code/Autism_spectrum_disorder_(ASD)/GSE111175.ipynb

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   "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 = \"Autism_spectrum_disorder_(ASD)\"\n",
    "cohort = \"GSE111175\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)\"\n",
    "in_cohort_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)/GSE111175\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE111175.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE111175.csv\"\n",
    "json_path = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba25fb99",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "edc0e834",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Perturbations of PI3K/AKT, RAS/ERK, WNT/B-catenin networks in leukocytes are linked to ASD genetics and fetal origins of autism\"\n",
      "!Series_summary\t\"Hundreds of genes are implicated in autism spectrum disorder (ASD) but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here, we analyzed leukocyte transcriptomics from 1-4 year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes genes highly expressed during fetal brain development and which is dysregulated in hiPSC-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS/ERK, PI3K/AKT, and WNT/-catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms.\"\n",
      "!Series_overall_design\t\"Leukocyte gene expression levels were analysed in autistic and typically developing infants and toddlers\"\n",
      "!Series_overall_design\t\"\"\n",
      "!Series_overall_design\t\"Diagnosis key:\"\n",
      "!Series_overall_design\t\"ASD: autism spectrum disorder\"\n",
      "!Series_overall_design\t\"TD: typically developing (control)\"\n",
      "!Series_overall_design\t\"PDDNOS: pervasive developmental disorder not otherwise specified\"\n",
      "!Series_overall_design\t\"PreemieNoDelay: Preemie No Delay (the individual was a preemie but didn't show any mental delay)\"\n",
      "!Series_overall_design\t\"LD: language delay\"\n",
      "!Series_overall_design\t\"AutFeat: Autism features (individual shows some signs of autism, but doesn't meet full criteria)\"\n",
      "!Series_overall_design\t\"\"\n",
      "!Series_overall_design\t\"ADOS_CoSo: the score of each individual for ADOS communication and social deficit, a metric used for the diagnosis of individuals with autism.  The higher the score, the more severe the case.\"\n",
      "!Series_overall_design\t\"\"\n",
      "!Series_overall_design\t\"These data have been included in a SuperSeries with GSE42133 because the data were normalized together and are linked to that data set.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['cell type: leukocyte'], 1: ['gender: M'], 2: ['age (months): 15.277', 'age (months): 18.957', 'age (months): 25.692', 'age (months): 13.142', 'age (months): 24.575', 'age (months): 34.694', 'age (months): 19.22', 'age (months): 13.536', 'age (months): 15.08', 'age (months): 15.146', 'age (months): 23.294', 'age (months): 23.655', 'age (months): 32.526', 'age (months): 17.643', 'age (months): 13.569', 'age (months): 19.055', 'age (months): 12.977', 'age (months): 13.109', 'age (months): 15.113', 'age (months): 16.361', 'age (months): 29.273', 'age (months): 44.879', 'age (months): 17.248', 'age (months): 22.078', 'age (months): 14.423', 'age (months): 28.452', 'age (months): 12.517', 'age (months): 23.129', 'age (months): 28.287', 'age (months): 13.273'], 3: ['diagnosis: TD', 'diagnosis: LD', 'diagnosis: ASD', 'diagnosis: PreemieNoDelay', 'diagnosis: PDDNOS', 'diagnosis: AutFeat'], 4: ['ados_coso: 5', 'ados_coso: 3', 'ados_coso: 1', 'ados_coso: 2', 'ados_coso: 20', 'ados_coso: 0', 'ados_coso: 17', 'ados_coso: 4', 'ados_coso: 12', 'ados_coso: 10', 'ados_coso: 7', 'ados_coso: 13', 'ados_coso: 9', 'ados_coso: 21', 'ados_coso: 11', 'ados_coso: 6', 'ados_coso: 15', 'ados_coso: 8', 'ados_coso: 14', 'ados_coso: 18', 'ados_coso: 19']}\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": "8c11ae21",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87237f04",
   "metadata": {
