def _plot_with_rpy2(self, regions, filename):
from rpy2 import robjects
import rpy2.robjects.lib.ggplot2 as ggplot2
from rpy2.robjects.lib import grid
from rpy2.robjects.packages import importr
grdevices = importr('grDevices')
base = importr('base')
grdevices.pdf(file=filename + '.pdf')
t = [x for x in range(-self.num_bins, self.num_bins + 1)]
for region in regions[:self.num_regs]:
if not np.any(region.weighted):
logger.warning(
"Warning: No data for region located on bin " + str(region.bin) + ". Not plotting this one.")
continue
middle = (len(region.weighted[0]) - 1) / 2
if middle < self.num_bins:
logger.error("Warning: There are less bins calculated for regions than you want to plot.")
sys.exit(1)
d = {'map': robjects.StrVector(
[str(m) for sublist in [[x] * len(t) for x in range(len(region.weighted))] for m in sublist]),
't': robjects.FloatVector(t * len(region.weighted)),
'e': robjects.FloatVector([i for sublist in region.weighted for i in
sublist[middle - self.num_bins:middle + self.num_bins + 1]]),
'p': robjects.FloatVector([-np.log10(x) for sublist in region.pvalues for x in
sublist[middle - self.num_bins:middle + self.num_bins + 1]]),
'c': robjects.FloatVector([-np.log10(x) for sublist in region.corrected_pvalues for x in
sublist[middle - self.num_bins:middle + self.num_bins + 1]])}
dataf = robjects.DataFrame(d)
gp = ggplot2.ggplot(dataf) # first yellow second red
p1 = gp + ggplot2.geom_line(mapping=ggplot2.aes_string(x='t', y='e', group='map', colour='map'),
alpha=0.8) + ggplot2.scale_y_continuous(trans='log2') + ggplot2.ggtitle(
"\n".join(wrap("Bin " + str(region.bin) + " : " + str(region.positions)))) + ggplot2.labs(
y="log Intensity") + ggplot2.theme_classic() + ggplot2.theme(
**{'axis.title.x': ggplot2.element_blank(), 'axis.text.y': ggplot2.element_text(angle=45),
'axis.text.x': ggplot2.element_blank(),
'legend.position': 'none'}) + ggplot2.scale_colour_brewer(palette="Set1")
p2 = gp + ggplot2.geom_line(mapping=ggplot2.aes_string(x='t', y='p', group='map', colour='map'),
alpha=0.8) + ggplot2.labs(
y="-log10(p-value)") + ggplot2.theme_classic() + ggplot2.theme(
**{'axis.title.x': ggplot2.element_blank(), 'axis.text.x': ggplot2.element_blank(),
'legend.position': 'none'}) + ggplot2.scale_colour_brewer(palette="Set1")
p3 = gp + ggplot2.geom_line(mapping=ggplot2.aes_string(x='t', y='c', group='map', colour='map'),
alpha=0.8) + ggplot2.labs(y="-log10(q-value)",
x='bins (' + str(self.bin_res) + ' bp each)') + \
ggplot2.geom_hline(mapping=ggplot2.aes_string(yintercept=str(-np.log10(self.threshold))),
colour='black', alpha=0.8, linetype='dashed') + ggplot2.theme_classic() + \
ggplot2.theme(**{'legend.position': 'none'}) + ggplot2.scale_colour_brewer(palette="Set1")
g1 = ggplot2.ggplot2.ggplotGrob(p1)
g2 = ggplot2.ggplot2.ggplotGrob(p2)
g3 = ggplot2.ggplot2.ggplotGrob(p3)
robjects.globalenv["g"] = base.rbind(g1, g2, g3, size='first')
robjects.r("grid::grid.draw(g)")
grid.newpage()
logger.debug('Plotted region ' + str(region.bin))
grdevices.dev_off()
def get_nogrid_theme():
"""
Get no grid theme for ggplot2.
