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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2a3a42a1",
"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 = \"Bladder_Cancer\"\n",
"cohort = \"GSE162253\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Bladder_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Bladder_Cancer/GSE162253\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Bladder_Cancer/GSE162253.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Bladder_Cancer/gene_data/GSE162253.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Bladder_Cancer/clinical_data/GSE162253.csv\"\n",
"json_path = \"../../output/preprocess/Bladder_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "26606425",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5bc0cbab",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Bacterial effect on gene expression\"\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: ['gender: female', 'mouse strain: C57BL/6J-APCmin/J', 'mouse strain: C57BL/6J'], 1: ['strain: C57BL/6J', 'tissue: intestine'], 2: ['tissue: bladder', 'experiment: exp1', 'experiment: exp2', 'experiment: exp3'], 3: ['experiment: exp1', 'experiment: exp2', 'experiment: exp3', 'treatment: rLon', 'treatment: 536', 'treatment: N/A', 'treatment: PBS'], 4: ['treatment: PAI1', 'treatment: 536', 'treatment: N/A', 'treatment: PBS', 'treatment: rLon', nan]}\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": "4532357f",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d438750f",
"metadata": {
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"from typing import Callable, Optional, Dict, Any\n",
"\n",
"# Define variables for dataset assessment\n",
"is_gene_available = True # The dataset appears to be about gene expression based on the title\n",
"\n",
"# For trait data (bladder cancer vs. control)\n",
"# Based on the sample characteristics, trait data is not explicitly available\n",
"# The infection status in row 1 could be used as a trait, but it's not related to bladder cancer\n",
"trait_row = None # No direct bladder cancer data\n",
"\n",
"# For age and gender data\n",
"age_row = None # No age information available\n",
"gender_row = None # No gender information available\n",
"\n",
"# Define conversion functions (even though we won't use them in this case)\n",
"def convert_trait(value_str):\n",
" # Not used as trait_row is None\n",
" return None\n",
"\n",
"def convert_age(value_str):\n",
" # Not used as age_row is None\n",
" return None\n",
"\n",
"def convert_gender(value_str):\n",
" # Not used as gender_row is None\n",
" return None\n",
"\n",
"# Save metadata\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",
"# Since trait_row is None, we skip the clinical feature extraction step\n"
]
},
{
"cell_type": "markdown",
"id": "fd61eb8d",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fb69f511",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:56:44.990781Z",
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"shell.execute_reply": "2025-03-25T06:56:45.163077Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1415670_PM_at', '1415671_PM_at', '1415672_PM_at', '1415673_PM_at',\n",
" '1415674_PM_a_at', '1415675_PM_at', '1415676_PM_a_at', '1415677_PM_at',\n",
" '1415678_PM_at', '1415679_PM_at', '1415680_PM_at', '1415681_PM_at',\n",
" '1415682_PM_at', '1415683_PM_at', '1415684_PM_at', '1415685_PM_at',\n",
" '1415686_PM_at', '1415687_PM_a_at', '1415688_PM_at', '1415689_PM_s_at'],\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": "40f35ac3",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c56fe9ac",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:56:45.165004Z",
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"shell.execute_reply": "2025-03-25T06:56:45.166425Z"
}
},
"outputs": [],
"source": [
"# These identifiers appear to be Affymetrix probe IDs (format: 11715100_at) rather than standard human gene symbols\n",
"# These probe IDs typically need to be mapped to human gene symbols for biological interpretation\n",
"# Standard human gene symbols would look like BRCA1, TP53, etc.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "41770fea",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "83b517c8",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:56:45.167860Z",
