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{
"cells": [
{
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"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 = \"Rheumatoid_Arthritis\"\n",
"cohort = \"GSE176440\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Rheumatoid_Arthritis\"\n",
"in_cohort_dir = \"../../input/GEO/Rheumatoid_Arthritis/GSE176440\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/GSE176440.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/gene_data/GSE176440.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE176440.csv\"\n",
"json_path = \"../../output/preprocess/Rheumatoid_Arthritis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "475a35f1",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1b633e93",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression profiles of CD4+ T cells before and after methotrexate treatment in rheumatoid arthritis patients [Microarray]\"\n",
"!Series_summary\t\"To understand the molecular mechanisms by which methotraxate improves the disease activity in rheumatoid arthritis, CD4+ T cells were obtained before and 3month after methotrexate treatment.\"\n",
"!Series_overall_design\t\"28 treatment naïve rheumatoid arthritis patients participated in the study. Blood samples were obtained before and 3 months after methotrexate treatment. CD4+ T cells were magnetically purified and subjected the DNA microarray analyses.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['individual: A29', 'individual: A30', 'individual: A34', 'individual: C14', 'individual: C16', 'individual: C19', 'individual: C43', 'individual: C49', 'individual: C71', 'individual: C80', 'individual: C85', 'individual: C87', 'individual: C91', 'individual: C92', 'individual: C93', 'individual: C95', 'individual: C96', 'individual: C100', 'individual: C102', 'individual: C103', 'individual: C107', 'individual: C108', 'individual: C109', 'individual: C111', 'individual: C115', 'individual: C116', 'individual: C117', 'individual: K20'], 1: ['disease state: rheumatoid arthritis patient'], 2: ['treatment: before methotrexate', 'treatment: 3 months after methotrexate'], 3: ['cell type: CD4+ T cells']}\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": "3a650c57",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e67176c5",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:51:17.862013Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical data:\n",
"{0: [nan], 1: [1.0], 2: [nan], 3: [nan]}\n",
"Clinical data saved to ../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE176440.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any, List\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains microarray data of CD4+ T cells\n",
"# which implies gene expression data, not just miRNA or methylation\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Identify keys in the sample characteristics dictionary\n",
"\n",
"# Trait (Rheumatoid Arthritis)\n",
"# From the sample characteristics, all samples are from RA patients (key 1)\n",
"trait_row = 1 # \"disease state: rheumatoid arthritis patient\"\n",
"\n",
"# Treatment status (before/after methotrexate) at key 2 - this could be useful clinical information\n",
"# but it's not age or gender\n",
"\n",
"# Age - Not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender - Not available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert trait value to binary (0 for control, 1 for RA).\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # All samples are RA patients based on the data\n",
" if \"rheumatoid arthritis\" in value.lower():\n",
" return 1\n",
" return None # Default case for unknown values\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age value to continuous numeric.\"\"\"\n",
" # Not used since age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male).\"\"\"\n",
" # Not used since gender data is not available\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine if trait data is available\n",
"is_trait_available = trait_row is not None\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 not None, we need to extract clinical features\n",
"if trait_row is not None:\n",
" # Load the clinical data (assuming it was saved from a previous step)\n",
" clinical_data = pd.DataFrame(\n",
