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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d0569e62",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:40:33.304790Z",
"iopub.status.busy": "2025-03-25T08:40:33.304384Z",
"iopub.status.idle": "2025-03-25T08:40:33.471695Z",
"shell.execute_reply": "2025-03-25T08:40:33.471372Z"
}
},
"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 = \"Eczema\"\n",
"cohort = \"GSE123088\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Eczema\"\n",
"in_cohort_dir = \"../../input/GEO/Eczema/GSE123088\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Eczema/GSE123088.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Eczema/gene_data/GSE123088.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Eczema/clinical_data/GSE123088.csv\"\n",
"json_path = \"../../output/preprocess/Eczema/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "0bdd2df7",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ff77bcd7",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:40:33.473107Z",
"iopub.status.busy": "2025-03-25T08:40:33.472959Z",
"iopub.status.idle": "2025-03-25T08:40:33.747524Z",
"shell.execute_reply": "2025-03-25T08:40:33.747184Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases\"\n",
"!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
"!Series_overall_design\t\"Refer to individual Series\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell type: CD4+ T cells'], 1: ['primary diagnosis: ASTHMA', 'primary diagnosis: ATHEROSCLEROSIS', 'primary diagnosis: BREAST_CANCER', 'primary diagnosis: CHRONIC_LYMPHOCYTIC_LEUKEMIA', 'primary diagnosis: CROHN_DISEASE', 'primary diagnosis: ATOPIC_ECZEMA', 'primary diagnosis: HEALTHY_CONTROL', 'primary diagnosis: INFLUENZA', 'primary diagnosis: OBESITY', 'primary diagnosis: PSORIASIS', 'primary diagnosis: SEASONAL_ALLERGIC_RHINITIS', 'primary diagnosis: TYPE_1_DIABETES', 'primary diagnosis: ACUTE_TONSILLITIS', 'primary diagnosis: ULCERATIVE_COLITIS', 'primary diagnosis: Breast cancer', 'primary diagnosis: Control'], 2: ['Sex: Male', 'diagnosis2: ATOPIC_ECZEMA', 'Sex: Female', 'diagnosis2: ATHEROSCLEROSIS', 'diagnosis2: ASTHMA_OBESITY', 'diagnosis2: ASTHMA', 'diagnosis2: ASTMHA_SEASONAL_ALLERGIC_RHINITIS', 'diagnosis2: OBESITY'], 3: ['age: 56', 'Sex: Male', 'age: 20', 'age: 51', 'age: 37', 'age: 61', 'age: 31', 'age: 41', 'age: 80', 'age: 53', 'age: 73', 'age: 60', 'age: 76', 'age: 77', 'age: 74', 'age: 69', 'age: 81', 'age: 70', 'age: 82', 'age: 67', 'age: 78', 'age: 72', 'age: 66', 'age: 36', 'age: 45', 'age: 65', 'age: 48', 'age: 50', 'age: 24', 'age: 42'], 4: [nan, 'age: 63', 'age: 74', 'age: 49', 'age: 60', 'age: 68', 'age: 38', 'age: 16', 'age: 12', 'age: 27']}\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": "6e5170a7",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "42ed76ba",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:40:33.748684Z",
"iopub.status.busy": "2025-03-25T08:40:33.748580Z",
"iopub.status.idle": "2025-03-25T08:40:33.773690Z",
"shell.execute_reply": "2025-03-25T08:40:33.773407Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of clinical features:\n",
