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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d963c554",
"metadata": {
"execution": {
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"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 = \"Endometriosis\"\n",
"cohort = \"GSE75427\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Endometriosis\"\n",
"in_cohort_dir = \"../../input/GEO/Endometriosis/GSE75427\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Endometriosis/GSE75427.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Endometriosis/gene_data/GSE75427.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Endometriosis/clinical_data/GSE75427.csv\"\n",
"json_path = \"../../output/preprocess/Endometriosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "6d47b385",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e6aa32fa",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:04:20.015875Z",
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}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Expression profiles in decidualized and non-decidualized endometriotic cyst stromal cells (ECSCs) and normal endometrial stromal cells (NESCs)\"\n",
"!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
"!Series_overall_design\t\"Refer to individual Series\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell type: endometriotic cyst stromal cells'], 1: ['gender: Female'], 2: ['age: 34y', 'age: 42y', 'age: 30y', 'age: 28y'], 3: ['treatment: 12d 10% charcoal-stripped heat-inactivated FBS', 'treatment: 12d dibutyryl-cAMP and dienogest']}\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": "2dd6c053",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "28115b07",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:04:20.090749Z",
"iopub.status.busy": "2025-03-25T08:04:20.090638Z",
"iopub.status.idle": "2025-03-25T08:04:20.098704Z",
"shell.execute_reply": "2025-03-25T08:04:20.098408Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{'GSM1954898': [1.0, 34.0], 'GSM1954899': [1.0, 42.0], 'GSM1954900': [1.0, 30.0], 'GSM1954901': [1.0, 28.0], 'GSM1954902': [1.0, 34.0], 'GSM1954903': [1.0, 42.0], 'GSM1954904': [1.0, 30.0], 'GSM1954905': [1.0, 28.0]}\n",
"Clinical data saved to ../../output/preprocess/Endometriosis/clinical_data/GSE75427.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the title mentioning \"Expression profiles\", this dataset likely contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait (Endometriosis), we can see \"cell type: proliferative phase normal endometrium\" in row 0\n",
"# Row 0 likely distinguishes between normal and endometriotic cells\n",
"trait_row = 0\n",
"\n",
"# For gender, we see \"gender: Female\" in row 1, but it appears to be constant (only Female)\n",
"# Since there's only one unique value, we consider it not available\n",
"gender_row = None\n",
"\n",
"# For age, we see \"age: 37y\", \"age: 47y\", etc. in row 2\n",
"age_row = 2\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert cell type to binary where 1 indicates endometriotic cells and 0 indicates normal cells.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Based on the title, we're comparing normal endometrial stromal cells (NESCs)\n",
" # vs endometriotic cyst stromal cells (ECSCs)\n",
" if 'normal' in value.lower():\n",
" return 0 # Normal cells\n",
" elif 'endometrio' in value.lower():\n",
" return 1 # Endometriotic cells\n",
" else:\n",
" return None # Unknown\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous numeric value.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Extract numeric age, typically formatted as \"XXy\" (XX years)\n",
" if 'y' in value:\n",
" try:\n",
" age = int(value.replace('y', '').strip())\n",
" return age\n",
" except ValueError:\n",
" pass\n",
" \n",
" return None # If conversion fails\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"\n",
" Convert gender to binary (0 for female, 1 for male).\n",
" Not used in this dataset as gender is constant.\n",
" \"\"\"\n",
" # This function is included for completeness but not used since gender_row = None\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" if 'female' in value:\n",
" return 0\n",
" elif 'male' in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info (initial filtering)\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",
" # Extract clinical features\n",
" clinical_df = 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 extracted clinical data\n",
" preview = preview_df(clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_df.to_csv(out_clinical_data_file, index=True)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "b7f4352b",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cd1e5a9d",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:04:20.148572Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found data marker at line 63\n",
"Header line: \"ID_REF\"\t\"GSM1954898\"\t\"GSM1954899\"\t\"GSM1954900\"\t\"GSM1954901\"\t\"GSM1954902\"\t\"GSM1954903\"\t\"GSM1954904\"\t\"GSM1954905\"\n",
"First data line: \"A_23_P100001\"\t354.3793375\t172.500875\t58.17458\t89.16528875\t1994.738375\t146.5653413\t39.38974125\t28.5603025\n",
"Index(['A_23_P100001', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074',\n",
