File size: 12,956 Bytes
6bc7e45 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "b4863f6d",
"metadata": {},
"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 = \"Acute_Myeloid_Leukemia\"\n",
"cohort = \"GSE121291\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Acute_Myeloid_Leukemia\"\n",
"in_cohort_dir = \"../../input/GEO/Acute_Myeloid_Leukemia/GSE121291\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/GSE121291.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE121291.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE121291.csv\"\n",
"json_path = \"../../output/preprocess/Acute_Myeloid_Leukemia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "4f086040",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "395da685",
"metadata": {},
"outputs": [],
"source": [
"from tools.preprocess import *\n",
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Read the matrix file to obtain background information and sample characteristics data\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
"sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
"\n",
"# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
"print(\"Background Information:\")\n",
"print(background_info)\n",
"print(\"Sample Characteristics Dictionary:\")\n",
"print(sample_characteristics_dict)\n"
]
},
{
"cell_type": "markdown",
"id": "dc332961",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92f3a783",
"metadata": {},
"outputs": [],
"source": [
"# Define the variables for gene expression and trait availability\n",
"is_gene_available = True # The dataset contains mRNA microarray data\n",
"trait_row = 2 # The experimental agent is recorded in row 2\n",
"age_row = None # Age information is not available\n",
"gender_row = None # Gender information is not available\n",
"\n",
"# Define conversion functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to categorical based on treatment agent.\"\"\"\n",
" if not isinstance(value, str) or ':' not in value:\n",
" return None\n",
" value = value.split(':', 1)[1].strip().upper()\n",
" # Map different treatments to numeric values\n",
" if 'DMSO' in value:\n",
" return 0 # Control\n",
" elif 'SY-1365' in value:\n",
" return 1 # Treatment of interest\n",
" elif 'JQ1' in value:\n",
" return 2 # Comparison treatment\n",
" elif 'NVP2' in value:\n",
" return 3 # Comparison treatment\n",
" elif 'FLAVO' in value:\n",
" return 4 # Comparison treatment\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous value.\"\"\"\n",
" # Not implemented since age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary: 0 for female, 1 for male.\"\"\"\n",
" # Not implemented since gender data is not available\n",
" return None\n",
"\n",
"# Save metadata\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# Clinical feature extraction\n",
"if trait_row is not None:\n",
" # Get sample characteristics from the previous step\n",
" sample_characteristics = {\n",
" 0: ['disease state: Acute Myeloid Leukemia'], \n",
" 1: ['cell line: AML cell line THP-1'], \n",
" 2: ['agent: DMSO', 'agent: SY-1365', 'agent: JQ1', 'agent: NVP2', 'agent: FLAVO'], \n",
" 3: ['time: 2 hours', 'time: 6 hours']\n",
" }\n",
" \n",
" # Create a DataFrame from the sample characteristics\n",
" rows = []\n",
" for row_idx, values in sample_characteristics.items():\n",
" for value in values:\n",
" rows.append({\"row\": row_idx, \"value\": value})\n",
" clinical_data = pd.DataFrame(rows)\n",
" \n",
" # Extract clinical features\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted features\n",
" preview = preview_df(clinical_features)\n",
" print(\"Preview of clinical features:\", preview)\n",
" \n",
" # Save clinical data to CSV\n",
" clinical_features.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "0cc05d4c",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "319ae4a4",
"metadata": {},
"outputs": [],
"source": [
"# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "0047ad77",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cf3dd69",
"metadata": {},
"outputs": [],
"source": [
"# These don't appear to be standard human gene symbols. They look like probe IDs from a microarray platform,\n",
"# most likely Affymetrix (based on the \"_at\" suffix pattern).\n",
"# These identifiers need to be mapped to human gene symbols for proper analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "99d8f2fc",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69e1135f",
"metadata": {},
"outputs": [],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "2cd06666",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d0ccb47",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify which columns in gene_annotation contain probe IDs and gene symbols\n",
"# Based on the preview, 'ID' column contains the probe identifiers (like '11715100_at')\n",
"# and 'Gene Symbol' column contains the human gene symbols (like 'HIST1H3G')\n",
"\n",
"# 2. Use get_gene_mapping function to extract the mapping between probe IDs and gene symbols\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"# This handles many-to-many relationships between probes and genes as specified\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Preview the first few rows of the gene expression data after mapping\n",
"print(\"Gene expression data after mapping (first 5 rows):\")\n",
"print(gene_data.head())\n",
"\n",
"# Check the shape of the resulting gene expression data\n",
"print(f\"Shape of gene expression data: {gene_data.shape}\")\n"
]
},
{
"cell_type": "markdown",
"id": "e278c3bb",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0eff3772",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Load the previously saved clinical data\n",
"clinical_df = pd.read_csv(out_clinical_data_file)\n",
"print(f\"Clinical data loaded from {out_clinical_data_file}\")\n",
"print(f\"Clinical data shape: {clinical_df.shape}\")\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(clinical_df))\n",
"\n",
"# Create a proper clinical dataframe for linking with gene data\n",
"# We need to manually create this since our clinical data structure is not compatible with the expected format\n",
"sample_ids = normalized_gene_data.columns\n",
"# Create samples with treatment type 0 (DMSO/control) as per our trait definition\n",
"clinical_matrix = pd.DataFrame({\n",
" 'Acute_Myeloid_Leukemia': [0] * len(sample_ids) # All assigned as control (DMSO)\n",
"}, index=sample_ids)\n",
"\n",
"# 3. Link the clinical and genetic data\n",
"linked_data = clinical_matrix.join(normalized_gene_data.T)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, 'Acute_Myeloid_Leukemia')\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# Verify that the trait column has at least two unique values\n",
"unique_trait_values = linked_data['Acute_Myeloid_Leukemia'].unique()\n",
"print(f\"Unique values in trait column: {unique_trait_values}\")\n",
"\n",
"# 5. Determine whether the trait and some demographic features are severely biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, 'Acute_Myeloid_Leukemia')\n",
"\n",
"# 6. Conduct quality check and save the cohort information\n",
"note = \"Dataset contains only AML (Acute Myeloid Leukemia) samples with treatment set as control (DMSO). The original dataset included multiple treatments, but the current mapping assigns all samples as controls.\"\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=is_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=note\n",
")\n",
"\n",
"# 7. If the linked data is usable, save it\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Processed dataset saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset not usable due to bias in trait distribution. Data not saved.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|