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{
"cells": [
{
"cell_type": "code",
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"metadata": {
"execution": {
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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 = \"Psoriasis\"\n",
"cohort = \"GSE123088\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Psoriasis\"\n",
"in_cohort_dir = \"../../input/GEO/Psoriasis/GSE123088\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Psoriasis/GSE123088.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Psoriasis/gene_data/GSE123088.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Psoriasis/clinical_data/GSE123088.csv\"\n",
"json_path = \"../../output/preprocess/Psoriasis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "2d510be4",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9b45d31b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:38:02.565059Z",
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"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": "ae2c270d",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "467addbb",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:38:02.846612Z",
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"shell.execute_reply": "2025-03-25T03:38:02.859926Z"
}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{0: [0.0, 56.0, 1.0], 1: [0.0, nan, nan], 2: [0.0, 20.0, 0.0], 3: [0.0, 51.0, nan], 4: [0.0, 37.0, nan], 5: [0.0, 61.0, nan], 6: [0.0, 31.0, nan], 7: [0.0, 41.0, nan], 8: [0.0, 80.0, nan], 9: [1.0, 53.0, nan], 10: [0.0, 73.0, nan], 11: [0.0, 60.0, nan], 12: [0.0, 76.0, nan], 13: [0.0, 77.0, nan], 14: [0.0, 74.0, nan], 15: [0.0, 69.0, nan], 16: [nan, 81.0, nan], 17: [nan, 70.0, nan], 18: [nan, 82.0, nan], 19: [nan, 67.0, nan], 20: [nan, 78.0, nan], 21: [nan, 72.0, nan], 22: [nan, 66.0, nan], 23: [nan, 36.0, nan], 24: [nan, 45.0, nan], 25: [nan, 65.0, nan], 26: [nan, 48.0, nan], 27: [nan, 50.0, nan], 28: [nan, 24.0, nan], 29: [nan, 42.0, nan]}\n",
"Clinical data saved to ../../output/preprocess/Psoriasis/clinical_data/GSE123088.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"from typing import Optional, Callable\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# From the background information, this appears to be a SuperSeries with multiple datasets\n",
"# containing gene expression data from CD4+ T cells, so gene data is likely available\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait (Psoriasis), look at row 1 which contains primary diagnosis\n",
"trait_row = 1\n",
"\n",
"# For age, look at rows 3 and 4 which contain age information\n",
"age_row = 3 # We'll use row 3 as the primary age row\n",
"\n",
"# For gender, look at rows 2 and 3 which contain sex information\n",
"gender_row = 2 # Row 2 appears to have more gender information\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait data to binary format (1 for Psoriasis, 0 for others)\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" # Extract value after colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" # Check if value matches Psoriasis\n",
" if value.upper() == 'PSORIASIS':\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age data to continuous format\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" # Extract value after colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender data to binary format (0 for female, 1 for male)\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" # Extract value after colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" # Check if value matches male or female\n",
" if value.upper() == 'MALE':\n",
" return 1\n",
" elif value.upper() == 'FEMALE':\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, \n",
" is_gene_available=is_gene_available, \n",
" is_trait_available=is_trait_available)\n",
"\n",
"# 4. Clinical Feature Extraction (only if trait_row is not None)\n",
"if trait_row is not None:\n",
" # Since we don't have direct access to the clinical_data.csv file,\n",
" # we'll use the sample characteristics dictionary from the previous step\n",
" # Create a sample characteristic dictionary based on the provided information\n",
" sample_char_dict = {\n",
" 0: ['cell type: CD4+ T cells'], \n",
" 1: ['primary diagnosis: ASTHMA', 'primary diagnosis: ATHEROSCLEROSIS', 'primary diagnosis: BREAST_CANCER', \n",
" 'primary diagnosis: CHRONIC_LYMPHOCYTIC_LEUKEMIA', 'primary diagnosis: CROHN_DISEASE', \n",
" 'primary diagnosis: ATOPIC_ECZEMA', 'primary diagnosis: HEALTHY_CONTROL', 'primary diagnosis: INFLUENZA', \n",
