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Snakefile
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Snakefile
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import csv
import json
import os
import pandas as pd
from pathlib import Path
import pprint
from snakemake.logging import logger
import sys
# Set snakemake directory
SNAKEMAKE_DIR = os.path.dirname(workflow.snakefile)
wildcard_constraints:
type="(natural|simulated)",
sample="\w+",
timepoint="\d{4}-\d{2}-\d{2}"
# Load configuration parameters.
configfile: "config/config.json"
# Construct a list of timepoints for the requested start/end dates.
def _get_timepoints_for_build_interval(start_date, end_date, pivot_interval, min_years_per_build):
# Find all potential timepoints.
all_timepoints = pd.date_range(start_date, end_date, freq="%sMS" % pivot_interval)
# Calculate date offset from the minimum years per build to find first
# timepoint we can use to partition strains.
offset = pd.DateOffset(years=min_years_per_build)
first_timepoint = all_timepoints[0] + offset
# Convert datetime instances to strings for all valid build timepoints.
timepoints = [
timepoint.strftime("%Y-%m-%d")
for timepoint in all_timepoints
if timepoint >= first_timepoint
]
return timepoints
path_to_fauna = config["path_to_fauna"]
TIMEPOINT_TYPES = []
TIMEPOINT_SAMPLES = []
TIMEPOINTS = []
PREDICTOR_TYPES = []
PREDICTOR_SAMPLES = []
PREDICTORS = []
VALIDATION_PREDICTOR_TYPES = []
VALIDATION_PREDICTOR_SAMPLES = []
VALIDATION_PREDICTOR_PREDICTORS = []
TEST_PREDICTOR_TYPES = []
TEST_PREDICTOR_SAMPLES = []
TEST_PREDICTORS = []
ACTIVE_BUILDS = config["active_builds"].split(" ")
logger.info("Active builds: " + config["active_builds"])
for build_type, builds_by_type in config["builds"].items():
for sample, build in builds_by_type.items():
# Limit Snakemake rules to active builds.
if sample in ACTIVE_BUILDS:
timepoints_for_build = _get_timepoints_for_build_interval(
build["start_date"],
build["end_date"],
build["pivot_interval"],
build["min_years_per_build"]
)
for timepoint in timepoints_for_build:
TIMEPOINT_TYPES.append(build_type)
TIMEPOINT_SAMPLES.append(sample)
TIMEPOINTS.append(timepoint)
# Builds with validation samples are used for testing and not model fitting.
if "validation_build" in build:
validation_build = config["builds"][build_type][build["validation_build"]]
for predictor in validation_build["predictors"]:
TEST_PREDICTOR_TYPES.append(build_type)
TEST_PREDICTOR_SAMPLES.append(sample)
TEST_PREDICTORS.append(predictor)
else:
for predictor in build["predictors"]:
PREDICTOR_TYPES.append(build_type)
PREDICTOR_SAMPLES.append(sample)
PREDICTORS.append(predictor)
# Note samples for which we need validation figures.
if "full_tree_build" in build:
for predictor in build["validation_predictors"]:
VALIDATION_PREDICTOR_TYPES.append(build_type)
VALIDATION_PREDICTOR_SAMPLES.append(sample)
VALIDATION_PREDICTOR_PREDICTORS.append(predictor)
