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Finish pre-WGS report (#70)
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* Finish pre-WGS report

- Use new graph generation functions
- Add sliders
- Use BamQC3 and IchorCNA data
- Data table and failure info
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@apmasell @callunity
apmasell authored and callunity committed Dec 6, 2019
1 parent e8c2694 commit 2f80f4d
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Showing 2 changed files with 136 additions and 60 deletions.
27 changes: 25 additions & 2 deletions application/dash_application/plot_builder.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
from typing import List
from typing import List, Tuple

import pandas
import plotly.graph_objects as go
Expand Down Expand Up @@ -180,4 +180,27 @@ def get_shapes_for_values(shapeby: List[str]):
return get_dict_wrapped(shapeby, ALL_SYMBOLS)

def get_colours_for_values(colourby: List[str]):
return get_dict_wrapped(colourby, PLOTLY_DEFAULT_COLOURS)
return get_dict_wrapped(colourby, PLOTLY_DEFAULT_COLOURS)

def terminal_output(data:DataFrame, limits:List[Tuple[str, str, float]]) -> str:
if data.empty:
return "No data!"

output = ""

for (name, column, cutoff) in limits:
output += "$failed_%s\n" %name
newline = False
linenumber = 0
for failed in data.loc[data[column] < cutoff][pinery.column.SampleProvenanceColumn.SampleName]:
if not newline:
output += "[{0}] ".format(linenumber)
output += "\"" + failed + "\"\t\t"
if newline:
output += "\n"
newline = not newline
linenumber += 1
if output:
return output
else:
return "All samples within cutoffs"
169 changes: 111 additions & 58 deletions application/dash_application/views/preqc_wgs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@

import dash_core_components as core
import dash_html_components as html
import numpy
from dash.dependencies import Input, Output, State
import pandas as pd

Expand All @@ -11,7 +10,7 @@
from gsiqcetl import QCETLCache
from . import navbar
from ..dash_id import init_ids
from ..plot_builder import get_shapes_for_values, fill_in_shape_col, fill_in_colour_col, generate
from ..plot_builder import terminal_output, fill_in_shape_col, fill_in_colour_col, generate
from ..table_builder import build_table
from ..utility import df_manipulation as util

Expand All @@ -29,22 +28,26 @@
"second-sort",
"colour-by",
"shape-by",
"reads-per-start-point-slider",
"insert-mean-slider",
"passed-filter-reads-slider",

# Graphs
"total-reads",
"mean-insert",
"reads-per-start-point",
"duplication",
"purity",
"ploidy",
"unmapped-reads",
"non-primary-reads",
"on-target-reads",

"terminal-output",
"data-table",
])

BAMQC_COL = gsiqcetl.column.BamQcColumn
BAMQC_COL = gsiqcetl.column.BamQc3Column
ICHOR_COL = gsiqcetl.column.IchorCnaColumn
PINERY_COL = pinery.column.SampleProvenanceColumn
INSTRUMENT_COLS = pinery.column.InstrumentWithModelColumn
Expand All @@ -67,26 +70,22 @@
special_cols["On-target Reads"],
special_cols["Purity"]
]
most_bamqc_cols = [*BAMQC_COL.values()]
most_bamqc_cols.remove(BAMQC_COL.BamFile)
later_col_set = [
PINERY_COL.PrepKit, PINERY_COL.TissuePreparation,
PINERY_COL.LibrarySourceTemplateType, PINERY_COL.ExternalName,
PINERY_COL.GroupID, PINERY_COL.TissueOrigin, PINERY_COL.TissueType,
PINERY_COL.Institute, INSTRUMENT_COLS.ModelName
]
wgs_table_columns = [*first_col_set, *most_bamqc_cols, *later_col_set]
wgs_table_columns = [*first_col_set, *BAMQC_COL.values(), *ICHOR_COL.values(), *later_col_set]

# Set initial values for dropdown menus
initial_first_sort = PINERY_COL.StudyTitle
initial_second_sort = BAMQC_COL.TotalReads
initial_colour_col = PINERY_COL.StudyTitle
initial_shape_col = PINERY_COL.PrepKit

