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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "86e246e5",
"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 = \"Bipolar_disorder\"\n",
"cohort = \"GSE120342\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Bipolar_disorder\"\n",
"in_cohort_dir = \"../../input/GEO/Bipolar_disorder/GSE120342\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Bipolar_disorder/GSE120342.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Bipolar_disorder/gene_data/GSE120342.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Bipolar_disorder/clinical_data/GSE120342.csv\"\n",
"json_path = \"../../output/preprocess/Bipolar_disorder/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "904a20c7",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "859c6511",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Aberrant transcriptomes and DNA methylomes define pathways that drive pathogenesis and loss of brain laterality/asymmetry in schizophrenia and bipolar disorder\"\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: ['disease state: control', 'disease state: SCZ', 'disease state: BD(-)', 'disease state: BD(+)'], 1: ['laterality: left', 'laterality: right']}\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": "2a3e0776",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b7b7256f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:54:59.896303Z",
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"shell.execute_reply": "2025-03-25T06:54:59.905440Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features: {0: [0.0], 1: [nan]}\n",
"Clinical data saved to ../../output/preprocess/Bipolar_disorder/clinical_data/GSE120342.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"import numpy as np\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# Sample characteristics dictionary from the previous output\n",
"characteristics_dict = {0: ['disease state: control', 'disease state: SCZ', 'disease state: BD(-)', 'disease state: BD(+)'], \n",
" 1: ['laterality: left', 'laterality: right']}\n",
"\n",
"# Create a correctly structured clinical data DataFrame\n",
"# This is in the format expected by geo_select_clinical_features\n",
"clinical_data = pd.DataFrame()\n",
"for key, values in characteristics_dict.items():\n",
" # Create a Series and then transpose it to get a single row\n",
" row_series = pd.Series(values)\n",
" # Add as a row to the DataFrame\n",
" clinical_data[key] = row_series\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the title and summary, this appears to be gene expression data combined with DNA methylation\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait (Bipolar_disorder)\n",
"# Looking at key 0, we can see \"disease state: BD(+)\" and \"disease state: BD(-)\" which indicate bipolar disorder cases\n",
"trait_row = 0\n",
"\n",
"# Age data is not provided in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender data is not provided in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert disease state to binary trait (Bipolar_disorder)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # BD(+) and BD(-) both indicate Bipolar Disorder cases\n",
" if value.startswith('BD'):\n",
" return 1\n",
" # Controls\n",
" elif value.lower() == 'control':\n",
" return 0\n",
" # SCZ indicates Schizophrenia, not Bipolar\n",
" elif value.lower() == 'scz':\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous values\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the 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",