"{'GSM3494884': [nan, 56.0, 1.0], 'GSM3494885': [nan, nan, nan], 'GSM3494886': [nan, 20.0, 0.0], 'GSM3494887': [nan, 51.0, 0.0], 'GSM3494888': [nan, 37.0, 1.0], 'GSM3494889': [nan, 61.0, 1.0], 'GSM3494890': [nan, nan, nan], 'GSM3494891': [nan, 31.0, 1.0], 'GSM3494892': [nan, 56.0, 0.0], 'GSM3494893': [nan, 41.0, 0.0], 'GSM3494894': [nan, 61.0, 0.0], 'GSM3494895': [nan, nan, nan], 'GSM3494896': [nan, 80.0, 1.0], 'GSM3494897': [nan, 53.0, 1.0], 'GSM3494898': [nan, 61.0, 1.0], 'GSM3494899': [nan, 73.0, 1.0], 'GSM3494900': [nan, 60.0, 1.0], 'GSM3494901': [nan, 76.0, 1.0], 'GSM3494902': [nan, 77.0, 0.0], 'GSM3494903': [nan, 74.0, 0.0], 'GSM3494904': [nan, 69.0, 1.0], 'GSM3494905': [nan, 77.0, 0.0], 'GSM3494906': [nan, 81.0, 0.0], 'GSM3494907': [nan, 70.0, 0.0], 'GSM3494908': [nan, 82.0, 0.0], 'GSM3494909': [nan, 69.0, 0.0], 'GSM3494910': [nan, 82.0, 0.0], 'GSM3494911': [nan, 67.0, 0.0], 'GSM3494912': [nan, 67.0, 0.0], 'GSM3494913': [nan, 78.0, 0.0], 'GSM3494914': [nan, 67.0, 0.0], 'GSM3494915': [nan, 74.0, 1.0], 'GSM3494916': [nan, nan, nan], 'GSM3494917': [nan, 51.0, 1.0], 'GSM3494918': [nan, 72.0, 1.0], 'GSM3494919': [nan, 66.0, 1.0], 'GSM3494920': [nan, 80.0, 0.0], 'GSM3494921': [nan, 36.0, 1.0], 'GSM3494922': [nan, 67.0, 0.0], 'GSM3494923': [nan, 31.0, 0.0], 'GSM3494924': [nan, 31.0, 0.0], 'GSM3494925': [nan, 45.0, 0.0], 'GSM3494926': [nan, 56.0, 0.0], 'GSM3494927': [nan, 65.0, 0.0], 'GSM3494928': [nan, 53.0, 0.0], 'GSM3494929': [nan, 48.0, 0.0], 'GSM3494930': [nan, 50.0, 0.0], 'GSM3494931': [nan, 76.0, 1.0], 'GSM3494932': [1.0, nan, nan], 'GSM3494933': [1.0, 24.0, 0.0], 'GSM3494934': [1.0, 42.0, 0.0], 'GSM3494935': [1.0, 76.0, 1.0], 'GSM3494936': [1.0, 22.0, 1.0], 'GSM3494937': [1.0, nan, nan], 'GSM3494938': [1.0, 23.0, 0.0], 'GSM3494939': [0.0, 34.0, 1.0], 'GSM3494940': [0.0, 43.0, 1.0], 'GSM3494941': [0.0, 47.0, 1.0], 'GSM3494942': [0.0, 24.0, 0.0], 'GSM3494943': [0.0, 55.0, 1.0], 'GSM3494944': [0.0, 48.0, 1.0], 'GSM3494945': [0.0, 58.0, 1.0], 'GSM3494946': [0.0, 30.0, 0.0], 'GSM3494947': [0.0, 28.0, 1.0], 'GSM3494948': [0.0, 41.0, 0.0], 'GSM3494949': [0.0, 63.0, 1.0], 'GSM3494950': [0.0, 55.0, 0.0], 'GSM3494951': [0.0, 55.0, 0.0], 'GSM3494952': [0.0, 67.0, 1.0], 'GSM3494953': [0.0, 47.0, 0.0], 'GSM3494954': [0.0, 46.0, 0.0], 'GSM3494955': [0.0, 49.0, 1.0], 'GSM3494956': [0.0, 23.0, 1.0], 'GSM3494957': [0.0, 68.0, 1.0], 'GSM3494958': [0.0, 39.0, 1.0], 'GSM3494959': [0.0, 24.0, 1.0], 'GSM3494960': [0.0, 36.0, 0.0], 'GSM3494961': [0.0, 58.0, 0.0], 'GSM3494962': [0.0, 38.0, 0.0], 'GSM3494963': [0.0, 27.0, 0.0], 'GSM3494964': [0.0, 67.0, 0.0], 'GSM3494965': [0.0, 61.0, 1.0], 'GSM3494966': [0.0, 69.0, 1.0], 'GSM3494967': [0.0, 63.0, 1.0], 'GSM3494968': [0.0, 60.0, 0.0], 'GSM3494969': [0.0, 17.0, 1.0], 'GSM3494970': [0.0, 10.0, 0.0], 'GSM3494971': [0.0, 9.0, 1.0], 'GSM3494972': [0.0, 13.0, 0.0], 'GSM3494973': [0.0, 10.0, 1.0], 'GSM3494974': [0.0, 13.0, 0.0], 'GSM3494975': [0.0, 15.0, 1.0], 'GSM3494976': [0.0, 12.0, 1.0], 'GSM3494977': [0.0, 13.0, 1.0], 'GSM3494978': [nan, 81.0, 0.0], 'GSM3494979': [nan, 94.0, 0.0], 'GSM3494980': [nan, 51.0, 1.0], 'GSM3494981': [nan, 40.0, 1.0], 'GSM3494982': [nan, nan, nan], 'GSM3494983': [nan, 97.0, 1.0], 'GSM3494984': [nan, 23.0, 1.0], 'GSM3494985': [nan, 93.0, 0.0], 'GSM3494986': [nan, 58.0, 1.0], 'GSM3494987': [nan, 28.0, 0.0], 'GSM3494988': [nan, 54.0, 1.0], 'GSM3494989': [nan, 15.0, 1.0], 'GSM3494990': [nan, 8.0, 1.0], 'GSM3494991': [nan, 11.0, 1.0], 'GSM3494992': [nan, 12.0, 1.0], 