{
 "cells": [
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   "id": "bf6d68b1",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T07:33:29.039655Z"
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   "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 = \"Liver_cirrhosis\"\n",
    "cohort = \"GSE182065\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Liver_cirrhosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Liver_cirrhosis/GSE182065\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Liver_cirrhosis/GSE182065.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Liver_cirrhosis/gene_data/GSE182065.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Liver_cirrhosis/clinical_data/GSE182065.csv\"\n",
    "json_path = \"../../output/preprocess/Liver_cirrhosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fac390",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "856c970f",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-25T07:33:29.061110Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Expression profiling of prognostic liver signature in clinical fibrotic liver tissues cultured with various anti-fibrotic and chemopreventive agents\"\n",
      "!Series_summary\t\"Background/Aims: There is a major unmet need to assess prognostic impact of anti-fibrotics in clinical trials due to the slow rate of liver fibrosis progression. We aimed to develop a surrogate biomarker to predict future fibrosis progression.\"\n",
      "!Series_summary\t\"Methods: A Fibrosis Progression Signature (FPS) was defined to predict fibrosis progression within 5 years in HCV and NAFLD patients with no to minimal fibrosis at baseline (n=421), and validated in an independent NAFLD cohort (n=78). The FPS was used to assess response to 13 candidate anti-fibrotics in organotypic ex vivo cultures of clinical fibrotic liver tissues (n=78), and cenicriviroc in NASH patients enrolled in a clinical trial (n=19, NCT02217475). A serum-protein-based surrogate FPS (FPSec) was developed and technically evaluated in a liver disease patient cohort (n=79).\"\n",
      "!Series_summary\t\"Results: A 20-gene FPS was defined and validated in an independent NAFLD cohort (aOR=10.93, AUROC=0.86). Among computationally inferred fibrosis-driving FPS genes, BCL2 was confirmed as a potential pharmacological target using clinical liver tissues. Systematic ex vivo evaluation of 13 candidate anti-fibrotics identified rational combination therapies based on epigallocatechin gallate, some of which were validated for enhanced anti-fibrotic effect in ex vivo culture of clinical liver tissues. In NASH patients treated with cenicriviroc, FPS modulation was associated with 1-year fibrosis improvement accompanied by suppression of the E2F pathway. Induction of PPAR-alfa pathway was absent in patients without fibrosis improvement, suggesting benefit of combining PPAR-alfa agonism to improve anti-fibrotic efficacy of cenicriviroc. A 7-protein FPSec panel showed concordant prognostic prediction with FPS.\"\n",
      "!Series_summary\t\"Conclusion: FPS predicts long-term fibrosis progression in an etiology-agnostic manner, which can inform anti-fibrotic drug development.\"\n",
      "!Series_overall_design\t\"Gene expression profiling of snap-frozen surgical liver tissues treated with various anti-fibrotic and chemopreventive agents in ex vivo precision-cut liver slice (PCLS) culture. The samples in the FPS validation set 2.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: Liver'], 1: ['sample group: Compound treatment', 'sample group: Baseline (before culture)', 'sample group: Vehicle control'], 2: ['compound: Galunisertib', 'compound: Erlotinib', 'compound: AM095', 'compound: MG132', 'compound: Bortezomib', 'compound: Cenicriviroc', 'compound: Pioglitazone', 'compound: Metformin', 'compound: EGCG', 'compound: I-BET 151', 'compound: JQ1', 'compound: Captopril', 'compound: Nizatidine', 'compound: none', 'compound: DMSO'], 3: ['concentration: 10microM', 'concentration: 5microM', 'concentration: 3microM', 'concentration: 20microM', 'concentration: 100microM', 'concentration: 30microM', 'concentration: na', 'concentration: 0.1%']}\n"
     ]
    }
   ],
   "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": "88280a36",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2879e07",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2025-03-25T07:33:29.067009Z",
     "shell.execute_reply": "2025-03-25T07:33:29.066743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information and series title, this dataset appears to contain gene expression data\n",
    "# The summary mentions a 20-gene Fibrosis Progression Signature (FPS) and gene expression profiling\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (Liver cirrhosis):\n",
    "# The background information indicates this is a study on liver fibrosis, but there's no explicit cirrhosis indicator\n",
    "# in the sample characteristics. However, it's a study about fibrotic liver tissues, so all samples have some level\n",
    "# of fibrosis but we can't distinguish cirrhosis specifically.\n",
    "trait_row = None  # Cannot determine cirrhosis status from the available data\n",
    "\n",
    "# For age:\n",
    "# No age information is provided in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# For gender:\n",
    "# No gender information is provided in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "# Since trait_row is None, we don't need to define convert_trait, but we'll create a placeholder function\n",
    "def convert_trait(value):\n",
    "    # This function won't be used since trait_row is None\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    # This function won't be used since age_row is None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # This function won't be used since gender_row is None\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# The trait_row is None, meaning trait data is not available\n",
