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{
"cells": [
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"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 = \"Endometrioid_Cancer\"\n",
"cohort = \"GSE73551\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Endometrioid_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Endometrioid_Cancer/GSE73551\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Endometrioid_Cancer/GSE73551.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Endometrioid_Cancer/gene_data/GSE73551.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE73551.csv\"\n",
"json_path = \"../../output/preprocess/Endometrioid_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "97874008",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Tumor]\"\n",
"!Series_summary\t\"An expert-pathologist-reviewed epithelial ovarian cancer reference library (n = 50) used to assign the histopathology of epithelial ovarian cell lines using compositional statistics and random gene-sets\"\n",
"!Series_overall_design\t\"In the study presented here, we applied Gene Expression Compositional Assignment (GECA) to epithelial ovarian cell lines (GSE73637), using first a reference library of solid tumors (expO [http://www.intgen.org/expo/]) and then a second library of expert pathologically-reviewed epithelial ovarian cancer samples\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell type: MUCINOUS', 'cell type: CLEAR CELL', 'cell type: SEROUS LOW-GRADE', 'cell type: ENDOMETRIOID', 'cell type: SEROUS'], 1: ['tissue: Epithelial Ovarian Cancer']}\n"
]
}
],
"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": "cef083fa",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "466dd418",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of clinical features:\n",
"{'MUCINOUS': [0.0], 'CLEAR CELL': [0.0], 'SEROUS LOW-GRADE': [0.0], 'ENDOMETRIOID': [1.0], 'SEROUS': [0.0]}\n",
"Clinical features saved to ../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE73551.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the series title and overall design, this dataset appears to contain transcriptional data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait (Endometrioid_Cancer), we can use the cell type information in row 0\n",
"trait_row = 0\n",
"\n",
"# Age data is not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender data is not available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert the cell type value to a binary trait for Endometrioid Cancer\n",
" 1 for endometrioid, 0 for other types\n",
" \"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract the value after colon if it exists\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Check if the cell type is endometrioid (case insensitive)\n",
" if 'endometrioid' in value.lower():\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"# Age conversion function (not used since age data is not available)\n",
"def convert_age(value):\n",
" return None\n",
"\n",
"# Gender conversion function (not used since gender data is not available)\n",
"def convert_gender(value):\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine if trait data is available (trait_row is not None)\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort information\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",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Since we don't have a CSV file, we'll use the sample characteristics dictionary directly\n",
" # Create the clinical_data dataframe from the sample characteristics dictionary\n",
" sample_char_dict = {0: ['cell type: MUCINOUS', 'cell type: CLEAR CELL', 'cell type: SEROUS LOW-GRADE', \n",
" 'cell type: ENDOMETRIOID', 'cell type: SEROUS'], \n",
" 1: ['tissue: Epithelial Ovarian Cancer']}\n",
" \n",
" # Convert sample characteristics dictionary to a dataframe\n",
" # We need to create a dataframe with samples as columns and characteristics as rows\n",
" # First, extract unique values for each characteristic\n",
" unique_values = {}\n",
