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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "bf7a53a4",
"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 = \"Acute_Myeloid_Leukemia\"\n",
"cohort = \"GSE99612\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Acute_Myeloid_Leukemia\"\n",
"in_cohort_dir = \"../../input/GEO/Acute_Myeloid_Leukemia/GSE99612\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/GSE99612.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE99612.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE99612.csv\"\n",
"json_path = \"../../output/preprocess/Acute_Myeloid_Leukemia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "4ceb8255",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5dae7d17",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"The effect of dietary fibre exposure on gene expression profiles in Caco-2 and THP-1 cells\"\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 line: Caco-2', 'cell line: THP-1'], 1: ['Sex: male', 'cell type: macrophage'], 2: ['treatment: medium', 'treatment: Novelose 500 ug/ml', 'treatment: Inulin-chicory 500 ug/ml', 'treatment: Resistant starch corn 500 ug/ml', 'treatment: Sugar beet pectin 500 ug/ml', 'treatment: Beta-glucan oat medium viscosity 500 ug/ml', 'treatment: GOS 500 ug/ml', 'treatment: LPS 11.85 pg/ml', 'Sex: male'], 3: ['tumor origin: Caucasian colon adenocarcinoma', 'patient age: 1 year infant'], 4: ['passage number: 30-60', 'tumor origin: acute monocytic leukemia'], 5: ['days of differentiation on tranwells: 21', 'treatment: medium', 'treatment: LPS 11.85 pg/ml', 'treatment: Novelose 500 ug/ml', 'treatment: Inulin-chicory 500 ug/ml', 'treatment: Resistant starch corn 500 ug/ml', 'treatment: Sugar beet pectin 500 ug/ml', 'treatment: beta-glucan oat medium viscosity 500 ug/ml', 'treatment: GOS 500 ug/ml'], 6: [nan, 'passage number: passage 20-40'], 7: [nan, 'days of differentiation on tranwells: 4 day differentiated']}\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": "8d31c484",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a8599c5a",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:20:32.484603Z"
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"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Callable, Dict, Any, Optional\n",
"import numpy as np\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this appears to be a cell line experiment comparing\n",
"# Caco-2 and THP-1 cells with various treatments. While it does contain gene expression data,\n",
"# it's not suitable for our study on human AML patients.\n",
"is_gene_available = True # The dataset likely contains gene expression data\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# This dataset doesn't contain patient-level clinical data about AML.\n",
"# It's comparing different cell lines with different treatments.\n",
"\n",
"# The dataset doesn't contain usable trait data for our purposes (AML vs non-AML in humans)\n",
"trait_row = None # No suitable trait data for human patients\n",
"\n",
"# Age data isn't patient age but refers to the original cell line source\n",
"age_row = None # No suitable age data for human patients\n",
"\n",
"# Sex data doesn't represent individual patients\n",
"gender_row = None # No suitable gender data for human patients\n",
"\n",
"# No need to define conversion functions since we won't use them\n",
"\n",
"# 3. Save Metadata\n",
"# Since this is a cell line experiment, not patient data, it's not suitable for our study\n",
"is_trait_available = trait_row is not None # This will be False\n",
"\n",
"# Validate and save cohort information\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",
"# Since trait_row is None, we skip this step\n",
"if trait_row is not None:\n",
" # This code won't execute since trait_row is None\n",
" try:\n",
" clinical_data = pd.read_csv(f\"{in_cohort_dir}/clinical_data.csv\", index_col=0)\n",
" \n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=lambda x: None, # Placeholder since we won't use it\n",
" age_row=age_row,\n",
" convert_age=None,\n",
" gender_row=gender_row,\n",
" convert_gender=None\n",
" )\n",
" \n",
" # Preview the DataFrame\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Create output directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the DataFrame 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 during clinical feature extraction: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "1e75d79f",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "292bc8ff",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:20:32.486056Z",
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"shell.execute_reply": "2025-03-25T06:20:32.634014Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['7892501', '7892502', '7892503', '7892504', '7892505', '7892506',\n",