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     "shell.execute_reply": "2025-03-25T06:50:58.292215Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{'GSM3024679': [0.0, 15.277], 'GSM3024680': [0.0, 18.957], 'GSM3024681': [0.0, 25.692], 'GSM3024682': [0.0, 13.142], 'GSM3024683': [0.0, 24.575], 'GSM3024684': [1.0, 34.694], 'GSM3024685': [0.0, 19.22], 'GSM3024686': [0.0, 13.536], 'GSM3024687': [1.0, 15.08], 'GSM3024688': [0.0, 15.146], 'GSM3024689': [0.0, 23.294], 'GSM3024690': [0.0, 23.655], 'GSM3024691': [1.0, 32.526], 'GSM3024692': [0.0, 17.643], 'GSM3024693': [0.0, 17.643], 'GSM3024694': [0.0, 13.569], 'GSM3024695': [0.0, 13.569], 'GSM3024696': [0.0, 19.055], 'GSM3024697': [0.0, 12.977], 'GSM3024698': [0.0, 13.109], 'GSM3024699': [0.0, 15.113], 'GSM3024700': [0.0, 16.361], 'GSM3024701': [1.0, 29.273], 'GSM3024702': [1.0, 29.273], 'GSM3024703': [0.0, 24.575], 'GSM3024704': [0.0, 44.879], 'GSM3024705': [0.0, 17.248], 'GSM3024706': [0.0, 22.078], 'GSM3024707': [0.0, 14.423], 'GSM3024708': [1.0, 28.452], 'GSM3024709': [0.0, 12.517], 'GSM3024710': [0.0, 23.129], 'GSM3024711': [1.0, 28.287], 'GSM3024712': [0.0, 13.273], 'GSM3024713': [1.0, 40.608], 'GSM3024714': [1.0, 40.608], 'GSM3024715': [0.0, 21.257], 'GSM3024716': [1.0, 33.38], 'GSM3024717': [0.0, 12.912], 'GSM3024718': [0.0, 19.351], 'GSM3024719': [0.0, 31.639], 'GSM3024720': [1.0, 33.906], 'GSM3024721': [1.0, 19.68], 'GSM3024722': [1.0, 15.671], 'GSM3024723': [1.0, 41.068], 'GSM3024724': [1.0, 41.068], 'GSM3024725': [1.0, 36.172], 'GSM3024726': [1.0, 29.766], 'GSM3024727': [0.0, 13.339], 'GSM3024728': [0.0, 17.084], 'GSM3024729': [0.0, 17.084], 'GSM3024730': [0.0, 24.674], 'GSM3024731': [1.0, 38.538], 'GSM3024732': [0.0, 15.737], 'GSM3024733': [0.0, 15.737], 'GSM3024734': [1.0, 39.951], 'GSM3024735': [0.0, 13.733], 'GSM3024736': [1.0, 30.916], 'GSM3024737': [1.0, 28.715], 'GSM3024738': [1.0, 25.199], 'GSM3024739': [0.0, 11.236], 'GSM3024740': [0.0, 28.912], 'GSM3024741': [1.0, 14.324], 'GSM3024742': [1.0, 18.661], 'GSM3024743': [0.0, 13.306], 'GSM3024744': [1.0, 29.043], 'GSM3024745': [0.0, 14.456], 'GSM3024746': [1.0, 33.873], 'GSM3024747': [1.0, 31.606], 'GSM3024748': [0.0, 12.32], 'GSM3024749': [0.0, 11.532], 'GSM3024750': [1.0, 41.035], 'GSM3024751': [1.0, 31.934], 'GSM3024752': [0.0, 19.187], 'GSM3024753': [1.0, 18.07], 'GSM3024754': [0.0, 28.846], 'GSM3024755': [1.0, 16.526], 'GSM3024756': [0.0, 18.497], 'GSM3024757': [0.0, 33.511], 'GSM3024758': [0.0, 14.62], 'GSM3024759': [0.0, 18.267], 'GSM3024760': [1.0, 40.312], 'GSM3024761': [1.0, 15.507], 'GSM3024762': [0.0, 15.376], 'GSM3024763': [0.0, 18.136], 'GSM3024764': [0.0, 16.394], 'GSM3024765': [0.0, 19.713], 'GSM3024766': [1.0, 12.616], 'GSM3024767': [1.0, 40.476], 'GSM3024768': [1.0, 40.476], 'GSM3024769': [1.0, 12.156], 'GSM3024770': [1.0, 31.08], 'GSM3024771': [0.0, 13.897], 'GSM3024772': [0.0, 14.259], 'GSM3024773': [0.0, 15.244], 'GSM3024774': [0.0, 15.244], 'GSM3024775': [0.0, 13.996], 'GSM3024776': [0.0, 30.949], 'GSM3024777': [0.0, 12.715], 'GSM3024778': [0.0, 12.485], 'GSM3024779': [0.0, 37.027], 'GSM3024780': [0.0, 12.649], 'GSM3024781': [0.0, 13.01], 'GSM3024782': [0.0, 16.0], 'GSM3024783': [0.0, 13.864], 'GSM3024784': [0.0, 14.554], 'GSM3024785': [0.0, 20.008], 'GSM3024786': [0.0, 18.957], 'GSM3024787': [0.0, 16.821], 'GSM3024788': [0.0, 27.598], 'GSM3024789': [0.0, 27.598], 'GSM3024790': [0.0, 12.386], 'GSM3024791': [0.0, 14.292], 'GSM3024792': [0.0, 13.733], 'GSM3024793': [0.0, 13.733], 'GSM3024794': [0.0, 13.634], 'GSM3024795': [0.0, 17.676], 'GSM3024796': [0.0, 15.901], 'GSM3024797': [0.0, 23.786], 'GSM3024798': [0.0, 15.803], 'GSM3024799': [0.0, 16.657], 'GSM3024800': [0.0, 26.152], 'GSM3024801': [0.0, 22.111], 'GSM3024802': [0.0, 23.228], 'GSM3024803': [0.0, 12.156], 'GSM3024804': [0.0, 23.622], 'GSM3024805': [0.0, 13.405], 'GSM3024806': [0.0, 18.727], 'GSM3024807': [0.0, 18.694], 'GSM3024808': [0.0, 25.298], 'GSM3024809': [0.0, 25.298], 'GSM3024810': [0.0, 16.821], 'GSM3024811': [0.0, 32.394], 'GSM3024812': [0.0, 12.025], 'GSM3024813': [0.0, 13.832], 'GSM3024814': [0.0, 13.832], 'GSM3024815': [0.0, 19.778], 'GSM3024816': [0.0, 30.193], 'GSM3024817': [0.0, 17.051], 'GSM3024818': [0.0, 13.01], 'GSM3024819': [0.0, 16.427]}\n",
      "Clinical features saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE111175.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Dict, Any, Callable\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the series title and summary, this appears to be gene expression data from leukocytes\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For Trait: Looking at diagnosis in row 3\n",
    "trait_row = 3\n",
    "\n",
    "# For Age: Found in row 2\n",
    "age_row = 2\n",
    "\n",
    "# For Gender: Found in row 1, but it's constant (all males)\n",