" for key, values in sample_char_dict.items():\n",
" unique_values[key] = values\n",
" \n",
" # Create a dummy dataframe with this information\n",
" # In a real dataset, we would have sample IDs as columns, but here we'll use the cell types as samples\n",
" samples = []\n",
" for value in unique_values[0]: # Use cell types as samples\n",
" sample_name = value.split(\": \")[1] if \": \" in value else value\n",
" samples.append(sample_name)\n",
" \n",
" # Create the dataframe\n",
" data = {sample: [] for sample in samples}\n",
" for i, cell_type in enumerate(samples):\n",
" data[cell_type] = [f\"cell type: {cell_type}\" if j == 0 else unique_values[j][0] for j in range(len(unique_values))]\n",
" \n",
" clinical_data = pd.DataFrame(data)\n",
" \n",
" # Extract clinical features\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df=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 dataframe\n",
" preview = preview_df(clinical_features)\n",
" print(\"Preview of clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical features to the specified file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "576a8192",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Found data marker at line 59\n",
"Header line: \"ID_REF\"\t\"GSM1897741\"\t\"GSM1897744\"\t\"GSM1897746\"\t\"GSM1897748\"\t\"GSM1897750\"\t\"GSM1897752\"\t\"GSM1897753\"\t\"GSM1897755\"\t\"GSM1897757\"\t\"GSM1897759\"\t\"GSM1897761\"\t\"GSM1897763\"\t\"GSM1897765\"\t\"GSM1897767\"\t\"GSM1897769\"\t\"GSM1897770\"\t\"GSM1897772\"\t\"GSM1897774\"\t\"GSM1897776\"\t\"GSM1897778\"\t\"GSM1897780\"\t\"GSM1897782\"\t\"GSM1897784\"\t\"GSM1897786\"\t\"GSM1897787\"\t\"GSM1897789\"\t\"GSM1897792\"\t\"GSM1897794\"\t\"GSM1897795\"\t\"GSM1897797\"\t\"GSM1897799\"\t\"GSM1897801\"\t\"GSM1897802\"\t\"GSM1897804\"\t\"GSM1897806\"\t\"GSM1897808\"\t\"GSM1897810\"\t\"GSM1897812\"\t\"GSM1897814\"\t\"GSM1897816\"\t\"GSM1897818\"\t\"GSM1897820\"\t\"GSM1897822\"\t\"GSM1897823\"\t\"GSM1897825\"\t\"GSM1897827\"\t\"GSM1897829\"\t\"GSM1897831\"\t\"GSM1897833\"\t\"GSM1897835\"\n",
"First data line: 1\t5.403790297\t4.892553637\t5.326199224\t5.170940973\t5.427630826\t5.516932949\t5.470710937\t4.98660365\t5.133514638\t5.230458349\t4.990772814\t5.662734397\t5.696864862\t5.147933271\t4.86309129\t5.394637232\t5.381253066\t5.842655059\t5.545258599\t5.707996433\t5.684696894\t5.428043787\t5.099403081\t5.572583866\t5.715332586\t5.218152374\t4.765819974\t5.376930844\t6.175791799\t5.014040109\t4.829511887\t5.217120904\t4.591215425\t5.630381621\t5.19785045\t5.666201739\t5.509745722\t5.455583973\t4.731499955\t4.649977023\t5.034881723\t4.920999754\t5.041737535\t4.820794888\t5.334202161\t5.51383308\t5.340668225\t4.904084267\t5.331123029\t5.742880178\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',\n",
" '14', '15', '16', '17', '18', '19', '20'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "68afb96f",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
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"source": [
"# The gene identifiers shown in the data are numeric IDs (1, 2, 3, etc.), not human gene symbols.\n",
"# These are likely probe IDs or some other form of identifier that needs to be mapped to gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "2ea15c1a",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Examining SOFT file structure:\n",
"Line 0: ^DATABASE = GeoMiame\n",
"Line 1: !Database_name = Gene Expression Omnibus (GEO)\n",
"Line 2: !Database_institute = NCBI NLM NIH\n",
"Line 3: !Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n",
"Line 4: !Database_email = [email protected]\n",
"Line 5: ^SERIES = GSE73551\n",
"Line 6: !Series_title = Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Tumor]\n",
"Line 7: !Series_geo_accession = GSE73551\n",
"Line 8: !Series_status = Public on Nov 08 2016\n",
"Line 9: !Series_submission_date = Sep 29 2015\n",
"Line 10: !Series_last_update_date = Nov 10 2016\n",
"Line 11: !Series_pubmed_id = 27353327\n",