" '7892507', '7892508', '7892509', '7892510', '7892511', '7892512',\n",
" '7892513', '7892514', '7892515', '7892516', '7892517', '7892518',\n",
" '7892519', '7892520'],\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": "2038a547",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4738029d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:20:32.635741Z",
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"shell.execute_reply": "2025-03-25T06:20:32.637262Z"
}
},
"outputs": [],
"source": [
"# Review of gene identifiers in the gene expression data\n",
"# The identifiers appear to be numerical codes (like 7892501, 7892502, etc.)\n",
"# These are likely probe IDs rather than standard human gene symbols\n",
"# Human gene symbols would be alphanumeric (like BRCA1, TP53, etc.)\n",
"# Therefore, these identifiers will need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "eab5473d",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d7fa1690",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:20:35.292610Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['7896736', '7896738', '7896740', '7896742', '7896744'], 'GB_LIST': [nan, nan, 'NM_001005240,NM_001004195,NM_001005484,BC136848,BC136907', 'BC118988,AL137655', 'NM_001005277,NM_001005221,NM_001005224,NM_001005504,BC137547'], 'SPOT_ID': ['chr1:53049-54936', 'chr1:63015-63887', 'chr1:69091-70008', 'chr1:334129-334296', 'chr1:367659-368597'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': [53049.0, 63015.0, 69091.0, 334129.0, 367659.0], 'RANGE_STOP': [54936.0, 63887.0, 70008.0, 334296.0, 368597.0], 'total_probes': [7.0, 31.0, 24.0, 6.0, 36.0], 'gene_assignment': ['---', '---', 'NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000318050 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// ENST00000335137 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000326183 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// BC136848 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136907 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000442916 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099', 'ENST00000388975 // SEPT14 // septin 14 // 7p11.2 // 346288 /// BC118988 // NCRNA00266 // non-protein coding RNA 266 // --- // 140849 /// AL137655 // LOC100134822 // similar to hCG1739109 // --- // 100134822', 'NM_001005277 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// NM_001005221 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// NM_001005224 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// NM_001005504 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// ENST00000320901 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// BC137547 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137547 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137547 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759'], 'mrna_assignment': ['---', 'ENST00000328113 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102467008:102467910:-1 gene:ENSG00000183909 // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000318181 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:19:104601:105256:1 gene:ENSG00000176705 // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000492842 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:62948:63887:1 gene:ENSG00000240361 // chr1 // 100 // 100 // 31 // 31 // 0', 'NM_001005240 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 17 (OR4F17), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001004195 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 4 (OR4F4), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000318050 // ENSEMBL // Olfactory receptor 4F17 gene:ENSG00000176695 // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000335137 // ENSEMBL // Olfactory receptor 4F4 gene:ENSG00000186092 // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000326183 // ENSEMBL // Olfactory receptor 4F5 gene:ENSG00000177693 // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136848 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136907 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000442916 // ENSEMBL // OR4F4 (Fragment) gene:ENSG00000176695 // chr1 // 100 // 88 // 21 // 21 // 0', 'ENST00000388975 // ENSEMBL // Septin-14 gene:ENSG00000154997 // chr1 // 50 // 100 // 3 // 6 // 0 /// BC118988 // GenBank // Homo sapiens chromosome 20 open reading frame 69, mRNA (cDNA clone MGC:141807 IMAGE:40035995), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// AL137655 // GenBank // Homo sapiens mRNA; cDNA DKFZp434B2016 (from clone DKFZp434B2016). // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000428915 // ENSEMBL // cdna:known chromosome:GRCh37:10:38742109:38755311:1 gene:ENSG00000203496 