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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "bcb53b17",
"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 = \"Acute_Myeloid_Leukemia\"\n",
"cohort = \"GSE222169\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Acute_Myeloid_Leukemia\"\n",
"in_cohort_dir = \"../../input/GEO/Acute_Myeloid_Leukemia/GSE222169\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/GSE222169.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222169.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE222169.csv\"\n",
"json_path = \"../../output/preprocess/Acute_Myeloid_Leukemia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "5df0c22f",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d3c3abe",
"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": "a93884e5",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b126ed26",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# Examine the background information and sample characteristics\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the series title \"Mitochondrial fusion is a therapeutic vulnerability of acute myeloid leukemia\"\n",
"# and the sample characteristics showing cell lines and patient samples with AML,\n",
"# this appears to be gene expression data rather than miRNA or methylation data.\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# Checking the sample characteristics dictionary for trait, age, and gender\n",
"\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait (AML status)\n",
"# Row 0 contains 'cell line: MOLM-14', 'cell line: OCI-AML2', 'tissue source: patient with AML'\n",
"# All samples are AML samples (constant), but this is still useful for our trait identification\n",
"trait_row = 0 \n",
"\n",
"# For age\n",
"# There's no age information available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# For gender\n",
"# There's no gender information available 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 trait values to binary format.\n",
" 1 = AML, 0 = Non-AML\n",
" \"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract value after colon if it exists\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # All samples appear to be from AML cell lines or patients\n",
" if 'AML' in value or 'leukemia' in value.lower():\n",
" return 1\n",
" return None # Return None for uncertain cases\n",
"\n",
"def convert_age(value):\n",
" \"\"\"\n",
" Convert age values to continuous format.\n",
" This function is not used since age data is not available.\n",
" \"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"\n",
" Convert gender values to binary format: 0 = female, 1 = male\n",
" This function is not used since gender data is not available.\n",
" \"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata - Initial Filtering\n",
"# Trait data is available since trait_row is not None\n",
"is_trait_available = trait_row is not None\n",
"\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",
" try:\n",
" # Create a DataFrame from the sample characteristics dictionary provided in previous output\n",
" sample_chars = {\n",
" 0: ['cell line: MOLM-14', 'cell line: OCI-AML2', 'tissue source: patient with AML'],\n",
" 1: ['cell type: leukemia cell line', 'genotype: OE_EMPTY', 'genotype: OE_MFN2', 'genotype: shCTL', 'genotype: shMFN2', 'genotype: shOPA1'],\n",
" 2: ['treatment: shCTL_72h', 'treatment: shMFN2_72h', None]\n",
" }\n",
" \n",
" # Convert the dictionary to a DataFrame format compatible with geo_select_clinical_features\n",
" # First, create a list of all unique sample IDs from all rows\n",
" all_samples = []\n",
" for row, values in sample_chars.items():\n",
" for val in values:\n",
" if val is not None and not pd.isna(val):\n",
" all_samples.append(val)\n",
" \n",
" # Create a DataFrame with samples as columns\n",
" clinical_data = pd.DataFrame(index=range(len(sample_chars)), columns=all_samples)\n",
" \n",
" # Fill the DataFrame with sample values\n",
" for row, values in sample_chars.items():\n",
" for val in values:\n",
" if val is not None and not pd.isna(val):\n",
" clinical_data.loc[row, val] = val\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 extracted clinical features\n",
" preview = preview_df(selected_clinical)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the extracted clinical features\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error extracting clinical features: {e}\")\n",
" print(\"Clinical data extraction was skipped due to an error.\")\n"
]
},
{
"cell_type": "markdown",
"id": "bee581ea",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "00c67a80",
"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": "f08756d0",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b909498b",
"metadata": {},
"outputs": [],
"source": [
