{ "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 }