"""
nogrid_x_theme = theme(**{'panel.grid.major.x': element_blank(),
'panel.grid.minor.x': element_blank(),
'panel.grid.major.y': element_blank(),
'panel.grid.minor.y': element_blank()})
return nogrid_x_theme
def generate_histogram(subgroups_to_sses_to_n_count, tname, file_name):
columns_to_data = {'subgroup': [], tname: [], 'count': []}
max_count = 0
for subgroup, sses_to_n_count in subgroups_to_sses_to_n_count.items():
for ss, n_count in sses_to_n_count.items():
columns_to_data['subgroup'].append(subgroup)
columns_to_data[tname].append(ss)
columns_to_data['count'].append(n_count)
if n_count > max_count:
max_count = n_count
r_columns_to_data = {
'subgroup':
ro.FactorVector(columns_to_data['subgroup'],
levels=ro.StrVector(
_sort_subgroup(set(columns_to_data['subgroup'])))),
tname:
ro.StrVector(columns_to_data[tname]),
'count':
ro.IntVector(columns_to_data['count'])
}
df = ro.DataFrame(r_columns_to_data)
max_count = int(max_count / 1000 * 1000 + 1000)
histogram_file_path = os.path.join(OUTPUT_PATH, file_name)
logging.debug(
str.format("The Data Frame for file {}: \n{}", histogram_file_path,
df))
grdevices.png(file=histogram_file_path, width=1200, height=800)
gp = ggplot2.ggplot(df)
pp = gp + \
ggplot2.aes_string(x='subgroup', y='count', fill=tname) + \
ggplot2.geom_bar(position="dodge",width=0.8, stat="identity") + \
ggplot2.theme_bw() + \
ggplot2.theme_classic() + \
ggplot2.theme(**{'legend.title': ggplot2.element_blank()}) + \
ggplot2.theme(**{'legend.text': ggplot2.element_text(size=40)}) + \
ggplot2.theme(**{'axis.text.x': ggplot2.element_text(size=40,angle=45)}) + \
ggplot2.theme(**{'axis.text.y': ggplot2.element_text(size=40)}) + \
ggplot2.scale_y_continuous(expand=ro.IntVector([0, 0]),
limits=ro.IntVector([0, max_count])) + \
ggplot2.geom_text(ggplot2.aes_string(label='count'), size=6, angle=35, hjust=-0.1,
position=ggplot2.position_dodge(width=0.8),
vjust=-0.2)
pp.plot()
logging.info(str.format("Output step3 file {}", histogram_file_path))
grdevices.dev_off()
def generate_step3_5_lrr_acc20_line_chart(subgroups_to_lrrs_acc20mean,
prefix=''):
pandas2ri.activate()
subgroups_to_lrr_count = {}
columns_to_data = {'subgroup': [], 'pos': [], 'acc20': []}
for subgroup, (acc20means,
acc20_count) in subgroups_to_lrrs_acc20mean.items():
subgroups_to_lrr_count[subgroup] = acc20_count
for index, acc20mean in enumerate(acc20means):
columns_to_data['subgroup'].append(subgroup)
columns_to_data['pos'].append(index + 1)
columns_to_data['acc20'].append(acc20mean)
# Write the count of LRRs for each subgroup to file
with open(os.path.join(OUTPUT_PATH, prefix + "step3_5_lrr_count.txt"),
'w') as f:
for subgroup, lrr_count in subgroups_to_lrr_count.items():
f.write(str.format("{}: {}\n", subgroup, lrr_count))
# Generate the line chart file
r_columns_to_data = {
'subgroup': ro.StrVector(columns_to_data['subgroup']),
'pos': ro.IntVector(columns_to_data['pos']),
'acc20': ro.FloatVector(columns_to_data['acc20'])
}
df = ro.DataFrame(r_columns_to_data)
line_chart_file_path = os.path.join(OUTPUT_PATH,
prefix + "step3_5_lrr_acc20_line.png")
logging.debug(
str.format("The Data Frame for file {}: \n{}", line_chart_file_path,
df))
grdevices.png(file=line_chart_file_path, width=1024, height=512)
gp = ggplot2.ggplot(df)
pp = gp + \
ggplot2.theme_bw() + \
ggplot2.theme_classic() + \
ggplot2.theme(**{'axis.text.x': ggplot2.element_text(size=35)}) + \
ggplot2.theme(**{'axis.text.y': ggplot2.element_text(size=35)}) + \
ggplot2.aes_string(x='pos', y='acc20', group='subgroup', colour='subgroup') + \
ggplot2.geom_point(size=4, shape=20) + \
ggplot2.geom_line(size=3) + \
ggplot2.theme(**{'legend.title': ggplot2.element_blank()}) + \