" 'A_23_P100127', 'A_23_P100141', 'A_23_P100189', 'A_23_P100196',\n",
" 'A_23_P100203', 'A_23_P100220', 'A_23_P100240', 'A_23_P10025',\n",
" 'A_23_P100292', 'A_23_P100315', 'A_23_P100326', 'A_23_P100344',\n",
" 'A_23_P100355', 'A_23_P100386', 'A_23_P100392', 'A_23_P100420'],\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": "4a055de0",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "67c606ab",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:04:20.150197Z",
"iopub.status.busy": "2025-03-25T08:04:20.150088Z",
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"shell.execute_reply": "2025-03-25T08:04:20.151628Z"
}
},
"outputs": [],
"source": [
"# These identifiers don't appear to be standard human gene symbols\n",
"# They have a format like \"A_19_P00315452\" which looks like probe IDs from a microarray platform\n",
"# These will need to be mapped to standard gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "b8c9b575",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "24177971",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:04:20.153289Z",
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"shell.execute_reply": "2025-03-25T08:04:21.889891Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107', '(+)E1A_r60_a135'], 'SPOT_ID': ['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107', '(+)E1A_r60_a135'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'pos', 'pos'], 'REFSEQ': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan], 'GENE': [nan, nan, nan, nan, nan], 'GENE_SYMBOL': [nan, nan, nan, nan, nan], 'GENE_NAME': [nan, nan, nan, nan, nan], 'UNIGENE_ID': [nan, nan, nan, nan, nan], 'ENSEMBL_ID': [nan, nan, nan, nan, nan], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan, nan, nan], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, nan, nan], 'CYTOBAND': [nan, nan, nan, nan, nan], 'DESCRIPTION': [nan, nan, nan, nan, nan], 'GO_ID': [nan, nan, nan, nan, nan], 'SEQUENCE': [nan, nan, nan, nan, nan]}\n"
]
}
],
"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": "b59ce36e",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ac5ebb30",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene IDs in gene expression data (first 5):\n",
"Index(['A_23_P100001', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074',\n",
" 'A_23_P100127'],\n",
" dtype='object', name='ID')\n",
"\n",
"Further examination of gene annotation (10 more rows):\n",
" ID GENE_SYMBOL\n",
"10 (-)3xSLv1 NaN\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n",
"16 A_23_P100141 UNKL\n",
"17 A_23_P100189 PRM1\n",
"18 A_23_P100196 USP10\n",
"19 A_23_P100203 HSBP1\n",
"\n",
"Sample rows with gene symbols (if any):\n",
" ID GENE_SYMBOL\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Rows with matching ID pattern:\n",
" ID GENE_SYMBOL\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n",
"\n",
"Rows with both ID and GENE_SYMBOL:\n",
" ID GENE_SYMBOL\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n",
"\n",
"Rows with both ID and GENE:\n",
" ID GENE\n",
"11 A_23_P100001 400451\n",
"12 A_23_P100022 9899\n",
"13 A_23_P100056 348093\n",
"14 A_23_P100074 57099\n",
"15 A_23_P100127 57082\n",
"\n",
"Rows with both ID and GENE_NAME:\n",
" ID GENE_NAME\n",
"11 A_23_P100001 family with sequence similarity 174, member B\n",
"12 A_23_P100022 synaptic vesicle glycoprotein 2B\n",
"13 A_23_P100056 RNA binding protein with multiple splicing 2\n",
"14 A_23_P100074 apoptosis, caspase activation inhibitor\n",
"15 A_23_P100127 cancer susceptibility candidate 5\n",
"\n",
"Rows with both ID and REFSEQ:\n",
" ID REFSEQ\n",
"11 A_23_P100001 NM_207446\n",
"12 A_23_P100022 NM_014848\n",
"13 A_23_P100056 NM_194272\n",
"14 A_23_P100074 NM_020371\n",
"15 A_23_P100127 NM_170589\n",
"\n",
"Rows with both ID and GB_ACC:\n",
" ID GB_ACC\n",
"11 A_23_P100001 NM_207446\n",
"12 A_23_P100022 NM_014848\n",
"13 A_23_P100056 NM_194272\n",
"14 A_23_P100074 NM_020371\n",
"15 A_23_P100127 NM_170589\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene mapping dataframe (first 5 rows):\n",
" ID Gene\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n",
"\n",
"Gene expression data after mapping (first 5 genes):\n",
" GSM1954898 GSM1954899 GSM1954900 GSM1954901 GSM1954902 \\\n",
"Gene \n",
"A1BG 3028.378695 2731.904201 3157.886390 3028.031645 3820.889868 \n",
"A1BG-AS1 852.177600 601.155425 758.254475 1017.400850 803.633850 \n",
"A1CF 13.638512 10.773817 15.022629 10.245584 21.209031 \n",
"A2LD1 1528.978615 1301.985750 3653.101250 2065.590500 1868.458601 \n",
"A2M 1702.062389 4474.020852 3434.653770 13126.539044 213.549137 \n",
"\n",
" GSM1954903 GSM1954904 GSM1954905 \n",
"Gene \n",
"A1BG 3227.197977 3969.369038 5763.236649 \n",
"A1BG-AS1 550.132250 894.482050 1676.084700 \n",
"A1CF 10.897716 9.151911 12.580970 \n",
"A2LD1 1256.928650 2231.617550 1938.389575 \n",
"A2M 406.783548 1552.944676 5180.086498 \n",
"\n",
"Number of genes after mapping: 19818\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Endometriosis/gene_data/GSE75427.csv\n"
]
}
],
"source": [
"# Examining the structure of gene IDs in both datasets\n",
"print(\"Gene IDs in gene expression data (first 5):\")\n",
"print(gene_data.index[:5])\n",
"\n",
