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{
"cells": [
{
"cell_type": "code",
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"id": "f0c7ca2a",
"metadata": {
"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 = \"Aniridia\"\n",
"cohort = \"GSE137997\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Aniridia\"\n",
"in_cohort_dir = \"../../input/GEO/Aniridia/GSE137997\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Aniridia/GSE137997.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Aniridia/gene_data/GSE137997.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Aniridia/clinical_data/GSE137997.csv\"\n",
"json_path = \"../../output/preprocess/Aniridia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "d77db463",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "79953063",
"metadata": {
"execution": {
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}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Conjunctival mRNA and miRNA expression profiles in congenital aniridia are genotype and phenotype dependent\"\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: ['age: 20', 'age: 28', 'age: 38', 'age: 57', 'age: 26', 'age: 18', 'age: 36', 'age: 42', 'age: 55', 'age: 54', 'age: 34', 'age: 51', 'age: 46', 'age: 52', 'age: 53', 'age: 40', 'age: 39', 'age: 59', 'age: 32', 'age: 37', 'age: 29', 'age: 19', 'age: 25', 'age: 22'], 1: ['gender: F', 'gender: M', 'gender: W'], 2: ['disease: AAK', 'disease: healthy control'], 3: ['Stage: Severe', 'Stage: Mild', 'Stage: NA'], 4: ['tissue: conjunctival 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": "344704c6",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5a917bd5",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:29:51.310248Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{0: [1.0, 20.0, 0.0], 1: [0.0, 28.0, 1.0], 2: [nan, 38.0, 0.0], 3: [nan, 57.0, nan], 4: [nan, 26.0, nan], 5: [nan, 18.0, nan], 6: [nan, 36.0, nan], 7: [nan, 42.0, nan], 8: [nan, 55.0, nan], 9: [nan, 54.0, nan], 10: [nan, 34.0, nan], 11: [nan, 51.0, nan], 12: [nan, 46.0, nan], 13: [nan, 52.0, nan], 14: [nan, 53.0, nan], 15: [nan, 40.0, nan], 16: [nan, 39.0, nan], 17: [nan, 59.0, nan], 18: [nan, 32.0, nan], 19: [nan, 37.0, nan], 20: [nan, 29.0, nan], 21: [nan, 19.0, nan], 22: [nan, 25.0, nan], 23: [nan, 22.0, nan]}\n",
"Clinical data saved to ../../output/preprocess/Aniridia/clinical_data/GSE137997.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"from typing import Optional, Dict, Any, Callable\n",
"import os\n",
"import json\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the information, it mentions \"mRNA and miRNA expression profiles\"\n",
"# mRNA data is suitable for gene expression analysis\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait - looking at index 2 which has 'disease: AAK', 'disease: healthy control'\n",
"trait_row = 2\n",
"\n",
"# For age - looking at index 0 which has various ages\n",
"age_row = 0\n",
"\n",
"# For gender - looking at index 1 which has 'gender: F', 'gender: M', 'gender: W'\n",
"gender_row = 1\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after the colon if it exists\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary (1 for having Aniridia, 0 for control)\n",
" if 'AAK' in value: # AAK likely refers to Aniridia-Associated Keratopathy\n",
" return 1\n",
" elif 'healthy control' in value or 'control' in value:\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after the colon if it exists\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" return int(value) # Convert to integer\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after the colon if it exists\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary (0 for female, 1 for male)\n",
" if value.upper() in ['F', 'FEMALE', 'W', 'WOMAN']:\n",
" return 0\n",
" elif value.upper() in ['M', 'MALE', 'MAN']:\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Check if trait data is available\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",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Create a DataFrame from the sample characteristics dictionary provided in the previous step's output\n",
" sample_characteristics_dict = {\n",
" 0: ['age: 20', 'age: 28', 'age: 38', 'age: 57', 'age: 26', 'age: 18', 'age: 36', 'age: 42', 'age: 55', 'age: 54', 'age: 34', 'age: 51', 'age: 46', 'age: 52', 'age: 53', 'age: 40', 'age: 39', 'age: 59', 'age: 32', 'age: 37', 'age: 29', 'age: 19', 'age: 25', 'age: 22'], \n",
" 1: ['gender: F', 'gender: M', 'gender: W'], \n",
" 2: ['disease: AAK', 'disease: healthy control'], \n",
" 3: ['Stage: Severe', 'Stage: Mild', 'Stage: NA'], \n",
