":
self.transit_message("Normalizing using: %s" % self.normalization)
(data,
factors) = norm_tools.normalize_data(data, self.normalization,
self.ctrldata + self.expdata,
self.annotation_path)
Gctrl = tnseq_tools.Genes(self.ctrldata + self.expdata,
self.annotation_path,
ignoreCodon=self.ignoreCodon,
nterm=self.NTerminus,
cterm=self.CTerminus,
data=data[:Kctrl, :],
position=position)
Gexp = tnseq_tools.Genes(self.ctrldata + self.expdata,
self.annotation_path,
ignoreCodon=self.ignoreCodon,
nterm=self.NTerminus,
cterm=self.CTerminus,
data=data[Kctrl:, :],
position=position)
Ngenes = len(Gctrl)
# Get the average counts for all the genes, in each replicate
meanCtrl = numpy.zeros((Kctrl, Ngenes))
meanExp = numpy.zeros((Kexp, Ngenes))
for i in range(Ngenes):
if numpy.any(Gctrl[i].reads):
meanCtrl[:, i] = numpy.mean(Gctrl[i].reads, 1)
else:
meanCtrl[:, i] = numpy.zeros(Kctrl)
#
if numpy.any(Gexp[i].reads):
meanExp[:, i] = numpy.mean(Gexp[i].reads, 1)
else:
meanExp[:, i] = numpy.zeros(Kexp)
# Calculate a logFC2 between Experimental and Control
# Then calculates it's rank, and observed rankProduct
logFC2 = numpy.log2((meanExp + 0.0001) / (meanCtrl + 0.0001))
rank = numpy.array([scipy.stats.rankdata(Lvec) for Lvec in logFC2])
obsRP = numpy.power(numpy.prod(rank, 0), 1.0 / Kctrl)
permutations = numpy.zeros((self.samples, Ngenes))
tempranks = scipy.array(
[numpy.arange(1, Ngenes + 1) for rep in range(Kctrl)])
for s in range(self.samples):
rankperm = numpy.array(
[numpy.random.permutation(tr) for tr in tempranks])
permutations[s] = numpy.power(numpy.prod(rankperm, 0), 1.0 / Kctrl)
rankRP = numpy.argsort(obsRP) + 1
#rankproduct
data = []
count = 0
self.progress_range(Ngenes)
for i, gene in enumerate(Gctrl):
count += 1
meanctrl = numpy.mean(Gctrl[i].reads)
meanexp = numpy.mean(Gexp[i].reads)
log2fc = numpy.log2((meanexp + 0.0001) / (meanctrl + 0.0001))
countbetter = numpy.sum(permutations <= obsRP[i])
pval = countbetter / float(self.samples * Ngenes)
e_val = countbetter / float(self.samples)
q_paper = e_val / float(rankRP[i])
data.append([
gene.orf, gene.name, gene.desc, gene.n, meanctrl, meanexp,
log2fc, obsRP[i], e_val, q_paper, pval
])
# Update Progress
text = "Running rankproduct Method... %5.1f%%" % (100.0 * count /
Ngenes)
self.progress_update(text, count)
#
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Performing Benjamini-Hochberg Correction")
data.sort()
q_bh = stat_tools.BH_fdr_correction([row[-1] for row in data])
self.output.write("#RankProduct\n")
if self.wxobj:
members = sorted([
attr for attr in dir(self) if not callable(getattr(self, attr))
and not attr.startswith("__")
])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write(
"#GUI with: ctrldata=%s, annotation=%s, output=%s\n" %
(",".join(self.ctrldata).encode('utf-8'),
self.annotation_path.encode('utf-8'),
self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python %s\n" % " ".join(sys.argv))
self.output.write("#Data: %s\n" %
(",".join(self.ctrldata).encode('utf-8')))
self.output.write("#Annotation path: %s\n" %
self.annotation_path.encode('utf-8'))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#%s\n" % (columns))
for i, row in enumerate(data):
(orf, name, desc, n, mean1, mean2, log2FCgene, obsRPgene, e_val,
q_paper, pval) = row
self.output.write(
"%s\t%s\t%s\t%d\t%1.1f\t%1.1f\t%1.2f\t%1.8f\t%1.1f\t%1.8f\n" %
(orf, name, desc, n, mean1, mean2, log2FCgene, obsRPgene,
e_val, q_paper))
self.output.close()
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="RankProduct")
self.finish()
self.transit_message("Finished rankproduct Method")
def post_process_genes(self, data, position, states, output_path):
output = open(output_path, "w")
pos2state = dict([(position[t], states[t])
for t in range(len(states))])
theta = numpy.mean(data > 0)
G = tnseq_tools.Genes(self.ctrldata,
self.annotation_path,
data=data,
position=position,
ignoreCodon=False,
nterm=self.NTerminus,
cterm=self.CTerminus)
num2label = {0: "ES", 1: "GD", 2: "NE", 3: "GA"}
output.write("#HMM - Genes\n")
lines, counts = [], {}
for gene in G:
reads_nz = [c for c in gene.reads.flatten() if c > 0]
avg_read_nz = 0
if len(reads_nz) > 0:
avg_read_nz = numpy.average(reads_nz)
# State
genestates = [pos2state[p] for p in gene.position]
statedist = {}
for st in genestates:
if st not in statedist: statedist[st] = 0
statedist[st] += 1
# State counts
n0 = statedist.get(0, 0)
n1 = statedist.get(1, 0)
n2 = statedist.get(2, 0)
n3 = statedist.get(3, 0)
if gene.n > 0:
# this was intended to call genes ES if have sufficiently long run, but n0 (#ES) not even consecutive
#E = tnseq_tools.ExpectedRuns(gene.n, 1.0 - theta)
#V = tnseq_tools.VarR(gene.n, 1.0 - theta)
if n0 == gene.n:
S = "ES"
#elif n0 >= int(E+(3*math.sqrt(V))): S = "ES"
else:
temp = max([(statedist.get(s, 0), s)
for s in [0, 1, 2, 3]])[1]
S = num2label[temp]
else:
E = 0.0
V = 0.0
S = "N/A"
lines.append("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%1.4f\t%1.2f\t%s\n" %
(gene.orf, gene.name, gene.desc, gene.n, n0, n1, n2,
n3, gene.theta(), avg_read_nz, S))
if S not in counts: counts[S] = 0
counts[S] += 1
output.write(
"#genes: ES=%s, GD=%s, NE=%s, GA=%s, N/A=%s\n" %
tuple([counts.get(x, 0) for x in "ES GD NE GA N/A".split()]))
output.write(
"#key: ES=essential, GD=insertions cause growth-defect, NE=non-essential, GA=insertions confer growth-advantage, N/A=not analyzed (genes with 0 TA sites)\n"
)
output.write(
"#ORF\tgene\tannotation\tTAs\tES sites\tGD sites\tNE sites\tGA sites\tsaturation\tmean\tcall\n"
)
for line in lines:
output.write(line)
output.close()
def Run(self):
self.transit_message("Starting Binomial Method")
start_time = time.time()
self.progress_range(self.samples + self.burnin)
#Get orf data
#self.transit_message("Getting Data")
#G = tnseq_tools.Genes(self.ctrldata, self.annotation_path, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus)
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(self.ctrldata,
wxobj=self.wxobj)
(K, N) = data.shape
if self.normalization and self.normalization != "nonorm":
self.transit_message("Normalizing using: %s" % self.normalization)
(data,
factors) = norm_tools.normalize_data(data, self.normalization,
self.ctrldata,
self.annotation_path)
G = tnseq_tools.Genes(self.ctrldata,
self.annotation_path,
minread=1,
reps=self.replicates,
ignoreCodon=self.ignoreCodon,
nterm=self.NTerminus,
cterm=self.CTerminus,
data=data,
position=position)
#Parameters
self.transit_message("Setting Parameters")
w1 = 0.15
w0 = 1.0 - w1
mu_c = 0
Ngenes = len(G)
sample_size = self.samples + self.burnin
numReps = len(self.ctrldata)
theta = numpy.zeros((Ngenes, sample_size))
theta[:, 0] = 0.10
rho0 = numpy.zeros(sample_size)
rho0[0] = 0.5
Kp0 = numpy.zeros(sample_size)
Kp0[0] = 10
rho1 = numpy.zeros(sample_size)
rho1[0] = 0.10
Kp1 = numpy.zeros(sample_size)
Kp1[0] = 3
Z = numpy.zeros((Ngenes, sample_size))
pz1 = numpy.zeros(sample_size)
n1 = 0
w1 = scipy.stats.beta.rvs(self.alpha_w, self.beta_w)
W1 = numpy.zeros(sample_size)
W1[0] = w1
#
self.transit_message("Setting Initial Values")
K = numpy.array(
[sum([1 for x in gene.reads.flatten() if x > 0]) for gene in G])
N = numpy.array([len(gene.reads.flatten()) for gene in G])
for g, gene in enumerate(G):
if N[g] == 0: theta[g][0] = 0.5
elif K[g] / float(N[g]) == 0: theta[g][0] = 0.001
elif K[g] / float(N[g]) == 1: theta[g][0] = 0.001
else: theta[g][0] = K[g] / float(N[g])
#print(g, ORF[g], K[g], N[g], theta[g][0])
Z[g][0] = scipy.stats.bernoulli.rvs(1 - theta[g][0])
acc_p0 = 0
acc_k0 = 0
acc_p1 = 0
acc_k1 = 0
rho0c_std = 0.010
kp0c_std = 1.40
rho1c_std = 0.009
kp1c_std = 1.1
numpy.seterr(divide='ignore')
for i in range(1, sample_size):
i0 = Z[:, i - 1] == 0
n0 = numpy.sum(i0)
i1 = Z[:, i - 1] == 1
n1 = numpy.sum(i1)
theta[i0, i] = scipy.stats.beta.rvs(
Kp0[i - 1] * rho0[i - 1] + K[i0],
Kp0[i - 1] * (1 - rho0[i - 1]) + N[i0] - K[i0])