#
# Configure amino acid distance masks.
#
# Load mask configuration including which masks map to which attributes per
# lineage and segment.
masks_config = pd.read_table("config/distance_maps.tsv")
def _get_build_mask_config(wildcards):
config = masks_config[(masks_config["lineage"] == wildcards.lineage) &
(masks_config["segment"] == wildcards.segment)]
if config.shape[0] > 0:
return config
else:
return None
def _get_distance_comparisons_by_lineage_and_segment(wildcards):
config = _get_build_mask_config(wildcards)
return " ".join(config.loc[:, "compare_to"].values)
def _get_distance_attributes_by_lineage_and_segment(wildcards):
config = _get_build_mask_config(wildcards)
return " ".join(config.loc[:, "attribute"].values)
def _get_distance_maps_by_lineage_and_segment(wildcards):
config = _get_build_mask_config(wildcards)
return [
"config/distance_maps/{wildcards.lineage}/{wildcards.segment}/{distance_map}.json".format(wildcards=wildcards, distance_map=distance_map)
for distance_map in config.loc[:, "distance_map"].values
]
def _get_target_distance_earliest_date_by_wildcards(wildcards):
timepoint = pd.to_datetime(wildcards.timepoint)
offset = pd.DateOffset(years=config["years_back_for_target_distance"])
earliest_date = timepoint - offset
return earliest_date.strftime("%Y-%m-%d")
def _get_distance_earliest_date_by_wildcards(wildcards):
timepoint = pd.to_datetime(wildcards.timepoint)
season_offset = pd.DateOffset(months=config["months_for_distance_season"])
tree_offset = pd.DateOffset(years=config["max_years_for_distances"])
earliest_date = timepoint - season_offset - tree_offset
return earliest_date.strftime("%Y-%m-%d")
def _get_distance_latest_date_by_wildcards(wildcards):
timepoint = pd.to_datetime(wildcards.timepoint)
offset = pd.DateOffset(months=config["months_for_distance_season"])
latest_date = timepoint - offset
return latest_date.strftime("%Y-%m-%d")
#
# Distance functions for simulations.
#
def _get_distance_attributes_for_simulations(wildcards):
config = masks_config[(masks_config["lineage"] == "h3n2") &
(masks_config["segment"] == "ha") &
(masks_config["compare_to"] != "pairwise")]
return " ".join(config.loc[:, "attribute"].values)
def _get_pairwise_distance_attributes_for_simulations(wildcards):
config = masks_config[(masks_config["lineage"] == "h3n2") &
(masks_config["segment"] == "ha") &
(masks_config["compare_to"] == "pairwise")]
return " ".join(config.loc[:, "attribute"].values)
def _get_distance_maps_for_simulations(wildcards):
config = masks_config[(masks_config["lineage"] == "h3n2") &
(masks_config["segment"] == "ha") &
(masks_config["compare_to"] != "pairwise")]
return [
"config/distance_maps/h3n2/ha/{distance_map}.json".format(distance_map=distance_map)
for distance_map in config.loc[:, "distance_map"].values
]
def _get_pairwise_distance_maps_for_simulations(wildcards):
config = masks_config[(masks_config["lineage"] == "h3n2") &
(masks_config["segment"] == "ha") &
(masks_config["compare_to"] == "pairwise")]
return [
"config/distance_maps/h3n2/ha/{distance_map}.json".format(distance_map=distance_map)
for distance_map in config.loc[:, "distance_map"].values
]
def _get_distance_comparisons_for_simulations(wildcards):
config = masks_config[(masks_config["lineage"] == "h3n2") &
(masks_config["segment"] == "ha") &
(masks_config["compare_to"] != "pairwise")]
return " ".join(config.loc[:, "compare_to"].values)
def _get_start_date_from_range(wildcards):
return "%s-10-01" % wildcards["year_range"].split("-")[0]
def _get_end_date_from_range(wildcards):
return "%s-10-01" % wildcards["year_range"].split("-")[1]
def _get_predictor_list(wildcards):
return " ".join(wildcards["predictors"].split("-"))
def _get_clock_rate_by_wildcards(wildcards):
rates_by_lineage_and_segment = {
('h3n2', 'ha'): 0.0043, ('h3n2', 'na'):0.0029,
('h1n1pdm', 'ha'): 0.0040, ('h1n1pdm', 'na'):0.0032,
('vic', 'ha'): 0.0024, ('vic', 'na'):0.0015,
('yam', 'ha'): 0.0019, ('yam', 'na'):0.0013
}
sample = _get_sample_by_wildcards(wildcards)
dataset = config["datasets"][sample]
lineage = dataset["lineage"]
segment = dataset["segment"]
try:
rate = rates_by_lineage_and_segment[(lineage, segment)]
except KeyError:
rate = None
return rate
def _get_clock_rate_argument(wildcards):
rate = _get_clock_rate_by_wildcards(wildcards)
if rate is None:
argument = ""
else:
argument = "--clock-rate %.5f" % rate
return argument
def _get_clock_std_dev_argument(wildcards):
rate = _get_clock_rate_by_wildcards(wildcards)
if rate is None:
argument = ""
else:
argument = "--clock-std-dev %.5f" % (0.2 * rate)
return argument
def timestamp_to_float(time):
"""Convert a pandas timestamp to a floating point date.