ggplot2.theme(**{'legend.text': ggplot2.element_text(size=20)}) + \
ggplot2.scale_x_continuous(breaks=ro.IntVector(range(1, 25)), labels=ro.StrVector(list('LxxLxLxxNxLsGxIPxxLxxLxx')))
pp.plot()
logging.info(str.format("Output step3 file {}", line_chart_file_path))
grdevices.dev_off()
def _generate_step3_5_ss_acc20_line_chart(ts_to_acc20s, tname,
line_chart_file_path):
logging.debug(
str.format("Begin to generate {}, data {}", line_chart_file_path,
ts_to_acc20s))
ts_to_acc20mean = calc_acc20mean_by_types(ts_to_acc20s)
columns_to_data = {tname: [], 'site': [], 'acc20': []}
for ss, acc20means in ts_to_acc20mean.items():
for index, acc20mean in enumerate(acc20means):
columns_to_data[tname].append(ss)
columns_to_data['site'].append(index - 5)
columns_to_data['acc20'].append(acc20mean)
# Generate the line chart file
r_columns_to_data = {
tname: ro.StrVector(columns_to_data[tname]),
'site': ro.IntVector(columns_to_data['site']),
'acc20': ro.FloatVector(columns_to_data['acc20'])
}
df = ro.DataFrame(r_columns_to_data)
logging.debug(
str.format("The Data Frame for file {}: \n{}", line_chart_file_path,
df))
grdevices.png(file=line_chart_file_path, width=1024, height=512)
gp = ggplot2.ggplot(df)
pp = gp + \
ggplot2.theme_bw() + \
ggplot2.theme_classic() + \
ggplot2.theme(**{'axis.text.x': ggplot2.element_text(size=35)}) + \
ggplot2.theme(**{'axis.text.y': ggplot2.element_text(size=35)}) + \
ggplot2.aes_string(x='site', y='acc20', group=tname, colour=tname) + \
ggplot2.geom_point(size=4, shape=20) + \
ggplot2.geom_line(size=3) + \
ggplot2.theme(**{'legend.title': ggplot2.element_blank()}) + \
ggplot2.theme(**{'legend.text': ggplot2.element_text(size=20)}) + \
ggplot2.scale_x_continuous(breaks=ro.IntVector(list(range(-5, 6))),
labels=ro.StrVector(['-5', '-4', '-3', '-2', '-1', 'N', '1', '2', '3', '4', '5']))
pp.plot()
logging.info(str.format("Output step3 file {}", line_chart_file_path))
grdevices.dev_off()
def test_element_blank(self):
eb = ggplot2.element_blank()
assert isinstance(eb, ggplot2.ElementBlank)
y = 'lat', \
group = 'group', \
color = 'ObamaShare', \
fill = 'ObamaShare')) + \
ggplot2.scale_fill_gradient(high = 'blue', \
low = 'red') + \
ggplot2.scale_fill_continuous(name = "Obama Vote Share") + \
ggplot2.scale_colour_continuous(name = "Obama Vote Share") + \
ggplot2.theme(**{ 'legend.position': 'left', \
'legend.key.size': R.r.unit(2, 'lines'), \
'legend.title' : ggplot2.element_text(size = 14, hjust=0), \
'legend.text': ggplot2.element_text(size = 12), \
'title' : ggplot2.element_text('Obama Vote Share and Distance to Railroads in IL'), \
'plot.title': ggplot2.element_text(size = 24),
'plot.margin': R.r.unit(R.r.rep(0,4),'lines'), \
'panel.background': ggplot2.element_blank(), \
'panel.grid.minor': ggplot2.element_blank(), \
'panel.grid.major': ggplot2.element_blank(), \
'axis.ticks': ggplot2.element_blank(), \
'axis.title.x': ggplot2.element_blank(), \
'axis.title.y': ggplot2.element_blank(), \
'axis.title.x': ggplot2.element_blank(), \
'axis.title.x': ggplot2.element_blank(), \
'axis.text.x': ggplot2.element_blank(), \
'axis.text.y': ggplot2.element_blank()} ) + \
ggplot2.geom_line(ggplot2.aes(x='long',
y='lat',
group='group'),
data=IL_railroads,
color='grey',
size=0.2) + \
def plot_volcano_with_r(
data,
xlabel='Estimated effect (change in H/L ratio)',
title='',
max_labels=20,
color_background='#737373',
color_significant='#252525',
color_significant_muted='#252525',
label_only_large_fc=False,
special_labels=None,
special_palette=None,
base_size=12,
label_size=3,
x='logFC',
y='neg_log10_p_adjust',
special_labels_mode='all',
xlim=None,
skip_labels=None,
nudges=None,
):
r_data, r_like_data = transform_data_for_ggplot(