theta[i1, i] = scipy.stats.beta.rvs(
Kp1[i - 1] * rho1[i - 1] + K[i1],
Kp1[i - 1] * (1 - rho1[i - 1]) + N[i1] - K[i1])
rho0_c = rho0[i - 1] + scipy.stats.norm.rvs(0, rho0c_std)
Kp0_c = Kp0[i - 1] + scipy.stats.norm.rvs(0, kp0c_std)
if rho0_c <= 0: rho0[i] = rho0[i - 1]
else:
fc = numpy.log(
scipy.stats.beta.pdf(rho0_c, self.M0 * self.pi0,
self.M0 * (1.0 - self.pi0)))
f0 = numpy.log(
scipy.stats.beta.pdf(rho0[i - 1], self.M0 * self.pi0,
self.M0 * (1.0 - self.pi0)))
fc += numpy.sum(
numpy.log(
scipy.stats.beta.pdf(theta[i0, i], Kp0[i - 1] * rho0_c,
Kp0[i - 1] * (1 - rho0_c))))
f0 += numpy.sum(
numpy.log(
scipy.stats.beta.pdf(theta[i0, i],
Kp0[i - 1] * rho0[i - 1],
Kp0[i - 1] * (1 - rho0[i - 1]))))
if numpy.log(scipy.stats.uniform.rvs()) < fc - f0:
rho0[i] = rho0_c
acc_p0 += 1
else:
rho0[i] = rho0[i - 1]
if Kp0_c <= 0: Kp0[i] = Kp0[i - 1]
else:
fc = numpy.log(scipy.stats.gamma.pdf(Kp0_c, self.a0, self.b0))
f0 = numpy.log(
scipy.stats.gamma.pdf(Kp0[i - 1], self.a0, self.b0))
fc += numpy.sum(
numpy.log(
scipy.stats.beta.pdf(theta[i0, i], Kp0_c * rho0[i],
Kp0_c * (1 - rho0[i]))))
f0 += numpy.sum(
numpy.log(
scipy.stats.beta.pdf(theta[i0,
i], Kp0[i - 1] * rho0[i],
Kp0[i - 1] * (1 - rho0[i]))))
if numpy.log(scipy.stats.uniform.rvs()) < fc - f0:
Kp0[i] = Kp0_c
acc_k0 += 1
else:
Kp0[i] = Kp0[i - 1]
rho1_c = rho1[i - 1] + scipy.stats.norm.rvs(0, rho1c_std)
Kp1_c = Kp1[i - 1] + scipy.stats.norm.rvs(0, kp1c_std)
if rho1_c <= 0:
rho1[i] = rho1[i - 1]
else:
fc = numpy.log(
scipy.stats.beta.pdf(rho1_c, self.M1 * self.pi1,
self.M1 * (1 - self.pi1)))
f1 = numpy.log(
scipy.stats.beta.pdf(rho1[i - 1], self.M1 * self.pi1,
self.M1 * (1 - self.pi1)))
fc += numpy.sum(
numpy.log(
scipy.stats.beta.pdf(theta[i1, i], Kp1[i - 1] * rho1_c,
Kp1[i - 1] * (1 - rho1_c))))
f1 += numpy.sum(
numpy.log(
scipy.stats.beta.pdf(theta[i1, i],
Kp1[i - 1] * rho1[i - 1],
Kp1[i - 1] * (1 - rho1[i - 1]))))
if numpy.log(scipy.stats.uniform.rvs()) < fc - f1:
rho1[i] = rho1_c
acc_p1 += 1
else:
rho1[i] = rho1[i - 1]
if Kp1_c <= 0: Kp1[i] = Kp1[i - 1]
else:
fc = numpy.log(scipy.stats.gamma.pdf(Kp1_c, self.a1, self.b1))
f1 = numpy.log(
scipy.stats.gamma.pdf(Kp1[i - 1], self.a1, self.b1))
fc += numpy.sum(
numpy.log(
scipy.stats.beta.pdf(theta[i1, i], Kp1_c * rho1[i],
Kp1_c * (1 - rho1[i]))))
f1 += numpy.sum(
numpy.log(
scipy.stats.beta.pdf(theta[i1,
i], Kp1[i - 1] * rho1[i],
Kp1[i - 1] * (1 - rho1[i]))))
if numpy.log(scipy.stats.uniform.rvs()) < fc - f1:
Kp1[i] = Kp1_c
acc_k1 += 1
else:
Kp1[i] = Kp1[i - 1]
g0 = scipy.stats.beta.pdf(theta[:, i], Kp0[i] * rho0[i], Kp0[i] *
(1 - rho0[i])) * (1 - w1)
g1 = scipy.stats.beta.pdf(theta[:, i], Kp1[i] * rho1[i], Kp1[i] *
(1 - rho1[i])) * (w1)
p1 = g1 / (g0 + g1)
p1 = numpy.nan_to_num(p1)
try:
Z[:, i] = scipy.stats.bernoulli.rvs(p1)
except:
inan = numpy.isnan(p1)
sys.stderr.write("K=\t", K[inan], "\n")
sys.stderr.write("N=\t", N[inan], "\n")
sys.stderr.write("theta=", theta[inan, i], '\n')
sys.exit()
pz1[i] = p1[0]
i1 = Z[:, i] == 1
n1 = numpy.sum(i1)
#w1 = 0.15
w1 = scipy.stats.beta.rvs(self.alpha_w + n1,
self.beta_w + Ngenes - n1)
W1[i] = w1
#Update progress
text = "Running Binomial Method... %5.1f%%" % (100.0 * (i + 1) /
(sample_size))
self.progress_update(text, i)
numpy.seterr(divide='warn')
z_bar = numpy.apply_along_axis(numpy.mean, 1, Z[:, self.burnin:])
theta_bar = numpy.apply_along_axis(numpy.mean, 1, theta[:,
self.burnin:])
#(ess_threshold, noness_threshold) = stat_tools.fdr_post_prob(z_bar)
(ess_threshold,
noness_threshold) = stat_tools.bayesian_ess_thresholds(z_bar)
self.output.write("#Binomial\n")
#output.write("#Command: %s\n" % " ".join(["%s=%s" %(key,val) for (key,val) in kwargs.items()]))
if self.wxobj:
members = sorted([
attr for attr in dir(self) if not callable(getattr(self, attr))
and not attr.startswith("__")
])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write(
"#GUI with: ctrldata=%s, annotation=%s, output=%s, samples=%s, burnin=%s\n"
%
(",".join(self.ctrldata).encode('utf-8'),
self.annotation_path.encode('utf-8'),
self.output.name.encode('utf-8'), self.samples, self.burnin))
else:
self.output.write("#Console: python3 %s\n" % " ".join(sys.argv))
self.output.write("#Thresholds: (%1.5f, %1.5f)\n" %
(ess_threshold, noness_threshold))
self.output.write("#rho0 Acceptance Rate:\t%f%%\n" %
((100.0 * acc_p0) / sample_size))
self.output.write("#Kp0 Acceptance Rate:\t%f%%\n" %
((100.0 * acc_k0) / sample_size))
self.output.write("#rho1 Acceptance Rate:\t%f%%\n" %
((100.0 * acc_p1) / sample_size))
self.output.write("#Kp1 Acceptance Rate:\t%f%%\n" %
((100.0 * acc_k1) / sample_size))
self.output.write(
"#Hyperparameters rho: \t%1.2f\t%3.1f\t%1.2f\t%3.1f\n" %
(self.pi0, self.M0, self.pi1, self.M1))
self.output.write(
"#Hyperparameters Kp: \t%3.1f\t%3.1f\t%3.1f\t%3.1f\n" %
(self.a0, self.b0, self.a1, self.b1))
self.output.write("#Hyperparameters W: \t%1.3f\t%1.3f\n" %
(self.alpha_w, self.beta_w))
self.output.write("#%s\n" % "\t".join(columns))
data = []
for g, gene in enumerate(G):
c = "Uncertain"
if z_bar[g] > ess_threshold:
c = "Essential"
if z_bar[g] < noness_threshold:
c = "Non-Essential"
data.append(
"%s\t%s\t%s\t%1.1f\t%d\t%d\t%d\t%f\t%f\t%s" %
(gene.orf, gene.name, gene.desc, K[g] / float(numReps),
N[g] / numReps, K[g], N[g], theta_bar[g], z_bar[g], c))
data.sort()
for row in data:
self.output.write("%s\n" % row)
self.output.close()
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="Binomial")
self.finish()
self.transit_message("Finished Binomial Method")
def Run(self):
self.transit_message("Starting Griffin Method")
start_time = time.time()
#Get orf data
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(self.ctrldata,
wxobj=self.wxobj)
(K, N) = data.shape
if self.normalization and self.normalization != "nonorm":
self.transit_message("Normalizing using: %s" % self.normalization)
(data,
factors) = norm_tools.normalize_data(data, self.normalization,
self.ctrldata,
self.annotation_path)
G = tnseq_tools.Genes(self.ctrldata,
self.annotation_path,
minread=1,
reps=self.replicates,
ignoreCodon=self.ignoreCodon,
nterm=self.NTerminus,
cterm=self.CTerminus,
data=data,
position=position)
N = len(G)
self.progress_range(N)
count = 0
pins = G.global_theta()
pnon = 1.0 - pins
results = []
for gene in G:
if gene.n == 0:
results.append([gene, 0.0, 1.000])
else:
B = 1.0 / math.log(1.0 / pnon)
u = math.log(gene.n * pins, 1.0 / pnon)
exprun = tnseq_tools.ExpectedRuns(gene.n, pnon)
pval = 1.0 - tnseq_tools.GumbelCDF(gene.r, u, B)
results.append([gene, exprun, pval])
text = "Running Griffin Method... %5.1f%%" % (100.0 * (count + 1) /
(N))
self.progress_update(text, count)
count += 1
pval = [row[-1] for row in results]
padj = stat_tools.BH_fdr_correction(pval)
for i in range(len(results)):
results[i].append(padj[i])
results.sort()
self.output.write("#Griffin\n")
if self.wxobj:
members = sorted([
attr for attr in dir(self) if not callable(getattr(self, attr))
and not attr.startswith("__")
])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write(
"#GUI with: ctrldata=%s, annotation=%s, output=%s\n" %
(",".join(self.ctrldata).encode('utf-8'),
self.annotation_path.encode('utf-8'),
self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python3 %s\n" % " ".join(sys.argv))
self.output.write("#Data: %s\n" %
(",".join(self.ctrldata).encode('utf-8')))
self.output.write("#Annotation path: %s\n" %
self.annotation_path.encode('utf-8'))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#%s\n" % "\t".join(columns))
for (gene, exprun, pval, padj) in results:
self.output.write(
"%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%1.1f\t%1.5f\t%1.5f\n" %
(gene.orf, gene.name, gene.desc, gene.k, gene.n, gene.r,
gene.s, gene.t, exprun, pval, padj))
self.output.close()
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="Griffin")
self.finish()
self.transit_message("Finished Griffin Method")
def Run(self):
#if not self.wxobj:
# # Force matplotlib to use good backend for png.
# import matplotlib.pyplot as plt
#elif "matplotlib.pyplot" not in sys.modules:
try:
import matplotlib.pyplot as plt
except:
print("Error: cannot do histograms")
self.doHistogram = False
self.transit_message("Starting resampling Method")
start_time = time.time()
histPath = ""
if self.doHistogram:
histPath = os.path.join(os.path.dirname(self.output.name), transit_tools.fetch_name(self.output.name)+"_histograms")
if not os.path.isdir(histPath):
os.makedirs(histPath)
#Get orf data
self.transit_message("Getting Data")
if self.diffStrains:
self.transit_message("Multiple annotation files found")
self.transit_message("Mapping ctrl data to {0}, exp data to {1}".format(self.annotation_path, self.annotation_path_exp))
if self.combinedWigParams:
(position, data, filenamesInCombWig) = tnseq_tools.read_combined_wig(self.combinedWigParams['combined_wig'])
conditionsByFile, _, _, _ = tnseq_tools.read_samples_metadata(self.combinedWigParams['samples_metadata'])
conditions = self.wigs_to_conditions(conditionsByFile, filenamesInCombWig)
data, conditions = self.filter_wigs_by_conditions(data, conditions, self.combinedWigParams['conditions'])
data_ctrl = numpy.array([d for i, d in enumerate(data) if conditions[i].lower() == self.combinedWigParams['conditions'][0]])
data_exp = numpy.array([d for i, d in enumerate(data) if conditions[i].lower() == self.combinedWigParams['conditions'][1]])
position_ctrl, position_exp = position, position
else:
(data_ctrl, position_ctrl) = transit_tools.get_validated_data(self.ctrldata, wxobj=self.wxobj)
(data_exp, position_exp) = transit_tools.get_validated_data(self.expdata, wxobj=self.wxobj)
(K_ctrl, N_ctrl) = data_ctrl.shape
(K_exp, N_exp) = data_exp.shape
if not self.diffStrains and (N_ctrl != N_exp):
self.transit_error("Error: Ctrl and Exp wig files don't have the same number of sites.")
self.transit_error("Make sure all .wig files come from the same strain.")
return
# (data, position) = transit_tools.get_validated_data(self.ctrldata+self.expdata, wxobj=self.wxobj)
self.transit_message("Preprocessing Ctrl data...")
data_ctrl = self.preprocess_data(position_ctrl, data_ctrl)
self.transit_message("Preprocessing Exp data...")
data_exp = self.preprocess_data(position_exp, data_exp)
G_ctrl = tnseq_tools.Genes(self.ctrldata, self.annotation_path, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus, data=data_ctrl, position=position_ctrl)
G_exp = tnseq_tools.Genes(self.expdata, self.annotation_path_exp, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus, data=data_exp, position=position_exp)
doLibraryResampling = False
# If library string not empty
if self.ctrl_lib_str or self.exp_lib_str:
letters_ctrl = set(self.ctrl_lib_str)
letters_exp = set(self.exp_lib_str)
# Check if using exactly 1 letters; i.e. no different libraries
if len(letters_ctrl) == 1 and letters_exp==1:
pass
# If using more than one letter, then check no differences in set
else:
lib_diff = letters_ctrl ^ letters_exp
# Check that their differences
if not lib_diff:
doLibraryResampling = True
else:
transit_tools.transit_error("Error: Library Strings (Ctrl = %s, Exp = %s) do not use the same letters. Make sure every letter / library is represented in both Control and Experimental Conditions. Proceeding with resampling assuming all datasets belong to the same library." % (self.ctrl_lib_str, self.exp_lib_str))
self.ctrl_lib_str = ""
self.exp_lib_str = ""
(data, qval) = self.run_resampling(G_ctrl, G_exp, doLibraryResampling, histPath)
self.write_output(data, qval, start_time)
self.finish()
self.transit_message("Finished resampling Method")
def Run(self):
self.transit_message("Starting Tn5 gaps method")
start_time = time.time()
self.transit_message("Getting data (May take a while)")
# Combine all wigs
(data, position) = transit_tools.get_validated_data(self.ctrldata,
wxobj=self.wxobj)
combined = tnseq_tools.combine_replicates(data, method=self.replicates)
combined[combined < self.minread] = 0
counts = combined
counts[counts > 0] = 1
num_sites = counts.size
genes_obj = tnseq_tools.Genes(self.ctrldata,
self.annotation_path,
ignoreCodon=self.ignoreCodon,
nterm=self.NTerminus,
cterm=self.CTerminus,
data=data,
position=position)
pins = numpy.mean(counts)
pnon = 1.0 - pins
# Calculate stats of runs
exprunmax = tnseq_tools.ExpectedRuns(num_sites, pnon)
varrun = tnseq_tools.VarR(num_sites, pnon)
stddevrun = math.sqrt(varrun)
exp_cutoff = exprunmax + 2 * stddevrun
# Get the runs
self.transit_message("Getting non-insertion runs in genome")
run_arr = tnseq_tools.runs_w_info(counts)
pos_hash = transit_tools.get_pos_hash(self.annotation_path)
# Finally, calculate the results
self.transit_message("Running Tn5 gaps method")
results_per_gene = {}
for gene in genes_obj.genes:
results_per_gene[gene.orf] = [
gene.orf, gene.name, gene.desc, gene.k, gene.n, gene.r, 0, 0, 1
]
N = len(run_arr)
count = 0
accum = 0
self.progress_range(N)
for run in run_arr:
accum += run['length']
count += 1
genes = tnseq_tools.get_genes_in_range(pos_hash, run['start'],
run['end'])
for gene_orf in genes:
gene = genes_obj[gene_orf]
inter_sz = self.intersect_size([run['start'], run['end']],
[gene.start, gene.end]) + 1
percent_overlap = self.calc_overlap([run['start'], run['end']],
[gene.start, gene.end])
run_len = run['length']
B = 1.0 / math.log(1.0 / pnon)
u = math.log(num_sites * pins, 1.0 / pnon)
pval = 1.0 - tnseq_tools.GumbelCDF(run['length'], u, B)
curr_val = results_per_gene[gene.orf]
curr_inter_sz = curr_val[6]
curr_len = curr_val[7]
if inter_sz > curr_inter_sz:
results_per_gene[gene.orf] = [
gene.orf, gene.name, gene.desc, gene.k, gene.n, gene.r,
inter_sz, run_len, pval
]
# Update Progress
text = "Running Tn5Gaps method... %1.1f%%" % (100.0 * count / N)
self.progress_update(text, count)
data = list(results_per_gene.values())
exp_run_len = float(accum) / N
min_sig_len = float('inf')
sig_genes_count = 0
pval = [row[-1] for row in data]
padj = stat_tools.BH_fdr_correction(pval)
for i in range(len(data)):
if padj[i] < 0.05:
sig_genes_count += 1
min_sig_len = min(min_sig_len, data[i][-2])
data[i].append(padj[i])
data[i].append('Essential' if padj[i] < 0.05 else 'Non-essential')
#(data[i][0], data[i][1], data[i][2], data[i][3], data[i][4], data[i][5], data[i][6], data[i][7], data[i][8], padj[i], 'Essential' if padj[i] < 0.05 else 'Non-essential')
data.sort(key=lambda l: l[0])
# Output results
self.output.write("#Tn5 Gaps\n")
if self.wxobj:
members = sorted([
attr for attr in dir(self) if not callable(getattr(self, attr))
and not attr.startswith("__")
])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write(
"#GUI with: ctrldata=%s, annotation=%s, output=%s\n" %
(",".join(self.ctrldata).encode('utf-8'),
self.annotation_path.encode('utf-8'),
self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python %s\n" % " ".join(sys.argv))
self.output.write("#Data: %s\n" %
(",".join(self.ctrldata).encode('utf-8')))
self.output.write("#Annotation path: %s\n" %
self.annotation_path.encode('utf-8'))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#Essential gene count: %d\n" % (sig_genes_count))
self.output.write("#Minimum reads: %d\n" % (self.minread))
self.output.write("#Replicate combination method: %s\n" %
(self.replicates))
self.output.write("#Minimum significant run length: %d\n" %
(min_sig_len))
self.output.write("#Expected run length: %1.5f\n" % (exp_run_len))
self.output.write("#Expected max run length: %s\n" % (exprunmax))
self.output.write("#%s\n" % "\t".join(columns))
#self.output.write("#Orf\tName\tDesc\tk\tn\tr\tovr\tlenovr\tpval\tpadj\tcall\n")
for res in data:
self.output.write(
"%s\t%s\t%s\t%s\t%s\t%s\t%d\t%d\t%1.5f\t%1.5f\t%s\n" %
(res[0], res[1], res[2], res[3], res[4], res[5], res[6],
res[7], res[8], res[9], res[10]))
self.output.close()
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="Tn5 Gaps")
self.finish()
self.transit_message("Finished Tn5Gaps Method")
if DO_LIB:
ctrl_lib_str = "ABAB"
exp_lib_str = "AAABBB"
else:
ctrl_lib_str = ""
exp_lib_str = ""
Kctrl = len(ctrldata)
Kexp = len(expdata)
(data, position) = transit_tools.get_validated_data(ctrldata+expdata)
(K,N) = data.shape
(data, factors) = norm_tools.normalize_data(data, "TTR", ctrldata+expdata, annotation)
G = tnseq_tools.Genes(ctrldata + expdata, annotation, data=data, position=position)
gene = G[i]
print("\n\n")
print("#"*100)
print("# (%s) NEW TEST: %s" % (DO_LIB, gene))
print("#"*100)
print("")
ii = numpy.ones(gene.n) == 1
data1 = gene.reads[:Kctrl,ii].flatten()
def Run(self):
self.transit_message("Starting Genetic Interactions Method")
start_time = time.time()
self.output.write("#GI\n")
wiglist = self.ctrldataA + self.ctrldataB + self.expdataA + self.expdataB
Nwig = len(wiglist)
Na1 = len(self.ctrldataA)
Nb1 = len(self.ctrldataB)
Na2 = len(self.expdataA)
Nb2 = len(self.expdataB)
# Get data
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(wiglist,
wxobj=self.wxobj)
# Normalize data if specified
if self.normalization != "nonorm":
self.transit_message("Normalizing using: %s" % self.normalization)
(data,
factors) = norm_tools.normalize_data(data, self.normalization,
wiglist,
self.annotation_path)
# Do LOESS correction if specified
if self.LOESS:
self.transit_message("Performing LOESS Correction")
for j in range(K):
data[j] = stat_tools.loess_correction(position, data[j])
# Get Gene objects for each condition
G_A1 = tnseq_tools.Genes([],
self.annotation_path,
data=data[:Na1],
position=position,
nterm=self.NTerminus,
cterm=self.CTerminus)
G_B1 = tnseq_tools.Genes([],
self.annotation_path,
data=data[Na1:(Na1 + Nb1)],
position=position,
nterm=self.NTerminus,
cterm=self.CTerminus)
G_A2 = tnseq_tools.Genes([],
self.annotation_path,
data=data[(Na1 + Nb1):(Na1 + Nb1 + Na2)],
position=position,
nterm=self.NTerminus,
cterm=self.CTerminus)
G_B2 = tnseq_tools.Genes([],
self.annotation_path,
data=data[(Na1 + Nb1 + Na2):],
position=position,
nterm=self.NTerminus,
cterm=self.CTerminus)
means_list_a1 = []
means_list_b1 = []
means_list_a2 = []
means_list_b2 = []
var_list_a1 = []
var_list_a2 = []
var_list_b1 = []
var_list_b2 = []