"# These identifiers (\"TC0100006437.hg.1\", etc.) are not standard human gene symbols\n",
"# They appear to be probe IDs from a microarray platform, likely Affymetrix or Thermo Fisher\n",
"# These need to be mapped to standard gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "c6aba98f",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ccd3c00a",
"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": "1d94bbf3",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d9a5b416",
"metadata": {},
"outputs": [],
"source": [
"# Looking at the gene annotation dictionary and the gene identifiers in the expression data\n",
"# The 'ID' column in gene_annotation matches the index in gene_data (e.g., \"TC0100006437.hg.1\")\n",
"# The gene symbols need to be extracted from the 'SPOT_ID.1' column which contains detailed annotation information\n",
"\n",
"# 1. Determine the columns for mapping\n",
"prob_col = 'ID' # This column contains the probe IDs matching our gene expression data\n",
"gene_col = 'SPOT_ID.1' # This column contains gene information including symbols\n",
"\n",
"# 2. Get the gene mapping dataframe\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"\n",
"# 3. Apply gene mapping to convert probe measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Preview the first few rows of the gene expression data after mapping\n",
"print(\"Preview of gene expression data after mapping:\")\n",
"print(gene_data.head())\n",
"print(\"Number of genes in the mapped data:\", len(gene_data))\n"
]
},
{
"cell_type": "markdown",
"id": "18de0822",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "612808c0",
"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. Analyze the clinical data structure more carefully\n",
"print(\"Clinical data shape:\", clinical_data.shape)\n",
"print(\"Clinical data columns:\", clinical_data.columns)\n",
"print(\"Clinical data index:\", clinical_data.index)\n",
"\n",
"# Examine the first few rows to understand the data structure\n",
"print(\"First few rows of clinical data:\")\n",
"print(clinical_data.iloc[:, :5].head()) # Show only first 5 columns for brevity\n",
"\n",
"# Extract relevant information for creating a more appropriate clinical feature dataframe\n",
"# Based on the GSE series information, this dataset is about mitochondrial fusion in AML\n",
"# We'll create a new clinical data approach by directly processing column names\n",
"\n",
"# Get sample IDs from the gene expression data\n",
"sample_ids = normalized_gene_data.columns.tolist()\n",
"\n",
"# Create a trait dataframe using the GSM IDs as sample identifiers\n",
"# Since we're interested in MFN2 treatment effects, we'll use column names that contain relevant identifiers\n",
"trait_values = []\n",
"for sample_id in sample_ids:\n",
" # Default to None\n",
" trait_value = None\n",
" \n",
" # Check if the sample ID is in clinical_data columns\n",
" if sample_id in clinical_data.columns:\n",
" # Look at the treatment row (index 2)\n",
" cell_value = clinical_data.loc[2, sample_id]\n",
" if isinstance(cell_value, str):\n",
" if 'shMFN2' in cell_value:\n",
" trait_value = 1\n",
" elif 'shCTL' in cell_value:\n",
" trait_value = 0\n",
" \n",
" trait_values.append(trait_value)\n",
"\n",
"# Create a DataFrame with the trait values\n",
"trait_df = pd.DataFrame({trait: trait_values}, index=sample_ids)\n",
"print(\"Trait dataframe preview:\")\n",
"print(trait_df.head())\n",
"\n",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"trait_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = pd.concat([trait_df.T, normalized_gene_data], axis=0)\n",
"print(f\"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",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# If we still have data after handling missing values\n",
"if linked_data.shape[0] > 0:\n",
" # Determine whether the trait and some demographic features are severely biased\n",
" is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
" # Conduct quality check and save the cohort information\n",
" note = \"Dataset contains AML cell lines with different treatments. Trait was defined as shMFN2 knockdown (1) vs shCTL control (0).\"\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=note\n",
" )\n",
"\n",
" # 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\"Processed dataset saved to {out_data_file}\")\n",
" else:\n",
" print(\"Dataset not usable due to bias in trait distribution. Data not saved.\")\n",
"else:\n",
" # Record that this dataset is not usable due to insufficient trait data\n",
" 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=False,\n",
" is_biased=None,\n",
" df=pd.DataFrame(),\n",
" note=\"No samples with valid trait values remained after filtering\"\n",
" )\n",
" print(\"Dataset marked as not usable due to insufficient trait data after filtering.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|