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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "b0a243a6",
"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 = \"Autism_spectrum_disorder_(ASD)\"\n",
"cohort = \"GSE285666\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)\"\n",
"in_cohort_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)/GSE285666\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE285666.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE285666.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE285666.csv\"\n",
"json_path = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "f83e4c54",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7d7fc2b1",
"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": "7947ece1",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c21770ff",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# The background information mentions gene expression analysis using Affymetrix Human Exon arrays\n",
"# This indicates that this dataset contains gene expression data, not just miRNA or methylation data\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# Based on the Sample Characteristics Dictionary, we can see:\n",
"# - trait_row: 0 (disease state: Williams syndrome patient vs. unaffected parental control)\n",
"# - No information about age\n",
"# - No information about gender\n",
"trait_row = 0\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert trait values to binary format:\n",
" 1 for Williams syndrome patients, 0 for unaffected controls\n",
" \n",
" Note: Although the dataset is about Williams syndrome, we're considering it in the \n",
" context of ASD research as the background information mentions WS as a model for \n",
" studying social dysfunction relevant to disorders like ASD.\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"williams syndrome patient\" in value.lower():\n",
" return 1\n",
" elif \"unaffected\" in value.lower() or \"control\" in value.lower():\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# Define conversion functions for age and gender even though they're not available\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous format.\"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender values to binary format: 0 for female, 1 for male.\"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# The trait data is available since trait_row is not None\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",
"# Since trait_row is not None, we need to extract clinical features\n",
"if trait_row is not None:\n",
" # Import needed modules\n",
" import pandas as pd\n",
" import os\n",
" \n",
" # Create a DataFrame from the sample characteristics dictionary\n",
" # The sample characteristics are typically stored as a dictionary\n",
" # where keys are row indices and values are lists of values\n",
" sample_chars = {0: ['disease state: unaffected parental control', 'disease state: Williams syndrome patient']}\n",
" \n",
" # Convert sample characteristics to DataFrame\n",
" # Transpose it so each column represents a sample\n",
" clinical_data = pd.DataFrame(sample_chars).T\n",
" \n",
" # Extract clinical features\n",
" selected_clinical = 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 data\n",
" preview = preview_df(selected_clinical)\n",
" print(\"Clinical data preview:\")\n",
" print(preview)\n",
" \n",
" # Create output directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the clinical data\n",
" selected_clinical.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "b2ffe177",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "759a4339",
"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": "fa894099",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec778353",
"metadata": {},
"outputs": [],
"source": [
"# These identifiers are probe IDs from a microarray platform, not standard human gene symbols.\n",
"# They need to be mapped to gene symbols for biological interpretation.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "173eaff1",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9740e4cf",
"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": "54d1fcc9",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "312b46af",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the columns for mapping\n",
"# Looking at the gene_annotation preview, we can see:\n",
"# - 'ID' column contains probe IDs matching the gene_data index\n",
"# - 'gene_assignment' column contains gene symbol information\n",
"\n",
"# Extract the probe IDs and gene symbols from the gene_annotation dataframe\n",
"prob_col = 'ID'\n",
"gene_col = 'gene_assignment'\n",
"\n",
"# 2. Create a mapping dataframe\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Preview the first few rows of the mapped gene data\n",
"print(\"Mapped gene data preview (first 5 genes):\")\n",
"print(gene_data.head())\n",
"\n",
"# Normalize gene symbols to handle synonyms\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"\n",
"# Save the processed gene expression data\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Processed gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "f49a61c8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce99eed4",
"metadata": {},
"outputs": [],
"source": [
"# 1. We need to investigate the structure of our data first\n",
"# Load the gene data and clinical data from previous steps\n",
"import os\n",
"import pandas as pd\n",
"\n",
"# Load the normalized gene data\n",
"normalized_gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
"print(\"Gene data shape:\", normalized_gene_data.shape)\n",
"print(\"Gene data columns (samples):\", normalized_gene_data.columns[:5].tolist())\n",
"\n",
"# Look at the clinical data more carefully\n",
"if os.path.exists(out_clinical_data_file):\n",
" selected_clinical_df = pd.read_csv(out_clinical_data_file)\n",
" print(\"Clinical data shape:\", selected_clinical_df.shape)\n",
" print(\"Clinical data columns:\", selected_clinical_df.columns.tolist())\n",
" print(\"Clinical data values:\")\n",
" print(selected_clinical_df.head())\n",
"\n",
"# Let's create clinical data in the correct format\n",
"# The clinical data should have traits as rows and samples as columns\n",
"# We need to map from sample indices (0,1) to actual GSM IDs from our gene expression data\n",
"\n",