    "# Since it's constant, we'll set it to None as it won't be useful for associative studies\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert diagnosis value to binary: 1 for ASD or related conditions, 0 for typically developing\n",
    "    \"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after the colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on diagnosis\n",
    "    if value == 'ASD' or value == 'PDDNOS' or value == 'AutFeat':\n",
    "        return 1  # Has autism or autism-related features\n",
    "    elif value == 'TD' or value == 'PreemieNoDelay' or value == 'LD':\n",
    "        return 0  # Typically developing or non-autism condition\n",
    "    else:\n",
    "        return None  # Unknown or invalid value\n",
    "\n",
    "def convert_age(value: str) -> float:\n",
    "    \"\"\"\n",
    "    Convert age value to continuous (months)\n",
    "    \"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after the colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        # Extract numeric value\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value: str) -> int:\n",
    "    \"\"\"\n",
    "    Convert gender value to binary: 0 for female, 1 for male\n",
    "    Not used in this dataset since all subjects are male\n",
    "    \"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after the colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value.lower() in ['m', 'male']:\n",
    "        return 1\n",
    "    elif value.lower() in ['f', 'female']:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save cohort metadata\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 (if trait data is available)\n",
    "if trait_row is not None:\n",
    "    try:\n",
    "        # The clinical_data variable should already be defined from a previous step\n",
    "        # If not, we'll check for it in the global variables\n",
    "        if 'clinical_data' not in globals():\n",
    "            # If clinical_data isn't already loaded, we'll need to use the sample characteristics\n",
    "            # that were shown in the output of the previous step\n",
    "            # Create a dictionary representation of the sample characteristics\n",
    "            sample_chars = {\n",
    "                0: ['cell type: leukocyte'], \n",
    "                1: ['gender: M'], \n",
    "                2: ['age (months): 15.277', 'age (months): 18.957', 'age (months): 25.692', 'age (months): 13.142', 'age (months): 24.575', 'age (months): 34.694', 'age (months): 19.22', 'age (months): 13.536', 'age (months): 15.08', 'age (months): 15.146', 'age (months): 23.294', 'age (months): 23.655', 'age (months): 32.526', 'age (months): 17.643', 'age (months): 13.569', 'age (months): 19.055', 'age (months): 12.977', 'age (months): 13.109', 'age (months): 15.113', 'age (months): 16.361', 'age (months): 29.273', 'age (months): 44.879', 'age (months): 17.248', 'age (months): 22.078', 'age (months): 14.423', 'age (months): 28.452', 'age (months): 12.517', 'age (months): 23.129', 'age (months): 28.287', 'age (months): 13.273'], \n",
    "                3: ['diagnosis: TD', 'diagnosis: LD', 'diagnosis: ASD', 'diagnosis: PreemieNoDelay', 'diagnosis: PDDNOS', 'diagnosis: AutFeat'], \n",
    "                4: ['ados_coso: 5', 'ados_coso: 3', 'ados_coso: 1', 'ados_coso: 2', 'ados_coso: 20', 'ados_coso: 0', 'ados_coso: 17', 'ados_coso: 4', 'ados_coso: 12', 'ados_coso: 10', 'ados_coso: 7', 'ados_coso: 13', 'ados_coso: 9', 'ados_coso: 21', 'ados_coso: 11', 'ados_coso: 6', 'ados_coso: 15', 'ados_coso: 8', 'ados_coso: 14', 'ados_coso: 18', 'ados_coso: 19']\n",
    "            }\n",
    "            # Convert it to a DataFrame\n",
    "            clinical_data = pd.DataFrame.from_dict(sample_chars, orient='index')\n",
    "        \n",
    "        # Extract clinical features\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 features\n",
    "        preview = preview_df(selected_clinical_df)\n",
    "        print(\"Preview of selected clinical features:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Create directory if it doesn't exist\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        \n",
    "        # Save the clinical features to a CSV file\n",
    "        selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error in clinical feature extraction: {str(e)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad2e272",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "58818eaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:50:58.293722Z",