# Base priors on empirical observations across genes.
for gene in sorted(G_A1):
if gene.n > 1:
A1_data = G_A1[gene.orf].reads.flatten()
B1_data = G_B1[gene.orf].reads.flatten()
A2_data = G_A2[gene.orf].reads.flatten()
B2_data = G_B2[gene.orf].reads.flatten()
means_list_a1.append(numpy.mean(A1_data))
var_list_a1.append(numpy.var(A1_data))
means_list_b1.append(numpy.mean(B1_data))
var_list_b1.append(numpy.var(B1_data))
means_list_a2.append(numpy.mean(A2_data))
var_list_a2.append(numpy.var(A2_data))
means_list_b2.append(numpy.mean(B2_data))
var_list_b2.append(numpy.var(B2_data))
# Priors
mu0_A1 = scipy.stats.trim_mean(means_list_a1, 0.01)
mu0_B1 = scipy.stats.trim_mean(means_list_b1, 0.01)
mu0_A2 = scipy.stats.trim_mean(means_list_a2, 0.01)
mu0_B2 = scipy.stats.trim_mean(means_list_b2, 0.01)
s20_A1 = scipy.stats.trim_mean(var_list_a1, 0.01)
s20_B1 = scipy.stats.trim_mean(var_list_b1, 0.01)
s20_A2 = scipy.stats.trim_mean(var_list_a2, 0.01)
s20_B2 = scipy.stats.trim_mean(var_list_b2, 0.01)
k0 = 1.0
nu0 = 1.0
data = []
postprob = []
count = 0
N = len(G_A1)
self.progress_range(N)
# Perform actual analysis
for gene in G_A1:
# If there is some data
if gene.n > 0:
A1_data = G_A1[gene.orf].reads.flatten()
B1_data = G_B1[gene.orf].reads.flatten()
A2_data = G_A2[gene.orf].reads.flatten()
B2_data = G_B2[gene.orf].reads.flatten()
# Time-1 Time-2
#
# Strain-A A C
#
# Strain-B B D
try:
muA1_post, varA1_post = stat_tools.sample_trunc_norm_post(
A1_data, self.samples, mu0_A1, s20_A1, k0, nu0)
muB1_post, varB1_post = stat_tools.sample_trunc_norm_post(
B1_data, self.samples, mu0_B1, s20_B1, k0, nu0)
muA2_post, varA2_post = stat_tools.sample_trunc_norm_post(
A2_data, self.samples, mu0_A2, s20_A2, k0, nu0)
muB2_post, varB2_post = stat_tools.sample_trunc_norm_post(
B2_data, self.samples, mu0_B2, s20_B2, k0, nu0)
except Exception as e:
muA1_post = varA1_post = numpy.ones(self.samples)
muB1_post = varB1_post = numpy.ones(self.samples)
muA2_post = varA2_post = numpy.ones(self.samples)
muB2_post = varB2_post = numpy.ones(self.samples)
logFC_A_post = numpy.log2(muA2_post / muA1_post)
logFC_B_post = numpy.log2(muB2_post / muB1_post)
delta_logFC_post = logFC_B_post - logFC_A_post
alpha = 0.05
# Get Bounds of the HDI
l_logFC_A, u_logFC_A = stat_tools.HDI_from_MCMC(
logFC_A_post, 1 - alpha)
l_logFC_B, u_logFC_B = stat_tools.HDI_from_MCMC(
logFC_B_post, 1 - alpha)
l_delta_logFC, u_delta_logFC = stat_tools.HDI_from_MCMC(
delta_logFC_post, 1 - alpha)
mean_logFC_A = numpy.mean(logFC_A_post)
mean_logFC_B = numpy.mean(logFC_B_post)
mean_delta_logFC = numpy.mean(delta_logFC_post)
# Is HDI significantly different than ROPE? (i.e. no overlap)
not_HDI_overlap_bit = l_delta_logFC > self.rope or u_delta_logFC < -self.rope
# Probability of posterior overlaping with ROPE
probROPE = numpy.mean(
numpy.logical_and(delta_logFC_post >= 0.0 - self.rope,
delta_logFC_post <= 0.0 + self.rope))
# If there is no data, assume empty defaults
else:
A1_data = [0, 0]
B1_data = [0, 0]
A2_data = [0, 0]
B2_data = [0, 0]
muA1_post = varA1_post = numpy.ones(self.samples)
muB1_post = varB1_post = numpy.ones(self.samples)
muA2_post = varA2_post = numpy.ones(self.samples)
muB2_post = varB2_post = numpy.ones(self.samples)
logFC_A_post = numpy.log2(muA2_post / muA1_post)
logFC_B_post = numpy.log2(muB2_post / muB1_post)
delta_logFC_post = logFC_B_post - logFC_A_post
mean_logFC_A = 0
mean_logFC_B = 0
mean_delta_logFC = 0
l_logFC_A = 0
u_logFC_A = 0
l_logFC_B = 0
u_logFC_B = 0
l_delta_logFC = 0
u_delta_logFC = 0
probROPE = 1.0
if numpy.isnan(l_logFC_A):
l_logFC_A = -10
u_logFC_A = 10
if numpy.isnan(l_logFC_B):
l_logFC_B = -10
u_logFC_B = 10
if numpy.isnan(l_delta_logFC):
l_delta_logFC = -10
u_delta_logFC = 10
postprob.append(probROPE)
data.append((gene.orf, gene.name, gene.n, numpy.mean(muA1_post),
numpy.mean(muA2_post), numpy.mean(muB1_post),
numpy.mean(muB2_post), mean_logFC_A, mean_logFC_B,
mean_delta_logFC, l_delta_logFC, u_delta_logFC,
probROPE, not_HDI_overlap_bit))
text = "Running GI Method... %2.0f%%" % (100.0 * (count + 1) / N)
self.progress_update(text, count)
self.transit_message_inplace("Running Export Method... %1.1f%%" %
(100.0 * count / (N - 1)))
count += 1
# for HDI, maybe I should sort on abs(mean_delta_logFC); however, need to sort by prob to calculate BFDR
probcol = -2 # probROPEs
data.sort(key=lambda x: x[probcol])
sortedprobs = numpy.array([x[probcol] for x in data])
# BFDR method: Newton M.A., Noueiry A., Sarkar D., Ahlquist P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics, 5:155–176.
if self.signif == "BFDR":
sortedprobs = numpy.array(sortedprobs)
#sortedprobs.sort() # why, since already sorted?
bfdr = numpy.cumsum(sortedprobs) / numpy.arange(
1,
len(sortedprobs) + 1)
adjusted_prob = bfdr # should be same order as sorted above by probROPE
adjusted_label = "BFDR"
elif self.signif == "FWER":
fwer = stat_tools.FWER_Bayes(sortedprobs)
#fwer.sort() # should not need this if monotonic
adjusted_prob = fwer
adjusted_label = "FWER"
# If not using adjustment for classification, sort correctly
else:
adjusted_prob = sortedprobs
adjusted_label = "un"
# should I stable-sort by overlap_bit?
# sorted_index = numpy.argsort([d[-1] for d in data])[::-1][:len(data)]
# adjusted_prob = [adjusted_prob[ii] for ii in sorted_index]
# data = [data[ii] for ii in sorted_index]
# Print(output)
if self.wxobj:
members = sorted([
attr for attr in dir(self) if not callable(getattr(self, attr))
and not attr.startswith("__")
])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write(
"#GUI with: norm=%s, samples=%s, includeZeros=%s, output=%s\n"
% (self.normalization, self.samples, self.includeZeros,
self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python3 %s\n" % " ".join(sys.argv))
now = str(datetime.datetime.now())
now = now[:now.rfind('.')]
