# null peaks
fig = plt.figure(figsize=(4,3))
plotBoxplot(readCountNorm[noExprChange][:, parameters.reorder_noreplicates], parameters.headers_noreplicates[parameters.reorder_noreplicates])
ax = plt.gca()
ax.set_yscale('log')
plt.tight_layout()
plt.savefig('%s.boxplot.noExprChange.pdf'%outputfile)
# all peaks
fig = plt.figure()
plotBoxplot(readCountNorm[peakbed.not_significant][:, parameters.reorder_noreplicates], parameters.headers_noreplicates[parameters.reorder_noreplicates])
# peaks that go up
peakbedFile = 'scoring/140815_peaks.coverageCorr.all.bed'
peakbed = filefun.loadBedwScores(peakbedFile)
fig = plt.figure(figsize=(4,3))
plotBoxplot(readCountNorm[peakbed.significant_up][:, parameters.reorder_noreplicates], parameters.headers_noreplicates[parameters.reorder_noreplicates])
ax = plt.gca()
ax.set_yscale('log')
plt.tight_layout()
plt.savefig('%s.boxplot.upwNfib.pdf'%outputfile)
# plot scatterplots for all samples versus mean of low
readCountNorm = readCountRed/np.mean(readCountRed[noExprChange], 0)*10
xvalues = np.mean(readCountNorm[:, parameters.indices_low_noreplicates], axis=1)
for i, label in enumerate(parameters.headers_noreplicates):
yvalues = readCountNorm[:, i]
indx = yvalues > 0
#fig = plt.figure(figsize=(4, 4))
#plotfun.plot_hexbin(xvalues[indx], yvalues[indx])
import scipy.stats as st
import scipy.spatial.distance
import matplotlib.pyplot as plt
import subprocess
from heatmapfun import plotHeatMap
import filefun
import globalvars
parameters = globalvars.Parameters()
wd = '/home/sarah/GreenDragonRaid1/lab/sarah/SCLC_ATAC/140908_bams/mm9/peakProcessing'
# load peak beds with scores
bedFileName = os.path.join(wd, 'scoring/140815_peaks.coverageCorr.all.bed')
peakBed = filefun.loadBedwScores(bedFileName)
# load nfib peak summits
chipBedFileName = '/home/sarah/GreenDragonRaid1/shr/Downloaded_data/mm9/nfib_chip/nfib_peak_summits.bed'
chipBed = filefun.loadBed(chipBedFileName)
# load tss
tssBedFileName ='/raid/Downloaded_data/mm9_data/TSS/refSeqmm9.TSS.fix.bed'
# find the number of chip peaks that fall into accessible peaks
peakBed.hasChipPeak = np.array(subprocess.check_output("bedtools intersect -c -b %s -a %s | awk '{print $NF}'"%(chipBedFileName, bedFileName), shell=True).split(), dtype=int).astype(bool)
# get peak Distance to Tss
peakBed.distancetoTss = np.array(subprocess.check_output("bedtools closest -d -t first -a %s -b %s | awk '{print $NF}'"%(bedFileName, tssBedFileName), shell=True).split(), dtype=int)
cutoff_distance = 5E3
peakBed.distal = peakBed.distancetoTss > cutoff_distance
np.save(outfile+'.npy', signals)
"""
for signal tracks, make plot
"""
span = 1E4
indx = np.arange(0, options.l+options.r, span, dtype=int)
xvalues = np.arange(-options.l, options.r, span)
fig = plt.figure(figsize=(5, 4))
ax = fig.add_subplot(111)
for signal in signals[0]:
ax.plot(xvalues, signal[indx], 'b', alpha=0.1)
ax.plot(xvalues, np.nanmean(signals[0, :, indx], 1), 'k')
plt.savefig('%s.%s.pdf'%(outfile, options.interval))
"""
Now, for conservation, etc, plot the conservation in distal sites. Dista,
"""
locBed = filefun.loadBedwScores(options.a)
locBed.distanceToTss = np.array(subprocess.check_output("bedtools closest -d -t first -a %s -b %s | awk '{print $NF}'"%(bedFileName, tssBedFileName), shell=True).split(), dtype=int)
locBed.distal = locBed.distanceToTss > 5E3
signals[0, np.all((locBed.distal, locBed.significant_up), axis=0)]
signals[0, locBed.distal]
signals[0, np.logical_not(locBed.distal)]
histogram.compare([signals[0, np.all((locBed.distal, locBed.significant_up), axis=0)],
signals[0, np.all((locBed.distal, locBed.no_change), axis=0)],
signals[0, np.logical_not(locBed.distal)]], labels=['up, distal', 'no change, distal', 'all promoter'])
plotfun.plot_barplot()
