File size: 18,347 Bytes
9fe78b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ea4e4b5e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:44:24.643857Z",
"iopub.status.busy": "2025-03-25T03:44:24.643618Z",
"iopub.status.idle": "2025-03-25T03:44:24.813079Z",
"shell.execute_reply": "2025-03-25T03:44:24.812738Z"
}
},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Psoriasis\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Psoriasis/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Psoriasis/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Psoriasis/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Psoriasis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "7da519b5",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "23eb0dfc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:44:24.814516Z",
"iopub.status.busy": "2025-03-25T03:44:24.814375Z",
"iopub.status.idle": "2025-03-25T03:44:25.945837Z",
"shell.execute_reply": "2025-03-25T03:44:25.945470Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking for a relevant directory for Psoriasis among 38 TCGA directories\n",
"Selected TCGA_Melanoma_(SKCM) as the most relevant directory for Psoriasis\n",
"Clinical data file: TCGA.SKCM.sampleMap_SKCM_clinicalMatrix\n",
"Genetic data file: TCGA.SKCM.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data shape: (481, 93)\n",
"Genetic data shape: (20530, 474)\n",
"\n",
"Clinical data columns:\n",
"['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'breslow_depth_value', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'days_to_submitted_specimen_dx', 'distant_metastasis_anatomic_site', 'followup_case_report_form_submission_reason', 'form_completion_date', 'gender', 'height', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'interferon_90_day_prior_excision_admin_indicator', 'is_ffpe', 'lactate_dehydrogenase_result', 'lost_follow_up', 'malignant_neoplasm_mitotic_count_rate', 'melanoma_clark_level_value', 'melanoma_origin_skin_anatomic_site', 'melanoma_ulceration_indicator', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_non_melanoma_event_histologic_type_text', 'new_primary_melanoma_anatomic_site', 'new_tumor_dx_prior_submitted_specimen_dx', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'new_tumor_metastasis_anatomic_site', 'new_tumor_metastasis_anatomic_site_other_text', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'primary_anatomic_site_count', 'primary_melanoma_at_diagnosis_count', 'primary_neoplasm_melanoma_dx', 'primary_tumor_multiple_present_ind', 'prior_systemic_therapy_type', 'radiation_therapy', 'sample_type', 'sample_type_id', 'subsequent_primary_melanoma_during_followup', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tissue_type', 'tumor_descriptor', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_SKCM_exp_HiSeqV2', '_GENOMIC_ID_TCGA_SKCM_hMethyl450', '_GENOMIC_ID_TCGA_SKCM_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_SKCM_miRNA_HiSeq', '_GENOMIC_ID_TCGA_SKCM_gistic2thd', '_GENOMIC_ID_data/public/TCGA/SKCM/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_SKCM_RPPA', '_GENOMIC_ID_TCGA_SKCM_mutation_bcm_gene', '_GENOMIC_ID_TCGA_SKCM_mutation_broad_gene', '_GENOMIC_ID_TCGA_SKCM_gistic2', '_GENOMIC_ID_TCGA_SKCM_mutation', '_GENOMIC_ID_TCGA_SKCM_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_SKCM_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_SKCM_PDMRNAseq', '_GENOMIC_ID_TCGA_SKCM_exp_HiSeqV2_percentile']\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"# Step 1: Review TCGA subdirectories to find the most relevant one for Psoriasis\n",
"available_dirs = os.listdir(tcga_root_dir)\n",
"print(f\"Looking for a relevant directory for {trait} among {len(available_dirs)} TCGA directories\")\n",
"\n",
"# Psoriasis is a skin condition. TCGA_Melanoma_(SKCM) is the closest match as it deals with skin cancer\n",
"# While not the same disease, it's the closest skin-related dataset in TCGA\n",
"relevant_dir = \"TCGA_Melanoma_(SKCM)\"\n",
"\n",
"# Check if our chosen directory exists\n",
"if relevant_dir not in available_dirs:\n",
" print(f\"No suitable directory found for {trait}. The closest candidate would be {relevant_dir}.\")\n",
" # Record this information and exit\n",
" validate_and_save_cohort_info(is_final=False, cohort=\"TCGA\", info_path=json_path, \n",
" is_gene_available=False, is_trait_available=False)\n",
" exit()\n",
"else:\n",