self.output.write("#Date: " + now + "\n")
#self.output.write("#Runtime: %s s\n" % (time.time() - start_time))
self.output.write("#Control Data-A: %s\n" %
(",".join(self.ctrldataA).encode('utf-8')))
self.output.write("#Control Data-B: %s\n" %
(",".join(self.ctrldataB).encode('utf-8')))
self.output.write("#Experimental Data-A: %s\n" %
(",".join(self.expdataA).encode('utf-8')))
self.output.write("#Experimental Data-B: %s\n" %
(",".join(self.expdataB).encode('utf-8')))
self.output.write("#Annotation path: %s\n" %
(self.annotation_path.encode('utf-8')))
self.output.write("#ROPE=%s, method for significance=%s\n" %
(self.rope, self.signif))
#self.output.write("#%s\n" % "\t".join(columns))
if self.signif == "HDI":
self.output.write(
"#Significant interactions are those genes whose delta-logFC HDI does not overlap the ROPE\n"
)
elif self.signif in "prob BDFR FWER":
self.output.write(
"#Significant interactions are those whose %s-adjusted probability of the delta-logFC falling within ROPE is < 0.05.\n"
% (adjusted_label))
# Write column names (redundant with self.columns)
self.output.write(
"#ORF\tName\tNumber of TA Sites\tMean count (Strain A Condition 1)\tMean count (Strain A Condition 2)\tMean count (Strain B Condition 1)\tMean count (Strain B Condition 2)\tMean logFC (Strain A)\tMean logFC (Strain B) \tMean delta logFC\tLower Bound delta logFC\tUpper Bound delta logFC\tIs HDI outside ROPE?\tProb. of delta-logFC being within ROPE\t%s-Adjusted Probability\tType of Interaction\n"
% adjusted_label)
# Write gene results
for i, row in enumerate(data):
#1 2 3 4 5 6 7 8 9 10 11 12 13 14
orf, name, n, mean_muA1_post, mean_muA2_post, mean_muB1_post, mean_muB2_post, mean_logFC_A, mean_logFC_B, mean_delta_logFC, l_delta_logFC, u_delta_logFC, probROPE, not_HDI_overlap_bit = row
interaction = self.classify_interaction(mean_delta_logFC,
mean_logFC_B, mean_logFC_A)
type_of_interaction = "No Interaction"
if self.signif in "prob BFDR FWER" and adjusted_prob[i] < 0.05:
type_of_interaction = interaction
if self.signif == "HDI" and not_HDI_overlap_bit:
type_of_interaction = interaction
new_row = tuple(
list(row[:-2]) + [
not_HDI_overlap_bit, probROPE, adjusted_prob[i],
type_of_interaction
])
self.output.write(
"%s\t%s\t%d\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%s\t%1.8f\t%1.8f\t%s\n"
% new_row)
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="GI")
self.finish()
self.transit_message("Finished Genetic Interactions Method")
def Run(self):
#if not self.wxobj:
# # Force matplotlib to use good backend for png.
# import matplotlib.pyplot as plt
#elif "matplotlib.pyplot" not in sys.modules:
try:
import matplotlib.pyplot as plt
except:
print "Error: cannot do histograms"
self.doHistogram = False
self.transit_message("Starting resampling Method")
start_time = time.time()
if self.doHistogram:
histPath = os.path.join(
os.path.dirname(self.output.name),
transit_tools.fetch_name(self.output.name) + "_histograms")
if not os.path.isdir(histPath):
os.makedirs(histPath)
else:
histPath = ""
Kctrl = len(self.ctrldata)
Kexp = len(self.expdata)
#Get orf data
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(self.ctrldata +
self.expdata,
wxobj=self.wxobj)
(K, N) = data.shape
if self.normalization != "nonorm":
self.transit_message("Normalizing using: %s" % self.normalization)
(data,
factors) = norm_tools.normalize_data(data, self.normalization,
self.ctrldata + self.expdata,
self.annotation_path)
if self.LOESS:
self.transit_message("Performing LOESS Correction")
for j in range(K):
data[j] = stat_tools.loess_correction(position, data[j])
G = tnseq_tools.Genes(self.ctrldata + self.expdata,
self.annotation_path,
ignoreCodon=self.ignoreCodon,
nterm=self.NTerminus,
cterm=self.CTerminus,
data=data,
position=position)
#G = tnseq_tools.Genes(self.ctrldata+self.expdata, self.annotation_path, norm=self.normalization, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus)
#Resampling
data = []
N = len(G)
count = 0
self.progress_range(N)
for gene in G:
count += 1
if gene.k == 0 or gene.n == 0:
(test_obs, mean1, mean2, log2FC, pval_ltail, pval_utail,
pval_2tail, testlist, data1, data2) = (0, 0, 0, 0, 1.00, 1.00,
1.00, [], [0], [0])
else:
if not self.includeZeros:
ii = numpy.sum(gene.reads, 0) > 0
else:
ii = numpy.ones(gene.n) == 1
data1 = gene.reads[:Kctrl, ii].flatten() + self.pseudocount
data2 = gene.reads[Kctrl:, ii].flatten() + self.pseudocount
(test_obs, mean1, mean2, log2FC, pval_ltail, pval_utail,
pval_2tail, testlist) = stat_tools.resampling(
data1,
data2,
S=self.samples,
testFunc=stat_tools.F_mean_diff_flat,
adaptive=self.adaptive)
if self.doHistogram:
import matplotlib.pyplot as plt
if testlist:
n, bins, patches = plt.hist(testlist,
density=1,
facecolor='c',
alpha=0.75,
bins=100)
else:
n, bins, patches = plt.hist([0, 0],
density=1,
facecolor='c',
alpha=0.75,
bins=100)
plt.xlabel('Delta Mean')
plt.ylabel('Probability')
plt.title('%s - Histogram of Delta Mean' % gene.orf)
plt.axvline(test_obs,
color='r',
linestyle='dashed',
linewidth=3)
plt.grid(True)
genePath = os.path.join(histPath, gene.orf + ".png")
if not os.path.exists(histPath):
os.makedirs(histPath)
plt.savefig(genePath)
plt.clf()
sum1 = numpy.sum(data1)
sum2 = numpy.sum(data2)
data.append([
gene.orf, gene.name, gene.desc, gene.n, mean1, mean2, sum1,
sum2, test_obs, log2FC, pval_2tail
])
# Update progress
text = "Running Resampling Method... %5.1f%%" % (100.0 * count / N)
self.progress_update(text, count)
#
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Performing Benjamini-Hochberg Correction")
data.sort()
qval = stat_tools.BH_fdr_correction([row[-1] for row in data])
self.output.write("#Resampling\n")
if self.wxobj:
members = sorted([
attr for attr in dir(self) if not callable(getattr(self, attr))
and not attr.startswith("__")
])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write(
"#GUI with: norm=%s, samples=%s, pseudocounts=%1.2f, adaptive=%s, histogram=%s, includeZeros=%s, output=%s\n"
% (self.normalization, self.samples, self.pseudocount,
self.adaptive, self.doHistogram, self.includeZeros,
self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python %s\n" % " ".join(sys.argv))
self.output.write("#Control Data: %s\n" %
(",".join(self.ctrldata).encode('utf-8')))
self.output.write("#Experimental Data: %s\n" %
(",".join(self.expdata).encode('utf-8')))
self.output.write("#Annotation path: %s\n" %
(self.annotation_path.encode('utf-8')))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#%s\n" % "\t".join(columns))
for i, row in enumerate(data):
(orf, name, desc, n, mean1, mean2, sum1, sum2, test_obs, log2FC,
pval_2tail) = row
self.output.write(
"%s\t%s\t%s\t%d\t%1.1f\t%1.1f\t%1.2f\t%1.1f\t%1.2f\t%1.1f\t%1.5f\t%1.5f\n"
% (orf, name, desc, n, mean1, mean2, log2FC, sum1, sum2,
test_obs, pval_2tail, qval[i]))
self.output.close()
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="Resampling")
self.finish()
self.transit_message("Finished resampling Method")
def Run(self):
self.transit_message("Starting Mann-Whitney U-test Method")
start_time = time.time()
Kctrl = len(self.ctrldata)
Kexp = len(self.expdata)
#Get orf data
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(self.ctrldata+self.expdata, wxobj=self.wxobj)
(K,N) = data.shape
if self.normalization != "nonorm":
self.transit_message("Normalizing using: %s" % self.normalization)
(data, factors) = norm_tools.normalize_data(data, self.normalization, self.ctrldata+self.expdata, self.annotation_path)
if self.LOESS:
self.transit_message("Performing LOESS Correction")
for j in range(K):
data[j] = stat_tools.loess_correction(position, data[j])
G = tnseq_tools.Genes(self.ctrldata + self.expdata, self.annotation_path, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus, data=data, position=position)
#u-test
data = []
N = len(G)
count = 0
self.progress_range(N)
for gene in G:
count+=1
if gene.k == 0 or gene.n == 0:
(test_obs, mean1, mean2, log2FC, u_stat, pval_2tail) = (0, 0, 0, 0, 0.0, 1.00)
else:
if not self.includeZeros:
ii = numpy.sum(gene.reads,0) > 0
else:
ii = numpy.ones(gene.n) == 1
data1 = gene.reads[:Kctrl,ii].flatten()
data2 = gene.reads[Kctrl:,ii].flatten()
try:
u_stat, pval_2tail = scipy.stats.mannwhitneyu(data1, data2,
alternative="two-sided")
except ValueError as e:
u_stat, pval_2tail = 0.0, 1.00
n1 = len(data1)
n2 = len(data2)
mean1 = 0
if n1 > 0:
mean1 = numpy.mean(data1)
mean2 = 0
if n2 > 0:
mean2 = numpy.mean(data2)
try:
# Only adjust log2FC if one of the means is zero
if mean1 > 0 and mean2 > 0:
log2FC = math.log((mean2)/(mean1),2)
else:
log2FC = math.log((mean2+1.0)/(mean1+1.0),2)
except:
log2FC = 0.0
#["Orf","Name","Desc","Sites","Mean Ctrl","Mean Exp","log2FC", "U-Statistic","p-value","Adj. p-value"]
data.append([gene.orf, gene.name, gene.desc, gene.n, mean1, mean2, log2FC, u_stat, pval_2tail])
# Update Progress
text = "Running Mann-Whitney U-test Method... %1.1f%%" % (100.0*count/N)
self.progress_update(text, count)
#
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Performing Benjamini-Hochberg Correction")
data.sort()
qval = stat_tools.BH_fdr_correction([row[-1] for row in data])
self.output.write("#utest\n")
if self.wxobj:
members = sorted([attr for attr in dir(self) if not callable(getattr(self,attr)) and not attr.startswith("__")])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write("#GUI with: norm=%s, includeZeros=%s, output=%s\n" % (self.normalization, self.includeZeros, self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python %s\n" % " ".join(sys.argv))
self.output.write("#Control Data: %s\n" % (",".join(self.ctrldata).encode('utf-8')))
self.output.write("#Experimental Data: %s\n" % (",".join(self.expdata).encode('utf-8')))
self.output.write("#Annotation path: %s\n" % (self.annotation_path.encode('utf-8')))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#%s\n" % "\t".join(columns))
for i,row in enumerate(data):
(orf, name, desc, n, mean1, mean2, log2FC, u_stat, pval_2tail) = row
self.output.write("%s\t%s\t%s\t%d\t%1.1f\t%1.1f\t%1.2f\t%1.2f\t%1.5f\t%1.5f\n" % (orf, name, desc, n, mean1, mean2, log2FC, u_stat, pval_2tail, qval[i]))
self.output.close()
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="utest")
self.finish()
self.transit_message("Finished Mann-Whitney U-test Method")
def Run(self):
self.transit_message("Starting Genetic Interactions Method")
start_time = time.time()
self.output.write("#GI\n")
wiglist = self.ctrldataA + self.expdataA + self.ctrldataB + self.expdataB
Nwig = len(wiglist)
Na1 = len(self.ctrldataA)
Nb1 = len(self.expdataA)
Na2 = len(self.ctrldataB)
Nb2 = len(self.expdataB)
# Get data
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(wiglist, wxobj=self.wxobj)
# Normalize data if specified
if self.normalization != "nonorm":
self.transit_message("Normalizing using: %s" % self.normalization)
(data, factors) = norm_tools.normalize_data(data, self.normalization, wiglist, self.annotation_path)
# Do LOESS correction if specified
if self.LOESS:
self.transit_message("Performing LOESS Correction")
for j in range(K):
data[j] = stat_tools.loess_correction(position, data[j])
# Get Gene objects for each condition
G_A1 = tnseq_tools.Genes([], self.annotation_path, data=data[:Na1], position=position,nterm=self.NTerminus,cterm=self.CTerminus)
G_B1 = tnseq_tools.Genes([], self.annotation_path, data=data[Na1:(Na1+Nb1)], position=position,nterm=self.NTerminus,cterm=self.CTerminus)
G_A2 = tnseq_tools.Genes([], self.annotation_path, data=data[(Na1+Nb1):(Na1+Nb1+Na2)], position=position,nterm=self.NTerminus,cterm=self.CTerminus)
G_B2 = tnseq_tools.Genes([], self.annotation_path, data=data[(Na1+Nb1+Na2):], position=position,nterm=self.NTerminus,cterm=self.CTerminus)
means_list_a1 = []
means_list_b1 = []
means_list_a2 = []
means_list_b2 = []
var_list_a1 = []
var_list_a2 = []
var_list_b1 = []
var_list_b2 = []
# Base priors on empirical observations accross genes.
for gene in sorted(G_A1):
if gene.n > 1:
A1_data = G_A1[gene.orf].reads.flatten()
B1_data = G_B1[gene.orf].reads.flatten()
A2_data = G_A2[gene.orf].reads.flatten()
B2_data = G_B2[gene.orf].reads.flatten()
means_list_a1.append(numpy.mean(A1_data))
var_list_a1.append(numpy.var(A1_data))
means_list_b1.append(numpy.mean(B1_data))
var_list_b1.append(numpy.var(B1_data))
means_list_a2.append(numpy.mean(A2_data))
var_list_a2.append(numpy.var(A2_data))
means_list_b2.append(numpy.mean(B2_data))
var_list_b2.append(numpy.var(B2_data))
# Priors
mu0_A1 = scipy.stats.trim_mean(means_list_a1, 0.01)
mu0_B1 = scipy.stats.trim_mean(means_list_b1, 0.01)
mu0_A2 = scipy.stats.trim_mean(means_list_a2, 0.01)
mu0_B2 = scipy.stats.trim_mean(means_list_b2, 0.01)
s20_A1 = scipy.stats.trim_mean(var_list_a1, 0.01)
s20_B1 = scipy.stats.trim_mean(var_list_b1, 0.01)
s20_A2 = scipy.stats.trim_mean(var_list_a2, 0.01)
s20_B2 = scipy.stats.trim_mean(var_list_b2, 0.01)
k0=1.0
nu0=1.0
data = []
postprob = []
count = 0
N = len(G_A1)
self.progress_range(N)
# Perform actual analysis
for gene in G_A1:
# If there is some data
if gene.n > 0:
A1_data = G_A1[gene.orf].reads.flatten()
B1_data = G_B1[gene.orf].reads.flatten()
A2_data = G_A2[gene.orf].reads.flatten()
B2_data = G_B2[gene.orf].reads.flatten()
# Time-1 Time-2
#
# Strain-A A C
#
# Strain-B B D
try:
muA1_post, varA1_post = stat_tools.sample_trunc_norm_post(A1_data, self.samples,
mu0_A1, s20_A1, k0, nu0)
muB1_post, varB1_post = stat_tools.sample_trunc_norm_post(B1_data, self.samples,
mu0_B1, s20_B1, k0, nu0)
muA2_post, varA2_post = stat_tools.sample_trunc_norm_post(A2_data, self.samples,
mu0_A2, s20_A2, k0, nu0)
muB2_post, varB2_post = stat_tools.sample_trunc_norm_post(B2_data, self.samples,
mu0_B2, s20_B2, k0, nu0)
except Exception as e:
muA1_post = varA1_post = numpy.ones(self.samples)
muB1_post = varB1_post = numpy.ones(self.samples)
muA2_post = varA2_post = numpy.ones(self.samples)
muB2_post = varB2_post = numpy.ones(self.samples)
logFC_A_post = numpy.log2(muA2_post/muA1_post)
logFC_B_post = numpy.log2(muB2_post/muB1_post)
delta_logFC_post = logFC_B_post - logFC_A_post
alpha = 0.05
# Get Bounds of the HDI
l_logFC_A, u_logFC_A = stat_tools.HDI_from_MCMC(logFC_A_post, 1-alpha)
l_logFC_B, u_logFC_B = stat_tools.HDI_from_MCMC(logFC_B_post, 1-alpha)
l_delta_logFC, u_delta_logFC = stat_tools.HDI_from_MCMC(delta_logFC_post, 1-alpha)
mean_logFC_A = numpy.mean(logFC_A_post)
mean_logFC_B = numpy.mean(logFC_B_post)
mean_delta_logFC = numpy.mean(delta_logFC_post)
# Is HDI significantly different than ROPE?
not_HDI_overlap_bit = l_delta_logFC > self.rope or u_delta_logFC < -self.rope
# Probability of posterior overlaping with ROPE
probROPE = numpy.mean(numpy.logical_and(delta_logFC_post>=0.0-self.rope, delta_logFC_post<=0.0+self.rope))
# If there is no data, assume empty defaults
else:
A1_data = [0,0]
B1_data = [0,0]
A2_data = [0,0]
B2_data = [0,0]
muA1_post = varA1_post = numpy.ones(self.samples)
muB1_post = varB1_post = numpy.ones(self.samples)
muA2_post = varA2_post = numpy.ones(self.samples)
muB2_post = varB2_post = numpy.ones(self.samples)
logFC_A_post = numpy.log2(muA2_post/muA1_post)
logFC_B_post = numpy.log2(muB2_post/muB1_post)
delta_logFC_post = logFC_B_post - logFC_A_post
mean_logFC_A = 0
mean_logFC_B = 0
mean_delta_logFC = 0
l_logFC_A = 0
u_logFC_A = 0
l_logFC_B = 0
u_logFC_B = 0
l_delta_logFC = 0
u_delta_logFC = 0
probROPE = 1.0
if numpy.isnan(l_logFC_A):
l_logFC_A = -10
u_logFC_A = 10
if numpy.isnan(l_logFC_B):
l_logFC_B = -10
u_logFC_B = 10
if numpy.isnan(l_delta_logFC):
l_delta_logFC = -10
u_delta_logFC = 10
postprob.append(probROPE)
data.append((gene.orf, gene.name, gene.n, numpy.mean(muA1_post), numpy.mean(muA2_post), numpy.mean(muB1_post), numpy.mean(muB2_post), mean_logFC_A, mean_logFC_B, mean_delta_logFC, l_delta_logFC, u_delta_logFC, probROPE, not_HDI_overlap_bit))
text = "Running GI Method... %2.0f%%" % (100.0*(count+1)/N)
self.progress_update(text, count)
self.transit_message_inplace("Running Export Method... %1.1f%%" % (100.0*count/(N-1)))
count+=1
data.sort(key=lambda x: x[-2])
if self.doBFDR or not self.doFWER:
postprob = numpy.array(postprob)
postprob.sort()
bfdr = numpy.cumsum(postprob)/numpy.arange(1, len(postprob)+1)
adjusted_prob = bfdr
adjusted_label = "BFDR"
elif doFWER:
fwer = FWER_Bayes(postprob)
fwer.sort()
adjusted_prob = fwer
adjusted_label = "FWER"
# If not using adjustment for classification, sort correctly
if not self.doBFDR and not self.doFWER:
sorted_index = numpy.argsort([d[-1] for d in data])[::-1][:len(data)]
adjusted_prob = [adjusted_prob[ii] for ii in sorted_index]
data = [data[ii] for ii in sorted_index]
# Print output
if self.wxobj:
members = sorted([attr for attr in dir(self) if not callable(getattr(self,attr)) and not attr.startswith("__")])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write("#GUI with: norm=%s, samples=%s, includeZeros=%s, output=%s\n" % (self.normalization, self.samples, self.includeZeros, self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python %s\n" % " ".join(sys.argv))
self.output.write("#Control Data-A: %s\n" % (",".join(self.ctrldataA).encode('utf-8')))
self.output.write("#Control Data-B: %s\n" % (",".join(self.ctrldataB).encode('utf-8')))
self.output.write("#Experimental Data-A: %s\n" % (",".join(self.expdataA).encode('utf-8')))
self.output.write("#Experimental Data-B: %s\n" % (",".join(self.expdataB).encode('utf-8')))
self.output.write("#Annotation path: %s\n" % (self.annotation_path.encode('utf-8')))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#%s\n" % "\t".join(columns))
if self.doBFDR or self.doFWER:
self.output.write("# Significant interactions are those whose adjusted probability of the delta-logFC falling within ROPE is < 0.05 (Adjusted using %s)\n" % (adjusted_label))
else:
self.output.write("# Significant interactions are those genes whose delta-logFC HDI does not overlap the ROPE\n")
self.output.write("#\n")
# Write column names
self.output.write("#ORF\tName\tNumber of TA Sites\tMean count (Strain A Time 1)\tMean count (Strain A Time 2)\tMean count (Strain B Time 1)\tMean count (Strain B Time 2)\tMean logFC (Strain A)\tMean logFC (Strain B) \tMean delta logFC\tLower Bound delta logFC\tUpper Bound delta logFC\tProb. of delta-logFC being within ROPE\tAdjusted Probability (%s)\tIs HDI outside ROPE?\tType of Interaction\n" % adjusted_label)
# Write gene results
for i,row in enumerate(data):
#1 2 3 4 5 6 7 8 9 10 11 12 13 14
orf, name, n, mean_muA1_post, mean_muA2_post, mean_muB1_post, mean_muB2_post, mean_logFC_A, mean_logFC_B, mean_delta_logFC, l_delta_logFC, u_delta_logFC, probROPE, not_HDI_overlap_bit = row
type_of_interaction = "No Interaction"
if ((self.doBFDR or self.doFWER) and adjusted_prob[i] < 0.05):
type_of_interaction = self.classify_interaction(mean_delta_logFC, mean_logFC_B, mean_logFC_A)
elif not (self.doBFDR or self.doFWER) and not_HDI_overlap_bit:
type_of_interaction = self.classify_interaction(mean_delta_logFC, mean_logFC_B, mean_logFC_A)
new_row = tuple(list(row[:-1])+[adjusted_prob[i], not_HDI_overlap_bit, type_of_interaction])
self.output.write("%s\t%s\t%d\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.2f\t%1.8f\t%1.8f\t%s\t%s\n" % new_row)
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="GI")
self.finish()
self.transit_message("Finished Genetic Interactions Method")
def Run(self):
self.status_message("Starting Gumbel Method")
#Set Default parameter values
w1 = 0.15
w0 = 1.0 - w1
ALPHA = 1
BETA = 1
ALPHA_w = 600
BETA_w = 3400
mu_c = 0
acctot = 0.0
phi_start = 0.3
sigma_c = 0.01
start_time = time.time()
self.progress_range(self.samples + self.burnin)
#Get orf data
self.transit_message("Reading Annotation")
#Validate data has empty sites
#(status, genome) = transit_tools.validate_wig_format(self.ctrldata, wxobj=self.wxobj)
#if status <2: tn_used = "himar1"
#else: tn_used = "tn5"
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(self.ctrldata,
wxobj=self.wxobj)
(K, N) = data.shape
if self.normalization and self.normalization != "nonorm":
self.transit_message("Normalizing using: %s" % self.normalization)
(data,
factors) = norm_tools.normalize_data(data, self.normalization,
self.ctrldata,
self.annotation_path)
G = tnseq_tools.Genes(self.ctrldata,
self.annotation_path,
minread=self.minread,
reps=self.replicates,
ignoreCodon=self.ignoreCodon,
nterm=self.NTerminus,
cterm=self.CTerminus,
data=data,
position=position)
ii_good = numpy.array(
[self.good_orf(g)
for g in G]) # Gets index of the genes that can be analyzed
K = G.local_insertions()[ii_good]
N = G.local_sites()[ii_good]
R = G.local_runs()[ii_good]
S = G.local_gap_span()[ii_good]
T = G.local_gene_span()[ii_good]
self.transit_message("Doing Regression")
mu_s, temp, sigma_s = stat_tools.regress(
R, S) # Linear regression to estimate mu_s, sigma_s for span data
mu_r, temp, sigma_r = stat_tools.regress(
S, R) # Linear regression to estimate mu_r, sigma_r for run data
N_GENES = len(G)
N_GOOD = sum(ii_good)
self.transit_message("Setting Initial Class")
Z_sample = numpy.zeros((N_GOOD, self.samples))
Z = [self.classify(g.n, g.r, 0.5) for g in G if self.good_orf(g)]
Z_sample[:, 0] = Z
N_ESS = numpy.sum(Z_sample[:, 0] == 1)
phi_sample = numpy.zeros(self.samples) #[]
phi_sample[0] = phi_start
phi_old = phi_start
phi_new = 0.00
SIG = numpy.array([
self.sigmoid(g.s, g.t) *
scipy.stats.norm.pdf(g.r, mu_r * g.s, sigma_r) for g in G
if self.good_orf(g)
])
# idxG,idxN = -1,0
# for i in range(len(G)):
# if G[i].name=="glf": idxG = i
# if ii_good[i]==True: idxN += 1 # could do sum(ii_good[:idxG])
i = 1
count = 0
while i < self.samples:
try:
# PHI
acc = 1.0
phi_new = phi_old + random.gauss(mu_c, sigma_c)
i0 = Z_sample[:, i - 1] == 0
if phi_new > 1 or phi_new <= 0 or (
self.F_non(phi_new, N[i0], R[i0]) -
self.F_non(phi_old, N[i0], R[i0])) < math.log(
random.uniform(0, 1)):
phi_new = phi_old
acc = 0.0
flag = 0
# Z
Z = self.sample_Z(phi_new, w1, N, R, S, T, mu_s, sigma_s, SIG)
# w1
N_ESS = sum(Z == 1)
w1 = scipy.stats.beta.rvs(N_ESS + ALPHA_w,
N_GOOD - N_ESS + BETA_w)
count += 1
acctot += acc
if (count > self.burnin) and (count % self.trim == 0):
phi_sample[i] = phi_new
Z_sample[:, i] = Z
i += 1
except ValueError as e:
self.transit_message("Error: %s" % e)
self.transit_message(
"This is likely to have been caused by poor data (e.g. too sparse)."
)
self.transit_message(
"If the density of the dataset is too low, the Gumbel method will not work."
)
self.transit_message("Quitting.")
return
# print i,phi_new,w1,G[idxG].name,N[idxN],R[idxN],Z[idxN]
phi_old = phi_new
#Update progress
text = "Running Gumbel Method... %5.1f%%" % (
100.0 * (count + 1) / (self.samples + self.burnin))
self.progress_update(text, count)
ZBAR = numpy.apply_along_axis(numpy.mean, 1, Z_sample)
(ess_t, non_t) = stat_tools.bayesian_ess_thresholds(ZBAR)
#Orf k n r s zbar
self.output.write("#Gumbel\n")
if self.wxobj:
members = sorted([
attr for attr in dir(self) if not callable(getattr(self, attr))
and not attr.startswith("__")
])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write(
"#GUI with: ctrldata=%s, annotation=%s, output=%s, samples=%s, minread=%s, trim=%s\n"
% (",".join(self.ctrldata).encode('utf-8'),
self.annotation_path.encode('utf-8'),
self.output.name.encode('utf-8'), self.samples,
self.minread, self.trim))
else:
self.output.write("#Console: python %s\n" % " ".join(sys.argv))
self.output.write("#Data: %s\n" %
(",".join(self.ctrldata).encode('utf-8')))
self.output.write("#Annotation path: %s\n" %
self.annotation_path.encode('utf-8'))
self.output.write("#FDR Corrected thresholds: %f, %f\n" %
(ess_t, non_t))
self.output.write("#MH Acceptance-Rate:\t%2.2f%%\n" %
(100.0 * acctot / count))
self.output.write("#Total Iterations Performed:\t%d\n" % count)
self.output.write("#Sample Size:\t%d\n" % i)
self.output.write("#phi estimate:\t%f\n" % numpy.average(phi_sample))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#%s\n" % "\t".join(columns))
i = 0
data = []
for g in G:
if not self.good_orf(g):
zbar = -1.0
else:
zbar = ZBAR[i]
i += 1
if zbar > ess_t:
call = "E"
elif non_t <= zbar <= ess_t:
call = "U"
elif 0 <= zbar < non_t:
call = "NE"
else:
call = "S"
data.append(
"%s\t%s\t%s\t%d\t%d\t%d\t%d\t%f\t%s\n" %
(g.orf, g.name, g.desc, g.k, g.n, g.r, g.s, zbar, call))
data.sort()
for line in data:
self.output.write(line)
self.output.close()
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="Gumbel")
self.finish()
self.transit_message("Finished Gumbel Method")
def main(args, kwargs, quite=False, jumble=False):
missingArgs = False
if "a1" not in kwargs:
missingArgs = True
error("Missing -a1 argument")
if "a2" not in kwargs:
missingArgs = True
error("Missing -a2 argument")
if "b1" not in kwargs:
missingArgs = True
error("Missing -b1 argument")
if "b2" not in kwargs:
missingArgs = True
error("Missing -b2 argument")
if "pt" not in kwargs:
missingArgs = True
error("Missing -pt argument")
if missingArgs:
usage()
sys.exit()
A_1list = kwargs["a1"].split(",")
A_2list = kwargs["a2"].split(",")
B_1list = kwargs["b1"].split(",")
B_2list = kwargs["b2"].split(",")
annotation = kwargs["pt"]
rope = float(kwargs.get("rope", 0.5))
S = int(kwargs.get("s", 100000))
norm_method = kwargs.get("n", "TTR")
label = kwargs.get("l", "debug")
onlyNZ = kwargs.get("-nz", False)
doBFDR = kwargs.get("-bfdr", False)
doFWER = kwargs.get("-fwer", False)
DEBUG = []
if "debug" in kwargs:
DEBUG = kwargs["debug"].split(",")
wiglist = A_1list + B_1list + A_2list + B_2list
Nwig = len(wiglist)
Na1 = len(A_1list)
Nb1 = len(A_1list)
Na2 = len(B_2list)
Nb2 = len(B_2list)
(data, position) = tnseq_tools.get_data(wiglist)
######### FILTER EMTPY SITES #########
if onlyNZ:
ii_good = numpy.sum(data, 0) > 0
data = data[:, ii_good]
position = position[ii_good]
######################################
(data, factors) = norm_tools.normalize_data(data, norm_method, wiglist,
sys.argv[1])
if jumble:
numpy.random.shuffle(data.flat)
numpy.random.shuffle(data.flat)
G_A1 = tnseq_tools.Genes([],
annotation,
data=data[:Na1],
position=position)
G_B1 = tnseq_tools.Genes([],
annotation,
data=data[Na1:(Na1 + Nb1)],
position=position)
G_A2 = tnseq_tools.Genes([],
annotation,
data=data[(Na1 + Nb1):(Na1 + Nb1 + Na2)],
position=position)
G_B2 = tnseq_tools.Genes([],
annotation,
data=data[(Na1 + Nb1 + Na2):],
position=position)
means_list_a1 = []
means_list_b1 = []
means_list_a2 = []
means_list_b2 = []
var_list_a1 = []
var_list_a2 = []
var_list_b1 = []
var_list_b2 = []