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"shell.execute_reply": "2025-03-25T06:56:49.629314Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1415670_PM_at', '1415671_PM_at', '1415672_PM_at', '1415673_PM_at', '1415674_PM_a_at'], 'GB_ACC': ['BC024686', 'NM_013477', 'NM_020585', 'NM_133900', 'NM_021789'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Mus musculus', 'Mus musculus', 'Mus musculus', 'Mus musculus', 'Mus musculus'], 'Annotation Date': ['Aug 10, 2010', 'Aug 10, 2010', 'Aug 10, 2010', 'Aug 10, 2010', 'Aug 10, 2010'], 'Sequence Type': ['Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence'], 'Sequence Source': ['GenBank', 'GenBank', 'GenBank', 'GenBank', 'GenBank'], 'Target Description': ['gb:BC024686.1 /DB_XREF=gi:19354080 /FEA=FLmRNA /CNT=416 /TID=Mm.26422.1 /TIER=FL+Stack /STK=110 /UG=Mm.26422 /LL=54161 /UG_GENE=Copg1 /DEF=Mus musculus, coatomer protein complex, subunit gamma 1, clone MGC:30335 IMAGE:3992144, mRNA, complete cds. /PROD=coatomer protein complex, subunit gamma 1 /FL=gb:AF187079.1 gb:BC024686.1 gb:NM_017477.1 gb:BC024896.1', 'gb:NM_013477.1 /DB_XREF=gi:7304908 /GEN=Atp6v0d1 /FEA=FLmRNA /CNT=197 /TID=Mm.1081.1 /TIER=FL+Stack /STK=114 /UG=Mm.1081 /LL=11972 /DEF=Mus musculus ATPase, H+ transporting, lysosomal 38kDa, V0 subunit D isoform 1 (Atp6v0d1), mRNA. /PROD=ATPase, H+ transporting, lysosomal 38kDa, V0subunit D isoform 1 /FL=gb:U21549.1 gb:U13840.1 gb:BC011075.1 gb:NM_013477.1', 'gb:NM_020585.1 /DB_XREF=gi:10181207 /GEN=AB041568 /FEA=FLmRNA /CNT=213 /TID=Mm.17035.1 /TIER=FL+Stack /STK=102 /UG=Mm.17035 /LL=57437 /DEF=Mus musculus hypothetical protein, MNCb-1213 (AB041568), mRNA. /PROD=hypothetical protein, MNCb-1213 /FL=gb:BC016894.1 gb:NM_020585.1', 'gb:NM_133900.1 /DB_XREF=gi:19527115 /GEN=AI480570 /FEA=FLmRNA /CNT=139 /TID=Mm.10623.1 /TIER=FL+Stack /STK=96 /UG=Mm.10623 /LL=100678 /DEF=Mus musculus expressed sequence AI480570 (AI480570), mRNA. /PROD=expressed sequence AI480570 /FL=gb:BC002251.1 gb:NM_133900.1', 'gb:NM_021789.1 /DB_XREF=gi:11140824 /GEN=Sbdn /FEA=FLmRNA /CNT=163 /TID=Mm.29814.1 /TIER=FL+Stack /STK=95 /UG=Mm.29814 /LL=60409 /DEF=Mus musculus synbindin (Sbdn), mRNA. /PROD=synbindin /FL=gb:NM_021789.1 gb:AF233340.1'], 'Representative Public ID': ['BC024686', 'NM_013477', 'NM_020585', 'NM_133900', 'NM_021789'], 'Gene Title': ['coatomer protein complex, subunit gamma', 'ATPase, H+ transporting, lysosomal V0 subunit D1', 'golgi autoantigen, golgin subfamily a, 7', 'phosphoserine phosphatase', 'trafficking protein particle complex 4'], 'Gene Symbol': ['Copg', 'Atp6v0d1', 'Golga7', 'Psph', 'Trappc4'], 'Entrez Gene': ['54161', '11972', '57437', '100678', '60409'], 'RefSeq Transcript ID': ['NM_017477 /// NM_201244', 'NM_013477', 'NM_001042484 /// NM_020585', 'NM_133900', 'NM_021789'], 'Gene Ontology Biological Process': ['0006810 // transport // inferred from electronic annotation /// 0006886 // intracellular protein transport // inferred from electronic annotation /// 0015031 // protein transport // inferred from electronic annotation /// 0016192 // vesicle-mediated transport // inferred from electronic annotation', '0006810 // transport // inferred from electronic annotation /// 0006811 // ion transport // inferred from electronic annotation /// 0007420 // brain development // inferred from electronic annotation /// 0015986 // ATP synthesis coupled proton transport // inferred from electronic annotation /// 0015992 // proton transport // inferred from electronic annotation', '0006893 // Golgi to plasma membrane transport // not recorded', '0006564 // L-serine biosynthetic process // inferred from electronic annotation /// 0008152 // metabolic process // inferred from electronic annotation /// 0008652 // cellular amino acid biosynthetic process // inferred from electronic annotation /// 0009612 // response to mechanical stimulus // inferred from electronic annotation /// 0031667 // response to nutrient levels // inferred from electronic annotation /// 0033574 // response to testosterone stimulus // inferred from electronic annotation', '0006810 // transport // inferred from electronic annotation /// 0006888 // ER to Golgi vesicle-mediated transport // inferred from electronic annotation /// 0016192 // vesicle-mediated transport // traceable author statement /// 0016192 // vesicle-mediated transport // inferred from electronic annotation /// 0016358 // dendrite development // inferred from direct assay /// 0045212 // neurotransmitter receptor biosynthetic process // traceable author statement'], 'Gene Ontology Cellular Component': ['0000139 // Golgi membrane // inferred from electronic annotation /// 0005737 // cytoplasm // inferred from electronic annotation /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0005798 // Golgi-associated vesicle // inferred from electronic annotation /// 0016020 // membrane // inferred from electronic annotation /// 0030117 // membrane coat // inferred from electronic annotation /// 0030126 // COPI vesicle coat // inferred from electronic annotation /// 0030663 // COPI coated vesicle membrane // inferred from electronic annotation /// 0031410 // cytoplasmic vesicle // inferred from electronic annotation', '0005769 // early endosome // inferred from direct assay /// 0008021 // synaptic vesicle // not recorded /// 0008021 // synaptic vesicle // inferred from electronic annotation /// 0016020 // membrane // inferred from electronic annotation /// 0016324 // apical plasma membrane // not recorded /// 0016324 // apical plasma membrane // inferred from electronic annotation /// 0019717 // synaptosome // not recorded /// 0019717 // synaptosome // inferred from electronic annotation /// 0033177 // proton-transporting