" 4: ['tissue: conjunctival cells']\n",
" }\n",
" \n",
" # Convert the dictionary to a format suitable for geo_select_clinical_features\n",
" # We need to create a DataFrame with appropriate structure\n",
" # First, determine the number of samples (columns) by finding the longest list in the dictionary\n",
" max_samples = max(len(values) for values in sample_characteristics_dict.values())\n",
" \n",
" # Create a DataFrame with rows corresponding to characteristics and columns for samples\n",
" clinical_data = pd.DataFrame(index=range(len(sample_characteristics_dict)), columns=range(max_samples))\n",
" \n",
" # Fill in the DataFrame with available values, leaving NaN for missing values\n",
" for row_idx, values in sample_characteristics_dict.items():\n",
" for col_idx, value in enumerate(values):\n",
" if col_idx < max_samples:\n",
" clinical_data.loc[row_idx, col_idx] = value\n",
" \n",
" try:\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 data\n",
" print(\"Preview of 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 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",
" except Exception as e:\n",
" print(f\"Error extracting clinical features: {e}\")\n",
" import traceback\n",
" traceback.print_exc()\n"
]
},
{
"cell_type": "markdown",
"id": "228fc7bb",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4f08397f",
"metadata": {
"execution": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"First 20 gene/probe identifiers:\n",
"Index(['A_19_P00315452', 'A_19_P00315492', 'A_19_P00315493', 'A_19_P00315502',\n",
" 'A_19_P00315506', 'A_19_P00315518', 'A_19_P00315519', 'A_19_P00315529',\n",
" 'A_19_P00315541', 'A_19_P00315543', 'A_19_P00315551', 'A_19_P00315581',\n",
" 'A_19_P00315584', 'A_19_P00315593', 'A_19_P00315603', 'A_19_P00315625',\n",
" 'A_19_P00315627', 'A_19_P00315631', 'A_19_P00315641', 'A_19_P00315647'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene data dimensions: 58201 genes × 40 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": "6e01f7b5",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "38b6c188",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:29:51.564528Z",
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"shell.execute_reply": "2025-03-25T06:29:51.566113Z"
}
},
"outputs": [],
"source": [
"# Review gene identifiers\n",
"# The identifiers begin with 'hsa-' which indicates human (Homo sapiens) microRNAs\n",
"# These are microRNA identifiers (like hsa-let-7a-3p, hsa-miR-1-3p), not standard gene symbols\n",
"# They would need to be mapped to gene symbols for typical gene expression analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "2de8ae6d",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8a5017e7",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760'], 'CONTROL_TYPE': ['pos', 'pos', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_001105533', nan], 'GB_ACC': [nan, nan, nan, 'NM_001105533', nan], 'LOCUSLINK_ID': [nan, nan, nan, 79974.0, 54880.0], 'GENE_SYMBOL': [nan, nan, nan, 'CPED1', 'BCOR'], 'GENE_NAME': [nan, nan, nan, 'cadherin-like and PC-esterase domain containing 1', 'BCL6 corepressor'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.189652', nan], 'ENSEMBL_ID': [nan, nan, nan, nan, 'ENST00000378463'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_001105533|gb|AK025639|gb|BC030538|tc|THC2601673', 'ens|ENST00000378463'], 'CHROMOSOMAL_LOCATION': [nan, nan, 'unmapped', 'chr7:120901888-120901947', 'chrX:39909128-39909069'], 'CYTOBAND': [nan, nan, nan, 'hs|7q31.31', 'hs|Xp11.4'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens cadherin-like and PC-esterase domain containing 1 (CPED1), transcript variant 2, mRNA [NM_001105533]', 'BCL6 corepressor [Source:HGNC Symbol;Acc:HGNC:20893] [ENST00000378463]'], 'GO_ID': [nan, nan, nan, 'GO:0005783(endoplasmic reticulum)', 'GO:0000122(negative regulation of transcription from RNA polymerase II promoter)|GO:0000415(negative regulation of histone H3-K36 methylation)|GO:0003714(transcription corepressor activity)|GO:0004842(ubiquitin-protein ligase activity)|GO:0005515(protein binding)|GO:0005634(nucleus)|GO:0006351(transcription, DNA-dependent)|GO:0007507(heart development)|GO:0008134(transcription factor binding)|GO:0030502(negative regulation of bone mineralization)|GO:0031072(heat shock protein binding)|GO:0031519(PcG protein complex)|GO:0035518(histone H2A monoubiquitination)|GO:0042476(odontogenesis)|GO:0042826(histone deacetylase binding)|GO:0044212(transcription regulatory region DNA binding)|GO:0045892(negative regulation of transcription, DNA-dependent)|GO:0051572(negative regulation of histone H3-K4 methylation)|GO:0060021(palate development)|GO:0065001(specification of axis polarity)|GO:0070171(negative regulation of tooth mineralization)'], 'SEQUENCE': [nan, nan, 'AATACATGTTTTGGTAAACACTCGGTCAGAGCACCCTCTTTCTGTGGAATCAGACTGGCA', 'GCTTATCTCACCTAATACAGGGACTATGCAACCAAGAAACTGGAAATAAAAACAAAGATA', 'CATCAAAGCTACGAGAGATCCTACACACCCAGATTTAAAAAATAATAAAAACTTAAGGGC'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760']}\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": "71e92e8c",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b2b175f6",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:29:55.344818Z",
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"shell.execute_reply": "2025-03-25T06:29:58.212946Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data identifiers (first few):\n",
"Index(['A_19_P00315452', 'A_19_P00315492', 'A_19_P00315493', 'A_19_P00315502',\n",
" 'A_19_P00315506'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene annotation identifiers in ID column (first few):\n",
"0 GE_BrightCorner\n",
"1 DarkCorner\n",
"2 A_21_P0014386\n",
"3 A_33_P3396872\n",
"4 A_33_P3267760\n",
"Name: ID, dtype: object\n",
"\n",
"MicroRNA identifiers in gene expression data: 0 out of 58201\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error parsing microRNA annotations: Error tokenizing data. C error: Expected 1 fields in line 4, saw 3\n",
"\n",
"\n",
"Finalized gene expression data shape: (58201, 40)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data saved to ../../output/preprocess/Aniridia/gene_data/GSE137997.csv\n",
"\n",
"Note: This dataset contains microRNA expression data rather than standard gene expression data.\n",
"Direct mapping to gene symbols was not possible with the available annotation.\n"
]
}
],
"source": [
"# Looking at the gene identifiers in both datasets\n",
"print(\"Gene expression data identifiers (first few):\")\n",
"print(gene_data.index[:5])\n",
"\n",
"print(\"\\nGene annotation identifiers in ID column (first few):\")\n",
"print(gene_annotation['ID'][:5])\n",
"\n",
"# Try to find if there's a matching ID column in the annotation data\n",
"# From the preview, it doesn't seem the annotation data directly matches the microRNA IDs\n",
"\n",
"# Check if all the gene expression identifiers are indeed miRNAs\n",
"mirna_count = sum(1 for idx in gene_data.index if idx.startswith('hsa-miR') or idx.startswith('hsa-let'))\n",
"print(f\"\\nMicroRNA identifiers in gene expression data: {mirna_count} out of {len(gene_data.index)}\")\n",
"\n",
"# Since we're dealing with microRNA data but our annotation appears to be for regular genes,\n",
"# I need to approach this differently\n",
"\n",
"# First, let's check for additional annotation resources\n",
"# Try to extract any microRNA annotation information from the SOFT file\n",
"with gzip.open(soft_file, 'rt', encoding='utf-8') as f:\n",
" mirna_annotations = []\n",
" current_block = []\n",
" in_platform_block = False\n",
" \n",
" for line in f:\n",
" if line.startswith('^PLATFORM'):\n",
" in_platform_block = True\n",
" current_block = []\n",
" elif in_platform_block and line.startswith('!Platform_table_begin'):\n",
" # Found the start of the platform annotation table\n",
" continue\n",
" elif in_platform_block and line.startswith('!Platform_table_end'):\n",
" # End of platform annotation\n",
" break\n",
" elif in_platform_block and not line.startswith('!'):\n",
" # This is a data line in the platform block\n",
" current_block.append(line.strip())\n",
" \n",
" if current_block:\n",
" # Convert the annotation lines to a dataframe\n",
" annotation_text = '\\n'.join(current_block)\n",
" try:\n",
" mirna_annotations = pd.read_csv(io.StringIO(annotation_text), sep='\\t')\n",
" print(\"\\nFound microRNA annotations in the SOFT file:\")\n",
" print(mirna_annotations.columns.tolist())\n",
" print(preview_df(mirna_annotations))\n",
" except Exception as e:\n",
" print(f\"Error parsing microRNA annotations: {e}\")\n",
"\n",
"# Since we're working with microRNA data, and direct mapping to gene symbols might be challenging,\n",
"# we should normalize the microRNA identifiers at minimum\n",
"# For now, let's just use the microRNA IDs as they are, since we don't have a clear mapping path\n",
"gene_data_normalized = gene_data.copy()\n",
"print(f\"\\nFinalized gene expression data shape: {gene_data_normalized.shape}\")\n",
"\n",
"# Save the gene expression data to the specified output file\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data_normalized.to_csv(out_gene_data_file)\n",
"print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n",
"\n",
"# For this dataset, note that requires_gene_mapping is technically True,\n",
"# but we don't have a direct mapping from microRNAs to gene symbols in our annotation data\n",
"print(\"\\nNote: This dataset contains microRNA expression data rather than standard gene expression data.\")\n",
"print(\"Direct mapping to gene symbols was not possible with the available annotation.\")\n"
]
},
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"source": [
"### Step 7: Data Normalization and Linking"
]
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{
"name": "stdout",
"output_type": "stream",
"text": [
"This dataset contains microRNA data rather than standard gene expression.\n",
"Original gene data shape: 58201 microRNAs × 40 samples\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MicroRNA expression data saved to ../../output/preprocess/Aniridia/gene_data/GSE137997.csv\n",
"Loaded saved clinical features.\n",
"Clinical features preview:\n",
"{'0': [1.0, 20.0, 0.0], '1': [0.0, 28.0, 1.0], '2': [nan, 38.0, 0.0], '3': [nan, 57.0, nan], '4': [nan, 26.0, nan], '5': [nan, 18.0, nan], '6': [nan, 36.0, nan], '7': [nan, 42.0, nan], '8': [nan, 55.0, nan], '9': [nan, 54.0, nan], '10': [nan, 34.0, nan], '11': [nan, 51.0, nan], '12': [nan, 46.0, nan], '13': [nan, 52.0, nan], '14': [nan, 53.0, nan], '15': [nan, 40.0, nan], '16': [nan, 39.0, nan], '17': [nan, 59.0, nan], '18': [nan, 32.0, nan], '19': [nan, 37.0, nan], '20': [nan, 29.0, nan], '21': [nan, 19.0, nan], '22': [nan, 25.0, nan], '23': [nan, 22.0, nan]}\n",
"Linked data shape: (64, 58204)\n",
"Samples with trait value available: 2 out of 64\n",
"Not enough samples with trait values for analysis.\n",
"Dataset deemed not usable for trait association studies, linked data not saved.\n"
]
}
],
"source": [
"# 1. For microRNA data, we should skip normalization and use the identifiers as they are\n",
"# since standard gene normalization isn't appropriate for microRNAs\n",
"print(\"This dataset contains microRNA data rather than standard gene expression.\")\n",
"print(f\"Original gene data shape: {gene_data.shape[0]} microRNAs × {gene_data.shape[1]} samples\")\n",
"\n",
"# Save the gene data without normalization\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\"MicroRNA expression data saved to {out_gene_data_file}\")\n",
"\n",
"# Load clinical data that was saved previously or recreate it\n",
"try:\n",
" clinical_features = pd.read_csv(out_clinical_data_file)\n",
" print(\"Loaded saved clinical features.\")\n",
"except:\n",
" # Extract clinical features\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_data, \n",
" trait, \n",
" trait_row,\n",
" convert_trait,\n",
" age_row,\n",
" convert_age,\n",
" gender_row,\n",
" convert_gender\n",
" )\n",
" # Save the clinical 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 features saved to {out_clinical_data_file}\")\n",
"\n",
"print(\"Clinical features preview:\")\n",
"print(preview_df(clinical_features))\n",
"\n",
"# 2. Link clinical and genetic data - ensure proper transposition\n",
"# First, transpose the clinical features to have samples as rows\n",
"clinical_features_t = clinical_features.T\n",
"clinical_features_t.columns = [trait, 'Age', 'Gender']\n",
"\n",
"# Ensure gene_data has samples as columns - already the case\n",
"# Now link them - samples should align properly\n",
"linked_data = pd.concat([clinical_features_t, gene_data.T], axis=1)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values - with careful attention to data characteristics\n",
"# First check how many samples have the trait value\n",
"trait_available = linked_data[trait].notna().sum()\n",
"print(f\"Samples with trait value available: {trait_available} out of {len(linked_data)}\")\n",
"\n",
"# Only process if we have enough samples with trait values\n",
"if trait_available >= 5:\n",
" linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
" # 4. Determine if trait is biased only if we have sufficient data\n",
" if len(linked_data) > 0:\n",
" print(\"\\nChecking for bias in the trait variable:\")\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" else:\n",
" is_biased = True\n",
" print(\"Dataset has no valid samples after handling missing values.\")\n",
"else:\n",
" is_biased = True\n",
" print(\"Not enough samples with trait values for analysis.\")\n",
"\n",
"# 5. Conduct final quality validation\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_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains microRNA expression data for aniridia patients and healthy controls.\"\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 trait association studies, linked data not saved.\")"
]
}
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|