"Line 12: !Series_summary = An expert-pathologist-reviewed epithelial ovarian cancer reference library (n = 50) used to assign the histopathology of epithelial ovarian cell lines using compositional statistics and random gene-sets\n",
"Line 13: !Series_overall_design = In the study presented here, we applied Gene Expression Compositional Assignment (GECA) to epithelial ovarian cell lines (GSE73637), using first a reference library of solid tumors (expO [http://www.intgen.org/expo/]) and then a second library of expert pathologically-reviewed epithelial ovarian cancer samples\n",
"Line 14: !Series_type = Expression profiling by array\n",
"Line 15: !Series_contributor = Jaine,K,Blayney\n",
"Line 16: !Series_sample_id = GSM1897741\n",
"Line 17: !Series_sample_id = GSM1897744\n",
"Line 18: !Series_sample_id = GSM1897746\n",
"Line 19: !Series_sample_id = GSM1897748\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"{'ID': [1, 2, 3, 4, 5], 'ProbeSetID': ['200000_s_at', '200001_at', '200002_at', '200003_s_at', '200004_at'], 'GeneSymbol': ['PRPF8', 'CAPNS1', 'RPL35', 'RPL28', 'EIF4G2'], 'Array': ['Ovarian Cancer DSA', 'Ovarian Cancer DSA', 'Ovarian Cancer DSA', 'Ovarian Cancer DSA', 'Ovarian Cancer DSA'], 'Annotation Date': ['10-Jan-11', '10-Jan-11', '10-Jan-11', '10-Jan-11', '10-Jan-11'], 'Sequence Type': ['Affymetrix human normalisation control', 'Affymetrix human normalisation control', 'Affymetrix human normalisation control', 'Affymetrix human normalisation control', 'Affymetrix human normalisation control'], 'Ensembl Version': ['release 60', 'release 60', 'release 60', 'release 60', 'release 60'], 'Ensembl Genome Version': ['GRCh37', 'GRCh37', 'GRCh37', 'GRCh37', 'GRCh37'], 'Orientation / Description': ['Sense (Fully Exonic)', 'Sense (Fully Exonic)', 'Sense (Fully Exonic)', 'Sense (Fully Exonic)', 'Sense (Fully Exonic)'], 'No. probes aligned': ['9', '8', '8', '9', '11'], 'Probeset mapping position': ['Chr 17: 1554017-1554762', 'Chr 19: 36640714-36641203', 'Chr 9: 127622482-127623828', 'Chr 19: 55897742-55898063', 'Chr 11: 10818748-10819118'], 'Ensembl Gene ID': ['ENSG00000174231', 'ENSG00000126247', 'ENSG00000136942', 'ENSG00000108107', 'ENSG00000110321'], 'Chromosomal location': ['Chr 17p11.1', 'Chr 19p11', 'Chr 9p11.1', 'Chr 19p11', 'Chr 11p11.11'], 'Strand': ['Reverse Strand', 'Forward Strand', 'Reverse Strand', 'Forward Strand', 'Reverse Strand'], 'Gene Description': ['PRP8 pre-mRNA processing factor 8 homolog (S. cerevisiae) [Source:HGNC Symbol;Acc:17340]', 'calpain, small subunit 1 [Source:HGNC Symbol;Acc:1481]', 'ribosomal protein L35 [Source:HGNC Symbol;Acc:10344]', 'ribosomal protein L28 [Source:HGNC Symbol;Acc:10330]', 'eukaryotic translation initiation factor 4 gamma, 2 [Source:HGNC Symbol;Acc:3297]'], 'Entrez Gene': ['10594', '826', '11224', '6158', '1982'], 'Alias Gene Symbols': ['PRP8 /// PRPF8-001 /// PRPC8 /// HPRP8 /// RP13 /// Prp8', 'CDPS /// 30K /// CANPS /// CAPNS1-201 /// CAPNS1-202 /// CANP /// CSS1 /// CAPN4 /// CALPAIN4', 'RPL35-002 /// RPL35-005 /// RPL35-001 /// RPL35-004 /// RPL35-003', 'RPL28-203 /// RPL28-201 /// RPL28-205 /// FLJ43307 /// RPL28-202 /// RPL28-204', 'DAP5 /// AAG1 /// EIF4G2-204 /// EIF4G2-203 /// NAT1 /// p97 /// EIF4G2-202 /// FLJ41344 /// EIF4G2-201 /// P97'], 'Ensembl Transcript ID': ['ENST00000304992', 'ENST00000457326 /// ENST00000246533', 'ENST00000493018 /// ENST00000348462 /// ENST00000487431 /// ENST00000373570 /// ENST00000495728', 'ENST00000431533', 'ENST00000396525 /// ENST00000339995 /// ENST00000429377'], 'RefSeq Transcript ID': ['NM_006445.3', '--- /// NM_001003962.1 // NM_001749.2', '--- /// NM_007209.3 /// --- /// --- /// ---', 'NM_001136136.1', 'NM_001042559.2 /// NM_001172705.1 // NM_001418.3 /// NM_001172705.1'], 'Unigene ID': ['Hs.181368', '--- /// ---', '--- /// --- /// --- /// --- /// Hs.182825', '---', '--- /// Hs.183684 /// Hs.183684'], 'ORF': ['PRPF8', 'CAPNS1', 'RPL35', 'RPL28', 'EIF4G2'], 'GB_ACC': ['NM_006445', 'NM_001003962', 'NM_007209', 'NM_001136136', 'NM_001042559'], 'SPOT_ID': ['ENST00000304992', 'ENST00000457326 ENST00000246533', 'ENST00000493018 ENST00000348462 ENST00000487431 ENST00000373570 ENST00000495728', 'ENST00000431533', 'ENST00000396525 ENST00000339995 ENST00000429377']}\n"
