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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "41d8995f",
"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 = \"Endometriosis\"\n",
"cohort = \"GSE73622\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Endometriosis\"\n",
"in_cohort_dir = \"../../input/GEO/Endometriosis/GSE73622\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Endometriosis/GSE73622.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Endometriosis/gene_data/GSE73622.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Endometriosis/clinical_data/GSE73622.csv\"\n",
"json_path = \"../../output/preprocess/Endometriosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "35367f2c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a11924f4",
"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": "5227bcd1",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8eb6370",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset appears to have gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Trait (Endometriosis) is available in row 0\n",
"trait_row = 0\n",
"# Age is available in row 3\n",
"age_row = 3\n",
"# Gender is not available in the sample characteristics dictionary\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert endometriosis status to binary value.\"\"\"\n",
" if value is None:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" if 'endometriosis' in value.lower():\n",
" return 1\n",
" elif 'no endometriosis' in value.lower():\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous numeric value.\"\"\"\n",
" if value is None:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary value (0 for female, 1 for male).\"\"\"\n",
" # This function is included for completeness but won't be used since gender data is not available\n",
" if value is None:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" value = value.lower()\n",
" if 'female' in value or 'f' == value:\n",
" return 0\n",
" elif 'male' in value or 'm' == value:\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# trait_row is not None, so trait data is available\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",
"# 4. Clinical Feature Extraction\n",
"# First, check the files in the directory\n",
"import os\n",
"import gzip\n",
"import pandas as pd\n",
"print(f\"Files in directory: {os.listdir(in_cohort_dir)}\")\n",
"\n",
"# Since trait_row is not None, we proceed with clinical feature extraction\n",
"try:\n",
" # Use the sample characteristics dictionary provided in the previous output\n",
" # Create a dataframe with columns for each sample and rows for different characteristics\n",
" sample_characteristics = {\n",
" 0: ['disease: Endometriosis', 'disease: No Endometriosis'],\n",
" 1: ['fresh tissue sample/time in culture: Fresh Tissue Sample', \n",
" 'fresh tissue sample/time in culture: 2-3 Weeks in Culture', \n",
" 'fresh tissue sample/time in culture: 4-8 Weeks in Culture'],\n",
" 2: ['cell type: Endometrial Mesenchymal Stem Cell', 'cell type: Endometrial Stromal Fibroblast'],\n",
" 3: ['age: 29', 'age: 39', 'age: 47', 'age: 35', 'age: 50', 'age: 27', 'age: 21', \n",
" 'age: 31', 'age: 26', 'age: 36', 'age: 24', 'age: 28', 'age: 41']\n",
" }\n",
" \n",
" # Create an empty dataframe with the right structure for geo_select_clinical_features\n",
" # We need a dataframe where each column represents a sample and each row contains the characteristics\n",
" # Since we don't have the exact structure from the compressed file, we'll create a sample-based structure\n",
" \n",
" # First, determine how many samples we need\n",
" # Let's count the number of unique values in the trait row (0)\n",
" n_traits = len(sample_characteristics[0])\n",
" \n",
" # Create sample IDs\n",
" sample_ids = [f\"Sample_{i+1}\" for i in range(n_traits)]\n",
" \n",
" # Create the dataframe structure expected by geo_select_clinical_features\n",
" clinical_data = pd.DataFrame(index=range(len(sample_characteristics)), columns=sample_ids)\n",
" \n",
" # Fill the dataframe with the characteristic values\n",
" # We'll distribute the traits across samples\n",
" for row_idx, values in sample_characteristics.items():\n",
" for sample_idx, value in enumerate(values):\n",
" if sample_idx < len(sample_ids):\n",
" clinical_data.iloc[row_idx, sample_idx] = value\n",
" \n",
" selected_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 features\n",
" clinical_preview = preview_df(selected_clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(clinical_preview)\n",
"\n",
" # Save the clinical data to CSV\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\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"except Exception as e:\n",
" print(f\"Error in clinical data extraction: {e}\")\n",
" # If we can't extract clinical data, we should update is_trait_available\n",
" is_trait_available = False\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"
]
},
{
"cell_type": "markdown",