"# Let's look at more rows of the gene annotation to find the matching columns\n",
"print(\"\\nFurther examination of gene annotation (10 more rows):\")\n",
"print(gene_annotation.iloc[10:20][['ID', 'GENE_SYMBOL']].head(10))\n",
"\n",
"# Try to find any rows with non-null gene symbols\n",
"print(\"\\nSample rows with gene symbols (if any):\")\n",
"symbol_sample = gene_annotation[gene_annotation['GENE_SYMBOL'].notna()].head(5)\n",
"print(symbol_sample[['ID', 'GENE_SYMBOL']])\n",
"\n",
"# Check which ID format in the annotation matches our expression data\n",
"# Since the standard gene_data IDs look like A_19_P00315452, we need to find the matching pattern\n",
"import re\n",
"\n",
"# Find the first few rows where ID matches our expression data pattern\n",
"pattern = r'A_\\d+_P\\d+'\n",
"matching_rows = gene_annotation[gene_annotation['ID'].str.contains(pattern, na=False)].head(5)\n",
"print(\"\\nRows with matching ID pattern:\")\n",
"print(matching_rows[['ID', 'GENE_SYMBOL']])\n",
"\n",
"# For probe-gene mapping, we need to determine which columns to use\n",
"# Based on the column names, 'ID' should contain probe IDs and 'GENE_SYMBOL' should contain gene symbols\n",
"# Let's confirm if there are any rows with both values\n",
"valid_mapping_rows = gene_annotation[(gene_annotation['ID'].notna()) & \n",
" (gene_annotation['GENE_SYMBOL'].notna())].head(5)\n",
"print(\"\\nRows with both ID and GENE_SYMBOL:\")\n",
"print(valid_mapping_rows[['ID', 'GENE_SYMBOL']])\n",
"\n",
"# If GENE_SYMBOL is mostly empty, check other potential gene identifier columns\n",
"potential_gene_cols = ['GENE', 'GENE_NAME', 'REFSEQ', 'GB_ACC']\n",
"for col in potential_gene_cols:\n",
" valid_rows = gene_annotation[(gene_annotation['ID'].notna()) & \n",
" (gene_annotation[col].notna())].head(5)\n",
" if not valid_rows.empty:\n",
" print(f\"\\nRows with both ID and {col}:\")\n",
" print(valid_rows[['ID', col]])\n",
"\n",
"# Based on the above analysis, create the mapping dataframe\n",
"# Assuming we've identified the correct columns\n",
"prob_col = 'ID' # Column with probe IDs\n",
"gene_col = 'GENE_SYMBOL' # Column with gene symbols (adjust if needed based on results)\n",
"\n",
"# Get the mapping dataframe\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"print(\"\\nGene mapping dataframe (first 5 rows):\")\n",
"print(mapping_df.head())\n",
"\n",
"# Convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"print(\"\\nGene expression data after mapping (first 5 genes):\")\n",
"print(gene_data.head())\n",
"\n",
"# If the mapping has very few valid entries (or none), we might need to try an alternative approach\n",
"# Check the mapping statistics\n",
"mapped_count = len(gene_data)\n",
"print(f\"\\nNumber of genes after mapping: {mapped_count}\")\n",
"\n",
"# If the mapping resulted in very few genes, try an alternative column\n",
"if mapped_count < 100:\n",
" print(\"Poor mapping results. Trying alternative gene column...\")\n",
" # Try using 'GENE' instead of 'GENE_SYMBOL'\n",
" gene_col_alt = 'GENE'\n",
" mapping_df_alt = get_gene_mapping(gene_annotation, prob_col, gene_col_alt)\n",
" gene_data = apply_gene_mapping(gene_data, mapping_df_alt)\n",
" print(f\"Number of genes after alternative mapping: {len(gene_data)}\")\n",
"\n",
"# Save the gene expression data to a file\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\"Gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ead2aff8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
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"shell.execute_reply": "2025-03-25T08:04:28.683467Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Endometriosis/gene_data/GSE75427.csv\n",
"Clinical data saved to ../../output/preprocess/Endometriosis/clinical_data/GSE75427.csv\n",
"Linked data shape: (8, 19449)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Quartiles for 'Endometriosis':\n",
" 25%: 1.0\n",
" 50% (Median): 1.0\n",
" 75%: 1.0\n",
"Min: 1.0\n",
"Max: 1.0\n",
"The distribution of the feature 'Endometriosis' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 29.5\n",
" 50% (Median): 32.0\n",
" 75%: 36.0\n",
"Min: 28.0\n",
"Max: 42.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"Data was determined to be unusable and was not saved\n"
]
}
],
"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",
"# Create clinical features directly from clinical_data using the conversion functions defined earlier\n",
"clinical_features_df = 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",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Now link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
"print(\"Linked data shape:\", linked_data.shape)\n",
"\n",
"# Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"\n",
"# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Conduct quality check and save the cohort information.\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=\"Dataset contains gene expression from monocytes of rheumatoid arthritis patients, with osteoporosis status included in comorbidity information.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\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\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
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