'GSM3494993': [nan, 8.0, 0.0], 'GSM3494994': [nan, 14.0, 1.0], 'GSM3494995': [nan, 8.0, 0.0], 'GSM3494996': [nan, 10.0, 1.0], 'GSM3494997': [nan, 14.0, 1.0], 'GSM3494998': [nan, 13.0, 1.0], 'GSM3494999': [nan, 40.0, 0.0], 'GSM3495000': [nan, 52.0, 0.0], 'GSM3495001': [nan, 42.0, 0.0], 'GSM3495002': [nan, 29.0, 0.0], 'GSM3495003': [nan, 43.0, 0.0], 'GSM3495004': [nan, 41.0, 0.0], 'GSM3495005': [nan, 54.0, 1.0], 'GSM3495006': [nan, 42.0, 1.0], 'GSM3495007': [nan, 49.0, 1.0], 'GSM3495008': [nan, 45.0, 0.0], 'GSM3495009': [nan, 56.0, 1.0], 'GSM3495010': [nan, 64.0, 0.0], 'GSM3495011': [nan, 71.0, 0.0], 'GSM3495012': [nan, 48.0, 0.0], 'GSM3495013': [nan, 20.0, 1.0], 'GSM3495014': [nan, 53.0, 0.0], 'GSM3495015': [nan, 32.0, 0.0], 'GSM3495016': [nan, 26.0, 0.0], 'GSM3495017': [nan, 28.0, 0.0], 'GSM3495018': [nan, 47.0, 0.0], 'GSM3495019': [nan, 24.0, 0.0], 'GSM3495020': [nan, 48.0, 0.0], 'GSM3495021': [nan, nan, nan], 'GSM3495022': [nan, 19.0, 0.0], 'GSM3495023': [nan, 41.0, 0.0], 'GSM3495024': [nan, 38.0, 0.0], 'GSM3495025': [nan, nan, nan], 'GSM3495026': [nan, 15.0, 0.0], 'GSM3495027': [nan, 12.0, 1.0], 'GSM3495028': [nan, 13.0, 0.0], 'GSM3495029': [nan, nan, nan], 'GSM3495030': [nan, 11.0, 1.0], 'GSM3495031': [nan, nan, nan], 'GSM3495032': [nan, 16.0, 1.0], 'GSM3495033': [nan, 11.0, 1.0], 'GSM3495034': [nan, nan, nan], 'GSM3495035': [nan, 35.0, 0.0], 'GSM3495036': [nan, 26.0, 0.0], 'GSM3495037': [nan, 39.0, 0.0], 'GSM3495038': [nan, 46.0, 0.0], 'GSM3495039': [nan, 42.0, 0.0], 'GSM3495040': [nan, 20.0, 1.0], 'GSM3495041': [nan, 69.0, 1.0], 'GSM3495042': [nan, 69.0, 0.0], 'GSM3495043': [nan, 47.0, 1.0], 'GSM3495044': [nan, 47.0, 1.0], 'GSM3495045': [nan, 56.0, 0.0], 'GSM3495046': [nan, 54.0, 0.0], 'GSM3495047': [nan, 53.0, 0.0], 'GSM3495048': [nan, 50.0, 0.0], 'GSM3495049': [nan, 22.0, 1.0], 'GSM3495050': [nan, 62.0, 0.0], 'GSM3495051': [nan, 74.0, 0.0], 'GSM3495052': [0.0, 57.0, 0.0], 'GSM3495053': [0.0, 47.0, 0.0], 'GSM3495054': [nan, 70.0, 0.0], 'GSM3495055': [nan, 50.0, 0.0], 'GSM3495056': [0.0, 52.0, 0.0], 'GSM3495057': [nan, 43.0, 0.0], 'GSM3495058': [0.0, 57.0, 0.0], 'GSM3495059': [nan, 53.0, 0.0], 'GSM3495060': [nan, 70.0, 0.0], 'GSM3495061': [0.0, 41.0, 0.0], 'GSM3495062': [nan, 61.0, 0.0], 'GSM3495063': [0.0, 39.0, 0.0], 'GSM3495064': [0.0, 58.0, 0.0], 'GSM3495065': [nan, 55.0, 0.0], 'GSM3495066': [nan, 63.0, 0.0], 'GSM3495067': [0.0, 60.0, 0.0], 'GSM3495068': [nan, 43.0, 0.0], 'GSM3495069': [nan, 68.0, 0.0], 'GSM3495070': [nan, 67.0, 0.0], 'GSM3495071': [nan, 50.0, 0.0], 'GSM3495072': [nan, 67.0, 0.0], 'GSM3495073': [0.0, 51.0, 0.0], 'GSM3495074': [0.0, 59.0, 0.0], 'GSM3495075': [0.0, 44.0, 0.0], 'GSM3495076': [nan, 35.0, 0.0], 'GSM3495077': [nan, 83.0, 0.0], 'GSM3495078': [nan, 78.0, 0.0], 'GSM3495079': [nan, 88.0, 0.0], 'GSM3495080': [nan, 41.0, 0.0], 'GSM3495081': [0.0, 60.0, 0.0], 'GSM3495082': [nan, 72.0, 0.0], 'GSM3495083': [nan, 53.0, 0.0]}\n",
"Clinical data saved to ../../output/preprocess/Eczema/clinical_data/GSE123088.csv\n"
]
}
],
"source": [
"# 1. Determine gene expression data availability\n",
"# This dataset appears to be a SuperSeries combining several studies\n",
"# Since it mentions CD4+ T cells and includes various diagnoses, it likely contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# Trait (Eczema) appears in row 1 as \"primary diagnosis: ATOPIC_ECZEMA\"\n",