>>> import datetime
>>> time = datetime.date(2010, 10, 1)
>>> timestamp_to_float(time)
2010.75
>>> time = datetime.date(2011, 4, 1)
>>> timestamp_to_float(time)
2011.25
>>> timestamp_to_float(datetime.date(2011, 1, 1))
2011.0
>>> timestamp_to_float(datetime.date(2011, 12, 1)) == (2011.0 + 11.0 / 12)
True
"""
return time.year + ((time.month - 1) / 12.0)
def _get_min_date_for_augur_frequencies(wildcards):
return timestamp_to_float(pd.to_datetime(wildcards.start))
def _get_max_date_for_augur_frequencies(wildcards):
return timestamp_to_float(pd.to_datetime(wildcards.timepoint))
def _get_excluded_fields_arg(wildcards):
if config.get("excluded_node_data_fields"):
return "--excluded-fields %s" % " ".join(config["excluded_node_data_fields"])
else:
return ""
genes_to_translate = {
'ha': ['SigPep', 'HA1', 'HA2'],
'na': ['NA']
}
def gene_names(wildcards=None, segment=None):
if segment is None:
segment = _get_segment(wildcards)
if segment in genes_to_translate:
genes = genes_to_translate[segment]
else:
print(f"WARNING: Genes to translate are not defined for {segment}, defaulting to '{segment.upper()}'", file=sys.stderr)
genes = [segment.upper()]
return genes
def translations(wildcards=None, segment=None, path=None):
genes = gene_names(wildcards, segment)
if path is None:
path = BUILD_TIMEPOINT_PATH
return [path + "aa-seq_%s.fasta" % gene
for gene in genes]
#
# Define helper functions for Snakemake outputs
#
def _get_clade_model_files(wildcards):
return expand("results/builds/{type}/{sample}/models_by_clades/{predictors}.json", zip, type=PREDICTOR_TYPES, sample=PREDICTOR_SAMPLES, predictors=PREDICTORS)
def _get_distance_model_files(wildcards):
return expand("results/builds/{type}/{sample}/models_by_distances/{predictors}.json", zip, type=PREDICTOR_TYPES, sample=PREDICTOR_SAMPLES, predictors=PREDICTORS) + expand("results/builds/{type}/{sample}/test_models_by_distances/{predictors}.json", zip, type=TEST_PREDICTOR_TYPES, sample=TEST_PREDICTOR_SAMPLES, predictors=TEST_PREDICTORS)
def _get_distance_model_errors(wildcards):
return expand("results/builds/{type}/{sample}/annotated_models_by_distances_errors/{predictors}.tsv", zip, type=PREDICTOR_TYPES, sample=PREDICTOR_SAMPLES, predictors=PREDICTORS) + expand("results/builds/{type}/{sample}/annotated_test_models_by_distances_errors/{predictors}.tsv", zip, type=TEST_PREDICTOR_TYPES, sample=TEST_PREDICTOR_SAMPLES, predictors=TEST_PREDICTORS)
def _get_distance_model_coefficients(wildcards):
return expand("results/builds/{type}/{sample}/annotated_models_by_distances_coefficients/{predictors}.tsv", zip, type=PREDICTOR_TYPES, sample=PREDICTOR_SAMPLES, predictors=PREDICTORS) + expand("results/builds/{type}/{sample}/annotated_test_models_by_distances_coefficients/{predictors}.tsv", zip, type=TEST_PREDICTOR_TYPES, sample=TEST_PREDICTOR_SAMPLES, predictors=TEST_PREDICTORS)