]
}
],
"source": [
"# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
"import gzip\n",
"\n",
"# Look at the first few lines of the SOFT file to understand its structure\n",
"print(\"Examining SOFT file structure:\")\n",
"try:\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" # Read first 20 lines to understand the file structure\n",
" for i, line in enumerate(file):\n",
" if i < 20:\n",
" print(f\"Line {i}: {line.strip()}\")\n",
" else:\n",
" break\n",
"except Exception as e:\n",
" print(f\"Error reading SOFT file: {e}\")\n",
"\n",
"# 2. Now let's try a more robust approach to extract the gene annotation\n",
"# Instead of using the library function which failed, we'll implement a custom approach\n",
"try:\n",
" # First, look for the platform section which contains gene annotation\n",
" platform_data = []\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" in_platform_section = False\n",
" for line in file:\n",
" if line.startswith('^PLATFORM'):\n",
" in_platform_section = True\n",
" continue\n",
" if in_platform_section and line.startswith('!platform_table_begin'):\n",
" # Next line should be the header\n",
" header = next(file).strip()\n",
" platform_data.append(header)\n",
" # Read until the end of the platform table\n",
" for table_line in file:\n",
" if table_line.startswith('!platform_table_end'):\n",
" break\n",
" platform_data.append(table_line.strip())\n",
" break\n",
" \n",
" # If we found platform data, convert it to a DataFrame\n",
" if platform_data:\n",
" import pandas as pd\n",
" import io\n",
" platform_text = '\\n'.join(platform_data)\n",
" gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
" low_memory=False, on_bad_lines='skip')\n",
" print(\"\\nGene annotation preview:\")\n",
" print(preview_df(gene_annotation))\n",
" else:\n",
" print(\"Could not find platform table in SOFT file\")\n",
" \n",
" # Try an alternative approach - extract mapping from other sections\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" for line in file:\n",
" if 'ANNOTATION information' in line or 'annotation information' in line:\n",
" print(f\"Found annotation information: {line.strip()}\")\n",
" if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
" print(f\"Platform title: {line.strip()}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing gene annotation: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "1db458e0",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ff355004",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:42:39.793896Z",
"iopub.status.busy": "2025-03-25T08:42:39.793767Z",
"iopub.status.idle": "2025-03-25T08:42:40.879058Z",
"shell.execute_reply": "2025-03-25T08:42:40.878603Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview (first 5 rows):\n",
" ID Gene\n",
"0 1 PRPF8\n",
"1 2 CAPNS1\n",
"2 3 RPL35\n",
"3 4 RPL28\n",
"4 5 EIF4G2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene data shape after mapping: (20172, 50)\n",
"Gene data preview (first 5 genes, first 3 samples):\n",
" GSM1897741 GSM1897744 GSM1897746\n",
"Gene \n",
"A1BG 5.331976 5.578752 5.536849\n",
"A1CF 3.920673 2.936927 3.194641\n",
"A2LD1 14.586115 16.210582 14.578840\n",
"A2M 14.252609 10.769140 10.335943\n",
"A2ML1 3.600177 2.762048 2.728659\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data saved to ../../output/preprocess/Endometrioid_Cancer/gene_data/GSE73551.csv\n"
]
}
],
"source": [
"# 1. Identify the appropriate columns in the gene annotation data\n",
"# From the preview, we can see that 'ID' in gene_annotation corresponds to the numeric IDs (1, 2, 3) in gene_data\n",
"# The 'GeneSymbol' column contains the human gene symbols we want to map to\n",
"\n",
"# 2. Create a gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col=\"ID\", gene_col=\"GeneSymbol\")\n",
"print(\"Gene mapping preview (first 5 rows):\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression\n",
