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{
"cells": [
{
"cell_type": "code",
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"id": "d11dcd1e",
"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 = \"Rectal_Cancer\"\n",
"cohort = \"GSE150082\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Rectal_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Rectal_Cancer/GSE150082\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Rectal_Cancer/GSE150082.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Rectal_Cancer/gene_data/GSE150082.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Rectal_Cancer/clinical_data/GSE150082.csv\"\n",
"json_path = \"../../output/preprocess/Rectal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "7de2e8b1",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b3dbf9cb",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:11.086337Z",
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}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Pre-existing tumoral B cell infiltration and impaired genome maintenance correlate with response to chemoradiotherapy in locally advanced rectal cancer (LARC)\"\n",
"!Series_summary\t\"Using Human Genome 4x44 two-color Agilent microarrays, we established the expression profiling of 39 LARC pretreatment tumor samples to elucidate the molecular features associated with response to treatment after neoadjuvant chemoradiotherapy (nCRT).\"\n",
"!Series_overall_design\t\"Two color microarrays where Cy5= tumor sample and Cy3= Stratagene Universal Human RNA Reference. This dataset comprises the transcriptomic profiling of 39 consecutive eligible LARC patients who underwent therapy at the Oncology Unit at Bonorino Udaondo Hospital (Buenos Aires, Argentina) from November 2015 to September 2018. This study was approved by the Udaondo Hospital Ethics Committee and the Instituto Leloir Institutional Review Board. All patients signed the approved Informed Consent. All patients were assigned to standard pelvic long course radiotherapy (LCRT: 50.4 Gy in 28 fractions of three-dimensional conformal radiotherapy, 1.8 Gy per fraction, per day) with concurrent capecitabine (825 mg/m2/bid for 28 days), termed hereafter CRT. Patients with a high risk of systemic relapse (EMVI, high mesorectal node burden and LLND) underwent TNT, which comprises pre-treatment before the CRT with three cycles of CAPOX (130 mg/m² of oxaliplatin on day 1 and capecitabine 1000 mg/m²/bid, days 1-14 every 3 weeks). Two cycles of capecitabine monotherapy (850 mg/m²/bid, days 1-14 every 3 weeks) was then administered until response assessment for all patients. Together, TNT and CRT are referred to as nCRT. Response to nCRT was evaluated on the surgical specimen by the pathological tumor regression (pTRG) score proposed by the seventh edition manual of the American Joint Committee on Cancer (AJCC), except for cases where pTRG was unavailable due to complete clinical response or unresectability. pTRG=0-1 and complete clinical responders were considered good responders, while pTRG=2-3 and unresectable patients were considered poor responders. The most relevant clinical variables are summarized in the metadata file; in case you require further information, do not hesitate to contact the authors.\"\n",
"!Series_overall_design\t\"contributor: GENUIT consortium\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['Sex: M', 'Sex: F'], 1: ['tissue: rectal cancer'], 2: ['age: 70', 'age: 74', 'age: 45', 'age: 54', 'age: 72', 'age: 57', 'age: 66', 'age: 71', 'age: 47', 'age: 61', 'age: 64', 'age: 59', 'age: 34', 'age: 63', 'age: 46', 'age: 55', 'age: 75', 'age: 42', 'age: 69', 'age: 49', 'age: 68', 'age: 60', 'age: 58', 'age: 30', 'age: 56'], 3: ['ptrg: Complete_clinical_response_nonOperative', 'ptrg: 1', 'ptrg: NA', 'ptrg: 0', 'ptrg: 3', 'ptrg: 2', 'ptrg: Unresectable'], 4: ['response: Good', 'response: Poor']}\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": "539c3ebe",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "edec9b7d",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:47:11.241415Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'GSM4523129': [1.0, 70.0, 1.0], 'GSM4523130': [1.0, 74.0, 1.0], 'GSM4523131': [1.0, 45.0, 0.0], 'GSM4523132': [1.0, 45.0, 0.0], 'GSM4523133': [1.0, 54.0, 1.0], 'GSM4523134': [1.0, 72.0, 1.0], 'GSM4523135': [1.0, 57.0, 1.0], 'GSM4523136': [1.0, 66.0, 1.0], 'GSM4523137': [1.0, 71.0, 0.0], 'GSM4523138': [1.0, 47.0, 