def _get_auspice_files(wildcards):
return expand("results/auspice/flu_{type}_{sample}_{timepoint}_{filetype}.json", zip, type=TIMEPOINT_TYPES, sample=TIMEPOINT_SAMPLES, timepoint=TIMEPOINTS, filetype=["tree", "tip-frequencies"] * len(TIMEPOINTS))
def _get_validation_figures(wildcards):
return expand("manuscript/figures/validation_figure_{type}-{sample}-{predictors}.pdf", zip, type=VALIDATION_PREDICTOR_TYPES, sample=VALIDATION_PREDICTOR_SAMPLES, predictors=VALIDATION_PREDICTOR_PREDICTORS)
def _get_validation_figure_clades(wildcards):
return expand("manuscript/figures/validation_figure_clades_{type}-{sample}-{predictors}.tsv", zip, type=VALIDATION_PREDICTOR_TYPES, sample=VALIDATION_PREDICTOR_SAMPLES, predictors=VALIDATION_PREDICTOR_PREDICTORS)
def _get_validation_figure_ranks(wildcards):
return expand("manuscript/figures/validation_figure_ranks_{type}-{sample}-{predictors}.tsv", zip, type=VALIDATION_PREDICTOR_TYPES, sample=VALIDATION_PREDICTOR_SAMPLES, predictors=VALIDATION_PREDICTOR_PREDICTORS)
include: "rules/utils.smk"
include: "rules/datasets.smk"
include: "rules/builds.smk"
rule all:
input:
"results/distance_model_errors.tsv",
"results/distance_model_coefficients.tsv",
_get_auspice_files,
_get_validation_figures
rule build_environment:
conda: "envs/anaconda.python3.yaml"
shell: "echo Environment built"
rule clade_models:
input: _get_clade_model_files
rule distance_models:
input: _get_distance_model_files
rule distance_models_errors:
input:
errors = _get_distance_model_errors
output:
errors = "results/distance_model_errors.tsv"
conda: "envs/anaconda.python3.yaml"
shell:
"""
python3 scripts/concatenate_tables.py \
--tables {input.errors} \
--output {output.errors}
"""
rule distance_models_coefficients:
input:
coefficients = _get_distance_model_coefficients
output:
coefficients = "results/distance_model_coefficients.tsv"
conda: "envs/anaconda.python3.yaml"
shell:
"""
python3 scripts/concatenate_tables.py \
--tables {input.coefficients} \
--output {output.coefficients}
"""
rule auspice:
input: _get_auspice_files
rule validation_figures:
input: _get_validation_figures
rule validation_figure_clades:
input:
_get_validation_figure_clades
output:
clades = "results/validation_figure_clades.tsv"
conda: "envs/anaconda.python3.yaml"
shell:
"""
python3 scripts/collect_tables.py \
--tables {input} \
--output {output.clades}
"""
rule validation_figure_ranks:
input:
_get_validation_figure_ranks
output:
ranks = "results/validation_figure_ranks.tsv"
conda: "envs/anaconda.python3.yaml"
shell:
"""
python3 scripts/collect_tables.py \
--tables {input} \
--output {output.ranks}
"""
rule trees:
input: expand(rules.aggregate_tree_plots.output.trees, zip, type=VALIDATION_PREDICTOR_TYPES, sample=VALIDATION_PREDICTOR_SAMPLES)
output:
trees="results/figures/trees.pdf"
shell: "gs -dBATCH -dNOPAUSE -q -sDEVICE=pdfwrite -sOutputFile={output} {input}"
