{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "806d6fc3",
   "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 = \"Bone_Density\"\n",
    "cohort = \"GSE56815\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Bone_Density\"\n",
    "in_cohort_dir = \"../../input/GEO/Bone_Density/GSE56815\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Bone_Density/GSE56815.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Bone_Density/gene_data/GSE56815.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Bone_Density/clinical_data/GSE56815.csv\"\n",
    "json_path = \"../../output/preprocess/Bone_Density/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdd62c3",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d443bba",
   "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": "83fa5598",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12b83518",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains gene expression data from Affymetrix arrays\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait (bone mineral density): available at index 1\n",
    "trait_row = 1\n",
    "# For age: not available in the sample characteristics\n",
    "age_row = None\n",
    "# For gender: all subjects are female (constant), so we consider it not available\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert bone mineral density values to binary (0 for low BMD, 1 for high BMD)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value part after the colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if isinstance(value, str):\n",
    "        value = value.lower()\n",
    "        if 'high' in value:\n",
    "            return 1\n",
    "        elif 'low' in value:\n",
    "            return 0\n",
    "    \n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age values to continuous numbers\"\"\"\n",
    "    # Not used as age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender values to binary (0 for female, 1 for male)\"\"\"\n",
    "    # Not used as gender data is not available\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata - Initial Filtering\n",
    "# Since trait_row is not None, trait data is available\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",
    "# 4. Clinical Feature Extraction\n",
    "# Since trait data is available, extract clinical features\n",
    "if trait_row is not None:\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 data\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of extracted clinical data:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the extracted clinical data to CSV\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": "f7254b68",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efb5a1b6",
   "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": "0cca0231",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d492fa14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyze the gene identifiers in the index\n",
    "# Looking at the identifiers like '1007_s_at', '1053_at', etc.\n",
    "# These appear to be probe IDs from Affymetrix microarrays, not standard human gene symbols\n",
    "\n",
    "# Probe IDs in the format of '1007_s_at' are typical for Affymetrix platforms\n",
    "# They need to be mapped to human gene symbols for meaningful analysis\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4c48bd",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e00bf63e",
   "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": "a2b8a7c0",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a797bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify columns in gene annotation data that contain probe IDs and gene symbols\n",
    "# From the preview, we can see that 'ID' contains probe IDs like '1007_s_at'\n",
    "# and 'Gene Symbol' contains the gene symbols like 'DDR1 /// MIR4640'\n",
    "probe_col = 'ID'\n",
    "gene_col = 'Gene Symbol'\n",
    "\n",
    "# 2. Get gene mapping dataframe using the get_gene_mapping function from the library\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)\n",
    "\n",
    "# 3. Convert probe-level measurements to gene-level expression by applying the mapping\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print information about the resulting gene expression dataframe\n",
    "print(f\"Number of genes after mapping: {len(gene_data)}\")\n",
    "print(\"First 10 gene symbols:\")\n",
    "print(gene_data.index[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81dd8993",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86cfadfc",
   "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(\"Clinical data structure:\")\n",
    "print(clinical_df.head())\n",
    "\n",
    "# Get trait column name (actual name in the DataFrame)\n",
    "trait_column = clinical_df.columns[0]  # First column should be Bone_Density\n",
    "\n",
    "# Create a DataFrame with correct structure for geo_link_clinical_genetic_data\n",
    "# The function expects clinical_df to have features as rows and samples as columns\n",
    "clinical_df_for_linking = pd.DataFrame(clinical_df[trait_column].values[None, :], \n",
    "                                      index=[trait], \n",
    "                                      columns=clinical_df.index)\n",
    "print(\"Clinical data prepared for linking:\")\n",
    "print(clinical_df_for_linking.head())\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_df_for_linking, normalized_gene_data)\n",
    "\n",
    "# For analysis functions, we need samples as rows and features as columns\n",
    "linked_data_for_analysis = linked_data.T\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data_for_analysis = handle_missing_values(linked_data_for_analysis, trait)\n",
    "\n",
    "# 4. Determine whether the trait and demographic features are severely biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_for_analysis, trait)\n",
    "\n",
    "# 5. Conduct final quality validation and save cohort info\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 gene expression from blood monocytes in pre- and postmenopausal females with low or high bone mineral density.\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it as CSV\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": "71f31bb3",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cb75063",
   "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 and prepare for linking\n",
    "# Read the clinical data without setting index_col to avoid issues\n",
    "clinical_df = pd.read_csv(out_clinical_data_file)\n",
    "\n",
    "# Get the structure of the clinical data to understand column names\n",
    "print(\"Clinical data columns:\", clinical_df.columns.tolist())\n",
    "print(\"Clinical data shape:\", clinical_df.shape)\n",
    "\n",
    "# Transform clinical data to have the correct format for linking\n",
    "# The clinical data appears to have samples as columns with the trait value in each row 0\n",
    "sample_names = clinical_df.columns.tolist()\n",
    "trait_values = clinical_df.iloc[0].tolist()\n",
    "clinical_df_transformed = pd.DataFrame({trait: trait_values}, index=sample_names)\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = pd.merge(clinical_df_transformed, normalized_gene_data.T, \n",
    "                      left_index=True, right_index=True)\n",
    "\n",
    "# Check the structure of the linked data\n",
    "print(\"Linked data shape:\", linked_data.shape)\n",
    "print(\"Linked data columns include trait column?\", trait in linked_data.columns)\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "\n",
    "# 4. 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",
    "# 5. Conduct final quality validation and save relevant 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 gene expression from blood monocytes in pre- and postmenopausal females with low or high bone mineral density.\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it as CSV\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\")"
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}