    "is_trait_available = False if trait_row is None else True\n",
    "\n",
    "# Initial filtering on usability\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 None, we should skip this substep\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef18fbd",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "320263bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:33:29.067972Z",
     "iopub.status.busy": "2025-03-25T07:33:29.067874Z",
     "iopub.status.idle": "2025-03-25T07:33:29.094363Z",
     "shell.execute_reply": "2025-03-25T07:33:29.094067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Liver_cirrhosis/GSE182065/GSE182065_series_matrix.txt.gz\n",
      "Gene data shape: (192, 293)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['AARS', 'ABLIM1', 'ACOT2', 'ACSM3', 'ACTR2', 'ADD3', 'ADH5', 'ADH6',\n",
      "       'ADRA2B', 'AEBP1', 'AKAP13', 'AKR1A1', 'AKR1D1', 'ALAS1', 'ALDH9A1',\n",
      "       'ANKRD46', 'ANXA1', 'ANXA3', 'AOX1', 'AP1B1'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. Get the SOFT and matrix file paths again \n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "print(f\"Matrix file found: {matrix_file}\")\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(f\"Gene data shape: {gene_data.shape}\")\n",
    "    \n",
    "    # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb980f4",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "266bc878",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T07:33:29.095309Z",
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     "shell.execute_reply": "2025-03-25T07:33:29.096642Z"
    }
   },
   "outputs": [],
   "source": [
    "# Examining the gene identifiers in the data\n",
    "# Looking at the first 20 gene identifiers: 'AARS', 'ABLIM1', 'ACOT2', etc.\n",
    "# These appear to be standard human gene symbols\n",
    "# No mapping to gene symbols is required as they're already in that format\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd322e5",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60fdea07",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2025-03-25T07:33:29.097817Z",
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     "shell.execute_reply": "2025-03-25T07:33:29.217721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape before normalization: (192, 293)\n",
      "Gene data shape after normalization: (191, 293)\n",
      "Normalized gene expression data saved to ../../output/preprocess/Liver_cirrhosis/gene_data/GSE182065.csv\n",
      "No clinical data available for this dataset, skipping clinical data processing.\n",
      "Abnormality detected in the cohort: GSE182065. Preprocessing failed.\n",
      "Dataset is not usable for liver cirrhosis analysis due to lack of clinical data. No linked data file saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "# Use normalize_gene_symbols_in_index to standardize gene symbols\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to file\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 expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Check if trait_row is None (indicating no clinical data is available)\n",
    "if trait_row is None:\n",
    "    print(\"No clinical data available for this dataset, skipping clinical data processing.\")\n",
    "    \n",
    "    # Validate and save cohort information with trait_available=False\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=False,\n",
    "        is_biased=True,  # Set to True since we can't use this data without clinical features\n",
    "        df=pd.DataFrame(),  # Empty DataFrame since we have no linked data\n",
    "        note=\"Dataset contains gene expression data from cell lines with HCV infection, which is not appropriate for liver cirrhosis trait analysis.\"\n",
    "    )\n",
    "    \n",
    "    print(\"Dataset is not usable for liver cirrhosis analysis due to lack of clinical data. No linked data file saved.\")\n",
    "else:\n",
    "    # If clinical data is available, proceed with the linking and processing\n",
    "    # 2. Link the clinical and genetic data\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",
    "    print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(selected_clinical_df.head())\n",
    "\n",
    "    # Link the clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "    print(f\"Linked data shape before processing: {linked_data.shape}\")\n",
    "    print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "    print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty dataframe\")\n",
    "\n",
    "    # 3. Handle missing values\n",
    "    try:\n",
    "        linked_data = handle_missing_values(linked_data, trait)\n",
    "        print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error handling missing values: {e}\")\n",
    "        linked_data = pd.DataFrame()  # Create empty dataframe if error occurs\n",
    "\n",
    "    # 4. Check for bias in features\n",
    "    if not linked_data.empty:\n",
    "        is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "        print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
    "    else:\n",
    "        is_biased = True\n",
    "        print(\"Cannot check for bias as dataframe is empty after missing value handling\")\n",
    "\n",
    "    # 5. Validate and save 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_biased,\n",
    "        df=linked_data,\n",
    "        note=\"Dataset contains gene expression data for liver fibrosis progression, which is relevant to liver cirrhosis research.\"\n",
    "    )\n",
    "\n",
    "    # 6. Save the linked data if usable\n",
    "    if is_usable:\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        linked_data.to_csv(out_data_file)\n",
    "        print(f\"Linked data saved to {out_data_file}\")\n",
    "    else:\n",
    "        print(\"Dataset is not usable for analysis. No linked data file saved.\")"
   ]
  }
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