two-sector ATPase complex, proton-transporting domain // inferred from electronic annotation /// 0033179 // proton-transporting V-type ATPase, V0 domain // inferred from electronic annotation /// 0043234 // protein complex // not recorded /// 0043679 // axon terminus // not recorded /// 0043679 // axon terminus // inferred from electronic annotation', '0000139 // Golgi membrane // not recorded /// 0000139 // Golgi membrane // inferred from electronic annotation /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0016020 // membrane // inferred from electronic annotation', '0019717 // synaptosome // not recorded /// 0019717 // synaptosome // inferred from electronic annotation', '0005783 // endoplasmic reticulum // inferred from electronic annotation /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0005795 // Golgi stack // inferred from direct assay /// 0005801 // cis-Golgi network // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from electronic annotation /// 0008021 // synaptic vesicle // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0030008 // TRAPP complex // inferred from direct assay /// 0030054 // cell junction // inferred from electronic annotation /// 0030425 // dendrite // inferred from direct assay /// 0045202 // synapse // inferred from direct assay /// 0045202 // synapse // inferred from electronic annotation /// 0045211 // postsynaptic membrane // inferred from electronic annotation'], 'Gene Ontology Molecular Function': ['0005198 // structural molecule activity // inferred from electronic annotation /// 0005488 // binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from electronic annotation', '0008553 // hydrogen-exporting ATPase activity, phosphorylative mechanism // inferred from direct assay /// 0015078 // hydrogen ion transmembrane transporter activity // inferred from electronic annotation /// 0032403 // protein complex binding // not recorded /// 0032403 // protein complex binding // inferred from electronic annotation', nan, '0003824 // catalytic activity // inferred from electronic annotation /// 0004647 // phosphoserine phosphatase activity // inferred from electronic annotation /// 0005515 // protein binding // inferred from electronic annotation /// 0016787 // hydrolase activity // inferred from electronic annotation /// 0016791 // phosphatase activity // inferred from electronic annotation', '0005515 // protein binding // inferred from physical interaction /// 0005515 // protein binding // inferred from electronic annotation']}\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": "2f16e961",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "acfef329",
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"outputs": [
{
"name": "stdout",
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"text": [
"Looking at the gene annotation and expression data ID formats:\n",
"Gene annotation ID example: 1415670_PM_at\n",
"Gene expression ID example: 1415670_PM_at\n",
"Species in annotation: Mus musculus\n",
"Number of common IDs between annotation and expression data: 45077\n",
"Original gene expression data shape: (45077, 45)\n",
"Dataset contains mouse gene data, not suitable for human bladder cancer study\n"
]
},
{
"data": {
"text/plain": [
"False"
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"execution_count": 7,
"metadata": {},
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],
"source": [
"# 1. Identify the relevant columns in the gene annotation DataFrame\n",
"print(\"Looking at the gene annotation and expression data ID formats:\")\n",
"print(f\"Gene annotation ID example: {gene_annotation['ID'].iloc[0]}\")\n",
"print(f\"Gene expression ID example: {gene_data.index[0]}\")\n",
"\n",
"# Check species information\n",
"print(f\"Species in annotation: {gene_annotation['Species Scientific Name'].iloc[0]}\")\n",
"\n",
"# Given this is mouse data and not human data, and we're studying human bladder cancer,\n",
"# this dataset is not appropriate. We should set is_gene_available to False.\n",
"is_gene_available = False\n",
"\n",
"# Check for matching IDs just to confirm our suspicion\n",
"common_ids = set(gene_annotation['ID'].astype(str)) & set(gene_data.index)\n",
"print(f\"Number of common IDs between annotation and expression data: {len(common_ids)}\")\n",
"\n",
"# Since this is mouse data and not suitable for human bladder cancer study,\n",
"# we'll create an empty gene_data_mapped DataFrame to indicate no valid mapping\n",
"gene_data_mapped = pd.DataFrame()\n",
"\n",
"# Print information about the result\n",
"print(f\"Original gene expression data shape: {gene_data.shape}\")\n",
"print(f\"Dataset contains mouse gene data, not suitable for human bladder cancer study\")\n",
"\n",
"# Update gene_data to reflect this issue\n",
"gene_data = gene_data_mapped\n",
"\n",
"# Update metadata to reflect that gene data is not 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",
")"
]
}
],
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|