# Base priors on empirical observations accross genes.
for gene in sorted(G_A1):
if gene.n > 1:
A1_data = G_A1[gene.orf].reads.flatten()
B1_data = G_B1[gene.orf].reads.flatten()
A2_data = G_A2[gene.orf].reads.flatten()
B2_data = G_B2[gene.orf].reads.flatten()
means_list_a1.append(numpy.mean(A1_data))
var_list_a1.append(numpy.var(A1_data))
means_list_b1.append(numpy.mean(B1_data))
var_list_b1.append(numpy.var(B1_data))
means_list_a2.append(numpy.mean(A2_data))
var_list_a2.append(numpy.var(A2_data))
means_list_b2.append(numpy.mean(B2_data))
var_list_b2.append(numpy.var(B2_data))
# Priors
mu0_A1 = scipy.stats.trim_mean(means_list_a1, 0.01)
mu0_B1 = scipy.stats.trim_mean(means_list_b1, 0.01)
mu0_A2 = scipy.stats.trim_mean(means_list_a2, 0.01)
mu0_B2 = scipy.stats.trim_mean(means_list_b2, 0.01)
s20_A1 = scipy.stats.trim_mean(var_list_a1, 0.01)
s20_B1 = scipy.stats.trim_mean(var_list_b1, 0.01)
s20_A2 = scipy.stats.trim_mean(var_list_a2, 0.01)
s20_B2 = scipy.stats.trim_mean(var_list_b2, 0.01)
k0 = 1.0
nu0 = 1.0
data = []
postprob = []
if not quite:
print "# Created with '%s'. Copyright 2016-2017. Michael A. DeJesus & Thomas R. Ioerger" % (
sys.argv[0])
print "# Version %1.2f; http://saclab.tamu.edu/essentiality/GI" % __version__
print "#"
print "# python %s" % " ".join(sys.argv)
print "# Samples = %d, k0=%1.1f, nu0=%1.1f" % (S, k0, nu0)
print "# Mean Prior: Variance Prior:"
print "# mu0_A1 = %1.2f s20_A1 = %1.1f" % (mu0_A1, s20_A1)
print "# mu0_B1 = %1.2f s20_B1 = %1.1f" % (mu0_B1, s20_B1)
print "# mu0_A2 = %1.2f s20_A2 = %1.1f" % (mu0_A2, s20_A2)
print "# mu0_B2 = %1.2f s20_B2 = %1.1f" % (mu0_B2, s20_B2)
print "# ROPE:", rope
print "# TTR Factors:", ", ".join(
["%1.4f" % x for x in numpy.array(factors).flatten()])
for gene in sorted(G_A1):
if len(DEBUG) > 0:
if gene.orf not in DEBUG: continue
if gene.n > 0:
A1_data = G_A1[gene.orf].reads.flatten()
B1_data = G_B1[gene.orf].reads.flatten()
A2_data = G_A2[gene.orf].reads.flatten()
B2_data = G_B2[gene.orf].reads.flatten()
# Time-1 Time-2
#
# Strain-A A C
#
# Strain-B B D
try:
muA1_post, varA1_post = sample_post(A1_data, S, mu0_A1, s20_A1,
k0, nu0)
muB1_post, varB1_post = sample_post(B1_data, S, mu0_B1, s20_B1,
k0, nu0)
muA2_post, varA2_post = sample_post(A2_data, S, mu0_A2, s20_A2,
k0, nu0)
muB2_post, varB2_post = sample_post(B2_data, S, mu0_B2, s20_B2,
k0, nu0)
except Exception as e:
muA1_post = varA1_post = numpy.ones(S)
muB1_post = varB1_post = numpy.ones(S)
muA2_post = varA2_post = numpy.ones(S)
muB2_post = varB2_post = numpy.ones(S)
logFC_A_post = numpy.log2(muA2_post / muA1_post)
logFC_B_post = numpy.log2(muB2_post / muB1_post)
delta_logFC_post = logFC_B_post - logFC_A_post
alpha = 0.05
# Get Bounds of the HDI
l_logFC_A, u_logFC_A = HDI_from_MCMC(logFC_A_post, 1 - alpha)
l_logFC_B, u_logFC_B = HDI_from_MCMC(logFC_B_post, 1 - alpha)
l_delta_logFC, u_delta_logFC = HDI_from_MCMC(
delta_logFC_post, 1 - alpha)
mean_logFC_A = numpy.mean(logFC_A_post)
mean_logFC_B = numpy.mean(logFC_B_post)
mean_delta_logFC = numpy.mean(delta_logFC_post)
# Is HDI significantly different than ROPE?
not_HDI_overlap_bit = l_delta_logFC > rope or u_delta_logFC < -rope
# Probability of posterior overlaping with ROPE
probROPE = numpy.mean(
numpy.logical_and(delta_logFC_post >= 0.0 - rope,
delta_logFC_post <= 0.0 + rope))
else:
A1_data = [0, 0]
B1_data = [0, 0]
A2_data = [0, 0]
B2_data = [0, 0]
mean_logFC_A = 0
mean_logFC_B = 0
mean_delta_logFC = 0
l_logFC_A = 0
u_logFC_A = 0
l_logFC_B = 0
u_logFC_B = 0
l_delta_logFC = 0
u_delta_logFC = 0
probROPE = 1.0
if numpy.isnan(l_logFC_A):
l_logFC_A = -10
u_logFC_A = 10
if numpy.isnan(l_logFC_B):
l_logFC_B = -10
u_logFC_B = 10
if numpy.isnan(l_delta_logFC):
l_delta_logFC = -10
u_delta_logFC = 10
if DEBUG:
out = open("%s.%s_muA1_post" % (label, gene.orf), "w")
for x in muA1_post:
print >> out, x
out = open("%s.%s_muA2_post" % (label, gene.orf), "w")
for x in muA2_post:
print >> out, x
out = open("%s.%s_logFC_A_post" % (label, gene.orf), "w")
for x in logFC_A_post:
print >> out, x
out = open("%s.%s_muB1_post" % (label, gene.orf), "w")
for x in muB1_post:
print >> out, x
out = open("%s.%s_muB2_post" % (label, gene.orf), "w")
for x in muB2_post:
print >> out, x
out = open("%s.%s_logFC_B_post" % (label, gene.orf), "w")
for x in logFC_A_post:
print >> out, x
out = open("%s.%s_delta_logFC_post" % (label, gene.orf), "w")
for x in delta_logFC_post:
print >> out, x
postprob.append(probROPE)
data.append((gene.orf, gene.name, gene.n, numpy.mean(muA1_post),
numpy.mean(muA2_post), numpy.mean(muB1_post),
numpy.mean(muB2_post), mean_logFC_A, mean_logFC_B,
mean_delta_logFC, l_delta_logFC, u_delta_logFC, probROPE,
not_HDI_overlap_bit))
if doBFDR or not doFWER:
postprob = numpy.array(postprob)
postprob.sort()
bfdr = numpy.cumsum(postprob) / numpy.arange(1, len(postprob) + 1)
adjusted_prob = bfdr
adjusted_label = "BFDR"
if doBFDR:
data.sort(key=lambda x: x[-2])
else:
data.sort(key=lambda x: x[-1], reverse=True)
elif doFWER:
fwer = FWER_Bayes(postprob)
fwer.sort()
adjusted_prob = fwer
adjusted_label = "FWER"
data.sort(key=lambda x: x[-2])
return (data, adjusted_prob, adjusted_label)
def Run(self):
self.transit_message("Starting Example Method")
start_time = time.time()
#Get orf data
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(self.ctrldata,
wxobj=self.wxobj)
(K, N) = data.shape
if self.normalization and self.normalization != "nonorm":
self.transit_message("Normalizing using: %s" % self.normalization)
(data,
factors) = norm_tools.normalize_data(data, self.normalization,
self.ctrldata,
self.annotation_path)
G = tnseq_tools.Genes(self.ctrldata,
self.annotation_path,
minread=1,
reps=self.replicates,
ignoreCodon=self.ignoreCodon,
nterm=self.NTerminus,
cterm=self.CTerminus,
data=data,
position=position)
data = []
N = len(G)
count = 0
self.progress_range(N)
for gene in G:
count += 1
if gene.n == 0:
mean = 0.0
else:
mean = numpy.mean(gene.reads)
if gene.k == 0:
nzmean = 0.0
else:
nzmean = numpy.sum(gene.reads) / float(gene.k)
data.append(
"%s\t%s\t%s\t%s\t%s\t%1.2f\t%1.2f\n" %
(gene.orf, gene.name, gene.desc, gene.k, gene.n, mean, nzmean))
# Update Progress
text = "Running Example Method... %5.1f%%" % (100.0 * count / N)
self.progress_update(text, count)
self.output.write("#Example\n")
if self.wxobj:
members = sorted([
attr for attr in dir(self) if not callable(getattr(self, attr))
and not attr.startswith("__")
])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write(
"#GUI with: ctrldata=%s, annotation=%s, output=%s\n" %
(",".join(self.ctrldata).encode('utf-8'),
self.annotation_path.encode('utf-8'),
self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python3 %s\n" % " ".join(sys.argv))
self.output.write("#Data: %s\n" %
(",".join(self.ctrldata).encode('utf-8')))
self.output.write("#Annotation path: %s\n" %
self.annotation_path.encode('utf-8'))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#%s\n" % "\t".join(columns))
data.sort()
for line in data:
self.output.write(line)
self.output.close()
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="Example")
self.finish()
self.transit_message("Finished Example Method")
