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classify-cancer-types.py
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classify-cancer-types.py
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"""
2018 Gregory Way
9.tcga-classify/classify-cancer-types.py
Predicting 33 different cancer-types using elastic net logistic regression and
compressed gene expression features in TCGA PanCanAtlas
Usage:
python classify-cancer-types.py
Output:
Cancer-type specific DataFrames storing ROC, precision-recall, and classifier
coefficients for every compression model trained in their ability to predict
cancer-type.
"""
import os
import numpy as np
import pandas as pd
from scripts.tcga_util import (
get_threshold_metrics,
summarize_results,
extract_coefficients,
align_matrices,
train_model,
build_feature_dictionary,
process_y_matrix_cancertype,
check_status,
)
np.random.seed(123)
# Load constants
folds = 5
max_iter = 100
algorithms = ["pca", "ica", "nmf", "dae", "vae"]
signals = ["signal", "shuffled"]
alphas = [0.1, 0.13, 0.15, 0.2, 0.25, 0.3]
l1_ratios = [0.15, 0.16, 0.2, 0.25, 0.3, 0.4]
# Load data to build y matrices
base_url = "https://github.com/greenelab/pancancer/raw"
commit = "2a0683b68017fb226f4053e63415e4356191734f"
# Load data
file = "{}/{}/data/sample_freeze.tsv".format(base_url, commit)
sample_freeze_df = pd.read_table(file, index_col=0)
file = "{}/{}/data/mutation_burden_freeze.tsv".format(base_url, commit)
mut_burden_df = pd.read_table(file, index_col=0)
# Track total metrics for each cancer-type in one file
count_list = []
metric_cols = [
"auroc",
"aupr",
"gene_or_cancertype",
"signal",
"z_dim",
"seed",
"algorithm",
"data_type",
]
# Obtain a dictionary of file directories for loading each feature matrix (X)
z_matrix_dict = build_feature_dictionary()
# Provide one vs all classifications for all 33 different cancertypes in TCGA
for acronym in sample_freeze_df.DISEASE.unique():
# Create list to store cancer-type specific results
cancertype_auc_list = []
cancertype_aupr_list = []
cancertype_coef_list = []
cancertype_metrics_list = []
# Create directory for the cancer-type
cancertype_dir = os.path.join("results", "cancer-type", acronym)
os.makedirs(cancertype_dir, exist_ok=True)
y_df, count_df = process_y_matrix_cancertype(
acronym=acronym,
sample_freeze=sample_freeze_df,
mutation_burden=mut_burden_df,
hyper_filter=5,
)
# Track the status counts of all classifiers
count_list.append(count_df)
# Check if cancer-type has been processed already
check_file = os.path.join(cancertype_dir, "{}_coefficients.tsv.gz".format(acronym))
if check_status(check_file):
continue
# Now, perform all the analyses for each X matrix
for signal in z_matrix_dict.keys():
z_dim_dict = z_matrix_dict[signal]
for z_dim in z_dim_dict.keys():
seed_z_dim_dict = z_dim_dict[z_dim]
for seed in seed_z_dim_dict.keys():
z_train_file = z_matrix_dict[signal][z_dim][seed]["train"]
z_test_file = z_matrix_dict[signal][z_dim][seed]["test"]
for alg in algorithms:
# Load and process data
train_samples, x_train_df, y_train_df = align_matrices(
x_file_or_df=z_train_file,
y=y_df,
add_cancertype_covariate=False,
algorithm=alg,
)
test_samples, x_test_df, y_test_df = align_matrices(
x_file_or_df=z_test_file,
y=y_df,
add_cancertype_covariate=False,
algorithm=alg,
)
# Train the model
print(
"Training model... cancer-type: {}, "
"algorithm: {}, signal: {}, z_dim: {}, "
"seed: {}".format(acronym, alg, signal, z_dim, seed)
)
# Fit the model
cv_pipeline, y_pred_train_df, y_pred_test_df, y_cv_df = train_model(
x_train=x_train_df,
x_test=x_test_df,
y_train=y_train_df,
alphas=alphas,
l1_ratios=l1_ratios,
n_folds=folds,
max_iter=max_iter,
)
# Get metric predictions
y_train_results = get_threshold_metrics(
y_train_df.status, y_pred_train_df, drop=False
)
y_test_results = get_threshold_metrics(
y_test_df.status, y_pred_test_df, drop=False
)
y_cv_results = get_threshold_metrics(
y_train_df.status, y_cv_df, drop=False
)
# Get coefficients
coef_df = extract_coefficients(
cv_pipeline=cv_pipeline,
feature_names=x_train_df.columns,
signal=signal,
z_dim=z_dim,
seed=seed,
algorithm=alg,
)
coef_df = coef_df.assign(acronym=acronym)
# Store all results
train_metrics_, train_roc_df, train_pr_df = summarize_results(
results=y_train_results,
gene_or_cancertype=acronym,
signal=signal,
z_dim=z_dim,
seed=seed,
algorithm=alg,
data_type="train",
)
test_metrics_, test_roc_df, test_pr_df = summarize_results(
results=y_test_results,
gene_or_cancertype=acronym,
signal=signal,
z_dim=z_dim,
seed=seed,
algorithm=alg,
data_type="test",
)
cv_metrics_, cv_roc_df, cv_pr_df = summarize_results(
results=y_cv_results,
gene_or_cancertype=acronym,
signal=signal,
z_dim=z_dim,
seed=seed,
algorithm=alg,
data_type="cv",
)
# Compile summary metrics
metrics_ = [train_metrics_, test_metrics_, cv_metrics_]
metric_df_ = pd.DataFrame(metrics_, columns=metric_cols)
cancertype_metrics_list.append(metric_df_)
auc_df = pd.concat([train_roc_df, test_roc_df, cv_roc_df])
cancertype_auc_list.append(auc_df)
aupr_df = pd.concat([train_pr_df, test_pr_df, cv_pr_df])
cancertype_aupr_list.append(aupr_df)
cancertype_coef_list.append(coef_df)
cancertype_auc_df = pd.concat(cancertype_auc_list)
cancertype_aupr_df = pd.concat(cancertype_aupr_list)
cancertype_coef_df = pd.concat(cancertype_coef_list)
cancertype_metrics_df = pd.concat(cancertype_metrics_list)
file = os.path.join(
cancertype_dir, "{}_auc_threshold_metrics.tsv.gz".format(acronym)
)
cancertype_auc_df.to_csv(
file, sep="\t", index=False, compression="gzip", float_format="%.5g"
)
file = os.path.join(
cancertype_dir, "{}_aupr_threshold_metrics.tsv.gz".format(acronym)
)
cancertype_aupr_df.to_csv(
file, sep="\t", index=False, compression="gzip", float_format="%.5g"
)
cancertype_coef_df.to_csv(
check_file, sep="\t", index=False, compression="gzip", float_format="%.5g"
)
file = os.path.join(cancertype_dir, "{}_classify_metrics.tsv.gz".format(acronym))
cancertype_metrics_df.to_csv(
file, sep="\t", index=False, compression="gzip", float_format="%.5g"
)
# Write out a combined status matrix as well
final_count_list_df = pd.concat(count_list)
file = os.path.join("results", "all_cancertype_status_counts.tsv")
final_count_list_df.to_csv(file, sep="\t", index=False)