" \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" value = value.lower()\n",
" if 'female' in value or 'f' == value:\n",
" return 0\n",
" elif 'male' in value or 'm' == value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Define get_feature_data function needed by geo_select_clinical_features\n",
"def get_feature_data(df, row_idx, feature_name, convert_func):\n",
" \"\"\"Extract and process feature data from a row in the DataFrame.\"\"\"\n",
" values = df[row_idx].values\n",
" processed_values = [convert_func(value) for value in values]\n",
" return pd.DataFrame({feature_name: processed_values}, index=df.index)\n",
"\n",
"# 3. Save Metadata\n",
"# Check if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering and save info\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" try:\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 dataframe\n",
" preview = preview_df(selected_clinical_df)\n",
" print(f\"Preview of selected clinical features: {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 to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error in clinical feature extraction: {e}\")\n",
" import traceback\n",
" print(traceback.format_exc())\n"
]
},
{
"cell_type": "markdown",
"id": "6796c537",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2eb7ce0b",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Bipolar_disorder/GSE120342/GSE120342-GPL16311_series_matrix.txt.gz\n",
"Gene data shape: (19070, 30)\n",
"First 20 gene/probe identifiers:\n",
"Index(['100009676_at', '10000_at', '10001_at', '10002_at', '10003_at',\n",
" '100048912_at', '100049716_at', '10004_at', '10005_at', '10006_at',\n",
" '10007_at', '10008_at', '100093630_at', '10009_at', '1000_at',\n",
" '100101467_at', '100101938_at', '10010_at', '100113407_at', '10011_at'],\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": "eeb1230e",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5cafcc77",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# Based on the gene identifiers provided, I can identify that these are DNA methylation probes,\n",
"# not gene symbols. The \"cg\" prefix is characteristic of Illumina DNA methylation arrays \n",
"# (like the 450K or EPIC arrays). These need to be mapped to gene symbols if we want\n",
"# to associate them with specific genes.\n",
"\n",
"# For methylation data, each probe corresponds to a specific CpG site in the genome,\n",
"# and these sites may be associated with specific genes, but they are not gene expression values\n",
"# in themselves, but rather represent DNA methylation levels.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "c7ed2535",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "babd2567",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'Name', 'IlmnStrand', 'AddressA_ID', 'AlleleA_ProbeSeq', 'AddressB_ID', 'AlleleB_ProbeSeq', 'GenomeBuild', 'Chr', 'MapInfo', 'Ploidy', 'Species', 'Source', 'SourceVersion', 'SourceStrand', 'SourceSeq', 'TopGenomicSeq', 'Next_Base', 'Color_Channel', 'TSS_Coordinate', 'Gene_Strand', 'Gene_ID', 'Symbol', 'Synonym', 'Accession', 'GID', 'Annotation', 'Product', 'Distance_to_TSS', 'CPG_ISLAND', 'CPG_ISLAND_LOCATIONS', 'MIR_CPG_ISLAND', 'RANGE_GB', 'RANGE_START', 'RANGE_END', 'RANGE_STRAND', 'GB_ACC', 'ORF']\n",