     "iopub.status.busy": "2025-03-25T06:50:58.293610Z",
     "iopub.status.idle": "2025-03-25T06:50:58.948927Z",
     "shell.execute_reply": "2025-03-25T06:50:58.948549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n",
      "       'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n",
      "       'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n",
      "       'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n",
      "       'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "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": "99f424c5",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "955e304e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:50:58.950270Z",
     "iopub.status.busy": "2025-03-25T06:50:58.950142Z",
     "iopub.status.idle": "2025-03-25T06:50:58.952117Z",
     "shell.execute_reply": "2025-03-25T06:50:58.951793Z"
    }
   },
   "outputs": [],
   "source": [
    "# Reviewing the gene identifiers\n",
    "# The identifiers shown are Illumina probe IDs (ILMN_XXXXXXX format)\n",
    "# These are not standard human gene symbols like BRCA1, TP53, etc.\n",
    "# Illumina IDs need to be mapped to gene symbols for biological interpretation\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819a6a53",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e14aa482",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:50:58.953236Z",
     "iopub.status.busy": "2025-03-25T06:50:58.953125Z",
     "iopub.status.idle": "2025-03-25T06:51:10.759015Z",
     "shell.execute_reply": "2025-03-25T06:51:10.758628Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0, 1450438.0, 1240647.0], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': [nan, nan, nan, nan, nan], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan]}\n"
     ]
    }
   ],
   "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": "9306d4e5",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a0da5317",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:51:10.760410Z",
     "iopub.status.busy": "2025-03-25T06:51:10.760275Z",
     "iopub.status.idle": "2025-03-25T06:51:13.063289Z",
     "shell.execute_reply": "2025-03-25T06:51:13.062920Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 mapped gene symbols:\n",
      "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n",
      "       'A4GALT', 'A4GNT'],\n",
      "      dtype='object', name='Gene')\n",
      "\n",
      "Preview of gene expression data:\n",
      "{'GSM3024679': [12.14829504, 17.699203898, 17.230453527999998, 23.403461292, 6.554122355], 'GSM3024680': [11.969531379, 17.527891581, 17.219302601, 22.918376916, 6.391279755], 'GSM3024681': [11.727884569, 17.387517878, 17.65973662, 23.093720998, 6.500198372], 'GSM3024682': [11.816503156, 17.572131345, 17.174511298, 23.327156476, 6.613017923], 'GSM3024683': [11.719622797, 18.240017216, 17.772976413000002, 23.249077607, 6.506893687], 'GSM3024684': [12.286030665, 17.873893835, 17.620441581, 23.201478258999998, 6.962539937], 'GSM3024685': [12.048422686, 17.355880630999998, 17.638495722000002, 23.174944167, 6.199299892], 'GSM3024686': [11.924370501999999, 17.754780502, 17.569035263, 23.021282288, 6.355609546], 'GSM3024687': [12.001591653999998, 17.844821955, 17.606445103, 23.076822311, 6.594722618], 'GSM3024688': [12.311364401999999, 17.228406662, 17.794923965, 22.776799546, 6.759964068], 'GSM3024689': [11.935815924, 17.470965681, 17.578313018, 22.874851672, 6.444160827], 'GSM3024690': [12.436110176, 17.67965371, 17.938196194, 23.022721108, 5.874136805], 'GSM3024691': [11.894314798, 18.044227451, 17.453671941, 23.174959463, 6.586291159], 'GSM3024692': [11.971704959, 17.544529813, 17.508546055, 23.08887756, 6.998182568], 'GSM3024693': [12.180822328, 17.628938356, 17.281315994, 22.907317312, 6.999953293], 'GSM3024694': [12.060684981000001, 17.867582012, 17.457109648, 23.101099171999998, 6.530406321], 'GSM3024695': [12.139271094, 17.973033992, 17.42826023, 22.925884761, 6.576595802], 'GSM3024696': [11.896378838, 17.646054093, 17.321054325, 23.29679502, 6.520284253], 'GSM3024697': [11.883716043, 17.746804088, 17.820674568, 23.338652142, 6.526629701], 'GSM3024698': [12.480998412, 17.947627428, 17.355774218, 23.085625319000002, 6.675471592], 'GSM3024699': [11.867819649000001, 17.524844502, 17.36009403, 23.286532665000003, 6.694614881], 'GSM3024700': [12.1055691, 17.596039603, 18.342650933999998, 23.391746279, 6.537733132], 'GSM3024701': [12.272881042, 18.150840151, 17.807688355, 23.228966413000002, 6.143268903], 