1.0], 'GSM4523139': [1.0, 66.0, 1.0], 'GSM4523140': [1.0, 61.0, 1.0], 'GSM4523141': [1.0, 64.0, 1.0], 'GSM4523142': [1.0, 61.0, 1.0], 'GSM4523143': [1.0, 59.0, 1.0], 'GSM4523144': [0.0, 34.0, 0.0], 'GSM4523145': [0.0, 63.0, 1.0], 'GSM4523146': [0.0, 66.0, 0.0], 'GSM4523147': [0.0, 46.0, 1.0], 'GSM4523148': [0.0, 57.0, 1.0], 'GSM4523149': [0.0, 64.0, 1.0], 'GSM4523150': [0.0, 55.0, 1.0], 'GSM4523151': [0.0, 75.0, 1.0], 'GSM4523152': [0.0, 61.0, 1.0], 'GSM4523153': [0.0, 42.0, 0.0], 'GSM4523154': [0.0, 63.0, 1.0], 'GSM4523155': [0.0, 42.0, 1.0], 'GSM4523156': [0.0, 69.0, 1.0], 'GSM4523157': [0.0, 49.0, 0.0], 'GSM4523158': [0.0, 68.0, 0.0], 'GSM4523159': [0.0, 60.0, 0.0], 'GSM4523160': [0.0, 69.0, 1.0], 'GSM4523161': [0.0, 58.0, 1.0], 'GSM4523162': [0.0, 30.0, 1.0], 'GSM4523163': [0.0, 49.0, 0.0], 'GSM4523164': [1.0, 74.0, 1.0], 'GSM4523165': [0.0, 56.0, 1.0], 'GSM4523166': [0.0, 64.0, 1.0], 'GSM4523167': [0.0, 66.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Rectal_Cancer/clinical_data/GSE150082.csv\n"
]
}
],
"source": [
"# Analyze gene expression data availability\n",
"is_gene_available = True # Based on Series_summary mentioning \"expression profiling\" and \"Agilent microarrays\"\n",
"\n",
"# Define trait row and conversion function\n",
"trait_row = 4 # 'response' field contains binary Good/Poor response data\n",
"age_row = 2 # Age data is available\n",
"gender_row = 0 # Sex data is available\n",
"\n",
"# Define conversion functions for clinical features\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary: Good response = 1, Poor response = 0\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" value_part = value.split(': ')[-1].strip() if ': ' in value else value.strip()\n",
" \n",
" if value_part == 'Good':\n",
" return 1\n",
" elif value_part == 'Poor':\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous numeric value\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" value_part = value.split(': ')[-1].strip() if ': ' in value else value.strip()\n",
" \n",
" try:\n",
" return float(value_part)\n",
" except ValueError:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary: Female = 0, Male = 1\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" value_part = value.split(': ')[-1].strip() if ': ' in value else value.strip()\n",
" \n",
" if value_part == 'F':\n",
" return 0\n",
" elif value_part == 'M':\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Determine trait availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save metadata for initial filtering\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",
"# Extract clinical features if trait data is available\n",
"if trait_row is not None:\n",
" # Assuming clinical_data is already in memory from previous steps\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 extracted data\n",
" preview_data = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview_data)\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 extracted clinical data\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": "7e8ef31d",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "97c9dc74",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:11.242904Z",
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"shell.execute_reply": "2025-03-25T03:47:11.467299Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['A_23_P100001', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074',\n",
" 'A_23_P100127', 'A_23_P100141', 'A_23_P100189', 'A_23_P100196',\n",
" 'A_23_P100203', 'A_23_P100220', 'A_23_P100240', 'A_23_P10025',\n",
" 'A_23_P100292', 'A_23_P100315', 'A_23_P100326', 'A_23_P100344',\n",
" 'A_23_P100355', 'A_23_P100386', 'A_23_P100392', 'A_23_P100420'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the file paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "cc2c7cdd",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c7cdf591",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:11.469034Z",
"iopub.status.busy": "2025-03-25T03:47:11.468916Z",
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"shell.execute_reply": "2025-03-25T03:47:11.470670Z"
}
},
"outputs": [],
"source": [
"# Reviewing the gene identifiers in the dataset\n",
"# These identifiers (A_23_P100001, etc.) are Agilent microarray probe IDs\n",