# Build figures for manuscript.
rule figure_for_model_schematic:
input:
tree_for_timepoint_t = "results/auspice/flu_simulated_simulated_sample_3_2029-10-01_tree.json",
tree_for_timepoint_u = "results/auspice/flu_simulated_simulated_sample_3_2030-10-01_tree.json",
frequencies_for_timepoint_t = "results/auspice/flu_simulated_simulated_sample_3_2029-10-01_tip-frequencies.json",
frequencies_for_timepoint_u = "results/auspice/flu_simulated_simulated_sample_3_2030-10-01_tip-frequencies.json"
output:
figure = "manuscript/figures/distance-based-fitness-model.pdf"
log:
notebook = "logs/notebooks/plot-model-diagram.ipynb"
conda: "envs/anaconda.python3.yaml"
notebook:
"notebooks/plot-model-diagram.ipynb"
rule bootstrap_analysis:
input:
model_distances = "results/distance_model_errors.tsv"
output:
output_table = "manuscript/tables/bootstrap_p_values.tsv",
bootstrap_figure_for_simulated_sample = "manuscript/figures/bootstrap_distributions_for_simulated_sample_3.pdf",
bootstrap_figure_for_natural_sample = "manuscript/figures/bootstrap_distributions_for_natural_sample_1_with_90_vpm_sliding.pdf",
composite_vs_individual_model_table = "manuscript/tables/composite_vs_individual_model_comparison.tex"
params:
n_bootstraps = 10000
log:
notebook = "logs/notebooks/bootstrap-analysis.ipynb"
conda: "envs/anaconda.python3.yaml"
notebook:
"notebooks/bootstrap-analysis.ipynb"
rule figures_and_tables_for_model_results:
input:
model_distances = "results/distance_model_errors.tsv",
model_coefficients = "results/distance_model_coefficients.tsv",
bootstrap_p_values = rules.bootstrap_analysis.output.output_table
output:
# Simulated populations
table_for_simulated_model_selection = "manuscript/tables/simulated_model_selection.tex",
source_data_for_simulated_model_coefficients = "manuscript/tables/simulated_model_coefficients.csv",
source_data_for_simulated_model_distances = "manuscript/tables/simulated_model_distances.csv",
figure_for_simulated_model_controls = "manuscript/figures/unadjusted-model-accuracy-and-coefficients-for-simulated-populations-controls.pdf",
figure_for_simulated_individual_models = "manuscript/figures/unadjusted-model-accuracy-and-coefficients-for-simulated-populations.pdf",
figure_for_simulated_composite_models = "manuscript/figures/unadjusted-composite-model-accuracy-and-coefficients-for-simulated-populations.pdf",
# Natural populations
table_for_natural_model_selection = "manuscript/tables/natural_model_selection.tex",
table_for_natural_model_complete_selection = "manuscript/tables/complete_natural_model_selection.tex",
source_data_for_natural_model_coefficients = "manuscript/tables/natural_model_coefficients.csv",
source_data_for_natural_model_distances = "manuscript/tables/natural_model_distances.csv",
figure_for_natural_epitope_vs_oracle_models = "manuscript/figures/unadjusted-composite-model-accuracy-and-coefficients-for-natural-populations-epitope-vs-oracle.pdf",
figure_for_natural_individual_models = "manuscript/figures/unadjusted-model-accuracy-and-coefficients-for-natural-populations.pdf",
figure_for_natural_composite_models = "manuscript/figures/best-composite-unadjusted-model-accuracy-and-coefficients-for-natural-populations.pdf",
figure_for_natural_updated_models = "manuscript/figures/models-natural-populations-composite-with-updated-coefficients-across-test-data.pdf",
# Cross-validation schematics
figure_for_simulated_cross_validation = "manuscript/figures/cross-validation-for-simulated-populations.pdf",
figure_for_natural_cross_validation = "manuscript/figures/cross-validation-for-natural-populations.pdf"
params:
simulated_sample = "simulated_sample_3",
natural_sample = "natural_sample_1_with_90_vpm_sliding"
log:
notebook = "logs/notebooks/model-results.ipynb"