'GSM3024702': [11.939531546, 17.523102865, 17.490859005, 23.231358833, 6.485310047], 'GSM3024703': [11.751055917, 17.850367634999998, 17.525698017, 23.236043882, 6.354228378], 'GSM3024704': [12.207938814999999, 17.73965046, 17.664480220999998, 23.241258728, 6.140386974], 'GSM3024705': [11.871884130000002, 17.97870907, 17.814301828, 24.10035744, 6.728461191], 'GSM3024706': [12.695606706, 17.308622046, 17.999152243, 22.927649349, 6.507663708], 'GSM3024707': [12.190363963, 17.918360283, 17.430457125, 23.357719472, 6.453224836], 'GSM3024708': [12.358380873000002, 17.701844345, 17.238405806, 23.160853771, 6.631547378], 'GSM3024709': [11.842979324, 18.070519056000002, 17.237460779, 23.261164064, 6.684769344], 'GSM3024710': [11.570915359, 17.624002891, 17.708232141, 23.065285642, 6.775636421], 'GSM3024711': [11.92516573, 17.557640341, 17.578662784, 23.307635703, 6.231651171], 'GSM3024712': [11.957949707000001, 17.776787091, 17.233471473999998, 23.384179933, 6.592178507], 'GSM3024713': [11.935296978, 17.437546284, 17.39480941, 23.098194606, 6.362346687], 'GSM3024714': [11.836711166, 17.782645039000002, 17.470763722, 23.112017649, 6.446273412], 'GSM3024715': [11.816344931, 17.805377613, 17.583367855, 23.362363006, 6.408822153], 'GSM3024716': [12.26523718, 17.326007853, 17.66554885, 23.214361947, 6.551567891], 'GSM3024717': [11.965086894999999, 17.83189388, 17.929628342, 23.757285979000002, 6.189994728], 'GSM3024718': [11.871610617, 17.621547419000002, 17.672495390999998, 22.990316604, 6.407329024], 'GSM3024719': [12.064922067, 17.679264523, 17.401322593, 23.147796213, 6.645820245], 'GSM3024720': [11.956123627, 17.626637792, 17.661966309, 23.511223778999998, 7.389751911], 'GSM3024721': [11.711696063, 17.737371437, 17.477009554, 23.005041417, 6.463671767], 'GSM3024722': [12.025094850999999, 17.72192921, 17.695114745, 23.043315349, 6.521063147], 'GSM3024723': [11.826356059999998, 17.635471987000003, 17.457819026000003, 22.932235121, 6.755566788], 'GSM3024724': [11.976445091, 17.700466019, 18.099020913, 23.179847992, 6.663111103], 'GSM3024725': [11.575562799, 17.582610146, 17.402698657000002, 23.237559498, 6.610994202], 'GSM3024726': [12.259168665, 17.505718010000002, 17.724975811, 23.235863755, 6.61175874], 'GSM3024727': [12.365564905, 17.826673815, 17.704456679, 23.129381248, 6.526287603], 'GSM3024728': [12.023533572000002, 17.805981361, 17.465898085, 23.367549292, 6.605595016], 'GSM3024729': [12.018544592, 17.340689335, 17.704120934, 23.424688023, 6.398008948], 'GSM3024730': [11.860838831, 17.371423926, 17.856703087, 23.138913435, 6.486311742], 'GSM3024731': [12.110223186999999, 17.254132612, 17.83377432, 22.917499598, 6.294110042], 'GSM3024732': [11.990632696999999, 17.90192403, 17.529131018, 23.253893919, 6.369369376], 'GSM3024733': [11.819628512000001, 17.764159856, 17.712981438, 23.427460378, 6.646218329], 'GSM3024734': [11.896854145999999, 17.932485882, 17.584731223, 22.645635418, 6.680303163], 'GSM3024735': [11.83390781, 17.649750152, 17.574178852, 23.002533834, 6.595205978], 'GSM3024736': [11.569020787, 17.645076782, 17.694437316, 23.563557676, 6.212096208], 'GSM3024737': [11.957070329, 17.711415027999998, 17.454819511, 23.116779583, 6.349858619], 'GSM3024738': [12.150181597, 18.025074805, 17.590567412, 23.287613973, 7.363684183], 'GSM3024739': [11.68211303, 17.998029654, 17.439440696, 23.08339286, 6.153456426], 'GSM3024740': [12.082824733999999, 17.525174404, 17.713553128, 23.319464699, 6.087617356], 'GSM3024741': [11.713824749, 17.777253714, 17.350112738, 23.351000416, 6.109498509], 'GSM3024742': [12.342666955999999, 18.053176289, 17.839001228, 22.737755914, 6.368672438], 'GSM3024743': [12.028123124, 17.581790521000002, 17.5501272, 23.649765117, 6.122950643], 'GSM3024744': [12.002765632, 17.801769093, 17.471506455, 23.100406209, 6.453549979], 'GSM3024745': [12.269979242, 17.426052755, 17.769204018, 22.599868961, 6.732054898], 'GSM3024746': [11.982378746, 17.895462128, 17.391416552, 23.47099016, 6.477538161], 'GSM3024747': [11.736938585, 17.810762237, 17.21809665, 23.218049091, 6.330656462], 'GSM3024748': [12.433842707, 18.071018347, 17.483980847, 23.830256151999997, 6.513887528], 'GSM3024749': [12.187955458000001, 17.204521646, 17.166438465, 23.70291769, 6.18237761], 'GSM3024750': [12.734539140999999, 17.909728829, 17.463105058, 23.253824817, 6.179015608], 'GSM3024751': [11.787108703000001, 17.559234526, 17.210473044, 22.959029942, 6.177801514], 'GSM3024752': [12.155071227, 17.71100567, 17.936402462, 23.091192849, 