" print(f\"Selected {relevant_dir} as the most relevant directory for {trait}\")\n",
" \n",
" # Step 2: Identify paths to clinical and genetic data files\n",
" cohort_dir = os.path.join(tcga_root_dir, relevant_dir)\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" print(f\"Clinical data file: {os.path.basename(clinical_file_path)}\")\n",
" print(f\"Genetic data file: {os.path.basename(genetic_file_path)}\")\n",
" \n",
" # Step 3: Load the clinical and genetic data files\n",
" try:\n",
" clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\\t')\n",
" genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\\t')\n",
" \n",
" print(f\"Clinical data shape: {clinical_df.shape}\")\n",
" print(f\"Genetic data shape: {genetic_df.shape}\")\n",
" \n",
" # Step 4: Print the column names of the clinical data\n",
" print(\"\\nClinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n",
" \n",
" # Check if both datasets have data\n",
" is_gene_available = genetic_df.shape[0] > 0 and genetic_df.shape[1] > 0\n",
" is_trait_available = clinical_df.shape[0] > 0 and clinical_df.shape[1] > 0\n",
" \n",
" # Record initial validation\n",
" validate_and_save_cohort_info(is_final=False, cohort=\"TCGA\", info_path=json_path, \n",
" is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n",
" \n",
" except Exception as e:\n",
" print(f\"Error loading data: {e}\")\n",
" validate_and_save_cohort_info(is_final=False, cohort=\"TCGA\", info_path=json_path, \n",
" is_gene_available=False, is_trait_available=False)\n"
]
},
{
"cell_type": "markdown",
"id": "61f40c3f",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c133dc38",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:44:25.947053Z",
"iopub.status.busy": "2025-03-25T03:44:25.946942Z",
"iopub.status.idle": "2025-03-25T03:44:25.956011Z",
"shell.execute_reply": "2025-03-25T03:44:25.955719Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [71.0, 82.0, 82.0, 46.0, 74.0], 'days_to_birth': [-26176.0, -30286.0, -30163.0, -17025.0, -27124.0]}\n",
"Gender columns preview:\n",
"{'gender': ['MALE', 'MALE', 'MALE', 'FEMALE', 'FEMALE']}\n"
]
}
],
"source": [
"# Identify candidate columns for age and gender\n",
"candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n",
"candidate_gender_cols = ['gender']\n",
"\n",
"# Get clinical data file path\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(f\"{tcga_root_dir}/TCGA_Melanoma_(SKCM)\")\n",
"\n",
"# Load clinical data\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Extract and preview candidate age columns\n",
"if candidate_age_cols:\n",
" age_preview = {col: clinical_df[col].head(5).tolist() for col in candidate_age_cols}\n",
" print(\"Age columns preview:\")\n",
" print(age_preview)\n",
"\n",
"# Extract and preview candidate gender columns\n",
"if candidate_gender_cols:\n",
" gender_preview = {col: clinical_df[col].head(5).tolist() for col in candidate_gender_cols}\n",
" print(\"Gender columns preview:\")\n",
" print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "332e1189",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "92a2ab8b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:44:25.957042Z",
"iopub.status.busy": "2025-03-25T03:44:25.956922Z",
"iopub.status.idle": "2025-03-25T03:44:25.959085Z",
"shell.execute_reply": "2025-03-25T03:44:25.958806Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected age column: age_at_initial_pathologic_diagnosis\n",
"Selected gender column: gender\n"
]
}
],
"source": [
"# 1. Select appropriate columns for age and gender\n",
"\n",
"# Age column selection\n",
"# Both columns seem to contain valid age information\n",
"# 'age_at_initial_pathologic_diagnosis' is more directly usable as it's already in years\n",
"# 'days_to_birth' would need conversion (it's negative days from birth to diagnosis)\n",
"age_col = 'age_at_initial_pathologic_diagnosis'\n",
"\n",
"# Gender column selection\n",
"# The 'gender' column looks appropriate with valid values ('MALE', 'FEMALE')\n",
"gender_col = 'gender'\n",
"\n",
"# 2. Print the chosen columns\n",
"print(f\"Selected age column: {age_col}\")\n",
"print(f\"Selected gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "78c3b4e5",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b1e4b40b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:44:25.960017Z",
"iopub.status.busy": "2025-03-25T03:44:25.959908Z",
"iopub.status.idle": "2025-03-25T03:44:39.169541Z",