" {0: ['individual: A29', 'individual: A30', 'individual: A34', 'individual: C14', 'individual: C16', 'individual: C19', 'individual: C43', 'individual: C49', 'individual: C71', 'individual: C80', 'individual: C85', 'individual: C87', 'individual: C91', 'individual: C92', 'individual: C93', 'individual: C95', 'individual: C96', 'individual: C100', 'individual: C102', 'individual: C103', 'individual: C107', 'individual: C108', 'individual: C109', 'individual: C111', 'individual: C115', 'individual: C116', 'individual: C117', 'individual: K20'], \n",
" 1: ['disease state: rheumatoid arthritis patient'] * 28,\n",
" 2: ['treatment: before methotrexate', 'treatment: 3 months after methotrexate'] * 14,\n",
" 3: ['cell type: CD4+ T cells'] * 28}\n",
" )\n",
" \n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the selected clinical data\n",
" print(\"Preview of selected clinical data:\")\n",
" print(preview_df(selected_clinical_df))\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 the selected clinical data to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "35fea9d6",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f227655b",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056',\n",
" 'A_23_P100074', 'A_23_P100092', 'A_23_P100103', 'A_23_P100111',\n",
" 'A_23_P100127', 'A_23_P100133', 'A_23_P100141', 'A_23_P100156',\n",
" 'A_23_P100177', 'A_23_P100189', 'A_23_P100196', 'A_23_P100203',\n",
" 'A_23_P100220', 'A_23_P100240', 'A_23_P10025', 'A_23_P100263'],\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": "54573b79",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b6cecc13",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:51:18.130841Z",
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"outputs": [],
"source": [
"# The identifiers in the gene expression data (A_23_P100001, A_23_P100011, etc.) are Agilent microarray \n",
"# probe identifiers, not human gene symbols.\n",
"# These are probe IDs from an Agilent microarray platform and need to be mapped to human gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "5ccddc43",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "825dcac6",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'SPOT_ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'CONTROL_TYPE': ['FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GB_ACC': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GENE': [400451.0, 10239.0, 9899.0, 348093.0, 57099.0], 'GENE_SYMBOL': ['FAM174B', 'AP3S2', 'SV2B', 'RBPMS2', 'AVEN'], 'GENE_NAME': ['family with sequence similarity 174, member B', 'adaptor-related protein complex 3, sigma 2 subunit', 'synaptic vesicle glycoprotein 2B', 'RNA binding protein with multiple splicing 2', 'apoptosis, caspase activation inhibitor'], 'UNIGENE_ID': ['Hs.27373', 'Hs.632161', 'Hs.21754', 'Hs.436518', 'Hs.555966'], 'ENSEMBL_ID': ['ENST00000557398', nan, 'ENST00000557410', 'ENST00000300069', 'ENST00000306730'], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': ['ref|NM_207446|ens|ENST00000557398|ens|ENST00000553393|ens|ENST00000327355', 'ref|NM_005829|ref|NM_001199058|ref|NR_023361|ref|NR_037582', 'ref|NM_014848|ref|NM_001167580|ens|ENST00000557410|ens|ENST00000330276', 'ref|NM_194272|ens|ENST00000300069|gb|AK127873|gb|AK124123', 'ref|NM_020371|ens|ENST00000306730|gb|AF283508|gb|BC010488'], 'CHROMOSOMAL_LOCATION': ['chr15:93160848-93160789', 'chr15:90378743-90378684', 'chr15:91838329-91838388', 'chr15:65032375-65032316', 'chr15:34158739-34158680'], 'CYTOBAND': ['hs|15q26.1', 'hs|15q26.1', 'hs|15q26.1', 'hs|15q22.31', 'hs|15q14'], 'DESCRIPTION': ['Homo sapiens family with sequence similarity 174, member B (FAM174B), mRNA [NM_207446]', 'Homo sapiens adaptor-related protein complex 3, sigma 2 subunit (AP3S2), transcript variant 1, mRNA [NM_005829]', 'Homo sapiens synaptic vesicle glycoprotein 2B (SV2B), transcript variant 1, mRNA [NM_014848]', 'Homo sapiens RNA binding protein with multiple splicing 2 (RBPMS2), mRNA [NM_194272]', 'Homo sapiens apoptosis, caspase activation inhibitor (AVEN), mRNA [NM_020371]'], 'GO_ID': ['GO:0016020(membrane)|GO:0016021(integral to membrane)', 'GO:0005794(Golgi apparatus)|GO:0006886(intracellular protein transport)|GO:0008565(protein transporter activity)|GO:0016020(membrane)|GO:0016192(vesicle-mediated transport)|GO:0030117(membrane coat)|GO:0030659(cytoplasmic vesicle membrane)|GO:0031410(cytoplasmic vesicle)', 'GO:0001669(acrosomal vesicle)|GO:0006836(neurotransmitter transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0022857(transmembrane transporter activity)|GO:0030054(cell junction)|GO:0030672(synaptic vesicle membrane)|GO:0031410(cytoplasmic vesicle)|GO:0045202(synapse)', 'GO:0000166(nucleotide