# Set initial points for graph cutoff lines
graph_cutoffs = {
"pf_reads": 0.01
}
initial_cutoff_pf_reads = 0.01
initial_cutoff_insert_mean = 150
initial_cutoff_rpsp = 5

shape_or_colour_by = [
{"label": "Project", "value": PINERY_COL.StudyTitle},
Expand All @@ -106,17 +105,17 @@ def get_wgs_data():
"""
# Get the BamQC data
cache = QCETLCache()
if True: # DELETE(amasella): once IchorCNA data is available
wgs_df = cache.bamqc.bamqc
wgs_df[ICHOR_COL.Ploidy] = numpy.NaN
wgs_df[ICHOR_COL.TumorFraction] = numpy.NaN
# This doesn't seem to exist even though it should
wgs_df[BAMQC_COL.MarkDuplicates_PERCENT_DUPLICATION] = numpy.NaN
else:
wgs_df = cache.bamqc.bamqc.merge(cache.ichorcna.ichorcna[[ICHOR_COL.Ploidy, ICHOR_COL.TumorFraction]],
how="left",
left_on=[BAMQC_COL.Run, BAMQC_COL.Lane, BAMQC_COL.Barcodes],
right_on=[ICHOR_COL.Run, ICHOR_COL.Lane, ICHOR_COL.Barcodes])

ichorcna_df = cache.ichorcna.ichorcna[[ICHOR_COL.Run,
ICHOR_COL.Lane,
ICHOR_COL.Barcodes,
ICHOR_COL.Ploidy,
ICHOR_COL.TumorFraction]]
bamqc_df = cache.bamqc3.bamqc3
wgs_df = bamqc_df.merge(
ichorcna_df, how="left", left_on=[
BAMQC_COL.Run, BAMQC_COL.Lane, BAMQC_COL.Barcodes], right_on=[
ICHOR_COL.Run, ICHOR_COL.Lane, ICHOR_COL.Barcodes])
# Cast the primary key/join columns to explicit types
wgs_df = util.df_with_normalized_ius_columns(
wgs_df, BAMQC_COL.Run, BAMQC_COL.Lane, BAMQC_COL.Barcodes)
Expand Down Expand Up @@ -182,7 +181,7 @@ def get_wgs_data():
EMPTY_WGS = pd.DataFrame(columns=WGS_DF.columns)


def generate_total_reads(df, colour_by, shape_by):
def generate_total_reads(df, colour_by, shape_by, cutoff):
return generate(
"Total Reads (Passed Filter)",
df,
Expand All @@ -191,11 +190,12 @@ def generate_total_reads(df, colour_by, shape_by):
"# Reads (10^6)",
colour_by,
shape_by,
"none"
"none",
cutoff
)


def generate_mean_insert_size(df, colour_by, shape_by):
def generate_mean_insert_size(df, colour_by, shape_by, cutoff):
return generate(
"Insert Mean",
df,
Expand All @@ -204,10 +204,18 @@ def generate_mean_insert_size(df, colour_by, shape_by):
"Base Pairs",
colour_by,
shape_by,
"none"
"none",
cutoff
)


def generate_reads_per_start_point(df, colour_by, shape_by, cutoff):
return generate("Reads per Start Point",
df, lambda d: d[PINERY_COL.SampleName],
lambda d: d[BAMQC_COL.ReadsPerStartPoint],
"Reads", colour_by, shape_by, "none", cutoff)


def generate_duplication(df, colour_by, shape_by):
return generate(
"Duplication",
Expand Down Expand Up @@ -286,6 +294,18 @@ def generate_ploidy(df, colour_by, shape_by):
)


def generate_terminal_output(
data,
initial_cutoff_rpsp,
initial_cutoff_insert_mean,
initial_cutoff_pf_reads):
return terminal_output(data, [
('rpsp', BAMQC_COL.ReadsPerStartPoint, initial_cutoff_rpsp),
('insert_mean', BAMQC_COL.InsertMean, initial_cutoff_insert_mean),
('reads_pf', special_cols["Total Reads (Passed Filter)"], initial_cutoff_pf_reads),
])


# Layout elements
layout = core.Loading(fullscreen=True, type="cube", children=[
html.Div(className="body", children=[
Expand Down Expand Up @@ -377,7 +397,32 @@ def generate_ploidy(df, colour_by, shape_by):
# TODO: add "Search Sample" input

# TODO: add "Show Names" dropdown
# TODO: add cut-off sliders
html.Label([
"Reads Per Start Point:",
core.Slider(
id=ids["reads-per-start-point-slider"],
min=0,
max=50,
step=1,
marks={i * 5: str(i * 5) for i in range(0, 10)},
tooltip="always_visible",
value=initial_cutoff_rpsp
)
]),
html.Br(),
html.Label([
"Insert Mean:",
core.Slider(
id=ids["insert-mean-slider"],
min=0,
max=500,
step=10,
marks={i * 50: str(i * 50) for i in range(0, 10)},
tooltip="always_visible",
value=initial_cutoff_insert_mean
)
]),
html.Br(),

