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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "9949b45e",
"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 = \"Creutzfeldt-Jakob_Disease\"\n",
"cohort = \"GSE87629\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Creutzfeldt-Jakob_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Creutzfeldt-Jakob_Disease/GSE87629\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Creutzfeldt-Jakob_Disease/GSE87629.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Creutzfeldt-Jakob_Disease/gene_data/GSE87629.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Creutzfeldt-Jakob_Disease/clinical_data/GSE87629.csv\"\n",
"json_path = \"../../output/preprocess/Creutzfeldt-Jakob_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "81524769",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "644a07d1",
"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": "587b0c60",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecc90421",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the Series_overall_design, this dataset contains DNA microarray analysis of B and T cells\n",
"is_gene_available = True # DNA microarray data is gene expression data\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait: looking at row 5 which contains 'biopsy data, villus height to crypt depth'\n",
"# This measures the severity of the disease (villus atrophy) which can serve as our trait\n",
"trait_row = 5\n",
"\n",
"# For age: There is no age information in the sample characteristics\n",
"age_row = None\n",
"\n",
"# For gender: There is no gender information in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert the villus height to crypt depth ratio to a continuous value.\n",
" Higher values indicate healthier intestinal tissue (less disease severity).\n",
" Lower values indicate more severe celiac disease activity.\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the numeric value after the colon\n",
" if ':' in value:\n",
" try:\n",
" # The value is in format \"biopsy data, villus height to crypt depth: X.X\"\n",
" return float(value.split(':')[1].strip())\n",
" except (ValueError, IndexError):\n",
" return None\n",
" else:\n",
" try:\n",
" return float(value)\n",
" except ValueError:\n",
" return None\n",
"\n",
"# Age and gender conversion functions are defined but won't be used\n",
"def convert_age(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" try:\n",
" return float(value.split(':')[1].strip())\n",
" except (ValueError, IndexError):\n",
" return None\n",
" else:\n",
" try:\n",
" return float(value)\n",
" except ValueError:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':')[1].strip().lower()\n",
" else:\n",
" value = value.lower()\n",
" \n",
" if value in ['female', 'f']:\n",
" return 0\n",
" elif value in ['male', 'm']:\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",
"# Save initial filtering 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",
" # Create a DataFrame from the Sample Characteristics Dictionary shown in the previous output\n",
" sample_characteristics = {\n",
" 0: ['individual: celiac patient A', 'individual: celiac patient C', 'individual: celiac patient G', 'individual: celiac patient H', 'individual: celiac patient K', 'individual: celiac patient L', 'individual: celiac patient M', 'individual: celiac patient N', 'individual: celiac patient O', 'individual: celiac patient P', 'individual: celiac patient Q', 'individual: celiac patient R', 'individual: celiac patient S', 'individual: celiac patient T', 'individual: celiac patient U', 'individual: celiac patient V', 'individual: celiac patient W', 'individual: celiac patient X', 'individual: celiac patient Y', 'individual: celiac patient Z'],\n",
" 1: ['disease state: biopsy confirmed celiac disease on gluten-free diet greater than one year'],\n",
" 2: ['treatment: control', 'treatment: 6 weeks gluten challenge'],\n",
" 3: ['tissue: peripheral whole blood'],\n",
" 4: ['cell type: purified pool of B and T cells'],\n",
" 5: ['biopsy data, villus height to crypt depth: 2.9', 'biopsy data, villus height to crypt depth: 2.6', 'biopsy data, villus height to crypt depth: 1.1', 'biopsy data, villus height to crypt depth: 0.5', 'biopsy data, villus height to crypt depth: 0.3', 'biopsy data, villus height to crypt depth: 2', 'biopsy data, villus height to crypt depth: 0.4', 'biopsy data, villus height to crypt depth: 2.4', 'biopsy data, villus height to crypt depth: 1.4', 'biopsy data, villus height to crypt depth: 2.7', 'biopsy data, villus height to crypt depth: 3.5', 'biopsy data, villus height to crypt depth: 0.7', 'biopsy data, villus height to crypt depth: 0.2', 'biopsy data, villus height to crypt depth: 2.8', 'biopsy data, villus height to crypt depth: 3', 'biopsy data, villus height to crypt depth: 0.8', 'biopsy data, villus height to crypt depth: 1.2', 'biopsy data, villus height to crypt depth: 1.7', 'biopsy data, villus height to crypt depth: 2.5', 'biopsy data, villus height to crypt depth: 2.1', 'biopsy data, villus height to crypt depth: 3.1'],\n",
" 6: ['hybridization batch: 1']\n",
" }\n",
" \n",
" # Convert the dictionary to a DataFrame\n",
" clinical_data = pd.DataFrame.from_dict(sample_characteristics, orient='index')\n",
" \n",
" # Extract clinical features using the library function\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",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of clinical data:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "2d2a3dd8",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55d5ab0d",
