def run(self):
if self.debug:
import pdb
pdb.set_trace()
inconsistent_rate_ls = []
for inputFname in self.inputFnameLs:
if os.path.isfile(inputFname):
try:
reader = csv.reader(open(inputFname), delimiter=figureOutDelimiter(inputFname))
header = reader.next()
col_name2index = getColName2IndexFromHeader(header, skipEmptyColumn=True)
inconsistent_rate_index = col_name2index.get("inconsistency")
for row in reader:
inconsistency = float(row[inconsistent_rate_index])
inconsistent_rate_ls.append(inconsistency)
del reader
except:
sys.stderr.write('Except type: %s\n'%repr(sys.exc_info()))
import traceback
traceback.print_exc()
if self.title is None:
title = "histogram of inconsistent rate from %s refs"%(len(inconsistent_rate_ls))
else:
title = self.title
if len(inconsistent_rate_ls)>10:
medianInconsistentRate = numpy.median(inconsistent_rate_ls)
title += " median %.4f"%(medianInconsistentRate)
yh_matplotlib.drawHist(inconsistent_rate_ls, title=title, \
xlabel_1D="Inconsistent Rate", xticks=None, outputFname=self.outputFname, min_no_of_data_points=20, needLog=False, \
dpi=200)
def constructPedigreeGraphFromPlinkIBD(self, inputFname=None, maxDistanceToPOVector=0.04, drawDistribution=False, outputFnamePrefix=None):
"""
2012.8.14
"""
sys.stderr.write("Constructing pedigree-graph out of plink-ibd %s ..."%(inputFname))
DG=nx.DiGraph()
childNodeSet = set()
reader = MatrixFile(inputFname)
reader.constructColName2IndexFromHeader()
monkey1IDIndex = reader.getColIndexGivenColHeader("IID1")
monkey2IDIndex = reader.getColIndexGivenColHeader("IID2")
Z0Index = reader.getColIndexGivenColHeader("Z0")
Z1Index = reader.getColIndexGivenColHeader("Z1")
Z2Index = reader.getColIndexGivenColHeader("Z2")
poVector = numpy.array([0,1,0.0])
counter = 0
real_counter = 0
data_ls = []
for row in reader:
monkey1ID = int(row[monkey1IDIndex]) #turn it into integer so could compare age
monkey2ID = int(row[monkey2IDIndex])
Z0 = float(row[Z0Index])
Z1 = float(row[Z1Index])
Z2 = float(row[Z2Index])
ZVector = numpy.array([Z0, Z1, Z2])
dist = numpy.linalg.norm(poVector-ZVector)
if drawDistribution and outputFnamePrefix:
data_ls.append(dist)
if dist<=maxDistanceToPOVector:
if monkey1ID>monkey2ID:
childID = monkey1ID
parentID = monkey2ID
else:
childID = monkey2ID
parentID = monkey1ID
DG.add_edge(parentID, childID, weight=dist)
childNodeSet.add(childID)
real_counter += 1
counter += 1
del reader
sys.stderr.write("%s out of %s lines become PO pairs. %s children, %s nodes. %s edges. %s connected components.\n"%(\
real_counter, counter, len(childNodeSet), DG.number_of_nodes(), DG.number_of_edges(), \
nx.number_connected_components(DG.to_undirected())))
if drawDistribution and outputFnamePrefix:
outputFname = '%s_IBDVector2POVectorDist_hist.png'%(outputFnamePrefix)
yh_matplotlib.drawHist(data_ls, title='', \
xlabel_1D="dist(ZVector,POVector)", xticks=None, \
outputFname=outputFname, min_no_of_data_points=10, \
needLog=True, \
dpi=200, min_no_of_bins=25)
return PassingData(DG=DG, childNodeSet=childNodeSet)
def drawKinshipIBDDeltaVectorHistogram(self, kinshipIBDDeltaData=None, row_id=None, outputFnamePrefix=None): """ 2012.8.22 """ vector = kinshipIBDDeltaData.getRowVectorGivenRowID(row_id=row_id) if vector is not None: data_ls = [] for i in xrange(len(vector)): if vector.mask[i]==False: data_ls.append(vector[i]) if len(data_ls)>10: outputFname = '%s_monkey_%s_kinship_ibd_hist.png'%(outputFnamePrefix, row_id) yh_matplotlib.drawHist(data_ls, title='', \ xlabel_1D="%s kinship-ibd"%(row_id), xticks=None, \ outputFname=outputFname, min_no_of_data_points=10, \ needLog=True, \ dpi=200, min_no_of_bins=25)
def drawQualityData(self, qualityDataStructure, outputFnamePrefix, sequence_id=''):
"""
2011-8-15
"""
sys.stderr.write("Making plots on quality data ...")