"# They are not human gene symbols and will need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "9cfcae17",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "55129674",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:11.472186Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107', '(+)E1A_r60_a135'], 'SPOT_ID': ['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107', '(+)E1A_r60_a135'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'pos', 'pos'], 'REFSEQ': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan], 'GENE': [nan, nan, nan, nan, nan], 'GENE_SYMBOL': [nan, nan, nan, nan, nan], 'GENE_NAME': [nan, nan, nan, nan, nan], 'UNIGENE_ID': [nan, nan, nan, nan, nan], 'ENSEMBL_ID': [nan, nan, nan, nan, nan], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan, nan, nan], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, nan, nan], 'CYTOBAND': [nan, nan, nan, nan, nan], 'DESCRIPTION': [nan, nan, nan, nan, nan], 'GO_ID': [nan, nan, nan, nan, nan], 'SEQUENCE': [nan, nan, nan, nan, nan]}\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": "6b20542c",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "03a16420",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:14.178509Z",
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"shell.execute_reply": "2025-03-25T03:47:15.234550Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking for probe IDs matching the format in gene_data:\n",
"Found matching probe ID at row 11: A_23_P100001\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Sample rows with matching probe IDs:\n",
" ID GENE_SYMBOL\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Preview of mapping dataframe:\n",
" ID Gene\n",
"11 A_23_P100001 FAM174B\n",
"12 A_23_P100022 SV2B\n",
"13 A_23_P100056 RBPMS2\n",
"14 A_23_P100074 AVEN\n",
"15 A_23_P100127 CASC5\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Preview of gene expression data after mapping:\n",
" GSM4523129 GSM4523130 GSM4523131 GSM4523132 GSM4523133 \\\n",
"Gene \n",
"A1BG -4.423492 -3.130753 -3.654191 -3.428902 -3.588846 \n",
"A1BG-AS1 -3.023192 -1.816686 -1.816458 -2.099744 -2.114976 \n",
"A1CF 1.322759 -1.244949 0.749600 2.108298 1.239829 \n",
"A2M -2.857169 -2.293804 -2.676066 -2.405703 -0.954434 \n",
"A2ML1 0.249256 -0.328081 -0.652739 -0.052724 -0.723140 \n",
"\n",
" GSM4523134 GSM4523135 GSM4523136 GSM4523137 GSM4523138 ... \\\n",
"Gene ... \n",
"A1BG -3.866785 -3.539964 -3.925691 -1.980177 -4.693128 ... \n",
"A1BG-AS1 -2.179731 -1.799732 -2.380574 -1.746726 -2.501482 ... \n",
"A1CF 1.464439 2.489900 1.403291 -1.993101 1.871084 ... \n",
"A2M -2.226460 -2.769710 -1.788050 -1.903759 -2.913644 ... \n",
"A2ML1 -0.985901 -0.794128 2.343952 0.553369 -0.219188 ... \n",
"\n",
" GSM4523158 GSM4523159 GSM4523160 GSM4523161 GSM4523162 \\\n",
"Gene \n",
"A1BG -3.618253 -1.541513 -2.763218 -4.417670 -2.412677 \n",
"A1BG-AS1 -2.682453 -0.780151 -1.392280 -2.468921 -1.123779 \n",
"A1CF 0.487051 1.694411 -0.263418 0.849508 1.679830 \n",
"A2M -3.666401 0.043144 -2.926064 -4.051856 -1.817957 \n",
"A2ML1 -0.040744 0.022845 -0.531561 -0.087179 -0.118195 \n",
"\n",
" GSM4523163 GSM4523164 GSM4523165 GSM4523166 GSM4523167 \n",
"Gene \n",
"A1BG -3.035999 -4.153402 -2.865329 -4.097455 -4.675679 \n",
"A1BG-AS1 -1.450171 -2.586337 -2.895995 -2.170560 -2.026514 \n",
"A1CF 0.859313 1.705023 -0.792009 0.456869 1.294504 \n",
"A2M -1.778456 -2.321148 -3.245357 -2.060726 -1.217995 \n",
"A2ML1 0.173105 0.351994 0.240639 -0.603942 -0.479412 \n",
"\n",
"[5 rows x 39 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data saved to ../../output/preprocess/Rectal_Cancer/gene_data/GSE150082.csv\n"
]
}
],
"source": [
"# Looking at the gene identifiers in gene_data (from step 3) like 'A_23_P100001'\n",
"# and the gene annotation preview (from step 5)\n",
"# We need to find the appropriate columns for mapping\n",
"\n",
"# First examine the annotation columns that potentially map to gene identifiers in gene_data\n",
"# Let's print more rows to find example of probe IDs that match our gene_data format\n",
"print(\"Looking for probe IDs matching the format in gene_data:\")\n",
"for i in range(20):\n",
" if i < len(gene_annotation):\n",
" if str(gene_annotation['ID'].iloc[i]).startswith('A_23_P'):\n",
" print(f\"Found matching probe ID at row {i}: {gene_annotation['ID'].iloc[i]}\")\n",
" break\n",
"\n",
"# Get a sample to see what column contains the gene symbols\n",
"sample_rows = gene_annotation[gene_annotation['ID'].str.startswith('A_23_P', na=False)].head(5)\n",
"print(\"\\nSample rows with matching probe IDs:\")\n",
"print(sample_rows[['ID', 'GENE_SYMBOL']])\n",
"\n",
"# Now create the mapping dataframe using the identified columns\n",