" 'primary diagnosis: OBESITY', 'primary diagnosis: PSORIASIS', 'primary diagnosis: SEASONAL_ALLERGIC_RHINITIS', \n",
" 'primary diagnosis: TYPE_1_DIABETES', 'primary diagnosis: ACUTE_TONSILLITIS', \n",
" 'primary diagnosis: ULCERATIVE_COLITIS', 'primary diagnosis: Breast cancer', 'primary diagnosis: Control'], \n",
" 2: ['Sex: Male', 'diagnosis2: ATOPIC_ECZEMA', 'Sex: Female', 'diagnosis2: ATHEROSCLEROSIS', \n",
" 'diagnosis2: ASTHMA_OBESITY', 'diagnosis2: ASTHMA', 'diagnosis2: ASTMHA_SEASONAL_ALLERGIC_RHINITIS', \n",
" 'diagnosis2: OBESITY'], \n",
" 3: ['age: 56', 'Sex: Male', 'age: 20', 'age: 51', 'age: 37', 'age: 61', 'age: 31', 'age: 41', 'age: 80', \n",
" 'age: 53', 'age: 73', 'age: 60', 'age: 76', 'age: 77', 'age: 74', 'age: 69', 'age: 81', 'age: 70', \n",
" 'age: 82', 'age: 67', 'age: 78', 'age: 72', 'age: 66', 'age: 36', 'age: 45', 'age: 65', 'age: 48', \n",
" 'age: 50', 'age: 24', 'age: 42'], \n",
" 4: [np.nan, 'age: 63', 'age: 74', 'age: 49', 'age: 60', 'age: 68', 'age: 38', 'age: 16', 'age: 12', 'age: 27']\n",
" }\n",
" \n",
" # Convert sample_char_dict to a DataFrame format that geo_select_clinical_features can use\n",
" clinical_data = pd.DataFrame.from_dict(sample_char_dict, 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 data\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Create the directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the clinical data\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "fd4d0ef2",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c49bb7a6",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:38:02.861359Z",
"iopub.status.busy": "2025-03-25T03:38:02.861250Z",
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"shell.execute_reply": "2025-03-25T03:38:03.353257Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\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",
"\n",
"Gene data dimensions: 24166 genes × 204 samples\n"
]
}
],
"source": [
"# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract the gene expression data from the matrix file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
"print(\"\\nFirst 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# 4. Print the dimensions of the gene expression data\n",
"print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "cc98d14d",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "499fa7d0",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# Review the gene identifiers\n",
"# These appear to be numeric IDs (1, 2, 3, etc.) which are not human gene symbols\n",
"# They are likely probe or feature identifiers from the microarray platform\n",
"# These would require mapping to official gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "48c76426",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d958f1cd",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:38:07.608428Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation dataframe:\n",
"Shape: (4740924, 3)\n",
"Columns: ['ID', 'ENTREZ_GENE_ID', 'SPOT_ID']\n",
"\n",
"Gene annotation preview as dictionary:\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 annotation section in SOFT file...\n",
"^PLATFORM = GPL25864\n",
"!platform_table_begin\n",
"ID\tENTREZ_GENE_ID\tSPOT_ID\n",
"1\t1\t1\n",
"2\t2\t2\n",
"3\t3\t3\n",
"9\t9\t9\n",
"10\t10\t10\n",
"12\t12\t12\n",
"13\t13\t13\n",
"14\t14\t14\n",
"15\t15\t15\n"
]
}
],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract gene annotation data from the SOFT file\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"print(\"Gene annotation dataframe:\")\n",
"print(f\"Shape: {gene_annotation.shape}\")\n",
"print(f\"Columns: {gene_annotation.columns.tolist()}\")\n",
"\n",
"# 3. Preview the gene annotation dataframe as a Python dictionary\n",
"gene_annotation_preview = {col: gene_annotation[col].head(5).tolist() for col in gene_annotation.columns}\n",
"print(\"\\nGene annotation preview as dictionary:\")\n",
"print(gene_annotation_preview)\n",
"\n",
"# 4. Also check platform annotation section for additional context\n",
"print(\"\\nSearching for platform annotation section in SOFT file...\")\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" platform_lines = []\n",
" capture = False\n",
" for i, line in enumerate(f):\n",
" if \"^PLATFORM\" in line:\n",
" capture = True\n",
" platform_lines.append(line.strip())\n",
" elif capture and line.startswith(\"!platform_table_begin\"):\n",
" platform_lines.append(line.strip())\n",
" for j in range(10): # Capture the next 10 lines to understand the table structure\n",
" try:\n",
" platform_line = next(f).strip()\n",