data,
label_only_large_fc=label_only_large_fc,
special_labels=special_labels,
max_labels=max_labels,
special_labels_mode=special_labels_mode,
skip_labels=skip_labels,
nudges=nudges)
plot = r_ggplot2.ggplot(r_data)
plot += r_ggplot2.theme_minimal(base_size=base_size)
plot += r_ggplot2.theme(
**{
'panel.grid.major':
r_ggplot2.element_blank(),
'panel.grid.minor':
r_ggplot2.element_blank(),
'panel.border':
r_ggplot2.element_rect(fill=robjects.rinterface.NA, color="black")
})
plot += r_ggplot2.theme(
text=r_ggplot2.element_text(family='Helvetica', face='plain'))
plot += r_ggplot2.theme(
**{
'plot.title': r_ggplot2.element_text(hjust=0.5),
# 'axis.title.y': r_ggplot2.element_text((t = 0, r = 20, b = 0, l = 0)),
})
aes_points = r_ggplot2.aes_string(x=x, y=y, color='group')
scale_points = r_ggplot2.scale_colour_manual(
aes_points,
values=r_label_palette(
r_like_data,
special_palette,
color_background=color_background,
color_significant=color_significant,
color_significant_muted=color_significant_muted))
plot += aes_points
plot += scale_points
if xlim is not None:
plot += r_ggplot2.scale_x_continuous(
labels=r_custom.formatterFunTwoDigits, limits=robjects.r.c(*xlim))
else:
plot += r_ggplot2.scale_x_continuous(
labels=r_custom.formatterFunTwoDigits)
plot += r_ggplot2.scale_y_continuous(labels=r_custom.formatterFunOneDigit)
plot += r_ggplot2.geom_hline(
yintercept=float(-np.log10(FDR_THRESHOLD_RESPONSE)),
color='#BDBDBD',
alpha=.3)
plot += r_ggplot2.geom_vline(xintercept=float(FC_THRESHOLD_RESPONSE),
color='#BDBDBD',
alpha=.3)
plot += r_ggplot2.geom_vline(xintercept=-float(FC_THRESHOLD_RESPONSE),
color='#BDBDBD',
alpha=.3)
plot += r_ggplot2.geom_point(**{'show.legend': False})
aes_text = r_ggplot2.aes_string(label='label')
plot += aes_text
plot += r_ggrepel.geom_text_repel(
aes_text,
nudge_x=r_dollar(r_data, 'nudgex'),
nudge_y=r_dollar(r_data, 'nudgey'),
size=label_size,
family='Helvetica',
**{
'show.legend': False,
'point.padding': 0.25,
'min.segment.length': 0,
#'max.iter':0,
'segment.color': '#BDBDBD'
},
)
plot += r_ggplot2.labs(x=xlabel,
y='Adjusted p value (-log10)',
title=title)
plot.plot()
#print robjects.r('packageVersion("ggplot2")')
#--------------------------------------------------------------------#
# Annotation #
#--------------------------------------------------------------------#
mytheme = {
'panel.background':ggplot2.element_rect(fill='white',colour='white'),
'axis.text':ggplot2.element_text(colour="black",size=15,
family=FONTFAM),
'axis.line':ggplot2.ggplot2.element_line(size = 1.2, colour="black"),
'axis.title':ggplot2.element_text(colour="black",size=15,
family=FONTFAM),
'plot.title':ggplot2.element_text(face="bold", size=20,
colour="black",family=FONTFAM),
'panel.grid.minor':ggplot2.element_blank(),
'panel.grid.major':ggplot2.element_blank(),
'legend.key':ggplot2.element_blank(),
'legend.text':ggplot2.element_text(colour="black",size=15,
family=FONTFAM),
'strip.text.y':ggplot2.element_text(colour="black",face="bold",
size=15,family=FONTFAM),
'strip.text.x':ggplot2.element_text(colour="black",face="bold",
size=15,family=FONTFAM),
'text':ggplot2.element_text(colour="black",family=FONTFAM)
}
#'panel.grid.major':ggplot2.theme_line(colour = "grey90"),
pointtheme = {
'panel.background':ggplot2.element_rect(fill='white',colour='black',size=2),
'axis.text':ggplot2.element_text(colour="black",size=15,family=FONTFAM),
#print(palette)
color_background = "white"
color_grid_major = palette[3]
color_axis_text = palette[6]
color_axis_title = palette[7]
color_title = palette[9]
#palette_lines <- brewer.pal("Dark2", n=3)
palette_lines <- brewer.pal("Set2", n=8)
palette_repeat <- c('#66C2A5', '#66C2A5', '#FC8D62','#FC8D62')
