{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "bf7a53a4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:20:32.199795Z", "iopub.status.busy": "2025-03-25T06:20:32.199662Z", "iopub.status.idle": "2025-03-25T06:20:32.366584Z", "shell.execute_reply": "2025-03-25T06:20:32.366259Z" } }, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n", "\n", "# Path Configuration\n", "from tools.preprocess import *\n", "\n", "# Processing context\n", "trait = \"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": { "iopub.execute_input": "2025-03-25T06:20:32.368067Z", "iopub.status.busy": "2025-03-25T06:20:32.367912Z", "iopub.status.idle": "2025-03-25T06:20:32.479627Z", "shell.execute_reply": "2025-03-25T06:20:32.479320Z" } }, "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": { "iopub.execute_input": "2025-03-25T06:20:32.480985Z", "iopub.status.busy": "2025-03-25T06:20:32.480875Z", "iopub.status.idle": "2025-03-25T06:20:32.484883Z", "shell.execute_reply": "2025-03-25T06:20:32.484603Z" } }, "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", "iopub.status.busy": "2025-03-25T06:20:32.485944Z", "iopub.status.idle": "2025-03-25T06:20:32.634408Z", "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", "iopub.status.busy": "2025-03-25T06:20:32.635618Z", "iopub.status.idle": "2025-03-25T06:20:32.637541Z", "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": { "iopub.execute_input": "2025-03-25T06:20:32.638676Z", "iopub.status.busy": "2025-03-25T06:20:32.638572Z", "iopub.status.idle": "2025-03-25T06:20:35.292996Z", "shell.execute_reply": "2025-03-25T06:20:35.292610Z" } }, "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", "iopub.status.busy": "2025-03-25T06:20:35.294287Z", "iopub.status.idle": "2025-03-25T06:20:36.480067Z", "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.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }