def profile(args):
interval = args.interval
datafiles = [x.strip() for x in args.datafiles]
annotation = args.annotation
outfile = args.outfile
colors = parse_colors(args.colors)
for x in args.datafiles:
if '.bam' in x and not os.path.isfile("{0}.bai".format(x)):
print(
"Data file '{0}' does not have an index file. Creating an index file for {0}."
.format(x))
pysam.index(x)
trackgroups = process_groups(args.trackgroups)
if not trackgroups:
trackgroups = [[x] for x in range(1, len(datafiles) + 1)]
scalegroups = process_groups(args.scalegroups)
scale = args.scale
if scale:
try:
scale = [float(x) for x in scale.split(",")]
except Exception:
print("Error in scale argument")
sys.exit(1)
if trackgroups and scalegroups:
if len(trackgroups) != sum([len(x) for x in scalegroups]):
sys.stderr.write("Track groups and scales do not match!\n")
sys.exit()
# Group the tracks according to track_groups
tracks = []
for group in trackgroups:
tracks.append([datafiles[i - 1] for i in group])
# Create the image
profile_screenshot(outfile,
interval,
tracks,
annotation=annotation,
scalegroups=scalegroups,
fontsize=args.textfontsize,
colors=colors,
bgmode=args.background,
fragmentsize=args.fragmentsize,
scale=scale,
show_scale=args.show_scale,
rmdup=args.rmdup,
rmrepeats=args.rmrepeats,
reverse=args.reverse,
adjscale=args.adjscale)
def profile(args):
interval = args.interval
datafiles = [x.strip() for x in args.datafiles]
annotation = args.annotation
outfile = args.outfile
colors = parse_colors(args.colors)
for x in args.datafiles:
if '.bam' in x and not os.path.isfile("{0}.bai".format(x)):
print("Data file '{0}' does not have an index file. Creating an index file for {0}.".format(x))
pysam.index(x)
trackgroups = process_groups(args.trackgroups)
if not trackgroups:
trackgroups = [[x] for x in range(1, len(datafiles) + 1)]
scalegroups = process_groups(args.scalegroups)
scale = args.scale
if scale:
try:
scale = [int(x) for x in scale.split(",")]
except Exception:
print("Error in scale argument")
sys.exit(1)
if trackgroups and scalegroups:
if len(trackgroups) != sum([len(x) for x in scalegroups]):
sys.stderr.write("Track groups and scales do not match!\n")
sys.exit()
# Group the tracks according to track_groups
tracks = []
for group in trackgroups:
tracks.append([datafiles[i - 1] for i in group])
# Create the image
profile_screenshot(outfile, interval, tracks,
annotation=annotation,
scalegroups=scalegroups,
fontsize=args.textfontsize,
colors=colors,
bgmode=args.background,
fragmentsize=args.fragmentsize,
scale=scale,
show_scale=args.show_scale,
rmdup=args.rmdup,
rmrepeats=args.rmrepeats,
reverse=args.reverse,
adjscale=args.adjscale
)
def bandplot(args):
if (0 > args.scalar) or (args.scalar > 100):
print "ERROR: -P value has to be between 0 and 100"
sys.exit(1)
else:
scalar = args.scalar
if not args.datafiles and not args.readCount:
print 'You should provide data file(s) or the read counts file.'
sys.exit()
if args.datafiles and args.readCount:
print 'You should choose only ONE option. Either data file(s) or the read counts file.'
sys.exit()
clust_file = args.clust_file
if args.datafiles:
for x in args.datafiles:
if '.bam' in x and not os.path.isfile("{0}.bai".format(x)):
print "Data file '{0}' does not have an index file. Creating an index file for {0}.".format(x)
pysam.index(x)
datafiles = [x.strip() for x in args.datafiles]
fragmentsize = args.fragmentsize
colors = parse_colors(args.colors)
scalegroups = process_groups(args.scalegroups)
percs = [int(x) for x in args.percs.split(",")]
rmdup = args.rmdup
rpkm = args.rpkm
rmrepeats = args.rmrepeats
bins = args.bins
summary = args.summary
fontsize = args.textfontsize
font = FontProperties(size=fontsize / 1.25, family=["Nimbus Sans L", "Helvetica", "sans-serif"])
# Calculate the profile data
if args.datafiles:
data = load_cluster_data(clust_file, datafiles, bins, rpkm, rmdup, rmrepeats, fragmentsize=fragmentsize)
tracks = [os.path.basename(x) for x in datafiles]
titles = [os.path.splitext(x)[0] for x in tracks]
else:
titles, data = load_read_counts(args.readCount)
tracks = titles
for x in data:
for i in data[x]:
bins = len(data[x][i])
break
break
# Get cluster information
cluster_data = load_bed_clusters(clust_file)
clusters = cluster_data.keys()
#Init x-axis
t = np.arange(bins)
rows = len(tracks)
cols = len(clusters)
if summary:
rows += 1
cols += 1
# Get a figure with a lot of subplots
fig, axes = create_grid_figure(rows, cols,
plotwidth=cfg.PLOTWIDTH,
plotheight=cfg.PLOTHEIGHT,
padleft=cfg.PADLEFT,
padtop=cfg.PADTOP,
pad=cfg.PAD,
padright=cfg.PADRIGHT,
padbottom=cfg.PADBOTTOM)
track_max = []
for track_num, track in enumerate(tracks):
percentiles = [scoreatpercentile([data[track][x] for x in cluster_data[cluster]], scalar) for cluster in clusters]
track_max.append(max(percentiles))
for track_num, track in enumerate(tracks):
for i,cluster in enumerate(clusters):
# Retrieve axes
ax = axes[track_num][i]
# Get the data
vals = np.array([data[track][x] for x in cluster_data[cluster]])
# Make the plot
coverage_plot(ax, t, vals, colors[track_num % len(colors)], percs)
# Get scale max
maxscale = track_max[track_num]
if scalegroups and len(scalegroups) > 0:
for group in scalegroups:
if (track_num + 1) in group:
maxscale = max([track_max[j - 1] for j in group])
break
# Set scale
ax.set_ylim(0, maxscale)
ax.set_xlim(0, bins - 1)
# Cluster titles
if track_num == 0:
ax.set_title("%s\nn=%s" % (cluster, len(cluster_data[cluster])), font_properties=font)
# Track title and scale
if i == 0:
pos = axes[track_num][0].get_position().get_points()
text_y = (pos[1][1] + pos[0][1]) / 2
text_x = pos[0][0] - (cfg.PAD / fig.get_figwidth())
plt.figtext(text_x, text_y, titles[track_num], clip_on=False, horizontalalignment="right", verticalalignment="center", font_properties=font)
plt.figtext(text_x, pos[1][1], "%.4g" % maxscale, clip_on=False, horizontalalignment="right", verticalalignment="top", font_properties=font)
plt.figtext(text_x, pos[0][1], 0, clip_on=False, horizontalalignment="right", verticalalignment="bottom", font_properties=font)
if summary:
for i,track in enumerate(tracks):
ax = axes[i][cols - 1]
l = len(clusters)
min_alpha = 0.3
max_alpha = 0.9
if l > 1:
step = (max_alpha - min_alpha) / (l - 1)
alphas = np.arange(min_alpha, max_alpha + step, step)
else:
alphas = [max_alpha]
for j,cluster in enumerate(clusters):
vals = np.array([data[track][x] for x in cluster_data[cluster]])
m = np.median(vals, axis=0)
ax.plot(np.arange(len(m)), m, color=colors[i % len(colors)], alpha=alphas[j])
ax.set_ylim(0, track_max[i])
for i,cluster in enumerate(clusters):
ax = axes[rows - 1][i]
max_max = 0
for j,track in enumerate(tracks):
vals = np.array([data[track][x] for x in cluster_data[cluster]])
m = np.median(vals, axis=0)
ax.plot(np.arange(len(m)), m, color=colors[j % len(colors)], alpha=0.8)
if track_max[j] > max_max:
max_max = track_max[j]
ax.set_ylim(0, max_max)
ax = axes[rows - 1][cols - 1]
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False)
print "Saving figure"
plt.savefig(args.outfile, dpi=600)