linetype_repeat <- c("solid","dashed","solid","dashed")
''')
fte_theme = theme(
**{
'axis.ticks':
element_blank(),
'panel.background':
element_rect(fill=robjects.r.color_background,
color=robjects.r.color_background),
'plot.background':
element_rect(fill=robjects.r.color_background,
color=robjects.r.color_background),
'panel.border':
element_rect(
color=robjects.r.color_background
), #'panel.grid.major':element_line(color=robjects.r.color_grid_major, size = 0.25),
'panel.grid.minor':
element_blank(),
'axis.ticks':
element_blank(),
'legend.position':
robjects.r('''
library(RColorBrewer)
library(grid)
palette <- brewer.pal("Greys", n=9)
color_background = palette[2]
color_grid_major = palette[3]
color_axis_text = palette[6]
color_axis_title = palette[7]
color_title = palette[9]
palette_lines <- brewer.pal("Set2", n=8)
''')
size = 9
fte_theme = theme(**{'axis.ticks':element_blank(),
'panel.background':element_rect(fill=robjects.r.color_background, color=robjects.r.color_background),
'plot.background':element_rect(fill=robjects.r.color_background, color=robjects.r.color_background),
'panel.border':element_rect(color=robjects.r.color_background),
'panel.grid.minor':element_blank(),
'axis.ticks':element_blank(),
'legend.position':"right",
'legend.background': element_rect(fill="transparent"),
'legend.text': element_text(size=size,color=robjects.r.color_axis_title),
'legend.title': element_text(size=size,color=robjects.r.color_axis_title),
'plot.title':element_text(color=robjects.r.color_title, size=10, vjust=1.25),
'axis.text.x':element_text(size=size,color=robjects.r.color_axis_text),
'axis.text.y':element_text(size=size,color=robjects.r.color_axis_text),
'axis.title.x':element_text(size=size,color=robjects.r.color_axis_title, vjust=0),
#'panel.grid.major':element_line(color=robjects.r.color_grid_major,size=.25),
'axis.title.y':element_text(size=size,color=robjects.r.color_axis_title,angle=90)})
def plot_qc_reads(qc_df):
"""
Plot number of reads part of a pipeline QC file.
"""
# Record NA values as 0
qc_df = qc_df.fillna(0)#.set_index("sample")
cols = ["sample",
"num_reads",
"num_mapped",
"num_unique_mapped",
"num_junctions"]
qc_df = qc_df[cols]
melted_qc = pandas.melt(qc_df, id_vars=["sample"])
qc_r = conversion_pydataframe(melted_qc)
labels = tuple(["num_reads",
"num_mapped",
"num_unique_mapped",
"num_junctions"])
labels = robj.StrVector(labels)
variable_i = qc_r.names.index('variable')
qc_r[variable_i] = robj.FactorVector(qc_r[variable_i],
levels = labels)
ggplot2.theme_set(ggplot2.theme_bw(12))
scales = importr("scales")
r_opts = r.options(scipen=4)
p = ggplot2.ggplot(qc_r) + \
ggplot2.geom_point(aes_string(x="sample", y="value")) + \
ggplot2.scale_y_continuous(trans=scales.log10_trans(),
breaks=scales.trans_breaks("log10",
robj.r('function(x) 10^x')),
labels=scales.trans_format("log10",
robj.r('math_format(10^.x)'))) + \
r.xlab("CLIP-Seq samples") + \
r.ylab("No. reads") + \
ggplot2.coord_flip() + \
ggplot2.facet_wrap(Formula("~ variable"), ncol=1) + \
theme(**{"panel.grid.major.x": element_blank(),
"panel.grid.minor.x": element_blank(),
"panel.grid.major.y": theme_line(size=0.5,colour="grey66",linetype=3)})
p.plot()
return
r.par(mfrow=np.array([1,2]))
num_samples = len(qc_df.num_reads)
r.par(bty="n", lwd=1.7, lty=2)
r_opts = r.options(scipen=4)
r.options(r_opts)
r.dotchart(convert_to_r_matrix(qc_df[["num_reads",
"num_mapped",
"num_unique_mapped"]]),
xlab="No. reads",
lcolor="black",
pch=19,
gcolor="darkblue",
cex=0.8)
r.par(bty="n")
r.dotchart(convert_to_r_matrix(qc_df[["num_ribosub_mapped",
"num_ribo",
"num_junctions"]]),
xlab="No. reads",
lcolor="black",
pch=19,
gcolor="darkblue",
cex=0.8)