{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e3ce48d",
   "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 = \"Osteoporosis\"\n",
    "cohort = \"GSE62589\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Osteoporosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Osteoporosis/GSE62589\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Osteoporosis/GSE62589.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Osteoporosis/gene_data/GSE62589.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Osteoporosis/clinical_data/GSE62589.csv\"\n",
    "json_path = \"../../output/preprocess/Osteoporosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5841ed96",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20f54e64",
   "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": "d2199aad",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b89068d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on available information, this is a SuperSeries containing gene expression data from blood samples\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Trait row is None since there's no explicit osteoporosis status in the sample characteristics\n",
    "trait_row = None\n",
    "\n",
    "# Age row is None since there's no age information in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender row is found at index 2, with 'Sex: female'\n",
    "gender_row = 2\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "# Since trait_row is None, no need to define convert_trait but we'll create a placeholder\n",
    "def convert_trait(value):\n",
    "    return None\n",
    "\n",
    "# No age data available, but create placeholder function\n",
    "def convert_age(value):\n",
    "    return None\n",
    "\n",
    "# Gender conversion - Convert to binary (0 for female, 1 for male)\n",
    "def convert_gender(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    # Extract the value after the colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    else:\n",
    "        value = value.lower().strip()\n",
    "    \n",
    "    if 'female' in value:\n",
    "        return 0\n",
    "    elif 'male' in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort info\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",
    "# Skip this step since trait_row is None (no clinical data available for the trait)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa06d653",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "add1e709",
   "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": "7607d5e8",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "150e26ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers provided\n",
    "# These are numerical identifiers (like '2315554', '2315633', etc.)\n",
    "# These are not standard human gene symbols (which are typically alphabetic like 'BRCA1', 'TP53')\n",
    "# These appear to be probe IDs from a microarray platform that need to be mapped to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79a007e",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c544fb",
   "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": "93c2a9d6",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "468e5704",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Determine which columns in gene annotation data correspond to gene IDs and gene symbols\n",
    "# From the preview, we can see:\n",
    "# - 'ID' column contains probe identifiers matching the gene expression data\n",
    "# - 'gene_assignment' column contains gene symbol information\n",
    "\n",
    "# 2. Get a gene mapping dataframe by extracting relevant columns\n",
    "# First, check for empty gene assignments and print some examples to verify the mapping\n",
    "print(\"Example gene assignments:\")\n",
    "print(gene_annotation['gene_assignment'].head(10))\n",
    "\n",
    "# Create the mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\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\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Preview the resulting gene expression data\n",
    "print(\"\\nGene expression data after mapping:\")\n",
    "print(gene_data.head())\n",
    "print(f\"Shape of gene expression data: {gene_data.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41e36db",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26d9d179",
   "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",
    "# Since trait_row is None, we can't extract proper clinical features\n",
    "# Create a minimal clinical dataframe with just Gender since that's the only available feature\n",
    "if gender_row is not None:\n",
    "    gender_data = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender)\n",
    "    clinical_features_df = gender_data.T  # Transpose to get samples as rows\n",
    "else:\n",
    "    # If no clinical features at all, create an empty DataFrame with the same sample IDs\n",
    "    clinical_features_df = pd.DataFrame(index=normalized_gene_data.columns)\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Now link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
    "print(\"Linked data shape:\", linked_data.shape)\n",
    "\n",
    "# We can't handle missing values for the trait since there's no trait data\n",
    "# We can only handle missing values for gender\n",
    "if 'Gender' in linked_data.columns:\n",
    "    # Fill missing gender values with the mode\n",
    "    mode_gender = linked_data['Gender'].mode()[0] if not linked_data['Gender'].isna().all() else None\n",
    "    linked_data['Gender'] = linked_data['Gender'].fillna(mode_gender)\n",
    "\n",
    "# Since trait is not available, we can't evaluate if it's biased\n",
    "# We only need to evaluate the bias in Gender\n",
    "if 'Gender' in linked_data.columns:\n",
    "    gender_biased = judge_binary_variable_biased(linked_data, 'Gender')\n",
    "    if gender_biased:\n",
    "        print(\"The distribution of the feature 'Gender' in this dataset is severely biased.\")\n",
    "        linked_data = linked_data.drop(columns='Gender')\n",
    "    else:\n",
    "        print(\"The distribution of the feature 'Gender' in this dataset is fine.\")\n",
    "\n",
    "# 5. Conduct quality check and save the cohort information.\n",
    "# Since trait_row is None, is_trait_available should be False\n",
    "is_trait_available = trait_row is not None\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=is_trait_available, \n",
    "    is_biased=False,  # Set to False instead of None when trait is not available\n",
    "    df=linked_data,\n",
    "    note=\"This is a blood monocyte study. No osteoporosis status information is available in the clinical data.\"\n",
    ")\n",
    "\n",
    "# Since trait data is not available, the dataset is not usable for our trait analysis\n",
    "print(\"Dataset is not usable for trait analysis due to missing trait information.\")"
   ]
  }
 ],
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}