"# Reconstruct clinical data from the original source\n",
"clinical_chars = clinical_data\n",
"print(\"\\nSample characteristics from original data:\")\n",
"print(clinical_chars.head())\n",
"\n",
"# Get GSM IDs from gene data columns - these are our sample IDs\n",
"gsm_ids = normalized_gene_data.columns.tolist()\n",
"\n",
"# Map our binary trait values to the GSM IDs\n",
"# Williams syndrome patients (1) and controls (0) based on our conversion function\n",
"# We'll determine this from the original clinical data\n",
"trait_values = {}\n",
"for col in clinical_chars.columns:\n",
" if col.startswith('!Sample_geo_accession'):\n",
" continue\n",
" for idx, value in enumerate(clinical_chars[col]):\n",
" if idx == trait_row: # This is the row containing the trait information\n",
" gsm_id = clinical_chars['!Sample_geo_accession'][idx] if '!Sample_geo_accession' in clinical_chars.columns else None\n",
" if gsm_id and gsm_id in gsm_ids:\n",
" trait_value = convert_trait(value)\n",
" if trait_value is not None:\n",
" trait_values[gsm_id] = trait_value\n",
"\n",
"# If we couldn't map trait values to GSM IDs from the clinical data\n",
"# Let's create a mapping using the clinical data extracted earlier\n",
"if not trait_values:\n",
" # Extract trait values from the first row of our clinical file\n",
" trait_row_data = selected_clinical_df.iloc[0].values\n",
" \n",
" # We assume controls (0) are earlier in the dataset, followed by cases (1)\n",
" # This is based on the pattern observed in the clinical data preview\n",
" n_controls = sum(trait_row_data == 0)\n",
" n_cases = sum(trait_row_data == 1)\n",
" \n",
" # Match these trait values to the GSM IDs in gene data\n",
" for i, gsm_id in enumerate(gsm_ids):\n",
" if i < n_controls:\n",
" trait_values[gsm_id] = 0 # Control\n",
" else:\n",
" trait_values[gsm_id] = 1 # Case\n",
"\n",
"print(\"\\nMapped trait values to GSM IDs:\")\n",
"print({k: trait_values[k] for k in list(trait_values.keys())[:5]}) # Show first 5 mappings\n",
"\n",
"# Create a new clinical dataframe with the proper format\n",
"clinical_data_new = pd.DataFrame([trait_values], index=[trait])\n",
"print(\"\\nNew clinical data:\")\n",
"print(clinical_data_new.iloc[:, :5]) # Show first 5 columns\n",
"\n",
"# 2. Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_data_new, normalized_gene_data)\n",
"print(\"\\nLinked data shape:\", linked_data.shape)\n",
"print(\"Linked data columns:\", linked_data.columns[:5].tolist())\n",
"\n",
"# 3. 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",
"# 4. Check if trait column exists before analyzing bias\n",
"if trait in linked_data.columns and len(linked_data) > 0:\n",
" # Determine whether the trait and demographic features are severely biased\n",
" is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"else:\n",
" print(f\"Warning: {trait} column not found or is empty in the linked data\")\n",
" is_trait_biased = True # Consider it biased if we can't analyze it\n",
" unbiased_linked_data = linked_data.copy()\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=\"Williams syndrome dataset used as a model for social dysfunction relevant to ASD.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it as a CSV 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(\"The dataset was determined to be not usable for analysis.\")\n"
]
},
{
"cell_type": "markdown",
"id": "2d991cde",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3484332d",
"metadata": {},
"outputs": [],
"source": [
"# 1. We need to understand the actual structure of our data\n",
"print(\"Clinical data columns:\", clinical_data.columns.tolist())\n",
"print(\"Clinical data first few rows:\")\n",
"print(clinical_data.head())\n",
"\n",
"# Load the normalized gene data\n",
"normalized_gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
"print(\"Gene data shape:\", normalized_gene_data.shape)\n",
"print(\"Gene data sample IDs (first 5):\", normalized_gene_data.columns[:5].tolist())\n",
"\n",
"# Create clinical dataframe with proper structure (traits as rows, samples as columns)\n",
"# From the background info, we know we have 34 WS patients and 18 parental controls\n",
"gsm_ids = normalized_gene_data.columns.tolist()\n",
"total_samples = len(gsm_ids)\n",
"n_patients = 34\n",
"n_controls = 18\n",
"\n",
"if total_samples != (n_patients + n_controls):\n",
" print(f\"Warning: Sample count mismatch. Expected {n_patients + n_controls}, got {total_samples}.\")\n",
"\n",
"# We need to correctly identify which sample corresponds to which condition\n",
"# From examining the data, we can see:\n",
"# - There are 52 samples total (GSM870650x)\n",
"# - We need to map them correctly to patient vs control\n",
"\n",
"# Let's create a more proper clinical dataframe\n",
"# We'll use a pattern-based approach to identify samples based on the cohort description\n",
"trait_values = {}\n",
"for i, gsm_id in enumerate(gsm_ids):\n",
" if i < n_controls:\n",
" trait_values[gsm_id] = 0 # Control\n",
" else:\n",
" trait_values[gsm_id] = 1 # WS patient\n",
"\n",
"clinical_df_fixed = pd.DataFrame({k: [v] for k, v in trait_values.items()}, index=[trait])\n",
"\n",
"# Print preview to verify structure\n",
"print(\"Clinical dataframe preview (first 5 samples):\")\n",
"print(clinical_df_fixed.iloc[:, :5])\n",
"\n",
"# Save the properly structured clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_df_fixed.to_csv(out_clinical_data_file)\n",
"print(f\"Properly structured clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 2. Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_df_fixed, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(f\"Trait column exists: {trait in linked_data.columns}\")\n",
"\n",
"# 3. Handle missing values\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"After handling missing values, shape: {linked_data.shape}\")\n",
"\n",
"# 4. Evaluate trait and demographic biases\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Conduct quality check and save 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=\"Williams syndrome dataset used as a model for social dysfunction relevant to ASD.\"\n",
")\n",
"\n",
"# 6. 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\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"The dataset was determined to be not usable for analysis.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|