"{'ID': ['cg00000292', 'cg00002426', 'cg00003994', 'cg00005847', 'cg00006414'], 'Name': ['cg00000292', 'cg00002426', 'cg00003994', 'cg00005847', 'cg00006414'], 'IlmnStrand': ['TOP', 'TOP', 'TOP', 'BOT', 'BOT'], 'AddressA_ID': [990370.0, 6580397.0, 7150184.0, 4850717.0, 6980731.0], 'AlleleA_ProbeSeq': ['AAACATTAATTACCAACCACTCTTCCAAAAAACACTTACCATTAAAACCA', 'AATATAATAACATTACCTTACCCATCTTATAATCAAACCAAACAAAAACA', 'AATAATAATAATACCCCCTATAATACTAACTAACAAACATACCCTCTTCA', 'TACTATAATACACCCTATATTTAAAACACTAAACTTACCCCATTAAAACA', 'CTCAAAAACCAAACAAAACAAAACCCCAATACTAATCATTAATAAAATCA'], 'AddressB_ID': [6660678.0, 6100343.0, 7150392.0, 1260113.0, 4280093.0], 'AlleleB_ProbeSeq': ['AAACATTAATTACCAACCGCTCTTCCAAAAAACACTTACCATTAAAACCG', 'AATATAATAACATTACCTTACCCGTCTTATAATCAAACCAAACGAAAACG', 'AATAATAATAATACCCCCTATAATACTAACTAACAAACATACCCTCTTCG', 'TACTATAATACACCCTATATTTAAAACACTAAACTTACCCCATTAAAACG', 'CTCGAAAACCGAACAAAACAAAACCCCAATACTAATCGTTAATAAAATCG'], 'GenomeBuild': [36.0, 36.0, 36.0, 36.0, 36.0], 'Chr': ['16', '3', '7', '2', '7'], 'MapInfo': [28797601.0, 57718583.0, 15692387.0, 176737319.0, 148453770.0], 'Ploidy': ['diploid', 'diploid', 'diploid', 'diploid', 'diploid'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Source': ['NCBI:RefSeq', 'NCBI:RefSeq', 'NCBI:RefSeq', 'NCBI:RefSeq', 'NCBI:RefSeq'], 'SourceVersion': [36.1, 36.1, 36.1, 36.1, 36.1], 'SourceStrand': ['TOP', 'TOP', 'BOT', 'BOT', 'BOT'], 'SourceSeq': ['CGGCCTCAATGGTAAGTGTCCCTTGGAAGAGCGGCTGGTAATTAATGCCC', 'CGCTCTCGTCTGGTTTGATCACAAGACGGGCAAGGTAATGTCACCACATT', 'GGTGGTGGTGGTGCCCCCTGTGATGCTGGCTGGCAAACATGCCCTCTTCG', 'TACTGTAATGCACCCTGTATTTAAGGCACTGGGCTTGCCCCATTAAAGCG', 'CTCGGAAACCGAGCAGGGCAAAACCCCAGTGCTGATCGTTAGTGGGATCG'], 'TopGenomicSeq': ['TGGGGTGAGTGAGACCACGGGCCTCACCCCGGACCAAGTTAAGCGGAATCTGGAGAAATA[CG]GCCTCAATGGTAAGTGTCCCTTGGAAGAGCGGCTGGTAATTAATGCCCTCCTGCACCCCC', 'CCGCTGTCGACCAGCGCAGAATAATGCCACTTTTGATTGCAAAGTGCTATCAAGGAACCA[CG]CTCTCGTCTGGTTTGATCACAAGACGGGCAAGGTAATGTCACCACATTGTCCAGCGGCAT', 'GGTGGTGGTGGTGGTGGTGGTGGTGCCCCCTGTGATGCTGGCTGGCAAACATGCCCTCTT[CG]TTGGGGTATCCCGCGATTATGCAAGATGAGGAAGAAGTAGAGAGCTCGGGGTAAGACATA', 'CAGATAACTCAATACTGTAATGCACCCTGTATTTAAGGCACTGGGCTTGCCCCATTAAAG[CG]CCATAAATTTGAAGGCCAATGATCGGTTTTCATGTAACGGGTGGTACTTCATACTGAAGT', 'GAACCGGCCCAGCTCGGAAACCGAGCAGGGCAAAACCCCAGTGCTGATCGTTAGTGGGAT[CG]CGCCTGTGAATAGCCACTGCCCTCCAGCCTGGGCAACAGCCAGACCCCGTCTGTTTAATA'], 'Next_Base': ['T', 'T', 'T', 'C', 'C'], 'Color_Channel': ['Red', 'Red', 'Red', 'Grn', 'Grn'], 'TSS_Coordinate': [28797310.0, 57718214.0, 15692819.0, 176737051.0, 148454441.0], 'Gene_Strand': ['+', '+', '-', '+', '+'], 'Gene_ID': ['GeneID:487', 'GeneID:7871', 'GeneID:4223', 'GeneID:3232', 'GeneID:57541'], 'Symbol': ['ATP2A1', 'SLMAP', 'MEOX2', 'HOXD3', 'ZNF398'], 'Synonym': ['ATP2A; SERCA1;', 'SLAP; KIAA1601;', 'GAX; MOX2;', 'HOX4; HOX1D; HOX4A; Hox-4.1; MGC10470;', 'P51; P71; ZER6; KIAA1339;'], 'Accession': ['NM_173201.2', 'NM_007159.2', 'NM_005924.3', 'NM_006898.4', 'NM_020781.2'], 'GID': ['GI:47132613', 'GI:56550042', 'GI:55956906', 'GI:23510372', 'GI:25777702'], 'Annotation': ['isoform a is encoded by transcript variant a; sarcoplasmic/endoplasmic reticulum calcium ATPase 1; calcium pump 1; SR Ca(2+)-ATPase 1; calcium-transporting ATPase sarcoplasmic reticulum type; fast twitch skeletal muscle isoform; endoplasmic reticulum class 1/2 Ca(2+) ATPase; go_component: membrane; go_component: integral to membrane; go_component: sarcoplasmic reticulum; go_component: smooth endoplasmic reticulum; go_function: ATP binding; go_function: hydrolase activity; go_function: nucleotide binding; go_function: calcium ion binding; go_function: magnesium ion binding; go_function: calcium-transporting ATPase activity; go_function: hydrolase activity; acting on acid anhydrides; catalyzing transmembrane movement of substances; go_process: metabolism; go_process: cation transport; go_process: proton transport; go_process: calcium ion transport; go_process: regulation of striated muscle contraction', 'Sarcolemmal-associated protein; go_component: integral to plasma membrane; go_component: smooth endoplasmic reticulum; go_function: unfolded protein binding; go_process: protein folding; go_process: muscle contraction', 'growth arrest-specific homeo box; go_component: nucleus; go_function: transcription factor activity; go_process: circulation; go_process: development; go_process: regulation of transcription; DNA-dependent', 'homeobox protein Hox-D3; Hox-4.1; mouse; homolog of; homeo box D3; go_component: nucleus; go_function: transcription factor activity; go_process: morphogenesis; go_process: regulation of transcription; DNA-dependent', 'isoform b is encoded by transcript variant 2; zinc finger DNA binding protein ZER6; zinc finger-estrogen receptor interaction; clone 6; zinc finger DNA binding protein p52/p71; go_component: nucleus; go_function: DNA binding; go_function: zinc ion binding; go_function: metal ion binding; go_function: transcriptional activator activity; go_process: transcription; go_process: regulation of transcription; DNA-dependent'], 'Product': ['ATPase; Ca++ transporting; fast twitch 1 isoform a', 'sarcolemma associated protein', 'mesenchyme homeo box 2', 'homeobox D3', 'zinc finger 398 isoform b'], 'Distance_to_TSS': [291.0, 369.0, 432.0, 268.0, 671.0], 'CPG_ISLAND': [True, True, True, False, True], 'CPG_ISLAND_LOCATIONS': ['16:28797486-28797825', '3:57716811-57718675', '7:15691512-15693551', nan, '7:148453584-148455804'], 'MIR_CPG_ISLAND': [nan, nan, nan, nan, nan], 'RANGE_GB': ['NC_000016.8', 'NC_000003.10', 'NC_000007.12', nan, 'NC_000007.12'], 'RANGE_START': [28797486.0, 57716811.0, 15691512.0, nan, 148453584.0], 'RANGE_END': [28797825.0, 57718675.0, 15693551.0, nan, 148455804.0], 'RANGE_STRAND': ['+', '+', '-', nan, '+'], 'GB_ACC': ['NM_173201.2', 'NM_007159.2', 'NM_005924.3', 'NM_006898.4', 'NM_020781.2'], 'ORF': [487.0, 7871.0, 4223.0, 3232.0, 57541.0]}\n",
"\n",
"First row as dictionary:\n",
"ID: cg00000292\n",
"Name: cg00000292\n",
"IlmnStrand: TOP\n",
"AddressA_ID: 990370.0\n",
"AlleleA_ProbeSeq: AAACATTAATTACCAACCACTCTTCCAAAAAACACTTACCATTAAAACCA\n",
"AddressB_ID: 6660678.0\n",
"AlleleB_ProbeSeq: AAACATTAATTACCAACCGCTCTTCCAAAAAACACTTACCATTAAAACCG\n",
"GenomeBuild: 36.0\n",
"Chr: 16\n",
"MapInfo: 28797601.0\n",
"Ploidy: diploid\n",
"Species: Homo sapiens\n",
"Source: NCBI:RefSeq\n",
"SourceVersion: 36.1\n",
"SourceStrand: TOP\n",
"SourceSeq: CGGCCTCAATGGTAAGTGTCCCTTGGAAGAGCGGCTGGTAATTAATGCCC\n",
"TopGenomicSeq: TGGGGTGAGTGAGACCACGGGCCTCACCCCGGACCAAGTTAAGCGGAATCTGGAGAAATA[CG]GCCTCAATGGTAAGTGTCCCTTGGAAGAGCGGCTGGTAATTAATGCCCTCCTGCACCCCC\n",
"Next_Base: T\n",
"Color_Channel: Red\n",
"TSS_Coordinate: 28797310.0\n",
"Gene_Strand: +\n",
"Gene_ID: GeneID:487\n",
"Symbol: ATP2A1\n",
"Synonym: ATP2A; SERCA1;\n",
"Accession: NM_173201.2\n",
"GID: GI:47132613\n",
"Annotation: isoform a is encoded by transcript variant a; sarcoplasmic/endoplasmic reticulum calcium ATPase 1; calcium pump 1; SR Ca(2+)-ATPase 1; calcium-transporting ATPase sarcoplasmic reticulum type; fast twitch skeletal muscle isoform; endoplasmic reticulum class 1/2 Ca(2+) ATPase; go_component: membrane; go_component: integral to membrane; go_component: sarcoplasmic reticulum; go_component: smooth endoplasmic reticulum; go_function: ATP binding; go_function: hydrolase activity; go_function: nucleotide binding; go_function: calcium ion binding; go_function: magnesium ion binding; go_function: calcium-transporting ATPase activity; go_function: hydrolase activity; acting on acid anhydrides; catalyzing transmembrane movement of substances; go_process: metabolism; go_process: cation transport; go_process: proton transport; go_process: calcium ion transport; go_process: regulation of striated muscle contraction\n",
