def make_qualimap_plots(qmglobals=None, coverage_per_contig=None):
"""Make qualimap summary plots"""
retval = {
"fig": {"coverage_per_contig": None, "globals": None},
"file": {"coverage_per_contig": coverage_per_contig, "globals": qmglobals},
"uri": {"coverage_per_contig": data_uri(coverage_per_contig), "globals": data_uri(qmglobals)},
}
# Globals
if qmglobals is not None:
df_all = pd.read_csv(qmglobals)
df_all["Sample"] = df_all["Sample"].astype("str")
fig = figure(
y_range=[0, max(df_all["number of reads"])],
title="Mapping summary",
title_text_font_size="12pt",
plot_width=400,
plot_height=400,
x_range=sorted(list(set(df_all["Sample"]))),
)
mdotplot(
fig,
x="Sample",
size=10,
df=df_all,
alpha=0.5,
y=["number of reads", "number of mapped reads", "number of duplicated reads", "number of unique reads"],
)
xaxis(fig, axis_label="sample", major_label_orientation=np.pi / 3, axis_label_text_font_size="10pt")
yaxis(fig, axis_label="count", major_label_orientation=1, axis_label_text_font_size="10pt")
retval["fig"]["globals"] = fig
# Coverage per contig
if coverage_per_contig is not None:
df_all = pd.read_csv(coverage_per_contig, index_col=0)
df_all["Sample"] = df_all["Sample"].astype("str")
fig = figure(width=300, height=300)
points(
fig, x="chrlen_percent", y="mapped_bases_percent", df=df_all, glyph="text", text="chr", text_font_size="8pt"
)
main(fig, title_text_font_size="8pt")
xaxis(fig, axis_label="Chromosome length of total (%)", axis_label_text_font_size="8pt")
yaxis(fig, axis_label="Mapped bases of total (%)", axis_label_text_font_size="8pt")
gp = facet_grid(
fig,
x="chrlen_percent",
y="mapped_bases_percent",
df=df_all,
groups=["Sample"],
width=300,
height=300,
share_x_range=True,
share_y_range=True,
title_text_font_size="12pt",
)
for fig in [item for sublist in gp.children for item in sublist]:
abline(fig, x="chrlen_percent", y="mapped_bases_percent", df=df_all, slope=1)
retval["fig"]["coverage_per_contig"] = gp
return retval
def test_mdotplot_title(self):
mdotplot(self._fig,
x="treatment",
y=["x", "y"],
df=self._data,
size=10,
alpha=0.5,
title="test",
title_text_font_size='12pt')
def test_mdotplot(self):
mdotplot(self._fig,
x="treatment",
y=["y", "z"],
df=self._data,
color=["red", "blue"],
legend=["y", "z"],
size=10,
alpha=0.5)
output_file("tabort.html")
show(self._fig)
def make_cutadapt_summary_plot(inputfile):
df_summary = pd.read_csv(inputfile)
df_summary["sample"] = df_summary["sample"].astype("str")
TOOLS = "pan,wheel_zoom,box_zoom,box_select,reset,save"
fig = figure(tools=TOOLS, width=400, height=400,
x_range=sorted(list(set(df_summary["sample"]))),
y_range=[0, 105], title="Cutadapt metrics",
title_text_font_size='12pt')
mdotplot(fig, x="sample", y=["read1_pct", "read2_pct"],
df=df_summary, size=10, alpha=0.5)
xaxis(fig, axis_label="sample",
major_label_orientation=np.pi/3,
axis_label_text_font_size='10pt')
yaxis(fig, axis_label="percent reads",
major_label_orientation=1,
axis_label_text_font_size='10pt')
return {'fig': fig, 'uri': data_uri(inputfile), 'file': inputfile}