{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9c24e17",
   "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 = \"Psoriatic_Arthritis\"\n",
    "cohort = \"GSE142049\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Psoriatic_Arthritis\"\n",
    "in_cohort_dir = \"../../input/GEO/Psoriatic_Arthritis/GSE142049\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Psoriatic_Arthritis/GSE142049.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Psoriatic_Arthritis/gene_data/GSE142049.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Psoriatic_Arthritis/clinical_data/GSE142049.csv\"\n",
    "json_path = \"../../output/preprocess/Psoriatic_Arthritis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1783c18b",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca0a1099",
   "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": "ac8d5a01",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47517969",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyze the dataset to determine availability of gene expression and clinical data\n",
    "\n",
    "# 1. Gene Expression Data\n",
    "# Based on the background information, this dataset contains transcriptional data (RNA)\n",
    "# from CD19+ B cells, which indicates it contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Identify rows in the sample characteristics dictionary for trait, age, and gender\n",
    "\n",
    "# For the trait (Psoriatic Arthritis), we can find it in the working_diagnosis field (key 6)\n",
    "trait_row = 6\n",
    "\n",
    "# Age information is available in key 2\n",
    "age_row = 2\n",
    "\n",
    "# Gender information is available in key 1\n",
    "gender_row = 1\n",
    "\n",
    "# 2.2 Create conversion functions for each variable\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert the working_diagnosis value to a binary indicator for Psoriatic Arthritis.\n",
    "    1 if the patient has Psoriatic Arthritis, 0 otherwise.\n",
    "    \"\"\"\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        diagnosis = value.split(\":\", 1)[1].strip()\n",
    "        if diagnosis == \"Psoriatic Arthritis\":\n",
    "            return 1\n",
    "        else:\n",
    "            return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age string to a numerical value.\n",
    "    \"\"\"\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        age_str = value.split(\":\", 1)[1].strip()\n",
    "        try:\n",
    "            return int(age_str)\n",
    "        except ValueError:\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"\n",
    "    Convert gender string to binary: 0 for female, 1 for male.\n",
    "    \"\"\"\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        gender = value.split(\":\", 1)[1].strip()\n",
    "        if gender.upper() == \"F\":\n",
    "            return 0\n",
    "        elif gender.upper() == \"M\":\n",
    "            return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata - Initial Filtering\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",
    "if trait_row is not None:\n",
    "    # Extract clinical features using the library function\n",
    "    clinical_features = 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(clinical_features)\n",
    "    print(\"Preview of extracted clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical features to a CSV file\n",
    "    clinical_features.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dea5755",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d6ae7d7",
   "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": "9b0fccbf",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbc5de3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers, I can see they are ILMN_ prefixed identifiers.\n",
    "# These are Illumina BeadArray probe IDs, not standard human gene symbols.\n",
    "# Illumina IDs like \"ILMN_1343291\" need to be mapped to human gene symbols.\n",
    "# These IDs are specific to Illumina microarray platforms and require mapping.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035e8f1b",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b827a264",
   "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": "a47beafc",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdc931b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the relevant columns from the gene annotation dataframe\n",
    "# The 'ID' column contains probe IDs (e.g., ILMN_...) which match the gene_data index\n",
    "# The 'Symbol' column contains the human gene symbols we need to map to\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the needed columns\n",
    "mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(mapping_data))\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
    "print(\"After mapping to gene symbols, gene data shape:\", gene_data.shape)\n",
    "print(\"First 10 gene symbols after mapping:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Store the gene data to a CSV file\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f063d68",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35687c8f",
   "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",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Re-extract clinical features since we need it for linking\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",
    "# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct quality check and save the cohort 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 endothelial cells derived from circulating progenitors of RA patients\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n",
    "if is_usable:\n",
    "    print(f\"Data is usable. Saving to {out_data_file}\")\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "else:\n",
    "    print(\"Data is not usable. Not saving linked data file.\")"
   ]
  }
 ],
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