"trait_row = 1\n",
"\n",
"# Age appears in row 3 and continues in row 4\n",
"age_row = 3\n",
"\n",
"# Gender/Sex appears in rows 2 and 3\n",
"gender_row = 2\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract value after colon\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Check if Eczema is present in any form\n",
" if \"ATOPIC_ECZEMA\" in value:\n",
" return 1\n",
" elif \"HEALTHY_CONTROL\" in value or \"Control\" in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract value after colon\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract value after colon\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if value.lower() == \"female\":\n",
" return 0\n",
" elif value.lower() == \"male\":\n",
" return 1\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",
"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_row is not None)\n",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" 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 features\n",
" preview = preview_df(clinical_df)\n",
" print(\"Preview of clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "f3e3d004",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3539d90c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:40:33.774766Z",
"iopub.status.busy": "2025-03-25T08:40:33.774664Z",
"iopub.status.idle": "2025-03-25T08:40:34.269550Z",
"shell.execute_reply": "2025-03-25T08:40:34.269177Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Eczema/GSE123088/GSE123088_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (24166, 204)\n",
"First 20 gene/probe identifiers:\n",
"Index(['1', '2', '3', '9', '10', '12', '13', '14', '15', '16', '18', '19',\n",
" '20', '21', '22', '23', '24', '25', '26', '27'],\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": "8529df35",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "db623bcf",
"metadata": {
"execution": {
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"source": [
"# These identifiers appear to be numeric IDs, not human gene symbols.\n",
"# They are likely probe IDs or some other form of identifiers that need to be mapped.\n",
"# Looking at the first 20 identifiers, they are simply numbers like '1', '2', '3', etc.\n",
"# These are not standard human gene symbols, which would typically be alphanumeric like 'BRCA1', 'TP53', etc.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "58e26c6f",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
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"id": "02a6fc9b",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'ENTREZ_GENE_ID', 'SPOT_ID']\n",
"{'ID': ['1', '2', '3', '9', '10'], 'ENTREZ_GENE_ID': ['1', '2', '3', '9', '10'], 'SPOT_ID': [1.0, 2.0, 3.0, 9.0, 10.0]}\n",
"\n",
"Searching for platform information in SOFT file:\n",
"Platform ID not found in first 100 lines\n",
"\n",
"Searching for gene symbol information in SOFT file:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"No explicit gene symbol references found in first 1000 lines\n",
"\n",
"Checking for additional annotation files in the directory:\n",
"[]\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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
"print(\"\\nGene annotation preview:\")\n",
"print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