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455207 // ENSEMBL // cdna:known chromosome:GRCh37:1:334129:446155:1 gene:ENSG00000224813 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455464 // ENSEMBL // cdna:known chromosome:GRCh37:1:334140:342806:1 gene:ENSG00000224813 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000440200 // ENSEMBL // cdna:known chromosome:GRCh37:1:536816:655580:-1 gene:ENSG00000230021 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000279067 // ENSEMBL // cdna:known chromosome:GRCh37:20:62921738:62934912:1 gene:ENSG00000149656 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000499986 // ENSEMBL // cdna:known chromosome:GRCh37:5:180717576:180761371:1 gene:ENSG00000248628 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000436899 // ENSEMBL // cdna:known chromosome:GRCh37:6:131910:144885:-1 gene:ENSG00000170590 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000432557 // ENSEMBL // cdna:known chromosome:GRCh37:8:132324:150572:-1 gene:ENSG00000250210 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000523795 // ENSEMBL // cdna:known chromosome:GRCh37:8:141690:150563:-1 gene:ENSG00000250210 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000490482 // ENSEMBL // cdna:known chromosome:GRCh37:8:149942:163324:-1 gene:ENSG00000223508 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000307499 // ENSEMBL // cdna:known supercontig::GL000227.1:57780:70752:-1 gene:ENSG00000229450 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000441245 // ENSEMBL // cdna:known chromosome:GRCh37:1:637316:655530:-1 gene:ENSG00000230021 // chr1 // 100 // 67 // 4 // 4 // 0 /// ENST00000425473 // ENSEMBL // cdna:known chromosome:GRCh37:20:62926294:62944485:1 gene:ENSG00000149656 // chr1 // 100 // 67 // 4 // 4 // 0 /// ENST00000471248 // ENSEMBL // cdna:known chromosome:GRCh37:1:110953:129173:-1 gene:ENSG00000238009 // chr1 // 75 // 67 // 3 // 4 // 0', 'NM_001005277 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 16 (OR4F16), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005221 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 29 (OR4F29), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005224 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 3 (OR4F3), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005504 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 21 (OR4F21), mRNA. // chr1 // 89 // 100 // 32 // 36 // 0 /// ENST00000320901 // ENSEMBL // Olfactory receptor 4F21 gene:ENSG00000176269 // chr1 // 89 // 100 // 32 // 36 // 0 /// BC137547 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169170 IMAGE:9021547), complete cds. // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000426406 // ENSEMBL // cdna:known chromosome:GRCh37:1:367640:368634:1 gene:ENSG00000235249 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000332831 // ENSEMBL // cdna:known chromosome:GRCh37:1:621096:622034:-1 gene:ENSG00000185097 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000456475 // ENSEMBL // cdna:known chromosome:GRCh37:5:180794269:180795263:1 gene:ENSG00000230178 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000521196 // ENSEMBL // cdna:known chromosome:GRCh37:11:86612:87605:-1 gene:ENSG00000224777 // chr1 // 78 // 100 // 28 // 36 // 0'], 'category': ['---', 'main', 'main', 'main', 'main']}\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": "fce6b800",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b963275e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:20:35.294415Z",
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"shell.execute_reply": "2025-03-25T06:20:36.479665Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping sample (first 5 rows):\n",
" ID Gene\n",
"0 7896736 ---\n",
"1 7896738 ---\n",
"2 7896740 NM_001005240 // OR4F17 // olfactory receptor, ...\n",
"3 7896742 ENST00000388975 // SEPT14 // septin 14 // 7p11...\n",
"4 7896744 NM_001005277 // OR4F16 // olfactory receptor, ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data shape after mapping: (56391, 48)\n",
"\n",
"First 5 genes and expression values:\n",
" GSM2648543 GSM2648544 GSM2648545 GSM2648546 GSM2648547 GSM2648548 \\\n",
"Gene \n",
"A- 38.716029 37.128762 37.511108 37.623609 38.177840 38.209748 \n",
"A-2 2.658533 2.637381 2.639023 2.650405 2.663684 2.659015 \n",
"A-52 3.651613 3.645483 3.609023 3.648133 3.642320 3.644010 \n",
"A-E 0.482038 0.478818 0.496260 0.467780 0.487119 0.477848 \n",
"A-I 11.601787 11.668377 11.653517 11.683830 11.639600 11.745367 \n",
"\n",
" GSM2648549 GSM2648550 GSM2648551 GSM2648552 ... GSM2648581 \\\n",
"Gene ... \n",
"A- 37.516400 37.700195 38.048053 38.039465 ... 34.037491 \n",
"A-2 2.636998 2.577943 2.640939 2.644001 ... 2.159168 \n",
"A-52 3.653870 3.661173 3.648297 3.637170 ... 3.873297 \n",
