def test_read_and_get(self):
"""test the read and get functionality of track class"""
track = Track(self.chunk.chrom, self.chunk.start, self.chunk.end)
track.read_track("example/example.Scores.bedgraph.gz")
val = 1.35994655714
self.assertTrue(abs(val - track.get(pos=706661)) < 0.001)
class NFRChunk(Chunk):
"""Class for storing and determining collection of nfr positions
"""
def __init__(self, chunk):
self.start = chunk.start
self.end = chunk.end
self.chrom = chunk.chrom
self.nfrs = []
def initialize(self, parameters):
self.params = parameters
def getOcc(self):
"""gets occupancy track-- reads in from bedgraph"""
self.occ = Track(self.chrom, self.start, self.end, "Occupancy")
self.occ.read_track(self.params.occ_track)
#lower_file = self.params.occ_track[:-11] + 'lower_bound.bedgraph.gz'
#self.occ_lower = Track(self.chrom,self.start,self.end,"Occupancy")
#self.occ_lower.read_track(lower_file)
upper_file = self.params.occ_track[:-11] + 'upper_bound.bedgraph.gz'
self.occ_upper = Track(self.chrom, self.start, self.end, "Occupancy")
self.occ_upper.read_track(upper_file)
def getIns(self):
"""gets insertion track-- reads in from bedgraph"""
if self.params.ins_track is None:
self.ins = InsertionTrack(self.chrom, self.start, self.end)
self.ins.calculateInsertions(self.params.bam)
else:
self.ins = Track(self.chrom, self.start, self.end, "Insertion")
self.ins.read_track(self.params.ins_track)
def getBias(self):
"""get bias"""
self.bias = InsertionBiasTrack(self.chrom,
self.start,
self.end,
log=True)
if self.params.fasta is not None:
self.bias.computeBias(self.params.fasta, self.params.chrs,
self.params.pwm)
def findNFRs(self):
"""find NFR regions"""
region = np.ones(self.length())
tbx = pysam.TabixFile(self.params.calls)
nucs = []
if self.chrom in tbx.contigs:
for row in tbx.fetch(self.chrom,
self.start,
self.end,
parser=pysam.asTuple()):
nucs.append(int(row[1]))
for j in xrange(1, len(nucs)):
left = nucs[j - 1] + 73
right = nucs[j] - 72
if right <= left:
continue
candidate = NFR(left, right, self)
if candidate.min_upper < self.params.max_occ_upper and candidate.occ < self.params.max_occ:
self.nfrs.append(candidate)
def process(self, params):
"""wrapper to carry out all methods needed to call nucleosomes and nfrs"""
self.initialize(params)
self.getOcc()
self.getIns()
self.getBias()
self.findNFRs()
def removeData(self):
"""remove data from chunk-- deletes all attributes"""
names = self.__dict__.keys()
for name in names:
delattr(self, name)
class NucChunk(Chunk):
"""Class for storing and determining collection of nucleosome positions
"""
def __init__(self, chunk):
self.start = chunk.start
self.end = chunk.end
self.chrom = chunk.chrom
def initialize(self, parameters):
self.params = parameters
def getFragmentMat(self):
self.mat = FragmentMat2D(
self.chrom,
self.start - max(self.params.window, self.params.upper // 2 + 1),
self.end + max(self.params.window, self.params.upper // 2 + 1),
0,
self.params.upper,
atac=self.params.atac)
self.mat.makeFragmentMat(self.params.bam)
def makeBiasMat(self):
self.bias_mat = BiasMat2D(self.chrom, self.start - self.params.window,
self.end + self.params.window, 0,
self.params.upper)
bias_track = InsertionBiasTrack(
self.chrom,
self.start - self.params.window - self.params.upper // 2,
self.end + self.params.window + self.params.upper // 2 + 1,
log=True)
if self.params.fasta is not None:
bias_track.computeBias(self.params.fasta, self.params.chrs,
self.params.pwm)
self.bias_mat.makeBiasMat(bias_track)
self.bias_mat_prenorm = BiasMat2D(self.chrom,
self.start - self.params.window,
self.end + self.params.window, 0,
self.params.upper)
self.bias_mat_prenorm.mat = copy(self.bias_mat.mat)
self.bias_mat.normByInsertDist(self.params.fragmentsizes)
def getNucSignal(self):
"""Gets Nucleosome Signal Track"""
self.nuc_cov = CoverageTrack(self.chrom, self.start, self.end)
self.nuc_cov.calculateCoverage(self.mat, self.params.lower,
self.params.upper, self.params.window)
self.bias = BiasTrack(self.chrom, self.start, self.end)
self.bias.calculateBackgroundSignal(self.bias_mat, self.params.vmat,
self.nuc_cov)
self.nuc_signal = SignalTrack(self.chrom, self.start, self.end)
self.nuc_signal.calculateSignal(self.mat, self.params.vmat)
self.norm_signal = NormSignalTrack(self.chrom, self.start, self.end)
self.norm_signal.calculateNormSignal(self.nuc_signal, self.bias)
def getNFR(self):
"""get number of reads of sub-nucleosomal length"""
self.nfr_cov = CoverageTrack(self.chrom, self.start, self.end)
self.nfr_cov.calculateCoverage(self.mat, 0, self.params.lower,
self.params.window)
def smoothSignal(self):
"""Smooth thenormalized signal track"""
window_len = 6 * self.params.smooth_sd + 1
self.smoothed = Track(self.chrom, self.start, self.end,
"Smooth Signal")
tmp = copy(self.norm_signal.vals)
self.smoothed.assign_track(tmp)
self.smoothed.vals[self.smoothed.vals < 0] = 0
self.smoothed.smooth_track(window_len,
window="gaussian",
sd=self.params.smooth_sd,
mode='same',
norm=True)
def getOcc(self):
"""gets occupancy track-- either reads in from bw handle given, or makes new"""
self.occ = Track(self.chrom, self.start, self.end, "Occupancy")
self.occ.read_track(self.params.occ_track)
lower_file = self.params.occ_track[:-11] + 'lower_bound.bedgraph.gz'
self.occ_lower = Track(self.chrom, self.start, self.end, "Occupancy")
self.occ_lower.read_track(lower_file)
upper_file = self.params.occ_track[:-11] + 'upper_bound.bedgraph.gz'
self.occ_upper = Track(self.chrom, self.start, self.end, "Occupancy")
self.occ_upper.read_track(upper_file)
def findAllNucs(self):
"""Find peaks in data"""
self.nuc_collection = {}
combined = self.norm_signal.vals + self.smoothed.vals
#find peaks in normalized sigal
cands1 = call_peaks(combined,
min_signal=0,
sep=self.params.redundant_sep,
boundary=self.params.nonredundant_sep // 2,
order=self.params.redundant_sep // 2)
for i in cands1:
nuc = Nucleosome(i + self.start, self)
if nuc.nuc_cov > self.params.min_reads:
nuc.getLR(self)
if nuc.lr > self.params.min_lr:
nuc.getZScore(self)
if nuc.z >= self.params.min_z:
nuc.getOcc(self)
self.nuc_collection[i] = nuc
self.sorted_nuc_keys = np.array(sorted(self.nuc_collection.keys()))
self.nonredundant = reduce_peaks(
self.sorted_nuc_keys,
[self.nuc_collection[x].z
for x in self.sorted_nuc_keys], self.params.nonredundant_sep)
self.redundant = np.setdiff1d(self.sorted_nuc_keys, self.nonredundant)
def fit(self):
x = np.linspace(0, self.length() - 1, self.length())
fit = np.zeros(self.length())
for nuc in self.sorted_nuc_keys:
self.nuc_collection[nuc].getFuzz(self)
fit += norm(x, self.nuc_collection[nuc].fuzz**2,
self.nuc_collection[nuc].weight,
self.nuc_collection[nuc].fit_pos)
self.fitted = Track(self.chrom, self.start, self.end,
"Fitted Nucleosome Signal")
self.fitted.assign_track(fit)
def makeInsertionTrack(self):
"""make insertion track for chunk"""
self.ins = self.mat.getIns()
def process(self, params):
"""wrapper to carry out all methods needed to call nucleosomes and nfrs"""
self.initialize(params)
self.getFragmentMat()
self.makeBiasMat()
self.getNucSignal()
self.getNFR()
self.smoothSignal()
if params.occ_track is not None:
self.getOcc()
self.findAllNucs()
self.fit()
self.makeInsertionTrack()
def removeData(self):
"""remove data from chunk-- deletes all attributes"""
names = list(self.__dict__.keys())
for name in names:
delattr(self, name)
def test_read_and_get(self):
"""test the read and get functionality of track class"""
track = Track(self.chunk.chrom, self.chunk.start, self.chunk.end)
track.read_track('example/example.Scores.bedgraph.gz')
val = 1.35994655714
self.assertTrue(abs(val - track.get(pos=706661)) < 0.001)
class NucChunk(Chunk):
"""Class for storing and determining collection of nucleosome positions
"""
def __init__(self, chunk):
self.start = chunk.start
self.end = chunk.end
self.chrom = chunk.chrom
def initialize(self, parameters):
self.params = parameters
def getFragmentMat(self):
self.mat = FragmentMat2D(self.chrom, self.start - max(self.params.window,self.params.upper/2+1),
self.end + max(self.params.window,self.params.upper/2+1), 0, self.params.upper, atac = self.params.atac)
self.mat.makeFragmentMat(self.params.bam)
def makeBiasMat(self):
self.bias_mat = BiasMat2D(self.chrom, self.start - self.params.window,
self.end + self.params.window, 0, self.params.upper)