"print(preview_df(gene_annotation, n=5))\n",
"\n",
"# Let's look for platform information in the SOFT file to understand the annotation better\n",
"print(\"\\nSearching for platform information in SOFT file:\")\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for i, line in enumerate(f):\n",
" if '!Series_platform_id' in line:\n",
" print(line.strip())\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Platform ID not found in first 100 lines\")\n",
" break\n",
"\n",
"# Check if the SOFT file includes any reference to gene symbols\n",
"print(\"\\nSearching for gene symbol information in SOFT file:\")\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" gene_symbol_lines = []\n",
" for i, line in enumerate(f):\n",
" if 'GENE_SYMBOL' in line or 'gene_symbol' in line.lower() or 'symbol' in line.lower():\n",
" gene_symbol_lines.append(line.strip())\n",
" if i > 1000 and len(gene_symbol_lines) > 0: # Limit search but ensure we found something\n",
" break\n",
" \n",
" if gene_symbol_lines:\n",
" print(\"Found references to gene symbols:\")\n",
" for line in gene_symbol_lines[:5]: # Show just first 5 matches\n",
" print(line)\n",
" else:\n",
" print(\"No explicit gene symbol references found in first 1000 lines\")\n",
"\n",
"# Look for alternative annotation files or references in the directory\n",
"print(\"\\nChecking for additional annotation files in the directory:\")\n",
"all_files = os.listdir(in_cohort_dir)\n",
"print([f for f in all_files if 'annotation' in f.lower() or 'platform' in f.lower() or 'gpl' in f.lower()])\n"
]
},
{
"cell_type": "markdown",
"id": "bd741756",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "09706985",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:40:48.121583Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene mapping dataframe preview:\n",
"{'ID': ['1', '2', '3', '9', '10'], 'Gene': ['1', '2', '3', '9', '10']}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data after mapping:\n",
"Shape: (0, 204)\n",
"First 10 gene identifiers: []\n",
"Gene data saved to ../../output/preprocess/Eczema/gene_data/GSE123088.csv\n"
]
}
],
"source": [
"# Looking at the annotation data, we can see it includes:\n",
"# ID: probe identifiers that match gene_data index\n",
"# ENTREZ_GENE_ID: Entrez Gene IDs which can serve as gene identifiers\n",
"\n",
"# 1. Identify the appropriate columns for mapping\n",
"# From the preview, we can see that ID column in annotation matches the index in gene_data\n",
"# ENTREZ_GENE_ID appears to be the closest to gene identifiers we have\n",
"\n",
"# Since the ENTREZ_GENE_ID is numeric, we'll check if it can be mapped to gene symbols\n",
"# We'll use the gene_mapping function from the library with necessary columns\n",
"mapping_df = get_gene_mapping(gene_annotation, 'ID', 'ENTREZ_GENE_ID')\n",
"\n",
"print(\"\\nGene mapping dataframe preview:\")\n",
"print(preview_df(mapping_df, n=5))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"print(\"\\nGene expression data after mapping:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(f\"First 10 gene identifiers: {list(gene_data.index[:10])}\")\n",
"\n",
"# Save the processed gene data to the output file\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 data saved to {out_gene_data_file}\")"
]
}
],
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