yh_matplotlib.drawHist(qualityDataStructure.quality_ls, title='histogram of phredScore from %s'%(sequence_id), xlabel_1D=None, \
outputFname='%s_qualityHist.png'%(outputFnamePrefix), \
min_no_of_data_points=50, needLog=False, dpi=200)
yh_matplotlib.drawBoxPlot(qualityDataStructure.quality_ls_per_position, title='quality box plot from %s'%(sequence_id), \
xlabel_1D='base position in read', xticks=None, outputFname='%s_quality_per_position.png'%(outputFnamePrefix), \
dpi=200)
no_of_bases_per_position = qualityDataStructure.no_of_bases_per_position
readLength = len(no_of_bases_per_position)
yh_matplotlib.drawBarChart(range(1, readLength+1), no_of_bases_per_position, title='no of base calls from %s'%(sequence_id),\
xlabel_1D='base position in read', xticks=None, outputFname='%s_no_of_bases_per_position.png'%(outputFnamePrefix), \
bottom=0, needLog=False, dpi=200)
diNuc2count = qualityDataStructure.diNuc2count
diNuc2quality_ls = qualityDataStructure.diNuc2quality_ls
diNuc_key_ls = diNuc2count.keys()
diNuc_key_ls.sort()
diNuc_count_ls = []
diNuc_quality_ls_ls = []
for diNuc in diNuc_key_ls:
diNuc_count_ls.append(diNuc2count.get(diNuc))
diNuc_quality_ls_ls.append(diNuc2quality_ls.get(diNuc))
yh_matplotlib.drawBarChart(range(1, len(diNuc_count_ls)+1), diNuc_count_ls, title='di-nucleotide counts from %s'%(sequence_id),\
xlabel_1D=None, xticks=diNuc_key_ls, outputFname='%s_diNuc_count.png'%(outputFnamePrefix), \
bottom=0, needLog=False, dpi=200)
yh_matplotlib.drawBoxPlot(diNuc_quality_ls_ls, title='di-Nucleotide quality box plot from %s'%(sequence_id), \
xlabel_1D=None, xticks=diNuc_key_ls, outputFname='%s_diNuc_quality.png'%(outputFnamePrefix), \
dpi=200)
sys.stderr.write("Done.\n")
def drawBridgeChromosomalLengthHist(self, bridge_ls=None):
"""
2011-4-18
"""
no_of_bridges = len(bridge_ls)
sys.stderr.write(
"Drawing histogram of chromosomal length for %s bridges ... \n" %
(no_of_bridges))
bridge_chr_length_ls = []
no_of_loci_per_bridge_ls = []
for i in xrange(no_of_bridges):
bridge = bridge_ls[i]
bridge_chr_length_ls.append(bridge[3])
no_of_loci_per_bridge_ls.append(bridge[2])
yh_matplotlib.drawHist(bridge_chr_length_ls, title='Histogram of bridge chromosomal length', \
xlabel_1D='chromosomal length',\
outputFname='/tmp/chromosomal_length_hist.png', min_no_of_data_points=50, needLog=True)
yh_matplotlib.drawHist(no_of_loci_per_bridge_ls, title='Histogram of no-of-loci per bridge', \
xlabel_1D='no-of-loci',\
outputFname='/tmp/no_of_loci_hist.png', min_no_of_data_points=50, needLog=True)
sys.stderr.write("Done.\n")
def run(self):
"""
"""
if self.debug:
import pdb
pdb.set_trace()
#if self.monkeyCoverageFname and os.path.isfile(self.monkeyCoverageFname):
# monkey_id2coverage = cls.getMonkeyID2Coverage(self.monkeyCoverageFname)
#else:
db_vervet = self.db_vervet
sequencedMonkeyIDSet = self.readInSequencedMonkeys(db_vervet, countryIDList=self.sequencedMonkeyCountryIDList)
if self.inputFname:
preChosenMonkeyIDSet = self.readInChosenOnes(self.inputFname)
else:
preChosenMonkeyIDSet = set()
preChosenMonkeyIDSet |= sequencedMonkeyIDSet
#monkey with latitudes/longitudes
monkeyID2Info = self.readInMonkeysFromDB(db_vervet, countryIDList=self.newSampleMonkeyCountryIDList,\
maxLongitude=self.maxLongitude, addOnlyVWPMonkeys=self.addOnlyVWPMonkeys)
#allMonkeyID2Info is for output purpose
allMonkeyID2Info = self.readInMonkeysFromDB(db_vervet, countryIDList=self.sequencedMonkeyCountryIDList,\