html.Label([
"Passed Filter Reads:",
Expand All @@ -386,36 +431,29 @@ def generate_ploidy(df, colour_by, shape_by):
min=0,
max=0.5,
step=0.025,
marks={
0: "0",
0.05: "0.05",
0.1: "0.1",
0.15: "0.15",
0.2: "0.2",
0.25: "0.25",
0.3: "0.3",
0.35: "0.35",
0.4: "0.4",
0.45: "0.45",
0.5: "0.5"
},
marks={i * 0.05: str(i * 0.05) for i in range(0, 10)},
tooltip="always_visible",
value=graph_cutoffs["pf_reads"]
value=initial_cutoff_pf_reads
)
]),
html.Br(),
]),

html.Div(className="seven columns", children=[
core.Graph(
id=ids["total-reads"],
figure=generate_total_reads(EMPTY_WGS, initial_colour_col,
initial_shape_col)
),
id=ids["total-reads"],
figure=generate_total_reads(EMPTY_WGS, initial_colour_col,
initial_shape_col, initial_cutoff_pf_reads)
),
core.Graph(
id=ids["mean-insert"],
figure=generate_mean_insert_size(
EMPTY_WGS, initial_colour_col, initial_shape_col)
EMPTY_WGS, initial_colour_col, initial_shape_col, initial_cutoff_insert_mean)
),
core.Graph(
id=ids["reads-per-start-point"],
figure=generate_reads_per_start_point(
EMPTY_WGS, initial_colour_col, initial_shape_col, initial_cutoff_rpsp)
),
core.Graph(
id=ids["duplication"],
Expand Down Expand Up @@ -448,14 +486,19 @@ def generate_ploidy(df, colour_by, shape_by):
EMPTY_WGS, initial_colour_col, initial_shape_col)
),
]),

# DataTable for all samples info
html.Div(className="data-table",
children=[
build_table(ids["data-table"], wgs_table_columns, WGS_DF,
BAMQC_COL.TotalReads)
])
])
]),
html.Div(className='terminal-output',
children=[
html.Pre(generate_terminal_output(EMPTY_WGS, initial_cutoff_rpsp, initial_cutoff_insert_mean,
initial_cutoff_pf_reads),
id=ids['terminal-output'],
)
]),
html.Div(className='data-table',
children=[
build_table(ids["data-table"], wgs_table_columns, WGS_DF,
BAMQC_COL.TotalReads)
]),
])
])

Expand All @@ -465,12 +508,14 @@ def init_callbacks(dash_app):
[
Output(ids["total-reads"], "figure"),
Output(ids["mean-insert"], "figure"),
Output(ids["reads-per-start-point"], "figure"),
Output(ids["duplication"], "figure"),
Output(ids["purity"], "figure"),
Output(ids["ploidy"], "figure"),
Output(ids["unmapped-reads"], "figure"),
Output(ids["non-primary-reads"], "figure"),
Output(ids["on-target-reads"], "figure"),
Output(ids["terminal-output"], "value"),
Output(ids["data-table"], "data"),
],
[
Expand All @@ -482,14 +527,20 @@ def init_callbacks(dash_app):
State(ids['second-sort'], 'value'),
State(ids['colour-by'], 'value'),
State(ids['shape-by'], 'value'),
State(ids["reads-per-start-point-slider"], 'value'),
State(ids["insert-mean-slider"], 'value'),
State(ids["passed-filter-reads-slider"], 'value'),
]
)
def update_pressed(click,
runs,
first_sort,
second_sort,
colour_by,
shape_by):
shape_by,
total_reads_cutoff,
insert_mean_cutoff,
rpsp_cutoff):
if not runs:
df = pd.DataFrame(columns=WGS_DF.columns)
else:
Expand All @@ -501,15 +552,17 @@ def update_pressed(click,
dd = defaultdict(list)

return [
generate_total_reads(df, colour_by, shape_by),
generate_mean_insert_size(df, colour_by, shape_by),
generate_total_reads(df, colour_by, shape_by, total_reads_cutoff),
generate_mean_insert_size(df, colour_by, shape_by, insert_mean_cutoff),
generate_reads_per_start_point(df, colour_by, shape_by, rpsp_cutoff),
generate_duplication(df, colour_by, shape_by),
generate_purity(df, colour_by, shape_by),
generate_ploidy(df, colour_by, shape_by),
generate_unmapped_reads(df, colour_by, shape_by),
generate_non_primary(df, colour_by, shape_by),
generate_on_target_reads(df, colour_by, shape_by),
df.to_dict("records", into=dd),
generate_terminal_output(df, rpsp_cutoff, insert_mean_cutoff, total_reads_cutoff),
df.to_dict('records', into=dd),
]

@dash_app.callback(
Expand Down

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