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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "ba8805c3",
"metadata": {},
"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 = \"Celiac_Disease\"\n",
"cohort = \"GSE113469\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Celiac_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Celiac_Disease/GSE113469\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Celiac_Disease/GSE113469.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Celiac_Disease/gene_data/GSE113469.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Celiac_Disease/clinical_data/GSE113469.csv\"\n",
"json_path = \"../../output/preprocess/Celiac_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "81232365",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2f1be4a",
"metadata": {},
"outputs": [],
"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": "e5cdc099",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3ee538e",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the series title and overall design, this dataset contains PBMC gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait: we can see 'disease state' in key 0 with values 'Healthy Control' and 'Celiac Disease'\n",
"trait_row = 0\n",
"\n",
"# For age: we can see 'age' in key 1 with multiple values\n",
"age_row = 1 \n",
"\n",
"# For gender: not available in the sample characteristics dictionary\n",
"gender_row = None \n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait data to binary (0: control, 1: celiac disease)\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after colon if it exists\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"healthy control\" in value.lower():\n",
" return 0\n",
" elif \"celiac disease\" in value.lower():\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age data to continuous numeric values\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after colon if it exists\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender data to binary (0: female, 1: male)\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after colon if it exists\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" value = value.lower()\n",
" if \"female\" in value or \"f\" == value:\n",
" return 0\n",
" elif \"male\" in value or \"m\" == value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\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",
" # Sample characteristics from the previous step\n",
" sample_chars_dict = {\n",
" 0: ['disease state: Healthy Control', 'disease state: Celiac Disease'],\n",
" 1: ['age: 30', 'age: 27', 'age: 31', 'age: 26', 'age: 28', 'age: 32', 'age: 41', 'age: 34', \n",
" 'age: 25', 'age: 42', 'age: 29', 'age: 21', 'age: 44', 'age: 56', 'age: 50', 'age: 51', 'age: 37'],\n",
" 2: ['cell type: PBMCs']\n",
" }\n",
" \n",
" # Need to reconstruct clinical_data in the expected format for geo_select_clinical_features\n",
" # Each row is a feature and columns are samples\n",
" # First, let's determine the maximum sample size\n",
" max_samples = max(len(values) for values in sample_chars_dict.values())\n",
" \n",
" # Create a properly structured DataFrame\n",
" clinical_data = pd.DataFrame(index=range(len(sample_chars_dict)), columns=range(max_samples))\n",
" \n",
" # Fill the DataFrame with available data\n",
" for row_idx, values in sample_chars_dict.items():\n",
" for col_idx, value in enumerate(values):\n",
" clinical_data.loc[row_idx, col_idx] = value\n",
" \n",
" # Now call geo_select_clinical_features with this properly structured data\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 dataframe\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\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": "ae34ddd9",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10985a2a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "bd4c3ce0",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36a14da9",
"metadata": {},
"outputs": [],
"source": [
"# The identifiers starting with \"ILMN_\" are Illumina probe IDs, not human gene symbols\n",
"# These are microarray probe identifiers from Illumina BeadArray platform\n",
"# They need to be mapped to standard gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "60f3727e",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "497b0e2b",
"metadata": {},
"outputs": [],
"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": "7640a8e4",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a525af11",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the columns for probe IDs and gene symbols in the gene annotation\n",
"# Based on the preview, 'ID' contains the probe identifiers (same format as gene expression data index: ILMN_XXXXXXX)\n",
"# 'Symbol' contains the gene symbols\n",
"probe_id_col = 'ID'\n",
"gene_symbol_col = 'Symbol'\n",
"\n",
"# 2. Create the gene mapping dataframe using the identified columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"First few rows of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First few genes after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Save the gene expression data\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\"Gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "e4d83a2d",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac04c5a1",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"print(\"First 10 normalized gene symbols:\")\n",
"print(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",
"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_path = out_clinical_data_file\n",
"if os.path.exists(clinical_data_path):\n",
" clinical_data = pd.read_csv(clinical_data_path)\n",
" print(f\"Clinical data loaded from {clinical_data_path}, shape: {clinical_data.shape}\")\n",
" \n",
" # Load the original clinical data from the matrix file to ensure we have proper structure\n",
" # Since we need to match the sample IDs between gene data and clinical data\n",
" _, original_clinical_data = get_background_and_clinical_data(matrix_file)\n",
" \n",
" # Transpose gene data so samples are in rows\n",
" gene_data_t = gene_data.transpose()\n",
" print(f\"Transposed gene data shape: {gene_data_t.shape}\")\n",
" \n",
" # Create a linked dataframe\n",
" linked_data = pd.DataFrame()\n",
" \n",
" # Add trait data (first column of clinical_data)\n",
" if clinical_data.shape[1] >= 1:\n",
" # The clinical data might need to be transposed to match gene data sample ordering\n",
" if len(clinical_data) == 2 and clinical_data.shape[1] == 17: # Based on preview in Step 2\n",
" # Transpose clinical data so samples are rows\n",
" clinical_data_t = clinical_data.transpose()\n",
" \n",
" # Rename columns appropriately\n",
" if clinical_data_t.shape[1] == 2:\n",
" clinical_data_t.columns = [trait, 'Age']\n",
" \n",
" # Create DataFrame with clinical data columns first\n",
" linked_data = clinical_data_t.copy()\n",
" \n",
" # Add gene expression data\n",
" for gene in gene_data.index:\n",
" if gene in linked_data.columns:\n",
" # Avoid duplicate column names\n",
" continue\n",
" linked_data[gene] = gene_data_t[gene].values\n",
" \n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" \n",
" # 3. Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"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",
" \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=\"This dataset contains gene expression data from celiac disease patients on gluten-free diet versus 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 based on quality validation, linked data not saved.\")\n",
" else:\n",
" print(\"Clinical data structure doesn't match expected format (2 columns).\")\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,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Clinical data structure doesn't match expected format.\"\n",
" )\n",
" else:\n",
" print(\"Clinical data structure doesn't match expected format.\")\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,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Clinical data structure doesn't match expected format.\"\n",
" )\n",
" else:\n",
" print(\"Clinical data is empty or missing trait column, cannot create linked data.\")\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,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Clinical data loaded but appears to be empty or missing trait information.\"\n",
" )\n",
"else:\n",
" print(f\"Clinical data file {clinical_data_path} not found, cannot create linked data.\")\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,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Clinical data file not found.\"\n",
" )\n",
"\n",
"print(f\"Dataset usability status: {'Usable' if is_usable else 'Not usable'}\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|