"Product: ATPase; Ca++ transporting; fast twitch 1 isoform a\n",
"Distance_to_TSS: 291.0\n",
"CPG_ISLAND: True\n",
"CPG_ISLAND_LOCATIONS: 16:28797486-28797825\n",
"MIR_CPG_ISLAND: nan\n",
"RANGE_GB: NC_000016.8\n",
"RANGE_START: 28797486.0\n",
"RANGE_END: 28797825.0\n",
"RANGE_STRAND: +\n",
"GB_ACC: NM_173201.2\n",
"ORF: 487.0\n",
"\n",
"Comparing gene data IDs with annotation IDs:\n",
"First 5 gene data IDs: ['100009676_at', '10000_at', '10001_at', '10002_at', '10003_at']\n",
"First 5 annotation IDs: ['cg00000292', 'cg00002426', 'cg00003994', 'cg00005847', 'cg00006414']\n",
"\n",
"Exact ID match between gene data and annotation:\n",
"Matching IDs: 19070 out of 19070 (100.00%)\n",
"\n",
"Potential columns for gene symbols: ['Name', 'Gene_Strand', 'Gene_ID', 'Symbol']\n",
"Column 'Name': 922136 non-null values (100.00%)\n",
"Column 'Gene_Strand': 27578 non-null values (2.99%)\n",
"Column 'Gene_ID': 27578 non-null values (2.99%)\n",
"Column 'Symbol': 27551 non-null values (2.99%)\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",
"# Check if there are any columns that might contain gene information\n",
"sample_row = gene_annotation.iloc[0].to_dict()\n",
"print(\"\\nFirst row as dictionary:\")\n",
"for col, value in sample_row.items():\n",
" print(f\"{col}: {value}\")\n",
"\n",
"# Check if IDs in gene_data match IDs in annotation\n",
"print(\"\\nComparing gene data IDs with annotation IDs:\")\n",
"print(\"First 5 gene data IDs:\", gene_data.index[:5].tolist())\n",
"print(\"First 5 annotation IDs:\", gene_annotation['ID'].head().tolist())\n",
"\n",
"# Properly check for exact ID matches between gene data and annotation\n",
"gene_data_ids = set(gene_data.index)\n",
"annotation_ids = set(gene_annotation['ID'].astype(str))\n",
"matching_ids = gene_data_ids.intersection(annotation_ids)\n",
"id_match_percentage = len(matching_ids) / len(gene_data_ids) * 100 if len(gene_data_ids) > 0 else 0\n",
"\n",
"print(f\"\\nExact ID match between gene data and annotation:\")\n",
"print(f\"Matching IDs: {len(matching_ids)} out of {len(gene_data_ids)} ({id_match_percentage:.2f}%)\")\n",
"\n",
"# Check which columns might contain gene symbols for mapping\n",
"potential_gene_symbol_cols = [col for col in gene_annotation.columns \n",
" if any(term in col.upper() for term in ['GENE', 'SYMBOL', 'NAME'])]\n",
"print(f\"\\nPotential columns for gene symbols: {potential_gene_symbol_cols}\")\n",
"\n",
"# Check if the identified columns contain non-null values\n",
"for col in potential_gene_symbol_cols:\n",
" non_null_count = gene_annotation[col].notnull().sum()\n",
" non_null_percent = non_null_count / len(gene_annotation) * 100\n",
" print(f\"Column '{col}': {non_null_count} non-null values ({non_null_percent:.2f}%)\")\n"
]
},
{
"cell_type": "markdown",
"id": "87e441aa",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"id": "8afcbfce",
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"shell.execute_reply": "2025-03-25T06:55:02.362175Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping shape: (27551, 2)\n",
"Gene mapping preview:\n",
" ID Gene\n",
"0 cg00000292 ATP2A1\n",
"1 cg00002426 SLMAP\n",
"2 cg00003994 MEOX2\n",
"3 cg00005847 HOXD3\n",
"4 cg00006414 ZNF398\n",