bias_track = InsertionBiasTrack(self.chrom, self.start - self.params.window - self.params.upper/2,
self.end + self.params.window + self.params.upper/2 + 1, log = True)
if self.params.fasta is not None:
bias_track.computeBias(self.params.fasta, self.params.chrs, self.params.pwm)
self.bias_mat.makeBiasMat(bias_track)
self.bias_mat_prenorm = BiasMat2D(self.chrom, self.start - self.params.window,
self.end + self.params.window, 0, self.params.upper)
self.bias_mat_prenorm.mat = copy(self.bias_mat.mat)
self.bias_mat.normByInsertDist(self.params.fragmentsizes)
def getNucSignal(self):
"""Gets Nucleosome Signal Track"""
self.nuc_cov = CoverageTrack(self.chrom, self.start,
self.end)
self.nuc_cov.calculateCoverage(self.mat, self.params.lower, self.params.upper,
self.params.window)
self.bias = BiasTrack(self.chrom, self.start,
self.end)
self.bias.calculateBackgroundSignal(self.bias_mat, self.params.vmat, self.nuc_cov)
self.nuc_signal = SignalTrack(self.chrom, self.start,
self.end)
self.nuc_signal.calculateSignal(self.mat, self.params.vmat)
self.norm_signal = NormSignalTrack(self.chrom, self.start, self.end)
self.norm_signal.calculateNormSignal(self.nuc_signal,self.bias)
def getNFR(self):
"""get number of reads of sub-nucleosomal length"""
self.nfr_cov = CoverageTrack(self.chrom, self.start, self.end)
self.nfr_cov.calculateCoverage(self.mat, 0, self.params.lower,
self.params.window)
def smoothSignal(self):
"""Smooth thenormalized signal track"""
window_len = 6 * self.params.smooth_sd + 1
self.smoothed = Track(self.chrom,self.start,self.end, "Smooth Signal")
tmp = copy(self.norm_signal.vals)
self.smoothed.assign_track(tmp)
self.smoothed.vals[ self.smoothed.vals < 0] = 0
self.smoothed.smooth_track(window_len, window = "gaussian",
sd = self.params.smooth_sd, mode = 'same',
norm = True)
def getOcc(self):
"""gets occupancy track-- either reads in from bw handle given, or makes new"""
self.occ = Track(self.chrom,self.start,self.end,"Occupancy")
self.occ.read_track(self.params.occ_track)
lower_file = self.params.occ_track[:-11] + 'lower_bound.bedgraph.gz'
self.occ_lower = Track(self.chrom,self.start,self.end,"Occupancy")
self.occ_lower.read_track(lower_file)
upper_file = self.params.occ_track[:-11] + 'upper_bound.bedgraph.gz'
self.occ_upper = Track(self.chrom,self.start,self.end,"Occupancy")
self.occ_upper.read_track(upper_file)
def findAllNucs(self):
"""Find peaks in data"""
self.nuc_collection = {}
combined = self.norm_signal.vals + self.smoothed.vals
#find peaks in normalized sigal
cands1 = call_peaks(combined, min_signal = 0,
sep = self.params.redundant_sep,
boundary = self.params.nonredundant_sep/2, order = self.params.redundant_sep/2)
for i in cands1:
nuc = Nucleosome(i + self.start, self)
if nuc.nuc_cov > self.params.min_reads:
nuc.getLR(self)
if nuc.lr > self.params.min_lr:
nuc.getZScore(self)
if nuc.z >= self.params.min_z:
nuc.getOcc(self)
self.nuc_collection[i] = nuc
self.sorted_nuc_keys = np.array(sorted(self.nuc_collection.keys()))
self.nonredundant = reduce_peaks( self.sorted_nuc_keys,
map(lambda x: self.nuc_collection[x].z, self.sorted_nuc_keys),
self.params.nonredundant_sep)
self.redundant = np.setdiff1d(self.sorted_nuc_keys, self.nonredundant)
def fit(self):
x = np.linspace(0,self.length() -1, self.length())
fit = np.zeros(self.length())
for nuc in self.sorted_nuc_keys:
self.nuc_collection[nuc].getFuzz(self)
fit += norm(x,self.nuc_collection[nuc].fuzz**2, self.nuc_collection[nuc].weight, self.nuc_collection[nuc].fit_pos)
self.fitted = Track(self.chrom, self.start, self.end,
"Fitted Nucleosome Signal")
self.fitted.assign_track(fit)
def makeInsertionTrack(self):
"""make insertion track for chunk"""
self.ins = self.mat.getIns()
def process(self, params):
"""wrapper to carry out all methods needed to call nucleosomes and nfrs"""
self.initialize(params)
self.getFragmentMat()
self.makeBiasMat()
self.getNucSignal()
self.getNFR()
self.smoothSignal()
if params.occ_track is not None:
self.getOcc()
self.findAllNucs()
self.fit()
self.makeInsertionTrack()
def removeData(self):
"""remove data from chunk-- deletes all attributes"""
names = self.__dict__.keys()
for name in names:
delattr(self,name)