6.655557593], 'GSM3024753': [12.01398554, 17.789996721999998, 17.477377691, 23.374555868, 6.693498984], 'GSM3024754': [11.734981072, 17.663516797, 17.613345317, 22.947281606, 6.478471988], 'GSM3024755': [12.239431535, 17.664293044, 17.452157462000002, 23.396123906, 6.411715146], 'GSM3024756': [12.227549370999999, 17.846412912, 17.577523059, 23.644639929, 6.339790223], 'GSM3024757': [12.090944883, 16.866217206, 17.494437161, 23.224058175, 6.048543058], 'GSM3024758': [12.461979718, 17.525919526, 17.950085069, 23.286456221, 6.032320007], 'GSM3024759': [12.126251572000001, 17.777619754, 17.567104027, 22.813582142, 6.111670103], 'GSM3024760': [11.810178735000001, 17.674408391, 17.761393184, 23.443596712999998, 6.534603931], 'GSM3024761': [11.807557837000001, 17.611303381, 17.473876488, 23.184373602, 6.308111958], 'GSM3024762': [12.005798344999999, 17.61768562, 17.238004395, 23.008250576000002, 6.752817983], 'GSM3024763': [11.941254262000001, 17.847341601, 18.064372292, 23.085620228, 6.634114418], 'GSM3024764': [11.878438982, 17.7165369, 18.037922626, 23.119507605000003, 6.479712408], 'GSM3024765': [11.778239026000001, 17.797760705, 17.069650401, 23.133505383, 6.50414404], 'GSM3024766': [11.987824521, 17.397939239, 17.596497179, 23.263529638999998, 7.426931295], 'GSM3024767': [11.702061164, 17.587371799, 17.456773663, 23.552133695, 6.30074462], 'GSM3024768': [12.25314636, 17.639805246, 17.618752653, 23.41364676, 6.475720431], 'GSM3024769': [11.895869529, 18.05282505, 17.592705664, 23.197921478, 5.951584024], 'GSM3024770': [11.840212172000001, 17.630583955, 17.602747700000002, 22.908305198, 7.274174405], 'GSM3024771': [11.800306314, 17.95136852, 17.926810008, 23.548445064, 6.496645339], 'GSM3024772': [11.884421334999999, 17.840550245, 17.507642287, 23.617515443, 6.531566281], 'GSM3024773': [12.203483934000001, 17.85471896, 17.728281457, 23.136315477, 6.406962597], 'GSM3024774': [11.894359796, 18.00651432, 17.737295023999998, 23.078081425, 6.755852468], 'GSM3024775': [11.828591073, 17.65162589, 17.532489698, 23.118352801999997, 6.533248277], 'GSM3024776': [12.228943281, 17.634047227, 17.435904981, 23.254392367999998, 6.379434874], 'GSM3024777': [11.997546719, 18.462771276, 18.259314957, 23.13575836, 6.842505934], 'GSM3024778': [11.834799017, 17.584522622, 17.545597178, 22.993266401, 6.469428937], 'GSM3024779': [11.80790209, 18.860132171, 17.640156377, 23.526316475999998, 6.12477909], 'GSM3024780': [11.920686078, 18.15872777, 17.454839233999998, 23.005243643, 6.667719385], 'GSM3024781': [12.033715587, 17.661465982, 17.655558226, 23.435810908, 6.438531626], 'GSM3024782': [12.456833074, 18.145134053, 17.179142417, 23.010716271, 6.565902755], 'GSM3024783': [12.071711796, 17.808273895, 17.874682751, 23.479990966000003, 6.844256742], 'GSM3024784': [11.925122261999999, 17.951333465, 17.592376852, 23.199191997, 6.264964684], 'GSM3024785': [11.66792218, 17.687952955, 17.714138359, 23.269072714, 6.22739386], 'GSM3024786': [11.930788386, 17.16210981, 17.836658179, 23.547361266, 6.705984443], 'GSM3024787': [12.080001617, 17.556548202, 17.469551006, 23.294915764000002, 6.556116099], 'GSM3024788': [11.807601888, 17.729907426, 17.283754656, 22.941999249, 6.432972235], 'GSM3024789': [11.882233132, 17.774689229, 17.343552341, 23.437636272, 6.094453205], 'GSM3024790': [12.125679304, 17.412946446, 17.548301235, 23.355586959, 6.720466387], 'GSM3024791': [11.805290224, 17.722267117, 17.556688591, 23.360874599, 6.593007285], 'GSM3024792': [11.688122444000001, 17.699239692, 17.595111105, 23.00402767, 6.742372983], 'GSM3024793': [11.919195533, 18.207317694, 17.818738552, 23.419835958, 6.585794389], 'GSM3024794': [12.092132346, 17.837403111, 17.785567839000002, 23.377775882999998, 6.708207267], 'GSM3024795': [11.864090996, 17.471463697, 17.619110798, 23.570250094, 6.653087835], 'GSM3024796': [11.873289404, 17.793796358999998, 17.628287645, 23.13140403, 5.982530673], 'GSM3024797': [11.840929569, 17.639059063, 17.713457157, 23.288482666, 6.461493526], 'GSM3024798': [11.819594712, 17.895527235, 17.895198596, 23.264609285, 6.284425318], 'GSM3024799': [11.956347013, 17.902391978, 17.345770292, 23.214501077, 6.620962866], 'GSM3024800': [11.95388487, 17.373384091, 17.856150028, 23.163693475000002, 6.250811476], 'GSM3024801': [12.103487487999999, 18.040320180000002, 17.782500341000002, 23.762479283, 6.697582353], 'GSM3024802': [12.068210286, 17.825516308, 17.951255663, 23.320456888, 6.510336119], 'GSM3024803': [11.778654115, 17.865694748, 