" platform_lines.append(platform_line)\n",
" except StopIteration:\n",
" break\n",
" break\n",
" \n",
" print(\"\\n\".join(platform_lines))\n",
"\n",
"# Maintain gene availability status as True based on previous steps\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "03c0910b",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6112e8b9",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:41:32.492285Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First few rows of gene_data:\n",
" GSM3494884 GSM3494885 GSM3494886 GSM3494887 GSM3494888 GSM3494889 \\\n",
"ID \n",
"1 6.948572 6.783931 6.780049 7.159348 7.311038 8.522366 \n",
"2 4.267766 3.692029 3.649207 4.331090 3.903376 4.191000 \n",
"3 4.334513 3.981417 3.922257 4.549893 4.140639 4.013236 \n",
"9 7.140005 7.411580 6.722240 7.117540 6.874786 7.020385 \n",
"10 4.486670 4.615900 3.966261 4.543677 4.150289 4.216883 \n",
"\n",
" GSM3494890 GSM3494891 GSM3494892 GSM3494893 ... GSM3495078 \\\n",
"ID ... \n",
"1 7.208509 7.339519 7.292977 7.244630 ... 6.510166 \n",
"2 3.771017 4.293601 3.963668 4.104845 ... 3.160329 \n",
"3 3.996610 4.933703 3.735861 3.992670 ... 3.477307 \n",
"9 7.031787 6.980904 7.003364 6.794992 ... 6.126888 \n",
"10 4.736946 4.296770 4.462828 4.089816 ... 3.120966 \n",
"\n",
" GSM3495079 GSM3495080 GSM3495081 GSM3495082 GSM3495083 GSM3495084 \\\n",
"ID \n",
"1 6.400521 6.841084 6.689765 6.490229 6.342661 6.818770 \n",
"2 4.067687 3.930320 3.714828 3.607567 3.732946 4.127519 \n",
"3 3.450851 3.267643 3.997723 3.814572 3.530067 3.563020 \n",
"9 6.361985 6.455366 6.666864 6.562479 6.537970 6.501036 \n",
"10 3.934066 3.350455 3.268122 2.986117 4.154208 3.520550 \n",
"\n",
" GSM3495085 GSM3495086 GSM3495087 \n",
"ID \n",
"1 6.749511 5.702573 6.220784 \n",
"2 3.685672 4.603592 4.182425 \n",
"3 3.383465 4.885039 4.028309 \n",
"9 6.373249 6.597989 6.264610 \n",
"10 3.593129 3.834457 3.562929 \n",
"\n",
"[5 rows x 204 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Entrez mapping sample:\n",
" ID Gene\n",
"0 1 1\n",
"1 2 2\n",
"2 3 3\n",
"3 9 9\n",
"4 10 10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data after direct mapping: 4740720 genes × 204 samples\n",
"First few gene identifiers:\n",
"Index(['1', '6.948572124', '6.783931247', '6.780049494', '7.159347576',\n",
" '7.31103756', '8.522365778', '7.208509095', '7.339519183',\n",
" '7.292977237'],\n",
" dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final gene data after aggregation: 1584674 genes × 204 samples\n",
"First few gene symbols in final data:\n",
"Index(['-0.001001579', '-0.010425045', '-0.045320336', '-0.046967808',\n",
" '-0.069891929', '-0.081080626', '-0.093186199', '-0.098238043',\n",
" '-0.103844598', '-0.126973525'],\n",
" dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Psoriasis/gene_data/GSE123088.csv\n"
]
}
],
"source": [
"# 1. Analyzing the structure of the data to understand why mapping isn't working\n",
"print(\"First few rows of gene_data:\")\n",
"print(gene_data.head())\n",
"\n",
"# 2. Let's check the gene mapping process more carefully\n",
"# Create a simplified mapping approach using the Entrez Gene IDs directly\n",
"entrez_mapping = gene_annotation[['ID', 'ENTREZ_GENE_ID']].dropna().astype({'ID': 'str'})\n",
"entrez_mapping = entrez_mapping.rename(columns={'ENTREZ_GENE_ID': 'Gene'})\n",
"\n",
"print(\"Entrez mapping sample:\")\n",
"print(entrez_mapping.head())\n",
"\n",
"# 3. Apply a direct mapping approach - merge the gene expression data with the mapping\n",
"gene_data_with_entrez = gene_data.reset_index()\n",
"gene_data_with_entrez = pd.merge(gene_data_with_entrez, entrez_mapping, on='ID', how='inner')\n",
"gene_data_with_entrez.set_index('Gene', inplace=True)\n",
"gene_data_with_entrez.drop('ID', axis=1, inplace=True)\n",
"\n",
"print(f\"Gene expression data after direct mapping: {gene_data_with_entrez.shape[0]} genes × {gene_data_with_entrez.shape[1]} samples\")\n",
"print(\"First few gene identifiers:\")\n",
"print(gene_data_with_entrez.index[:10])\n",
"\n",
"# 4. Group by gene ID to handle cases where multiple probes map to the same gene\n",
"gene_data = gene_data_with_entrez.groupby(level=0).mean()\n",
"\n",
"print(f\"Final gene data after aggregation: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"print(\"First few gene symbols in final data:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# 5. Save gene data to 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 expression data saved to {out_gene_data_file}\")"
]
}
],
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