"# This will handle the many-to-many relation between probes and genes\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"\\nGene data shape after mapping: {gene_data.shape}\")\n",
"print(\"Gene data preview (first 5 genes, first 3 samples):\")\n",
"if gene_data.shape[0] > 0 and gene_data.shape[1] > 0:\n",
" preview_cols = min(3, gene_data.shape[1])\n",
" print(gene_data.iloc[:5, :preview_cols])\n",
"\n",
"# Save the processed gene expression data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "13e3783f",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7aa8ef46",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:42:40.880680Z",
"iopub.status.busy": "2025-03-25T08:42:40.880560Z",
"iopub.status.idle": "2025-03-25T08:42:52.442987Z",
"shell.execute_reply": "2025-03-25T08:42:52.442312Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (20036, 50)\n",
"First few genes with their expression values after normalization:\n",
" GSM1897741 GSM1897744 GSM1897746 GSM1897748 GSM1897750 \\\n",
"Gene \n",
"A1BG 5.331976 5.578752 5.536849 5.161539 5.352578 \n",
"A1CF 3.920673 2.936927 3.194641 3.082743 2.935914 \n",
"A2M 14.252609 10.769140 10.335943 10.783329 11.617907 \n",
"A2ML1 3.600177 2.762048 2.728659 3.291211 3.406832 \n",
"A3GALT2 6.455418 6.153991 6.062100 5.696409 5.895500 \n",
"\n",
" GSM1897752 GSM1897753 GSM1897755 GSM1897757 GSM1897759 ... \\\n",
"Gene ... \n",
"A1BG 5.062424 5.628661 4.694100 5.454426 5.214719 ... \n",
"A1CF 3.430515 6.307725 3.109970 3.176425 3.403035 ... \n",
"A2M 7.886267 10.788412 11.061511 11.618258 10.487040 ... \n",
"A2ML1 6.553377 3.206583 7.694877 3.070904 3.106801 ... \n",
"A3GALT2 5.641279 6.284191 5.969538 5.889430 6.129745 ... \n",
"\n",
" GSM1897818 GSM1897820 GSM1897822 GSM1897823 GSM1897825 \\\n",
"Gene \n",
"A1BG 5.354290 5.522464 5.067847 5.140292 5.279861 \n",
"A1CF 2.754772 2.965057 3.518125 3.141298 3.156165 \n",
"A2M 10.046690 12.844992 7.895416 9.973413 11.412809 \n",
"A2ML1 2.945756 2.956432 3.009950 3.091285 3.152198 \n",
"A3GALT2 6.301145 5.991926 5.945347 6.257818 5.868759 \n",
"\n",
" GSM1897827 GSM1897829 GSM1897831 GSM1897833 GSM1897835 \n",
"Gene \n",
"A1BG 5.642801 5.019582 5.618265 5.508222 6.029443 \n",
"A1CF 3.491213 3.618391 3.173946 3.919150 3.474716 \n",
"A2M 9.416339 10.433374 10.761468 10.874345 10.987057 \n",
"A2ML1 3.036763 6.832390 3.636048 2.911268 3.050173 \n",
"A3GALT2 6.253857 5.889241 5.830122 6.084974 6.563145 \n",
"\n",
"[5 rows x 50 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Endometrioid_Cancer/gene_data/GSE73551.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Raw clinical data shape: (2, 51)\n",
"Clinical features:\n",
" GSM1897741 GSM1897744 GSM1897746 GSM1897748 \\\n",
"Endometrioid_Cancer 0.0 0.0 0.0 1.0 \n",
"\n",
" GSM1897750 GSM1897752 GSM1897753 GSM1897755 \\\n",
"Endometrioid_Cancer 1.0 0.0 0.0 0.0 \n",
"\n",
" GSM1897757 GSM1897759 ... GSM1897818 GSM1897820 \\\n",
"Endometrioid_Cancer 0.0 0.0 ... 0.0 0.0 \n",
"\n",
" GSM1897822 GSM1897823 GSM1897825 GSM1897827 \\\n",
"Endometrioid_Cancer 0.0 0.0 0.0 0.0 \n",
"\n",
" GSM1897829 GSM1897831 GSM1897833 GSM1897835 \n",
"Endometrioid_Cancer 0.0 0.0 0.0 0.0 \n",
"\n",
"[1 rows x 50 columns]\n",
"Clinical features saved to ../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE73551.csv\n",
"Linked data shape: (50, 20037)\n",
"Linked data preview (first 5 rows, first 5 columns):\n",
" Endometrioid_Cancer A1BG A1CF A2M A2ML1\n",
"GSM1897741 0.0 5.331976 3.920673 14.252609 3.600177\n",
"GSM1897744 0.0 5.578752 2.936927 10.769140 2.762048\n",
"GSM1897746 0.0 5.536849 3.194641 10.335943 2.728659\n",
"GSM1897748 1.0 5.161539 3.082743 10.783329 3.291211\n",
"GSM1897750 1.0 5.352578 2.935914 11.617907 3.406832\n",
"Missing values before handling:\n",
" Trait (Endometrioid_Cancer) missing: 0 out of 50\n",
" Genes with >20% missing: 0\n",
" Samples with >5% missing genes: 0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (50, 20037)\n",
"For the feature 'Endometrioid_Cancer', the least common label is '1.0' with 9 occurrences. This represents 18.00% of the dataset.\n",