maxLongitude=None)
graph = self.constructNeighborGraph(monkeyID2Info, maxDist=self.maxDist)
allMonkeyGraph = self.constructNeighborGraph(allMonkeyID2Info, maxDist=self.maxDist)
"""
#draw it and check how many monkeys have degree=1
pos=nx.graphviz_layout(graph, prog="neato")
#nx.draw_shell(graph)
nx.draw(graph, pos, with_labels=True
)
#node_size=40,
pylab.savefig('%s_graphNeatoLayout.png'%(self.outputFnamePrefix), dpi=150)
"""
shortestDistanceVectorData = self.constructNewMonkeyToChosenSetDistanceVector(graph=graph, preChosenMonkeyIDSet=preChosenMonkeyIDSet, \
minShortestDistance=self.minShortestDistance)
#probabilitySpanRBDict = self.constructNewMonkeyToChosenSetDistanceVector(graph=graph, preChosenMonkeyIDSet=preChosenMonkeyIDSet)
#sampling for 10 times
for i in xrange(1):
finalChosenMonkeyIDDict = self.chooseExtraSamples(graph, preChosenMonkeyIDSet=preChosenMonkeyIDSet, \
noOfMonkeysToChoose=self.noOfMonkeysToChoose, \
shortestDistanceVectorData=shortestDistanceVectorData, \
minShortestDistance=self.minShortestDistance)
self.outputChosenMonkeys(monkeyID2Info=allMonkeyID2Info, chosenMonkeyIDDict=finalChosenMonkeyIDDict, \
outputFname='%s_sample%s_%sMonkeys.tsv'%(self.outputFnamePrefix, i, len(finalChosenMonkeyIDDict)))
distance_ls = self.getPairwiseDistanceWithinGraphOfChosenMonkey(graph=allMonkeyGraph, \
chosenMonkeyIDDict=finalChosenMonkeyIDDict)
yh_matplotlib.drawHist(data_ls=distance_ls, title=None, \
xlabel_1D="pairwise distance within graph", \
xticks=None, outputFname='%s_sample%s_%sMonkeys_pairwise_distance_hist.png'%\
(self.outputFnamePrefix, i, len(finalChosenMonkeyIDDict)), \
min_no_of_data_points=10, needLog=True, \
dpi=200, max_no_of_bins=40)
def run(self):
if self.debug:
import pdb
pdb.set_trace()
no_of_result1_peaks_ls = []
no_of_result2_peaks_ls = []
fraction_of_result1_peaks_in_result2_ls = []
fraction_of_result2_peaks_in_result1_ls = []
no_of_combined_peaks_ls = []
fraction_of_overlap_in_combined_peaks_ls = []
for inputFname in self.inputFnameLs:
reader = csv.reader(open(inputFname),
delimiter=figureOutDelimiter(inputFname))
header = reader.next()
col_name2index = getColName2IndexFromHeader(header,
skipEmptyColumn=True)
no_of_result1_peaks_index = col_name2index.get(
"no_of_result1_peaks")
no_of_result2_peaks_index = col_name2index.get(
"no_of_result2_peaks")
no_of_result1_peaks_in_result2_index = col_name2index.get(
"no_of_result1_peaks_in_result2")
no_of_result2_peaks_in_result1_index = col_name2index.get(
"no_of_result2_peaks_in_result1")
for row in reader:
no_of_result1_peaks = float(row[no_of_result1_peaks_index])
no_of_result2_peaks = float(row[no_of_result2_peaks_index])
no_of_result1_peaks_in_result2 = float(
row[no_of_result1_peaks_in_result2_index])
no_of_result2_peaks_in_result1 = float(
row[no_of_result2_peaks_in_result1_index])
no_of_result1_peaks_ls.append(no_of_result1_peaks)
no_of_result2_peaks_ls.append(no_of_result2_peaks)
fraction_of_result1_peaks_in_result2_ls.append(
no_of_result1_peaks_in_result2 / no_of_result1_peaks)
fraction_of_result2_peaks_in_result1_ls.append(
no_of_result2_peaks_in_result1 / no_of_result2_peaks)
no_of_combined_peaks_ls.append(no_of_result1_peaks +
no_of_result2_peaks)
fraction_of_overlap_in_combined_peaks_ls.append(
(no_of_result1_peaks_in_result2 +
no_of_result2_peaks_in_result1) /