"id": "402b0922",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c021b88a",
"metadata": {},
"outputs": [],
"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": "28693fd2",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3f84c91",
"metadata": {},
"outputs": [],
"source": [
"# Looking at the gene identifiers in the dataset\n",
"# The IDs like '7896736', '7896738', etc. appear to be microarray probe IDs, not human gene symbols\n",
"# These numeric identifiers need to be mapped to standard gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "ac3bfd3c",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "262facee",
"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": "e458c5d7",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81c772ac",
"metadata": {},
"outputs": [],
"source": [
"# 1. Determine which columns contain probe IDs and gene symbols\n",
"# Looking at the gene_annotation dataframe:\n",
"# - 'ID' column contains probe IDs that match the gene expression data index\n",
"# - 'gene_assignment' column contains gene symbols and other gene information\n",
"\n",
"# 2. Create a gene mapping dataframe\n",
"# Extract the ID column and gene_assignment column for mapping\n",
"gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n",
"\n",
"print(\"Gene mapping preview (first 5 rows):\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Convert probe-level measurements to gene-level expression data\n",
"# Apply the mapping to the gene expression data to get gene-level expressions\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"print(\"Gene expression data after mapping (first 5 genes):\")\n",
"print(preview_df(gene_data))\n",
"\n",
"# Save 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\"Gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "03813ef6",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20fca8af",
"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 clinical data file we saved earlier\n",
"try:\n",
" clinical_features_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
" print(\"Clinical data shape:\", clinical_features_df.shape)\n",
"except Exception as e:\n",
" print(f\"Error loading clinical data: {e}\")\n",
" \n",
"# Get the sample IDs from genetic data to ensure alignment\n",
"gene_sample_ids = normalized_gene_data.columns.tolist()\n",
"print(f\"Gene expression data has {len(gene_sample_ids)} samples: {gene_sample_ids[:5]}...\")\n",
"\n",
"# Extract clinical information directly from the matrix file to match sample IDs\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the header line to get sample IDs\n",
" sample_ids = []\n",
" for line in file:\n",
" if line.startswith('\"ID_REF\"'):\n",
" headers = line.strip().split('\\t')\n",
" sample_ids = [h.strip('\"') for h in headers[1:]] # Skip ID_REF\n",
" break\n",
" \n",
" # Reset file pointer to beginning\n",
" file.seek(0)\n",
" \n",
" # Find disease status information\n",
" trait_values = {}\n",
" age_values = {}\n",
" for line in file:\n",
" if \"disease:\" in line:\n",
" values = line.strip().split('\\t')\n",
" if len(values) > 1:\n",
" for i, val in enumerate(values[1:]):\n",
" if i < len(sample_ids):\n",
" trait_values[sample_ids[i]] = convert_trait(val)\n",
" elif \"age:\" in line:\n",
" values = line.strip().split('\\t')\n",
" if len(values) > 1:\n",
" for i, val in enumerate(values[1:]):\n",
" if i < len(sample_ids):\n",
" age_values[sample_ids[i]] = convert_age(val)\n",
"\n",
"# Create clinical data with proper sample IDs\n",
"clinical_dict = {\n",
" trait: pd.Series(trait_values),\n",
" 'Age': pd.Series(age_values) if age_values else None\n",
"}\n",
"\n",
"clinical_features_df = pd.DataFrame(clinical_dict)\n",
"clinical_features_df = clinical_features_df.dropna(axis=1, how='all')\n",
"\n",
"print(\"New clinical data shape:\", clinical_features_df.shape)\n",
"print(\"Clinical data preview:\", clinical_features_df.head())\n",
"\n",
"# Save the properly structured 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\"Updated clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 3. Now link the clinical and genetic data using the proper function\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
"print(\"Linked data shape:\", linked_data.shape)\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(\"After handling missing values, shape:\", linked_data.shape)\n",
"\n",
"# 5. 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",
"# 6. 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 data from patients with and without endometriosis. The dataset comes from a study of endometrial mesenchymal stem cells and stromal fibroblasts.\"\n",
")\n",
"\n",
"# 7. 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\")\n"
]
},
{
"cell_type": "markdown",
"id": "e0d91c23",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe6c5ac5",
"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",
"# 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\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|