"shell.execute_reply": "2025-03-25T03:44:39.168991Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed clinical data (shape: (481, 3)) saved to ../../output/preprocess/Psoriasis/clinical_data/TCGA.csv\n",
"Clinical data preview:\n",
" Psoriasis Age Gender\n",
"sampleID \n",
"TCGA-3N-A9WB-06 1 71.0 1.0\n",
"TCGA-3N-A9WC-06 1 82.0 1.0\n",
"TCGA-3N-A9WD-06 1 82.0 1.0\n",
"TCGA-BF-A1PU-01 1 46.0 0.0\n",
"TCGA-BF-A1PV-01 1 74.0 0.0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original genetic data shape: (20530, 474)\n",
"Normalized genetic data shape: (19848, 474)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed gene data saved to ../../output/preprocess/Psoriasis/gene_data/TCGA.csv\n",
"Number of common samples between clinical and genetic data: 474\n",
"Linked data shape: (474, 19851)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (474, 19851)\n",
"For the feature 'Psoriasis', the least common label is '0' with 1 occurrences. This represents 0.21% of the dataset.\n",
"The distribution of the feature 'Psoriasis' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 48.0\n",
" 50% (Median): 58.0\n",
" 75%: 70.75\n",
"Min: 15.0\n",
"Max: 90.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '0.0' with 180 occurrences. This represents 37.97% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"Is the trait distribution biased? True\n",
"Data shape after removing biased features: (474, 19851)\n",
"Data was deemed not usable for Psoriasis analysis - no final file saved.\n"
]
}
],
"source": [
"# 1. Extract and standardize clinical features\n",
"# Use tcga_select_clinical_features to extract trait (Psoriasis) and demographic info\n",
"# For TCGA datasets, we use sample ID patterns to determine the trait (tumor vs normal)\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(f\"{tcga_root_dir}/TCGA_Melanoma_(SKCM)\")\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)\n",
"\n",
"# Save the processed clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Processed clinical data (shape: {selected_clinical_df.shape}) saved to {out_clinical_data_file}\")\n",
"print(f\"Clinical data preview:\")\n",
"print(selected_clinical_df.head())\n",
"\n",
"# 2. Normalize gene symbols in the genetic data\n",
"genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
"print(f\"Original genetic data shape: {genetic_df.shape}\")\n",
"\n",
"# Apply normalization using the NCBI Gene database\n",
"normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)\n",
"print(f\"Normalized genetic data shape: {normalized_genetic_df.shape}\")\n",
"\n",
"# Save the normalized gene expression data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_genetic_df.to_csv(out_gene_data_file)\n",
"print(f\"Processed gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 3. Link clinical and genetic data\n",
"# In TCGA datasets, we need to ensure that indexes (sample IDs) match between datasets\n",
"common_samples = set(selected_clinical_df.index).intersection(set(normalized_genetic_df.columns))\n",
"print(f\"Number of common samples between clinical and genetic data: {len(common_samples)}\")\n",
"\n",
"# Filter both dataframes to include only common samples\n",
"selected_clinical_df = selected_clinical_df.loc[selected_clinical_df.index.isin(common_samples)]\n",
"normalized_genetic_df = normalized_genetic_df[list(common_samples)]\n",
"\n",
"# Combine clinical and genetic data\n",
"linked_data = selected_clinical_df.join(normalized_genetic_df.T)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine if trait and demographic features are biased\n",
"is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)\n",
"print(f\"Is the trait distribution biased? {is_biased}\")\n",
"print(f\"Data shape after removing biased features: {cleaned_data.shape}\")\n",
"\n",
"# 6. Validate the quality of the data and save metadata\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=is_biased,\n",
" df=cleaned_data,\n",
" note=f\"Data from TCGA Melanoma (SKCM) cohort was used as a proxy for {trait}.\"\n",
")\n",
"\n",
"# 7. Save the linked data if it's usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" cleaned_data.to_csv(out_data_file)\n",
" print(f\"Final processed data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Data was deemed not usable for {trait} analysis - no final file saved.\")"
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|