17.390966017, 23.063713299, 6.620587511], 'GSM3024804': [12.194067718, 17.545804417, 17.631879855, 23.381652014, 6.076221116], 'GSM3024805': [11.78805159, 17.792496918, 17.489723326, 23.190423478, 6.635685728], 'GSM3024806': [11.845316913000001, 17.917776847, 17.661189387, 23.330569844, 6.470696049], 'GSM3024807': [12.100077176, 17.420491248, 17.484593852, 22.936298908, 6.474975954], 'GSM3024808': [11.991461064, 17.54762838, 17.547359384, 23.279417198, 6.667719385], 'GSM3024809': [12.298344704, 17.820199936, 17.604536804, 23.138750881, 6.941477574], 'GSM3024810': [11.996499888999999, 17.892576165999998, 17.322534716, 22.963249152, 6.20856573], 'GSM3024811': [11.724132137000002, 17.381806937, 17.783545964, 23.237206786, 6.257983255], 'GSM3024812': [11.735416483, 18.053376608999997, 17.642520384, 23.189606094, 6.684495795], 'GSM3024813': [12.068068483000001, 17.756658177, 17.974945881, 23.517455187, 6.678554132], 'GSM3024814': [12.11516884, 17.962488337, 17.808250659000002, 22.850014441, 6.623127249], 'GSM3024815': [11.825303263999999, 17.455332457, 17.550148321, 23.364604472, 6.181048729], 'GSM3024816': [11.821744769999999, 17.254191, 17.508777891999998, 23.135277723999998, 6.583838824], 'GSM3024817': [11.929267229, 17.971582134000002, 17.643833835, 22.968637193, 6.254894541], 'GSM3024818': [11.631113892, 17.410927604, 18.01394457, 23.598198509, 6.672887258], 'GSM3024819': [12.396639759, 17.748723579, 17.2416284, 23.226073112, 6.783958141]}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the columns in gene annotation for mapping\n",
    "# From the preview, we can see that 'ID' contains the probe identifiers (ILMN_*) \n",
    "# and 'Symbol' contains the gene symbols\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the two columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print the first few gene symbols to confirm the mapping worked\n",
    "print(\"First 10 mapped gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Preview a small subset of the gene expression data\n",
    "print(\"\\nPreview of gene expression data:\")\n",
    "print(preview_df(gene_data))\n",
    "\n",
    "# Save the gene expression data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\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": "380d3069",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34fcc5f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:51:13.064701Z",
     "iopub.status.busy": "2025-03-25T06:51:13.064576Z",
     "iopub.status.idle": "2025-03-25T06:51:32.237265Z",
     "shell.execute_reply": "2025-03-25T06:51:32.236602Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (21464, 141)\n",
      "Gene data columns (sample IDs): ['GSM3024679', 'GSM3024680', 'GSM3024681', 'GSM3024682', 'GSM3024683'] ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data shape: (20259, 141)\n",
      "First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv\n",
      "Clinical data shape: (2, 140)\n",
      "Clinical data rows (traits): [0.0, 15.277]\n",
      "Clinical data columns (samples): ['GSM3024680', 'GSM3024681', 'GSM3024682', 'GSM3024683', 'GSM3024684'] ...\n",
      "Number of common samples: 140\n",
      "Linked data shape: (140, 20261)\n",
      "Renamed first column to Autism_spectrum_disorder_(ASD)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data shape after handling missing values: (140, 20261)\n",
      "For the feature 'Autism_spectrum_disorder_(ASD)', the least common label is '1.0' with 38 occurrences. This represents 27.14% of the dataset.\n",
      "The distribution of the feature 'Autism_spectrum_disorder_(ASD)' in this dataset is fine.\n",
      "\n",
      "A new JSON file was created at: ../../output/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE111175.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Load gene data and normalize gene symbols\n",
    "gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
    "print(f\"Gene data shape: {gene_data.shape}\")\n",
    "print(f\"Gene data columns (sample IDs): {gene_data.columns[:5].tolist()} ...\")\n",
    "\n",
    "# Normalize gene symbols\n",
    "try:\n",
    "    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "    print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "    print(f\"First few normalized gene symbols: {normalized_gene_data.index[:5].tolist()}\")\n",
    "    \n",
    "    # Save the normalized 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",
    "    # Use the normalized gene data for linking\n",
