def make_rseqc_summary_plots(rd_file, gc_file, do_qc=True, min_exonmap=60.0, max_three_prime_map=10.0):
"""Make rseqc summary plots"""
df_rd = pd.read_csv(rd_file, index_col=0)
df_gc = pd.read_csv(gc_file, index_col=0)
samples = list(df_gc.index)
# Use tags for formula
df = df_rd.pivot_table(columns=["Group"], values=["Tag_count"], index="sample")
df['Tag_count', "ExonMap"] = 100.0 * (df['Tag_count', "CDS_Exons"] + df['Tag_count', "3'UTR_Exons"] + df['Tag_count', "5'UTR_Exons"]) / df['Tag_count', "Total_Assigned_Tags"]
df.columns = df.columns.droplevel()
df['i'] = list(range(0, len(df.index)))
df['samples'] = samples
df_gc["three_prime_map"] = 100.0 * df_gc.loc[:, "91":"100"].sum(axis=1) / df_gc.loc[:, "1":"100"].sum(axis=1)
df = pd.concat([df, df_gc], axis=1)
colors = brewer["PiYG"][3]
colormap = {'False' : colors[0], 'True' : colors[2]}
columns = [
TableColumn(field="samples", title="Sample"),
TableColumn(field="ExonMap", title="Tags mapping to exons (%)"),
TableColumn(field="3' Map", title="Tags mapping to 3' end (%)"),
]
source = ColumnDataSource(df)
# Default tools, plot_config and tooltips
TOOLS="pan,box_zoom,box_select,lasso_select,reset,save,hover"
plot_config=dict(plot_width=300, plot_height=300,
tools=TOOLS, title_text_font_size='12pt',
x_range=[0, len(samples)], y_range=[0, 105],
x_axis_type=None, y_axis_type="linear",
xaxis={'axis_label' : "sample", 'major_label_orientation' : np.pi/3, 'axis_label_text_font_size' : '10pt'},
yaxis={'axis_label' : "percent (%)", 'major_label_orientation' : 1, 'axis_label_text_font_size' : '10pt'})
# Exonmap plot
qc = QCArgs(x=[0,len(samples)],
y=[min_exonmap, min_exonmap],
line_dash=[2,4]) if do_qc else None
c1 = list(map(lambda x: colormap[str(x)],
df['ExonMap'] < min_exonmap)) if do_qc else colors[0]
p1 = scatterplot(x='i', y='ExonMap',
source=source, color=c1, qc=qc,
tooltips = [{'type':HoverTool, 'tips' : [
('Sample', '@samples'),('ExonMap', '@ExonMap'),]}],
title="Tags mapping to exons", **plot_config)
# Fraction reads mapping to the 10% right-most end
qc = QCArgs(x=[0,len(samples)],
y=[max_three_prime_map, max_three_prime_map],
line_dash=[2,4]) if do_qc else None
c2 = list(map(lambda x: colormap[str(x)],
df['three_prime_map'] > max_three_prime_map)) if do_qc else colors[0]
p2 = scatterplot(x = 'i', y = 'three_prime_map',
color = c2, source = source,
qc=qc,
tooltips = [{'type':HoverTool, 'tips' : [
('Sample', '@samples'),('ExonMap', '@ExonMap'),]}],
title="Reads mapping to 3' end", **plot_config)
return {'fig' : gridplot([[p1, p2]]),
'uri' : [data_uri(rd_file), data_uri(gc_file)],
'file' : [rd_file, gc_file]}
def make_star_alignment_plots(df, samples, do_qc=False, min_reads=200000, min_map=40, max_unmap=20):
"""Make star alignment plots"""
# Currently hover tool and categorical variables don't play
# nicely together in bokeh: see
# https://github.com/bokeh/bokeh/issues/624
# Workaround as long as categorical variables don't work with HoverTool
df['i'] = list(range(0, len(df.index)))
df['samples'] = samples
df['mismatch_sum'] = df['Mismatch_rate_per_base__PCT'] + df['Deletion_rate_per_base'] + df['Insertion_rate_per_base']
df['PCT_of_reads_unmapped'] = df['PCT_of_reads_unmapped:_other'] + df['PCT_of_reads_unmapped:_too_many_mismatches'] + df['PCT_of_reads_unmapped:_too_short']
colors = brewer["PiYG"][3]
colormap = {'False' : colors[0], 'True' : colors[1]}
columns = [
TableColumn(field="samples", title="Sample"),
TableColumn(field="Number_of_input_reads", title="Number of input reads"),
TableColumn(field="Uniquely_mapped_reads_PCT", title="Uniquely mapped reads (%)"),
TableColumn(field="Mismatch_rate_per_base__PCT", title="Mismatch rate per base (%)"),
TableColumn(field="Insertion_rate_per_base", title="Insertion rate per base (%)"),
TableColumn(field="Deletion_rate_per_base", title="Deletion rate per base (%)"),
TableColumn(field="PCT_of_reads_unmapped", title="Unmapped reads (%)"),
]
source = ColumnDataSource(df)
# Generate the table
table = DataTable(source=source, columns=columns, editable=False, width = 1000)