"The distribution of the feature 'Endometrioid_Cancer' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Endometrioid_Cancer/GSE73551.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(\"First few genes with their expression values after normalization:\")\n",
"print(normalized_gene_data.head())\n",
"\n",
"# Save the normalized 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. Check if trait data is available before proceeding with clinical data extraction\n",
"if trait_row is None:\n",
" print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n",
" # Create an empty dataframe for clinical features\n",
" clinical_features = pd.DataFrame()\n",
" \n",
" # Create an empty dataframe for linked data\n",
" linked_data = pd.DataFrame()\n",
" \n",
" # Validate and save cohort info\n",
" 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=False, # Trait data is not available\n",
" is_biased=True, # Not applicable but required\n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=\"Dataset contains gene expression data but lacks clear trait indicators for Duchenne Muscular Dystrophy status.\"\n",
" )\n",
" print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n",
"else:\n",
" try:\n",
" # Get the file paths for the matrix file to extract clinical data\n",
" _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
" \n",
" # Get raw clinical data from the matrix file\n",
" _, clinical_raw = get_background_and_clinical_data(matrix_file)\n",
" \n",
" # Verify clinical data structure\n",
" print(\"Raw clinical data shape:\", clinical_raw.shape)\n",
" \n",
" # Extract clinical features using the defined conversion functions\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df=clinical_raw,\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",
" print(\"Clinical features:\")\n",
" print(clinical_features)\n",
" \n",
" # Save clinical features to file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
" \n",
" # 3. Link clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
" print(linked_data.iloc[:5, :5])\n",
" \n",
" # 4. Handle missing values\n",
" print(\"Missing values before handling:\")\n",
" print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Age' in linked_data.columns:\n",
" print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Gender' in linked_data.columns:\n",
" print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n",
" \n",
" gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
" print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n",
" print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n",
" \n",
" cleaned_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
" \n",
" # 5. Evaluate bias in trait and demographic features\n",
" is_trait_biased = False\n",
" if len(cleaned_data) > 0:\n",
" trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
" is_trait_biased = trait_biased\n",
" else:\n",
" print(\"No data remains after handling missing values.\")\n",
" is_trait_biased = True\n",
" \n",
" # 6. Final validation and save\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=cleaned_data,\n",
" note=\"Dataset contains gene expression data comparing Duchenne muscular dystrophy vs healthy samples.\"\n",
" )\n",
" \n",
" # 7. Save if usable\n",
" if is_usable and len(cleaned_data) > 0:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" cleaned_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"Data was determined to be unusable or empty and was not saved\")\n",
" \n",
" except Exception as e:\n",
" print(f\"Error processing data: {e}\")\n",
" # Handle the error case by still recording cohort info\n",
" 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=False, # Mark as not available due to processing issues\n",
" is_biased=True, \n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=f\"Error processing data: {str(e)}\"\n",
" )\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
],
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|