conda: "envs/anaconda.python3.yaml"
notebook:
"notebooks/model-results.ipynb"
rule figure_for_vaccine_comparison:
input:
validation_tip_attributes = "results/builds/natural/natural_sample_1_with_90_vpm_sliding/tip_attributes_with_weighted_distances.tsv",
test_tip_attributes = "results/builds/natural/natural_sample_1_with_90_vpm_sliding_test_tree/tip_attributes_with_weighted_distances.tsv",
cTiter_x_ne_star_validation_forecasts_path = "results/builds/natural/natural_sample_1_with_90_vpm_sliding/forecasts_cTiter_x-ne_star.tsv",
ne_star_lbi_validation_forecasts_path = "results/builds/natural/natural_sample_1_with_90_vpm_sliding/forecasts_ne_star-lbi.tsv",
naive_validation_forecasts_path = "results/builds/natural/natural_sample_1_with_90_vpm_sliding/forecasts_naive.tsv",
cTiter_x_ne_star_test_forecasts_path = "results/builds/natural/natural_sample_1_with_90_vpm_sliding_test_tree/forecasts_cTiter_x-ne_star.tsv",
ne_star_lbi_test_forecasts_path = "results/builds/natural/natural_sample_1_with_90_vpm_sliding_test_tree/forecasts_ne_star-lbi.tsv",
naive_test_forecasts_path = "results/builds/natural/natural_sample_1_with_90_vpm_sliding_test_tree/forecasts_naive.tsv",
vaccines_json_path = "config/vaccines_h3n2.json",
titers = config["builds"]["natural"]["natural_sample_1_with_90_vpm_sliding_test_tree"]["titers"]
output:
figure = "manuscript/figures/vaccine-comparison.pdf",
relative_figure = "manuscript/figures/vaccine-comparison-relative-distance.pdf",
source_data = "manuscript/figures/vaccine-comparison.csv"
log:
notebook = "logs/notebooks/vaccine-strain-comparison.ipynb"
conda: "envs/anaconda.python3.yaml"
notebook:
"notebooks/vaccine-strain-comparison.ipynb"
rule table_of_mutations_by_trunk_status_for_simulated_populations:
input:
full_tree_json = "results/auspice/flu_simulated_simulated_sample_3_full_tree_2040-10-01_tree.json",
epitope_sites_distance_map = "config/distance_maps/h3n2/ha/luksza.json",
output:
table = "manuscript/tables/mutations_by_trunk_status_for_simulated_populations.tex"
log:
notebook = "logs/notebooks/simulated-mutations-by-trunk-status.ipynb"
conda: "envs/anaconda.python3.yaml"
notebook:
"notebooks/simulated-mutations-by-trunk-status.ipynb"
rule table_of_mutations_by_trunk_status_for_natural_populations:
input:
full_tree_json = "results/auspice/flu_natural_natural_sample_1_with_90_vpm_sliding_full_tree_2015-10-01_tree.json",
epitope_sites_distance_map = "config/distance_maps/h3n2/ha/luksza.json",
output:
table = "manuscript/tables/mutations_by_trunk_status.tex"
log:
notebook = "logs/notebooks/natural-mutations-by-trunk-status.ipynb"
conda: "envs/anaconda.python3.yaml"
notebook:
"notebooks/natural-mutations-by-trunk-status.ipynb"
rule figures:
input:
rules.figure_for_model_schematic.output,
*rules.figures_and_tables_for_model_results.output,
_get_validation_figures,
rules.figure_for_vaccine_comparison.output,
figure_names="manuscript/figures/figure_names.tsv"
output:
"manuscript/Figure_1.pdf"
shell:
"""
while read original_name new_name
do
ln manuscript/figures/$original_name manuscript/$new_name
done < {input.figure_names}
"""
# Compile the manuscript after creating all figures and tables.
rule manuscript:
input:
# Tables and figures
"manuscript/Figure_1.pdf",
rules.table_of_mutations_by_trunk_status_for_simulated_populations.output,
rules.table_of_mutations_by_trunk_status_for_natural_populations.output,
# Manuscript text and references
"manuscript/flu_forecasting.tex",
"manuscript/flu_forecasting.bib",
"manuscript/abstract.tex",
"manuscript/main.tex"
output:
"manuscript/flu_forecasting.pdf"
params:
title = "flu_forecasting"
shell:
"""
cd manuscript
pdflatex -draftmode {params.title}
bibtex {params.title}
pdflatex -draftmode {params.title}
pdflatex {params.title}
"""