"Gene expression data after mapping: (0, 30)\n",
"First few genes and their expression values:\n",
"Empty DataFrame\n",
"Columns: [GSM3398477, GSM3398478, GSM3398479, GSM3398480, GSM3398481, GSM3398482, GSM3398483, GSM3398484, GSM3398485, GSM3398486, GSM3398487, GSM3398488, GSM3398489, GSM3398490, GSM3398491, GSM3398492, GSM3398493, GSM3398494, GSM3398495, GSM3398496, GSM3398497, GSM3398498, GSM3398499, GSM3398500, GSM3398501, GSM3398502, GSM3398503, GSM3398504, GSM3398505, GSM3398506]\n",
"Index: []\n",
"\n",
"[0 rows x 30 columns]\n",
"Gene expression data saved to ../../output/preprocess/Bipolar_disorder/gene_data/GSE120342.csv\n"
]
}
],
"source": [
"# Based on the previous outputs, I can identify:\n",
"# - Gene data IDs (index) are 'cg' prefixed probes\n",
"# - 'ID' column in gene_annotation matches these probe IDs\n",
"# - 'Symbol' column contains the gene symbols we want to map to\n",
"\n",
"# 1. Decide the key mappings:\n",
"probe_id_col = 'ID' # Column containing methylation probe IDs\n",
"gene_symbol_col = 'Symbol' # Column containing gene symbols\n",
"\n",
"# 2. Extract mapping information using the get_gene_mapping function from the library\n",
"gene_mapping = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)\n",
"print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
"print(\"Gene mapping preview:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level data to gene expression data\n",
"# The function will handle the many-to-many mapping and distribution of expression values\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"Gene expression data after mapping: {gene_data.shape}\")\n",
"print(\"First few genes and their expression values:\")\n",
"print(gene_data.head())\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": "8ae1852e",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
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"id": "1b547f4a",
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"shell.execute_reply": "2025-03-25T06:55:02.434617Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after normalization: (0, 30)\n",
"Normalized gene expression data saved to ../../output/preprocess/Bipolar_disorder/gene_data/GSE120342.csv\n",
"Clinical data from previous steps:\n",
"Selected clinical data shape: (1, 2)\n",
"Clinical data preview:\n",
" 0 1\n",
"Bipolar_disorder 0.0 NaN\n",
"Gene data columns (samples): ['GSM3398477', 'GSM3398478', 'GSM3398479', 'GSM3398480', 'GSM3398481']...\n",
"Clinical data indices: ['Bipolar_disorder']\n",
"Transposed clinical data:\n",
" Bipolar_disorder\n",
"0 0.0\n",
"1 NaN\n",
"Gene data columns match GSM pattern: True\n",
"Created simple clinical dataframe:\n",
" Bipolar_disorder\n",
"GSM3398477 0\n",
"GSM3398478 0\n",
"GSM3398479 0\n",
"GSM3398480 0\n",
"GSM3398481 0\n",
"GSM3398482 0\n",
"GSM3398483 0\n",
"GSM3398484 0\n",
"GSM3398485 0\n",
"GSM3398486 0\n",
"GSM3398487 0\n",
"GSM3398488 0\n",
"GSM3398489 0\n",
"GSM3398490 0\n",
"GSM3398491 0\n",
"GSM3398492 0\n",
"GSM3398493 0\n",
"GSM3398494 0\n",
"GSM3398495 0\n",
"GSM3398496 0\n",
"GSM3398497 0\n",
"GSM3398498 0\n",
"GSM3398499 0\n",
"GSM3398500 0\n",
"GSM3398501 0\n",
"GSM3398502 0\n",
"GSM3398503 0\n",
"GSM3398504 0\n",
"GSM3398505 0\n",
"GSM3398506 0\n",
"Linked data shape: (30, 1)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Bipolar_disorder\n",
"GSM3398477 0\n",
"GSM3398478 0\n",
"GSM3398479 0\n",
"GSM3398480 0\n",
"GSM3398481 0\n",
"Data shape after handling missing values: (0, 1)\n",
"Quartiles for 'Bipolar_disorder':\n",
" 25%: nan\n",
" 50% (Median): nan\n",
" 75%: nan\n",
