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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "828e19e1",
"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 = \"Cervical_Cancer\"\n",
"cohort = \"GSE107754\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Cervical_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Cervical_Cancer/GSE107754\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Cervical_Cancer/GSE107754.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Cervical_Cancer/gene_data/GSE107754.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Cervical_Cancer/clinical_data/GSE107754.csv\"\n",
"json_path = \"../../output/preprocess/Cervical_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "fbe7c637",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eebc18b2",
"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": "4715e3e5",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85d5a7ea",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset appears to contain gene expression data\n",
"# The Series_title and Series_summary mention \"whole human genome gene expression microarrays\"\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# Examining the Sample Characteristics Dictionary:\n",
"\n",
"# For Trait (Cervical Cancer):\n",
"# From key 2, we can see 'tissue: Cervical cancer' and 'tissue: Cervix cancer'\n",
"trait_row = 2\n",
"\n",
"# For Age:\n",
"# There's no age information in the sample characteristics dictionary\n",
"age_row = None\n",
"\n",
"# For Gender:\n",
"# From key 0, we can see 'gender: Male' and 'gender: Female'\n",
"gender_row = 0\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait (cancer type) to binary (1 for cervical cancer, 0 for other cancers)\"\"\"\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().lower()\n",
" \n",
" # Check for cervical cancer\n",
" if 'cervix cancer' in value or 'cervical cancer' in value:\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"# No age data, but define the function for consistency\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous value\"\"\"\n",
" if value is None:\n",
" return None\n",
" \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 to binary (0 for female, 1 for male)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" if 'female' in value:\n",
" return 0\n",
" elif 'male' in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. 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",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Load clinical data\n",
" try:\n",
" # Assuming clinical_data has been defined in a previous step\n",
" clinical_features = 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 clinical features\n",
" preview = preview_df(clinical_features)\n",
" print(\"Clinical Features Preview:\")\n",
" print(preview)\n",
" \n",
" # Save clinical features to CSV\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
" except NameError:\n",
" print(\"Error: clinical_data not defined. Make sure to load clinical data in a previous step.\")\n"
]
},
{
"cell_type": "markdown",
"id": "dcd8fff0",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c88922ed",
"metadata": {},
"outputs": [],
"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": "38a8fe40",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1cbb25cc",
"metadata": {},
"outputs": [],
"source": [
"# These identifiers are from Agilent microarray probes (the A_23_P format is characteristic of Agilent)\n",
"# They are not standard human gene symbols, but probe IDs that need to be mapped to gene symbols\n",
"# Based on my biomedical knowledge, these are microarray probe identifiers, not gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "fe470d5d",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1acf226f",
"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": "cf71cab1",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8f3a8a7",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the appropriate columns for gene identifier and gene symbol\n",
"# Based on the preview of the gene annotation data, 'ID' matches the gene expression data identifiers\n",
"# and 'GENE_SYMBOL' contains the gene symbols we need\n",
"\n",
"# 2. Get the gene mapping dataframe using the function from the library\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
"\n",
"# Print a preview of the mapping to verify\n",
"print(\"Gene mapping preview (first 5 rows):\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"# This function handles the case where one probe maps to multiple genes\n",
"# For these cases, it splits the expression values equally among mapped genes\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print the shape of the resulting gene expression matrix\n",
"print(f\"\\nGene expression matrix shape after mapping: {gene_data.shape}\")\n",
"# Preview the first few genes\n",
"print(\"\\nFirst 5 gene symbols after mapping:\")\n",
"print(gene_data.index[:5])\n"
]
},
{
"cell_type": "markdown",
"id": "2c040eaf",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e87569e2",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
"normalized_gene_data = normalize_gene_symbols_in_index(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",
"# Create clinical features directly from clinical_data using the conversion functions defined earlier\n",
"clinical_features_df = 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 data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Now link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
"print(\"Linked data shape:\", linked_data.shape)\n",
"\n",
"# Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"\n",
"# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\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=\"This is an HPV-transformed keratinocyte cell line study focusing on transformation stages: 1 for anchorage independent (more advanced cancer stage), 0 for earlier stages.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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