{
 "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.\")"
   ]
  }
 ],
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}