    "    gene_data = normalized_gene_data\n",
    "except Exception as e:\n",
    "    print(f\"Warning: Gene symbol normalization failed: {e}\")\n",
    "    print(\"Continuing with unnormalized gene data\")\n",
    "\n",
    "# Reload or recreate the clinical data with proper orientation\n",
    "try:\n",
    "    # When loading clinical data, ensure index is maintained\n",
    "    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "    print(f\"Clinical data rows (traits): {selected_clinical_df.index.tolist()}\")\n",
    "    print(f\"Clinical data columns (samples): {selected_clinical_df.columns[:5].tolist()} ...\")\n",
    "except Exception as e:\n",
    "    print(f\"Error loading clinical data: {e}\")\n",
    "    # If loading fails, recreate the clinical features\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",
    "    print(f\"Recreated clinical data shape: {selected_clinical_df.shape}\")\n",
    "    print(f\"Recreated clinical data rows (traits): {selected_clinical_df.index.tolist()}\")\n",
    "    print(f\"Recreated clinical data columns (samples): {selected_clinical_df.columns[:5].tolist()} ...\")\n",
    "    \n",
    "    # Save the clinical data with index\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Verify common samples between datasets\n",
    "common_samples = list(set(gene_data.columns).intersection(set(selected_clinical_df.columns)))\n",
    "print(f\"Number of common samples: {len(common_samples)}\")\n",
    "\n",
    "if len(common_samples) == 0:\n",
    "    print(\"Error: No common samples found between gene data and clinical data\")\n",
    "    print(\"Gene data column names format:\", gene_data.columns[0])\n",
    "    print(\"Clinical data column names format:\", selected_clinical_df.columns[0])\n",
    "    \n",
    "    # In case of emergency, create a placeholder dataset so we can validate and save 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=True,\n",
    "        df=pd.DataFrame({trait: [], 'Age': []}), \n",
    "        note=\"Data linking failed - no common samples between gene expression and clinical data.\"\n",
    "    )\n",
    "    print(\"The dataset was determined to be not usable for analysis.\")\n",
    "else:\n",
    "    # Subset both datasets to include only common samples\n",
    "    gene_data_subset = gene_data[common_samples]\n",
    "    clinical_data_subset = selected_clinical_df[common_samples]\n",
    "    \n",
    "    # 2. Link the clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_data_subset, gene_data_subset)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    \n",
    "    # Check if trait column exists in linked data\n",
    "    if trait not in linked_data.columns:\n",
    "        # Try to fix the trait column name if it was transformed during linkage\n",
    "        if len(linked_data.columns) > 0 and pd.api.types.is_numeric_dtype(linked_data.iloc[:, 0]):\n",
    "            # If first column is numeric and likely the trait\n",
    "            linked_data = linked_data.rename(columns={linked_data.columns[0]: trait})\n",
    "            print(f\"Renamed first column to {trait}\")\n",
    "    \n",
    "    # Verify trait column exists after potential renaming\n",
    "    if trait not in linked_data.columns:\n",
    "        print(f\"Error: '{trait}' column missing in linked data\")\n",
    "        print(f\"Available columns: {linked_data.columns[:10].tolist()} ...\")\n",
    "        \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=True,\n",
    "            df=pd.DataFrame({trait: [], 'Age': []}), \n",
    "            note=\"Data linking failed - trait column missing in linked data.\"\n",
    "        )\n",
    "        print(\"The dataset was determined to be not usable for analysis.\")\n",
    "    else:\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 demographic features are severely biased\n",
    "        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=trait_biased, \n",
    "            df=unbiased_linked_data, \n",
    "            note=\"Dataset contains gene expression data from leukocytes related to Autism Spectrum Disorder (ASD).\"\n",
    "        )\n",
    "        \n",
    "        # 6. If the linked data is usable, save it as a CSV file\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(\"The dataset was determined to be not usable for analysis due to bias in the trait distribution.\")"
   ]
  }
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