"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": "6101c34d",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25a895d7",
"metadata": {},
"outputs": [],
"source": [
"# From the identifiers shown, we can observe that the gene identifiers are in the format \"ILMN_xxxxxxx\".\n",
"# This format indicates that they are Illumina probe IDs, not standard human gene symbols.\n",
"# Illumina probe IDs need to be mapped to human gene symbols for proper analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "7f8514c0",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cbdaf90",
"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": "2896a2b3",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b5f9f09",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the appropriate column names for gene identifiers and gene symbols\n",
"# From the gene_annotation preview, we see:\n",
"# - 'ID' column contains Illumina probe IDs (e.g., ILMN_1725881) which match our gene expression data index\n",
"# - 'Symbol' column contains the gene symbols we want to map to (e.g., LOC23117, FCGR2B, TRIM44)\n",
"\n",
"# 2. Get gene mapping dataframe by extracting the two columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"\n",
"# Print the first few rows to verify the mapping\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"# Using the library function that distributes probe values across multiple genes and sums by gene\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print the first few rows to verify the mapped gene expression data\n",
"print(\"\\nGene expression data preview (after mapping):\")\n",
"print(preview_df(gene_data))\n",
"\n",
"# Save the 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": "e9453534",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b44a07b8",
"metadata": {},
"outputs": [],
"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. Load the clinical data\n",
"# First, let's read the clinical data that was saved in step 2\n",
"clinical_df = pd.read_csv(out_clinical_data_file)\n",
"\n",
"# The clinical data is loaded but it might not have the trait name as a column\n",
"# We need to set the first column as the trait column\n",
"clinical_df.columns = [trait]\n",
"print(\"Clinical data preview:\")\n",
"print(clinical_df.head())\n",
"\n",
"# Create a transposed version for linking with gene data (samples as columns)\n",
"clinical_df_t = clinical_df.T\n",
"print(\"Transposed clinical data shape:\", clinical_df_t.shape)\n",
"\n",
"# 3. Link the clinical and genetic data\n",
"linked_data = pd.concat([clinical_df_t, normalized_gene_data]).T\n",
"print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine whether the trait and demographic features are biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 6. Conduct final quality validation 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=\"Dataset contains villus height to crypt depth ratio measurements in celiac disease patients, which reflects disease severity.\"\n",
")\n",
"\n",
"# 7. If the linked data is usable, save it\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"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": "c79be5e3",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b03847f",
"metadata": {},
"outputs": [],
"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. Extract clinical features using the functions defined in step 2\n",
"# First, let's load the clinical data again to ensure we have the latest version\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Extract clinical features using the correct trait name from the variable\n",
"selected_clinical_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",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# 3. Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine whether the trait and demographic features are biased\n",
"# Check if trait is biased\n",
"trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'\n",
"if trait_type == \"binary\":\n",
" is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n",
"else:\n",
" is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n",
"\n",
"# Remove biased demographic features if present\n",
"unbiased_linked_data = linked_data.copy()\n",
"if \"Age\" in unbiased_linked_data.columns:\n",
" age_biased = judge_continuous_variable_biased(unbiased_linked_data, 'Age')\n",
" if age_biased:\n",
" print(f\"The distribution of the feature 'Age' in this dataset is severely biased.\")\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns='Age')\n",
" else:\n",
" print(f\"The distribution of the feature 'Age' in this dataset is fine.\")\n",
"\n",
"if \"Gender\" in unbiased_linked_data.columns:\n",
" gender_biased = judge_binary_variable_biased(unbiased_linked_data, 'Gender')\n",
" if gender_biased:\n",
" print(f\"The distribution of the feature 'Gender' in this dataset is severely biased.\")\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns='Gender')\n",
" else:\n",
" print(f\"The distribution of the feature 'Gender' in this dataset is fine.\")\n",
"\n",
"# 6. Conduct final quality validation 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=\"Dataset contains villus height to crypt depth ratio measurements in celiac disease patients, which reflects disease severity when studied for Creutzfeldt-Jakob_Disease.\"\n",
")\n",
"\n",
"# 7. If the linked data is usable, save it\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"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
}
|