(no_of_result1_peaks + no_of_result2_peaks))
del reader
title = "%s pairs" % (len(fraction_of_result1_peaks_in_result2_ls))
if len(fraction_of_result1_peaks_in_result2_ls) > 10:
medianFraction = numpy.median(
fraction_of_result1_peaks_in_result2_ls)
title += " median %.3f" % (medianFraction)
yh_matplotlib.drawHist(fraction_of_result1_peaks_in_result2_ls, title=title, \
xlabel_1D="fraction of result1 peaks in result2", xticks=None, \
outputFname="%s_hist_of_fraction_of_result1_peaks_in_result2.png"%self.outputFnamePrefix, \
min_no_of_data_points=20, needLog=False, \
dpi=200)
title = "%s pairs" % (len(fraction_of_result2_peaks_in_result1_ls))
if len(fraction_of_result2_peaks_in_result1_ls) > 10:
medianFraction = numpy.median(
fraction_of_result2_peaks_in_result1_ls)
title += " median %.3f" % (medianFraction)
yh_matplotlib.drawHist(fraction_of_result2_peaks_in_result1_ls, title=title, \
xlabel_1D="fraction of result2 peaks in result1", xticks=None, \
outputFname="%s_hist_of_fraction_of_result2_peaks_in_result1.png"%self.outputFnamePrefix, \
min_no_of_data_points=20, needLog=False, \
dpi=200)
title = "%s pairs" % (len(fraction_of_overlap_in_combined_peaks_ls))
if len(fraction_of_overlap_in_combined_peaks_ls) > 10:
medianFraction = numpy.median(
fraction_of_overlap_in_combined_peaks_ls)
title += " median %.3f" % (medianFraction)
yh_matplotlib.drawHist(fraction_of_overlap_in_combined_peaks_ls, title=title, \
xlabel_1D="fraction of recurrent peaks in combined", xticks=None, \
outputFname="%s_hist_of_fraction_of_recurrent_peaks_in_combined.png"%self.outputFnamePrefix, \
min_no_of_data_points=20, needLog=False, \
dpi=200)
title = "%s results" % (len(no_of_result1_peaks_ls))
yh_matplotlib.drawScatter(no_of_result1_peaks_ls, no_of_result2_peaks_ls, \
fig_fname="%s_no_of_peaks_result1_vs_result2.png"%self.outputFnamePrefix, \
title=title, xlabel='No. of peaks in result1', \
ylabel='No. of peaks in result2', dpi=300)
title = "%s results" % (len(no_of_result1_peaks_ls))
yh_matplotlib.drawScatter(no_of_result1_peaks_ls, fraction_of_result1_peaks_in_result2_ls, \
fig_fname="%s_result1_no_of_peak_vs_fraction.png"%self.outputFnamePrefix, \
title=title, xlabel='No. of peaks in result1', \
ylabel='Fraction found in result2', dpi=300)
title = "%s results" % (len(no_of_result2_peaks_ls))
yh_matplotlib.drawScatter(no_of_result2_peaks_ls, fraction_of_result2_peaks_in_result1_ls, \
fig_fname="%s_result2_no_of_peak_vs_fraction.png"%self.outputFnamePrefix, \
title=title, xlabel='No. of peaks in result2', \
ylabel='Fraction found in result1', dpi=300)
title = "%s pairs" % (len(fraction_of_result1_peaks_in_result2_ls))
yh_matplotlib.drawScatter(fraction_of_result1_peaks_in_result2_ls, fraction_of_result2_peaks_in_result1_ls, \
fig_fname="%s_1_fraction_in2_vs_2_fraction_in1.png"%self.outputFnamePrefix, \
title=title, xlabel='result1 fraction found in result2', \
ylabel='result2 fraction found in result1', dpi=300)
title = "%s pairs" % (len(no_of_combined_peaks_ls))
yh_matplotlib.drawScatter(no_of_combined_peaks_ls, fraction_of_overlap_in_combined_peaks_ls, \
fig_fname="%s_combined_no_of_peak_vs_fraction.png"%self.outputFnamePrefix, \
title=title, xlabel='No. of peaks combined', \
ylabel='Fraction recurrent', dpi=300)
def run(self):
if self.debug:
import pdb
pdb.set_trace()
#without commenting out db_vervet connection code. schema "genome" wont' be default path.