binding)|GO:0003676(nucleic acid binding)', 'GO:0005515(protein binding)|GO:0005622(intracellular)|GO:0005624(membrane fraction)|GO:0006915(apoptosis)|GO:0006916(anti-apoptosis)|GO:0012505(endomembrane system)|GO:0016020(membrane)'], 'SEQUENCE': ['ATCTCATGGAAAAGCTGGATTCCTCTGCCTTACGCAGAAACACCCGGGCTCCATCTGCCA', 'TCAAGTATTGGCCTGACATAGAGTCCTTAAGACAAGCAAAGACAAGCAAGGCAAGCACGT', 'ATGTCGGCTGTGGAGGGTTAAAGGGATGAGGCTTTCCTTTGTTTAGCAAATCTGTTCACA', 'CCCTGTCAGATAAGTTTAATGTTTAGTTTGAGGCATGAAGAAGAAAAGGGTTTCCATTCT', 'GACCAGCCAGTTTACAAGCATGTCTCAAGCTAGTGTGTTCCATTATGCTCACAGCAGTAA']}\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": "10880b81",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "89dc6c60",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:51:22.344377Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of mapped gene expression data:\n",
"(18488, 56)\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT',\n",
" 'AAAS', 'AACS'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identify the columns in the gene annotation dataframe\n",
"# From the preview, 'ID' in the gene_annotation corresponds to the probe identifiers in gene_data\n",
"# 'GENE_SYMBOL' contains the human gene symbols we want to map to\n",
"prob_col = 'ID'\n",
"gene_col = 'GENE_SYMBOL'\n",
"\n",
"# 2. Extract the mapping between probe IDs and gene symbols\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"# This function handles the many-to-many relationship as specified\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Preview the result to verify the transformation\n",
"print(\"Preview of mapped gene expression data:\")\n",
"print(gene_data.shape)\n",
"print(gene_data.index[:10]) # Print first 10 gene symbols\n"
]
},
{
"cell_type": "markdown",
"id": "fbb6eb5c",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "dd1bd097",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:51:23.003614Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Rheumatoid_Arthritis/gene_data/GSE176440.csv\n",
"Clinical data columns: ['0', '1', '2', '3']\n",
"Linked data shape: (61, 18248)\n",
"Error processing data: ['Rheumatoid_Arthritis']\n",
"Abnormality detected in the cohort: GSE176440. Preprocessing failed.\n",
"Dataset not usable for analysis. No linked data saved.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Load the clinical data that was saved in Step 2\n",
"clinical_data_df = pd.read_csv(out_clinical_data_file)\n",
"\n",
"# Check the structure of the clinical data\n",
"print(\"Clinical data columns:\", clinical_data_df.columns.tolist())\n",
"\n",
"# Since we don't have a proper trait column in the clinical data,\n",
"# we need to add it before linking\n",
"if trait not in clinical_data_df.columns:\n",
" # Create a proper clinical data structure with the trait column\n",
" # From previous steps, we see all values are 1.0 for RA patients\n",
" clinical_data_df[trait] = 1.0\n",
"\n",
"# Link the clinical and genetic data on sample IDs\n",
"linked_data = geo_link_clinical_genetic_data(clinical_data_df, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"try:\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"After handling missing values, linked data shape: {linked_data.shape}\")\n",
" \n",
" # 4. Determine whether the trait and demographic features are severely biased\n",
" trait_biased, linked_data_filtered = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # 5. Conduct final quality validation and save relevant 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=trait_biased, \n",
" df=linked_data_filtered,\n",
" note=\"Dataset contains gene expression data from CD4+ T cells of rheumatoid arthritis patients before and after methotrexate treatment.\"\n",
" )\n",
" \n",
" # 6. If the linked data is usable, save it as a CSV file\n",
" if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_filtered.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"Dataset not usable for analysis. No linked data saved.\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing data: {e}\")\n",
" # If there's an error, mark the dataset as not usable\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=False, # Marking as unavailable due to processing error\n",
" is_biased=True, \n",
" df=pd.DataFrame(),\n",
" note=f\"Error during data processing: {e}. Dataset contains only RA patients with constant trait value.\"\n",
" )\n",
" print(\"Dataset not usable for analysis. No linked data saved.\")"
]
}
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|