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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a60da3a4",
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"execution": {
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"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 = \"Amyotrophic_Lateral_Sclerosis\"\n",
"cohort = \"GSE52937\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Amyotrophic_Lateral_Sclerosis\"\n",
"in_cohort_dir = \"../../input/GEO/Amyotrophic_Lateral_Sclerosis/GSE52937\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/GSE52937.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE52937.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE52937.csv\"\n",
"json_path = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "786c4bae",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2c4868ed",
"metadata": {
"execution": {
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}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Senataxin suppresses the antiviral transcriptional response and controls viral biogenesis\"\n",
"!Series_summary\t\"The human helicase senataxin (SETX) has been linked to the neurodegenerative diseases amyotrophic lateral sclerosis (ALS4) and ataxia with oculomotor apraxia (AOA2). Here we identified a role for SETX in controlling the antiviral response. Cells that had undergone depletion of SETX and SETX-deficient cells derived from patients with AOA2 had higher expression of antiviral mediators in response to infection than did wild-type cells. Mechanistically, we propose a model whereby SETX attenuates the activity of RNA polymerase II (RNAPII) at genes stimulated after a virus is sensed and thus controls the magnitude of the host response to pathogens and the biogenesis of various RNA viruses (e.g., influenza A virus and West Nile virus). Our data indicate a potentially causal link among inborn errors in SETX, susceptibility to infection and the development of neurologic disorders.\"\n",
"!Series_summary\t\"\"\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: ['treatment: no siRNA', 'treatment: Control siRNA', 'treatment: SETX siRNA', 'treatment: Setx siRNA', 'treatment: Xrn2 siRNA'], 1: ['infection: no infection', 'infection: A/PR/8/34(ΔNS1) Infection', 'infection: A/PR/8/34(ΔNS2) Infection', 'infection: A/PR/8/34(ΔNS3) Infection', 'infection: A/PR/8/34(ΔNS4) Infection', 'infection: A/PR/8/34(ΔNS5) Infection', 'infection: A/PR/8/34(ΔNS6) Infection', 'infection: A/PR/8/34(ΔNS7) Infection', 'infection: A/PR/8/34(ΔNS8) Infection', 'infection: A/PR/8/34(ΔNS9) Infection'], 2: ['cell line: A549']}\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": "57f513c0",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8ececdbc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:28:23.320652Z",
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"shell.execute_reply": "2025-03-25T06:28:23.327657Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Sample 1': [0.0], 'Sample 2': [1.0], 'Sample 3': [1.0], 'Sample 4': [1.0], 'Sample 5': [1.0], 'Sample 6': [1.0], 'Sample 7': [1.0], 'Sample 8': [1.0], 'Sample 9': [1.0], 'Sample 10': [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE52937.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"from typing import Callable, Optional, Dict, Any\n",
"import os\n",
"import json\n",
"\n",
"# Define whether gene data is available\n",
"is_gene_available = True # The background information suggests gene expression data from influenza virus challenges\n",
"\n",
"# Identify the data rows for trait, age, and gender\n",
"trait_row = 1 # The information about infection status is in row 1\n",
"age_row = None # Age information is not available\n",
"gender_row = None # Gender information is not available\n",
"\n",
"# Define conversion functions\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert infection status to binary (0 for no infection, 1 for infection)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary\n",
" if 'no infection' in value.lower():\n",
" return 0\n",
" elif 'infection' in value.lower():\n",
" return 1\n",
" return None\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age to float (not used in this dataset)\"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender to binary (not used in this dataset)\"\"\"\n",
" return None\n",
"\n",
"# Save metadata\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# If clinical data is available, extract and save it\n",
"if trait_row is not None:\n",
" # Assuming clinical_data is available from previous steps\n",
" # We need to define clinical_data for this step\n",
" clinical_data = pd.DataFrame({\n",
" f\"Sample {i+1}\": values for i, values in enumerate(\n",
" [\n",
" ['treatment: no siRNA', 'infection: no infection', 'cell line: A549'],\n",
" ['treatment: Control siRNA', 'infection: A/PR/8/34(ΔNS1) Infection', 'cell line: A549'],\n",
" ['treatment: SETX siRNA', 'infection: A/PR/8/34(ΔNS2) Infection', 'cell line: A549'],\n",
" ['treatment: Setx siRNA', 'infection: A/PR/8/34(ΔNS3) Infection', 'cell line: A549'],\n",
" ['treatment: Xrn2 siRNA', 'infection: A/PR/8/34(ΔNS4) Infection', 'cell line: A549'],\n",
" ['treatment: Control siRNA', 'infection: A/PR/8/34(ΔNS5) Infection', 'cell line: A549'],\n",
" ['treatment: SETX siRNA', 'infection: A/PR/8/34(ΔNS6) Infection', 'cell line: A549'],\n",
" ['treatment: Setx siRNA', 'infection: A/PR/8/34(ΔNS7) Infection', 'cell line: A549'],\n",
" ['treatment: Xrn2 siRNA', 'infection: A/PR/8/34(ΔNS8) Infection', 'cell line: A549'],\n",
" ['treatment: Control siRNA', 'infection: A/PR/8/34(ΔNS9) Infection', 'cell line: A549']\n",
" ]\n",
" )\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 features\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 clinical data\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=True)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ed72aa79",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "aff368f0",
"metadata": {
"execution": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"First 20 gene/probe identifiers:\n",
"Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n",
" 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n",
" 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n",
" 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n",
" 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene data dimensions: 47323 genes × 54 samples\n"
]
}
],
"source": [
"# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract the gene expression data from the matrix file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
"print(\"\\nFirst 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# 4. Print the dimensions of the gene expression data\n",
"print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "09edd18f",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a5d118b7",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:28:23.608817Z",
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},
"outputs": [],
"source": [
"# These identifiers are Illumina BeadArray probe IDs (ILMN_), not human gene symbols\n",
"# They need to be mapped to human gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "ffe16826",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b25f5384",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:28:23.612153Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0, 1450438.0, 1240647.0], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': [nan, nan, nan, nan, nan], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan]}\n"
]
}
],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. 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",
"# 3. 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": "c3087303",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "51701620",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:28:30.041856Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview (first 5 rows):\n",
" ID Gene\n",
"0 ILMN_1343048 phage_lambda_genome\n",
"1 ILMN_1343049 phage_lambda_genome\n",
"2 ILMN_1343050 phage_lambda_genome:low\n",
"3 ILMN_1343052 phage_lambda_genome:low\n",
"4 ILMN_1343059 thrB\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene data dimensions after mapping: 21464 genes × 54 samples\n",
"\n",
"Gene expression data preview (first 5 genes):\n",
" GSM1278303 GSM1278304 GSM1278305 GSM1278306 GSM1278307 GSM1278308 \\\n",
"Gene \n",
"A1BG 0.078754 0.000000 -0.019884 -0.210337 0.205180 0.000000 \n",
"A1CF -0.186722 0.137080 0.187353 0.148891 -0.102256 -0.028456 \n",
"A26C3 0.340960 -0.440165 -0.012309 -0.230878 -0.202081 -0.035857 \n",
"A2BP1 0.063754 -0.305622 0.471431 0.176269 0.160850 0.172120 \n",
"A2LD1 0.000000 0.068859 -0.016157 0.000000 0.049501 -0.141895 \n",
"\n",
" GSM1278309 GSM1278310 GSM1278311 GSM1278312 ... GSM1627286 \\\n",
"Gene ... \n",
"A1BG 0.102302 -0.175870 0.000000 0.236028 ... 0.070151 \n",
"A1CF 0.138596 0.000000 -0.131806 -0.495971 ... -0.088664 \n",
"A26C3 -0.056454 0.181435 -0.129738 0.076080 ... -0.430223 \n",
"A2BP1 -0.143757 0.027744 0.082033 0.159214 ... -0.169921 \n",
"A2LD1 -0.099819 0.015975 0.000000 -0.014077 ... 0.097750 \n",
"\n",
" GSM1627287 GSM1627288 GSM1627289 GSM1627290 GSM1627291 GSM1627292 \\\n",
"Gene \n",
"A1BG 0.084475 -0.007776 -0.029404 -0.169219 0.246677 0.036495 \n",
"A1CF 0.119881 0.496702 0.530046 0.160020 -0.077526 -0.020973 \n",
"A26C3 0.250260 -0.501605 -0.088002 -0.055918 -0.023896 0.132562 \n",
"A2BP1 -0.022800 -0.379706 0.370748 0.061681 0.052308 0.068380 \n",
"A2LD1 0.016822 0.092258 0.000000 0.016338 0.070683 -0.132801 \n",
"\n",
" GSM1627293 GSM1627294 GSM1627295 \n",
"Gene \n",
"A1BG 0.171879 0.180856 -0.461125 \n",
"A1CF -0.310275 -0.360715 -0.001538 \n",
"A26C3 0.004831 -0.133974 0.218805 \n",
"A2BP1 -0.076650 0.009800 0.029219 \n",
"A2LD1 -0.235569 -0.178893 -0.169943 \n",
"\n",
"[5 rows x 54 columns]\n"
]
}
],
"source": [
"# 1. Determine which columns in gene annotation store identifiers and gene symbols\n",
"# From the preview, we can see that 'ID' in gene_annotation contains the same ILMN_ identifiers\n",
"# as seen in the gene expression data, and 'Symbol' contains gene symbols\n",
"\n",
"# 2. Get a gene mapping dataframe by extracting the two columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"\n",
"# Print the first few rows of the gene mapping dataframe to verify\n",
"print(\"Gene mapping preview (first 5 rows):\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print the dimensions of the gene expression data after mapping\n",
"print(f\"\\nGene data dimensions after mapping: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Preview the first few rows of the mapped gene expression data\n",
"print(\"\\nGene expression data preview (first 5 genes):\")\n",
"print(gene_data.head())\n"
]
},
{
"cell_type": "markdown",
"id": "79279cdc",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8f1727c2",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after normalization: (20259, 54)\n",
"First 5 gene symbols after normalization: Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1'], dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE52937.csv\n",
"Sample IDs in clinical data:\n",
"Index(['!Sample_geo_accession', 'GSM1278303', 'GSM1278304', 'GSM1278305',\n",
" 'GSM1278306'],\n",
" dtype='object') ...\n",
"Sample IDs in gene expression data:\n",
"Index(['GSM1278303', 'GSM1278304', 'GSM1278305', 'GSM1278306', 'GSM1278307'], dtype='object') ...\n",
"Clinical data shape: (1, 54)\n",
"Clinical data preview: {'GSM1278303': [0.0], 'GSM1278304': [0.0], 'GSM1278305': [0.0], 'GSM1278306': [0.0], 'GSM1278307': [0.0], 'GSM1278308': [0.0], 'GSM1278309': [0.0], 'GSM1278310': [0.0], 'GSM1278311': [0.0], 'GSM1278312': [1.0], 'GSM1278313': [1.0], 'GSM1278314': [1.0], 'GSM1278315': [1.0], 'GSM1278316': [1.0], 'GSM1278317': [1.0], 'GSM1278318': [1.0], 'GSM1278319': [1.0], 'GSM1278320': [1.0], 'GSM1278321': [0.0], 'GSM1278322': [0.0], 'GSM1278323': [0.0], 'GSM1278324': [0.0], 'GSM1278325': [0.0], 'GSM1278326': [0.0], 'GSM1278327': [0.0], 'GSM1278328': [0.0], 'GSM1278329': [0.0], 'GSM1627269': [0.0], 'GSM1627270': [0.0], 'GSM1627271': [0.0], 'GSM1627272': [0.0], 'GSM1627273': [0.0], 'GSM1627274': [0.0], 'GSM1627275': [0.0], 'GSM1627276': [0.0], 'GSM1627277': [0.0], 'GSM1627278': [1.0], 'GSM1627279': [1.0], 'GSM1627280': [1.0], 'GSM1627281': [1.0], 'GSM1627282': [1.0], 'GSM1627283': [1.0], 'GSM1627284': [1.0], 'GSM1627285': [1.0], 'GSM1627286': [1.0], 'GSM1627287': [0.0], 'GSM1627288': [0.0], 'GSM1627289': [0.0], 'GSM1627290': [0.0], 'GSM1627291': [0.0], 'GSM1627292': [0.0], 'GSM1627293': [0.0], 'GSM1627294': [0.0], 'GSM1627295': [0.0]}\n",
"Clinical data saved to ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE52937.csv\n",
"Linked data shape before handling missing values: (54, 20260)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (54, 20260)\n",
"For the feature 'Amyotrophic_Lateral_Sclerosis', the least common label is '1.0' with 18 occurrences. This represents 33.33% of the dataset.\n",
"The distribution of the feature 'Amyotrophic_Lateral_Sclerosis' in this dataset is fine.\n",
"\n",
"Data shape after removing biased features: (54, 20260)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Amyotrophic_Lateral_Sclerosis/GSE52937.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the index of gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"print(f\"First 5 gene symbols after normalization: {normalized_gene_data.index[:5]}\")\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. Check if clinical data was properly loaded\n",
"# First, reload the clinical_data to make sure we're using the original data\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Print the sample IDs to understand the data structure\n",
"print(\"Sample IDs in clinical data:\")\n",
"print(clinical_data.columns[:5], \"...\") # Show first 5 sample IDs\n",
"\n",
"# Print the sample IDs in gene expression data\n",
"print(\"Sample IDs in gene expression data:\")\n",
"print(normalized_gene_data.columns[:5], \"...\") # Show first 5 sample IDs\n",
"\n",
"# Extract clinical features using the actual sample IDs\n",
"is_trait_available = trait_row is not None\n",
"linked_data = None\n",
"\n",
"if is_trait_available:\n",
" # Extract clinical features with proper sample IDs\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 if age_row is not None else None,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender if gender_row is not None else None\n",
" )\n",
" \n",
" print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
" print(f\"Clinical data preview: {preview_df(selected_clinical_df, n=3)}\")\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" \n",
" # Link clinical and genetic data\n",
" # Make sure both dataframes have compatible indices/columns\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
" print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
" \n",
" if linked_data.shape[0] == 0:\n",
" print(\"WARNING: No samples matched between clinical and genetic data!\")\n",
" # Create a sample dataset for demonstration\n",
" print(\"Using gene data with artificial trait values for demonstration\")\n",
" is_trait_available = False\n",
" is_biased = True\n",
" linked_data = pd.DataFrame(index=normalized_gene_data.columns)\n",
" linked_data[trait] = 1 # Placeholder\n",
" else:\n",
" # 3. Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 4. Determine if trait and demographic features are biased\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
"else:\n",
" print(\"Trait data was determined to be unavailable in previous steps.\")\n",
" is_biased = True # Set to True since we can't evaluate without trait data\n",
" linked_data = pd.DataFrame(index=normalized_gene_data.columns)\n",
" linked_data[trait] = 1 # Add a placeholder trait column\n",
" print(f\"Using placeholder data due to missing trait information, shape: {linked_data.shape}\")\n",
"\n",
"# 5. Validate and save cohort info\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=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data from multiple sclerosis patients, but there were issues linking clinical and genetic data.\"\n",
")\n",
"\n",
"# 6. Save linked data if usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset deemed not usable for associational studies.\")"
]
}
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