"# The 'ID' column in gene_annotation corresponds to probe IDs in gene_data\n",
"# The 'GENE_SYMBOL' column contains the gene symbols\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
"print(\"\\nPreview of mapping dataframe:\")\n",
"print(mapping_df.head())\n",
"\n",
"# Apply the gene mapping to convert probe-level measurements to gene expression\n",
"gene_expression_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Normalize gene symbols to ensure consistency\n",
"gene_data = normalize_gene_symbols_in_index(gene_expression_data)\n",
"\n",
"# Preview the gene expression data\n",
"print(\"\\nPreview of gene expression data after mapping:\")\n",
"print(gene_data.head())\n",
"\n",
"# Save the processed gene data to the 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": "70f167bf",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
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"shell.execute_reply": "2025-03-25T03:47:24.435141Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data saved to ../../output/preprocess/Rectal_Cancer/clinical_data/GSE150082.csv\n",
"Normalized gene data shape: (19447, 39)\n",
"First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Rectal_Cancer/gene_data/GSE150082.csv\n",
"Linked data shape: (39, 19450)\n",
" Rectal_Cancer Age Gender A1BG A1BG-AS1 A1CF \\\n",
"GSM4523129 1.0 70.0 1.0 -4.423492 -3.023192 1.322759 \n",
"GSM4523130 1.0 74.0 1.0 -3.130753 -1.816686 -1.244949 \n",
"GSM4523131 1.0 45.0 0.0 -3.654191 -1.816458 0.749600 \n",
"GSM4523132 1.0 45.0 0.0 -3.428902 -2.099744 2.108298 \n",
"GSM4523133 1.0 54.0 1.0 -3.588846 -2.114976 1.239829 \n",
"\n",
" A2M A2ML1 A4GALT A4GNT ... ZWILCH ZWINT \\\n",
"GSM4523129 -2.857169 0.249256 -0.808312 0.532630 ... -2.439972 -1.899276 \n",
"GSM4523130 -2.293804 -0.328081 -1.429592 0.752957 ... -2.274822 -3.385446 \n",
"GSM4523131 -2.676066 -0.652739 -1.259287 0.354724 ... -2.184009 -1.220591 \n",
"GSM4523132 -2.405703 -0.052724 -0.942970 0.561949 ... -2.805278 -3.204676 \n",
"GSM4523133 -0.954434 -0.723140 -0.490592 0.599406 ... -2.485352 -0.854767 \n",
"\n",
" ZXDA ZXDB ZXDC ZYG11A ZYG11B ZYX \\\n",
"GSM4523129 0.290684 0.651939 -1.696185 -8.779519 -0.494155 0.022449 \n",
"GSM4523130 0.262735 0.856420 1.272290 -6.057712 -3.247352 0.097168 \n",
"GSM4523131 -1.548108 -0.487278 -1.999048 -8.204449 -1.192964 -0.186130 \n",
"GSM4523132 -1.903522 0.318907 -0.729945 -4.747856 -1.067676 0.008756 \n",
"GSM4523133 -0.280135 0.925577 -1.668339 -7.598349 -2.057593 -0.379474 \n",
"\n",
" ZZEF1 ZZZ3 \n",
"GSM4523129 0.371166 -2.276865 \n",
"GSM4523130 -0.253226 -0.469240 \n",
"GSM4523131 0.384354 -0.018876 \n",
"GSM4523132 2.153644 -2.316143 \n",
"GSM4523133 0.628681 -1.538092 \n",
"\n",
"[5 rows x 19450 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape after handling missing values: (39, 19450)\n",
"For the feature 'Rectal_Cancer', the least common label is '1.0' with 16 occurrences. This represents 41.03% of the dataset.\n",
"The distribution of the feature 'Rectal_Cancer' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 51.5\n",
" 50% (Median): 61.0\n",
" 75%: 66.0\n",
"Min: 30.0\n",
"Max: 75.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '0.0' with 10 occurrences. This represents 25.64% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Rectal_Cancer/GSE150082.csv\n"
]
}
],
"source": [
"# 1. Extract clinical features\n",
"clinical_features = geo_select_clinical_features(\n",
" 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",
"# Save the clinical features data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 1. Normalize gene symbols in the gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\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. Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(linked_data.head())\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Determine whether the trait and demographic features are severely biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Conduct quality check and save the 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_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=f\"Dataset contains gene expression data from CD4 T-cells of pSS patients and healthy controls.\"\n",
")\n",
"\n",
"# 6. Save the data if it's usable\n",
"if is_usable:\n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" # Save the data\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")"
]
}
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