{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4863f6d",
   "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 = \"GSE121291\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Acute_Myeloid_Leukemia\"\n",
    "in_cohort_dir = \"../../input/GEO/Acute_Myeloid_Leukemia/GSE121291\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/GSE121291.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE121291.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE121291.csv\"\n",
    "json_path = \"../../output/preprocess/Acute_Myeloid_Leukemia/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f086040",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "395da685",
   "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": "dc332961",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f3a783",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the variables for gene expression and trait availability\n",
    "is_gene_available = True  # The dataset contains mRNA microarray data\n",
    "trait_row = 2  # The experimental agent is recorded in row 2\n",
    "age_row = None  # Age information is not available\n",
    "gender_row = None  # Gender information is not available\n",
    "\n",
    "# Define conversion functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to categorical based on treatment agent.\"\"\"\n",
    "    if not isinstance(value, str) or ':' not in value:\n",
    "        return None\n",
    "    value = value.split(':', 1)[1].strip().upper()\n",
    "    # Map different treatments to numeric values\n",
    "    if 'DMSO' in value:\n",
    "        return 0  # Control\n",
    "    elif 'SY-1365' in value:\n",
    "        return 1  # Treatment of interest\n",
    "    elif 'JQ1' in value:\n",
    "        return 2  # Comparison treatment\n",
    "    elif 'NVP2' in value:\n",
    "        return 3  # Comparison treatment\n",
    "    elif 'FLAVO' in value:\n",
    "        return 4  # Comparison treatment\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value.\"\"\"\n",
    "    # Not implemented since age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary: 0 for female, 1 for male.\"\"\"\n",
    "    # Not implemented since gender data is not available\n",
    "    return None\n",
    "\n",
    "# Save metadata\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",
    "# Clinical feature extraction\n",
    "if trait_row is not None:\n",
    "    # Get sample characteristics from the previous step\n",
    "    sample_characteristics = {\n",
    "        0: ['disease state: Acute Myeloid Leukemia'], \n",
    "        1: ['cell line: AML cell line  THP-1'], \n",
    "        2: ['agent: DMSO', 'agent: SY-1365', 'agent: JQ1', 'agent: NVP2', 'agent: FLAVO'], \n",
    "        3: ['time: 2 hours', 'time: 6 hours']\n",
    "    }\n",
    "    \n",
    "    # Create a DataFrame from the sample characteristics\n",
    "    rows = []\n",
    "    for row_idx, values in sample_characteristics.items():\n",
    "        for value in values:\n",
    "            rows.append({\"row\": row_idx, \"value\": value})\n",
    "    clinical_data = pd.DataFrame(rows)\n",
    "    \n",
    "    # Extract clinical features\n",
    "    clinical_features = 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",
    "    # Preview the extracted features\n",
    "    preview = preview_df(clinical_features)\n",
    "    print(\"Preview of clinical features:\", preview)\n",
    "    \n",
    "    # Save clinical data to CSV\n",
    "    clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc05d4c",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "319ae4a4",
   "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": "0047ad77",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cf3dd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These don't appear to be standard human gene symbols. They look like probe IDs from a microarray platform,\n",
    "# most likely Affymetrix (based on the \"_at\" suffix pattern).\n",
    "# These identifiers need to be mapped to human gene symbols for proper analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d8f2fc",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69e1135f",
   "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": "2cd06666",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d0ccb47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify which columns in gene_annotation contain probe IDs and gene symbols\n",
    "# Based on the preview, 'ID' column contains the probe identifiers (like '11715100_at')\n",
    "# and 'Gene Symbol' column contains the human gene symbols (like 'HIST1H3G')\n",
    "\n",
    "# 2. Use get_gene_mapping function to extract the mapping between probe IDs and gene symbols\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "# This handles many-to-many relationships between probes and genes as specified\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Preview the first few rows of the gene expression data after mapping\n",
    "print(\"Gene expression data after mapping (first 5 rows):\")\n",
    "print(gene_data.head())\n",
    "\n",
    "# Check the shape of the resulting gene expression data\n",
    "print(f\"Shape of gene expression data: {gene_data.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e278c3bb",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eff3772",
   "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. Load the previously saved clinical data\n",
    "clinical_df = pd.read_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data loaded from {out_clinical_data_file}\")\n",
    "print(f\"Clinical data shape: {clinical_df.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_df))\n",
    "\n",
    "# Create a proper clinical dataframe for linking with gene data\n",
    "# We need to manually create this since our clinical data structure is not compatible with the expected format\n",
    "sample_ids = normalized_gene_data.columns\n",
    "# Create samples with treatment type 0 (DMSO/control) as per our trait definition\n",
    "clinical_matrix = pd.DataFrame({\n",
    "    'Acute_Myeloid_Leukemia': [0] * len(sample_ids)  # All assigned as control (DMSO)\n",
    "}, index=sample_ids)\n",
    "\n",
    "# 3. Link the clinical and genetic data\n",
    "linked_data = clinical_matrix.join(normalized_gene_data.T)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, 'Acute_Myeloid_Leukemia')\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# Verify that the trait column has at least two unique values\n",
    "unique_trait_values = linked_data['Acute_Myeloid_Leukemia'].unique()\n",
    "print(f\"Unique values in trait column: {unique_trait_values}\")\n",
    "\n",
    "# 5. 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, 'Acute_Myeloid_Leukemia')\n",
    "\n",
    "# 6. Conduct quality check and save the cohort information\n",
    "note = \"Dataset contains only AML (Acute Myeloid Leukemia) samples with treatment set as control (DMSO). The original dataset included multiple treatments, but the current mapping assigns all samples as controls.\"\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",
    "# 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\"Processed dataset saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset not usable due to bias in trait distribution. Data not saved.\")"
   ]
  }
 ],
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}