db_genome = GenomeDB.GenomeDatabase(drivername=self.drivername, username=self.db_user,
password=self.db_passwd, hostname=self.hostname, database=self.dbname, schema="genome")
db_genome.setup(create_tables=False)
#chrOrder=2 means chromosomes are not ordered alphabetically but by their sizes (descendingly)
oneGenomeData = db_genome.getOneGenomeData(tax_id=self.tax_id, chr_gap=0, chrOrder=self.chrOrder, \
sequence_type_id=self.sequence_type_id)
chr2size = db_genome.getTopNumberOfChomosomes(contigMaxRankBySize=80000, contigMinRankBySize=1, tax_id=self.tax_id, \
sequence_type_id=self.sequence_type_id)
self.chr_id2cumu_start = oneGenomeData.chr_id2cumu_start
"""
size_chr_id_ls = [(value, key) for key, value in chr2size.iteritems()]
size_chr_id_ls.sort()
size_chr_id_ls.reverse()
"""
sys.stderr.write("Reading in data ...")
for inputFname in self.inputFnameLs:
if not os.path.isfile(inputFname):
continue
self.vcftoolsOutputStatFileWalker(inputFname, processFunc=None, chrColumnHeader=self.chrColumnHeader,\
minChrLength=self.minChrLength, chrLengthColumnHeader=self.chrLengthColumnHeader,\
xColumnHeader=self.xColumnHeader, valueForNonPositiveYValue=self.valueForNonPositiveYValue)
sys.stderr.write("Done.\n")
pylab.clf()
yh_matplotlib.setPlotDimension(left=0.025, right=0.985, bottom=0.1, top=0.9)
fig = pylab.figure(figsize=(30,2))
#ax = pylab.axes()
ax = fig.gca()
max_y = None
min_y = None
value_ls = []
for chromosome in oneGenomeData.chr_id_ls:
xy_ls = self.chr2xy_ls.get(chromosome)
if xy_ls:
if max_y is None:
max_y = max(xy_ls[1])
else:
max_y = max(max_y, max(xy_ls[1]))
if min_y is None:
min_y = min(xy_ls[1])
else:
min_y = min(min_y, min(xy_ls[1]))
ax.plot(xy_ls[0], xy_ls[1], '.', markeredgewidth=0, markersize=4, alpha=0.8)
value_ls += xy_ls[1]
#separate each chromosome
#for chromosome in chr_ls[:-1]:
# print chromosome
# ax.axvline(chr_id2cumu_size[chromosome], linestyle='--', color='k', linewidth=0.8)
#draw the bonferroni line
#bonferroni_value = -math.log10(0.01/len(genome_wide_result.data_obj_ls))
#ax.axhline(bonferroni_value, linestyle='--', color='k', linewidth=0.8)
ax.set_xlabel(self.xColumnPlotLabel)
ax.set_ylabel(self.whichColumnPlotLabel)
#ax.set_xlim([0, chr_id2cumu_size[chr_ls[-1]]])
if self.ylim_type==1:
ylim = ax.get_ylim()
ax.set_ylim([0, ylim[1]])
elif self.ylim_type==2:
if max_y is not None and min_y is not None:
delta = abs(max_y-min_y)/12.0
ax.set_ylim([min_y-delta, max_y+delta])
#outputFnamePrefix = os.path.splitext(self.outputFname)[0]
outputFnamePrefix = self.outputFnamePrefix
pylab.savefig('%s.png'%outputFnamePrefix, dpi=self.figureDPI)
if self.need_svg:
pylab.savefig('%s.svg'%outputFnamePrefix, dpi=self.figureDPI)
outputFname = '%s_hist.png'%(outputFnamePrefix)
yh_matplotlib.drawHist(value_ls, title='', \
xlabel_1D=self.whichColumnPlotLabel, xticks=None, \
outputFname=outputFname, min_no_of_data_points=self.minNoOfTotal, \
needLog=self.logCount, \
dpi=self.figureDPI, min_no_of_bins=40)