# Default tools, plot_config and tooltips
TOOLS="pan,box_zoom,box_select,lasso_select,reset,save,hover"
plot_config=dict(plot_width=300, plot_height=300, tools=TOOLS, title_text_font_size='12pt',
x_axis_type = None, y_axis_type = "log",
xaxis = {'axis_label' : 'sample', 'axis_label_text_font_size' : '10pt', 'major_label_orientation' : np.pi/3},
yaxis = {'axis_label' : 'reads', 'axis_label_text_font_size' : '10pt', 'major_label_orientation' : np.pi/3},
x_range = [0, len(samples)]
)
# Number of input reads
c1 = list(map(lambda x: colormap[str(x)], df['Number_of_input_reads'] < min_reads)) if do_qc else "blue"
qc = QCArgs(x=[0,len(samples)], y=[min_reads, min_reads], line_dash=[2,4]) if do_qc else None
p1 = scatterplot(x='i', y='Number_of_input_reads', source=source, color=c1, qc=qc,
title="Number of input reads",
tooltips = [{'type':HoverTool, 'tips' : [('Sample', '@samples'),('Reads', '@Number_of_input_reads'),]}],
**plot_config)
# Uniquely mapped reads
plot_config.update({'yaxis_type' : 'linear', 'axis_label' : 'percent (%)'})
c2 = list(map(lambda x: colormap[str(x)], df['Uniquely_mapped_reads_PCT'] < min_map)) if do_qc else "blue"
qc = QCArgs(x=[0,len(samples)], y=[min_map, min_map], line_dash=[2,4]) if do_qc else None
p2 = scatterplot(x='i', y='Uniquely_mapped_reads_PCT', source=source, color=c2, qc=qc,
title="Uniquely mapping reads",
tooltips = [{'type':HoverTool, 'tips' : [('Sample', '@samples'),('Pct_mapped', '@Uniquely_mapped_reads_PCT'),]}],
**plot_config)
# Mapping reads in general
c3 = list(map(lambda x: colormap[str(x)], df['PCT_of_reads_unmapped'] > max_unmap)) if do_qc else "blue"
qc = QCArgs(x=[0,len(samples)], y=[max_unmap, max_unmap], line_dash=[2,4]) if do_qc else None
p3 = scatterplot(x='i', y='PCT_of_reads_unmapped',
source=source, color=c3, qc=qc, title="Unmapped reads",
tooltips = [{'type':HoverTool, 'tips' : [('Sample', '@samples'),('Pct_unmapped', '@PCT_of_reads_unmapped'),]}], **plot_config)
# Mismatch/indel rate
plot_config['tools'] = TOOLS.replace("lasso_select,", "")
plot_config['yaxis'].update({'axis_label' : 'Rate per base'})
p4 = scatterplot(x='i', y = ['Mismatch_rate_per_base__PCT', 'Insertion_rate_per_base', 'Deletion_rate_per_base'],
color = ["blue", "red", "green"], source = source,
title="Mismatch and indel rates",
tooltips = [{'type':HoverTool, 'tips' : [('Sample', '@samples'),
('Mismatch rate per base', '@Mismatch_rate_per_base__PCT'),
('Insertion rate per base', '@Insertion_rate_per_base'),
('Deletion rate per base', '@Deletion_rate_per_base'),
]},
],
**plot_config)
select_tool = p4.select(dict(type=BoxSelectTool))
select_tool.dimensions=['width']
# Plot sum
plot_config['yaxis'].update({'axis_label' : 'Mismatch/indel sum'})
c5 = list(map(lambda x: colormap[str(x)], df['mismatch_sum'] > 1.0)) if do_qc else "blue"
qc = QCArgs(x=[0,len(samples)], y=[1.0, 1.0], line_dash=[2,4]) if do_qc else None
p5 = scatterplot(x='i', y='mismatch_sum',
source=source, color=c5, qc=qc, title="Mismatch / indel sum",
tooltips = [{'type':HoverTool, 'tips' : [('Sample', '@samples'),('Mismatch/indel rate per base', '@mismatch_sum'),]}], **plot_config)
select_tool = p5.select(dict(type=BoxSelectTool))
select_tool.dimensions=['width']
# Plot histogram of ratio
# plot_config['tools'] = "pan,box_zoom,reset,save"
# p6 = figure(title="Histogram of mismatch and indel rates", **plot_config)
# hist, edges = np.histogram(df['mismatch_sum'], density=False, bins=50)
# p6.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
# fill_color="#036564", line_color="#033649")
# p6.xaxis.axis_label = "Mismatch/indel sum"
# p6.yaxis.axis_label = "Count"
df_qc = None
if do_qc:
# QC summary table
d = {'samples':samples,
'read_filter' : df['Number_of_input_reads'] < min_reads,
'map_filter' : df['Uniquely_mapped_reads_PCT'] < min_map,
'mismatch_filter' : df['mismatch_sum'] > 1.0,
}
d['filter'] = d['read_filter'] | d['map_filter'] | d['mismatch_filter']
df_qc = pd.DataFrame(data=d, index=df.samples)
return {'plots' : VBox(children=[gridplot([[p1, p2, p3]]), HBox(children=[gridplot([[p4, p5]])])]), 'table' : table, 'qctable' : df_qc}