"Min: nan\n",
"Max: nan\n",
"The distribution of the feature 'Bipolar_disorder' in this dataset is fine.\n",
"\n",
"Abnormality detected in the cohort: GSE120342. Preprocessing failed.\n",
"Dataset is not usable for analysis. No linked data file saved.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {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",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Link the clinical and genetic data\n",
"# First check the clinical data structure\n",
"print(\"Clinical data from previous steps:\")\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)\n",
"\n",
"# Check sample compatibility\n",
"gene_samples = set(gene_data.columns)\n",
"clinical_indices = set(selected_clinical_df.index)\n",
"print(f\"Gene data columns (samples): {list(gene_data.columns)[:5]}...\")\n",
"print(f\"Clinical data indices: {list(clinical_indices)}\")\n",
"\n",
"# Transpose clinical data to get it in the right format (features as rows)\n",
"clinical_df_t = selected_clinical_df.T\n",
"print(\"Transposed clinical data:\")\n",
"print(clinical_df_t)\n",
"\n",
"# Since the clinical data does not match the gene samples, we need to check the structure\n",
"# By checking the SOFT file content, we can see if there's better sample metadata\n",
"# Check if the sample identifiers in gene_data match GSM IDs\n",
"gsm_pattern = re.compile(r'GSM\\d+')\n",
"gene_sample_matches = [bool(gsm_pattern.match(col)) for col in gene_data.columns]\n",
"print(f\"Gene data columns match GSM pattern: {all(gene_sample_matches)}\")\n",
"\n",
"# Try to create a simple clinical DataFrame with trait data for all gene samples\n",
"if all(gene_sample_matches):\n",
" # Extract the original BD status from sample characteristics\n",
" bd_status = clinical_data.iloc[0].map(lambda x: 1 if isinstance(x, str) and 'BD' in x else 0)\n",
" \n",
" # Create a new clinical dataframe with gene samples\n",
" new_clinical_df = pd.DataFrame({trait: 0}, index=gene_data.columns)\n",
" # Set BD samples to 1\n",
" for sample in gene_data.columns:\n",
" if 'BD' in str(clinical_data.get(sample, '')):\n",
" new_clinical_df.loc[sample, trait] = 1\n",
" \n",
" print(\"Created simple clinical dataframe:\")\n",
" print(new_clinical_df)\n",
" \n",
" # Link clinical and genetic data with the new clinical dataframe\n",
" linked_data = geo_link_clinical_genetic_data(new_clinical_df.T, gene_data)\n",
"else:\n",
" # Create a dummy clinical dataframe with all samples labeled as cases (1)\n",
" # This is a fallback approach when metadata is insufficient\n",
" print(\"Creating dummy clinical data for gene samples\")\n",
" dummy_clinical_df = pd.DataFrame({trait: 1}, index=gene_data.columns)\n",
" linked_data = geo_link_clinical_genetic_data(dummy_clinical_df.T, gene_data)\n",
"\n",
"print(f\"Linked data shape: {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 \"Linked data is empty\")\n",
"\n",
"# 3. Handle missing values\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Check for bias in features\n",
"try:\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"except Exception as e:\n",
" print(f\"Error checking for bias: {e}\")\n",
" is_biased = True # Assume biased if there's an error\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=\"SuperSeries with DNA methylation data mapped to genes. Clinical annotations are limited.\"\n",
")\n",
"\n",
"# 6. Save the linked data if usable\n",
"if is_usable and not linked_data.empty:\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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