"A-E 0.485932 0.487208 0.481296 0.490885 ... 0.465458 \n",
"A-I 11.681123 11.744437 11.692613 11.750230 ... 7.384977 \n",
"\n",
" GSM2648582 GSM2648583 GSM2648584 GSM2648585 GSM2648586 GSM2648587 \\\n",
"Gene \n",
"A- 34.163132 34.643837 33.520664 34.734178 34.441331 34.438711 \n",
"A-2 2.225617 2.162100 2.881895 2.353087 2.260880 2.632220 \n",
"A-52 3.872083 3.866130 3.837077 3.851257 3.880483 3.855293 \n",
"A-E 0.467714 0.473606 0.462785 0.457551 0.463859 0.457299 \n",
"A-I 7.316712 7.451579 7.439498 7.377272 7.488927 7.351624 \n",
"\n",
" GSM2648588 GSM2648589 GSM2648590 \n",
"Gene \n",
"A- 35.021535 34.479051 34.669246 \n",
"A-2 2.324638 2.213350 2.181451 \n",
"A-52 3.873340 3.851763 3.841573 \n",
"A-E 0.463503 0.469056 0.461046 \n",
"A-I 7.375039 7.332966 7.421468 \n",
"\n",
"[5 rows x 48 columns]\n",
"\n",
"Gene expression data shape after normalization: (20124, 48)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data saved to ../../output/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE99612.csv\n"
]
}
],
"source": [
"# 1. Identify the columns for probe IDs and gene symbols\n",
"probe_col = 'ID' # This contains the probe identifiers like '7896736'\n",
"gene_col = 'gene_assignment' # This contains gene symbol information\n",
"\n",
"# 2. Get gene mapping using the function from the library\n",
"mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
"\n",
"# Print a sample of the mapping to verify structure\n",
"print(\"Gene mapping sample (first 5 rows):\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene-level expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Print the shape and first few rows of the resulting gene expression dataframe\n",
"print(\"\\nGene expression data shape after mapping:\", gene_data.shape)\n",
"print(\"\\nFirst 5 genes and expression values:\")\n",
"print(gene_data.head())\n",
"\n",
"# Normalize gene symbols to ensure consistency and handle synonyms\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"\n",
"# Print the shape after normalization\n",
"print(\"\\nGene expression data shape after normalization:\", gene_data.shape)\n",
"\n",
"# Save gene data to 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\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "85a7e1d8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "59b46b74",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:20:36.481494Z",
"iopub.status.busy": "2025-03-25T06:20:36.481362Z",
"iopub.status.idle": "2025-03-25T06:20:36.571252Z",
"shell.execute_reply": "2025-03-25T06:20:36.570942Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Abnormality detected in the cohort: GSE99612. Preprocessing failed.\n",
"Dataset correctly identified as not usable for human AML trait-gene association study.\n"
]
}
],
"source": [
"# 1. Gene data was already normalized and saved in Step 6\n",
"# No need to normalize again as it was done in Step 6\n",
"\n",
"# 2-6. Since there's no clinical data available for this dataset (trait_row was None in Step 2),\n",
"# we can't link clinical and genetic data\n",
"# Instead, we should finalize the cohort information to reflect this limitation\n",
"\n",
"# Get a small sample of the normalized gene data for the validation function\n",
"if 'normalized_gene_data' not in locals():\n",
" # Load the saved gene data if not already in memory\n",
" try:\n",
" normalized_gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
" except:\n",
" normalized_gene_data = gene_data # Use the gene_data from previous step if file not found\n",
"\n",
"# Create a minimal dataframe with the gene data structure and add a dummy trait column\n",
"minimal_df = pd.DataFrame(index=normalized_gene_data.columns)\n",
"minimal_df[trait] = None # Add trait column with null values\n",
"\n",
"# Note for the validation function explaining why this dataset isn't usable\n",
"note = \"This dataset contains gene expression from cell lines (Caco-2 and THP-1) with various treatments, not human patient data for AML studies.\"\n",
"\n",
"# Final validation - mark as not usable for trait analysis\n",
"is_trait_available = False\n",
"is_gene_available = True\n",
"is_biased = False # Explicitly set to False since there's no trait data to evaluate bias\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=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=minimal_df,\n",
" note=note\n",
")\n",
"\n",
"# We do not save linked_data to out_data_file because this dataset is not usable for the study\n",
"if is_usable:\n",
" print(\"WARNING: This dataset was unexpectedly marked as usable, which conflicts with previous findings.\")\n",
"else:\n",
" print(\"Dataset correctly identified as not usable for human AML trait-gene association study.\")"
]
}
],
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|