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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "c9c24e17",
"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 = \"Psoriatic_Arthritis\"\n",
"cohort = \"GSE142049\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Psoriatic_Arthritis\"\n",
"in_cohort_dir = \"../../input/GEO/Psoriatic_Arthritis/GSE142049\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Psoriatic_Arthritis/GSE142049.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Psoriatic_Arthritis/gene_data/GSE142049.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Psoriatic_Arthritis/clinical_data/GSE142049.csv\"\n",
"json_path = \"../../output/preprocess/Psoriatic_Arthritis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "1783c18b",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca0a1099",
"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": "ac8d5a01",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "47517969",
"metadata": {},
"outputs": [],
"source": [
"# Analyze the dataset to determine availability of gene expression and clinical data\n",
"\n",
"# 1. Gene Expression Data\n",
"# Based on the background information, this dataset contains transcriptional data (RNA)\n",
"# from CD19+ B cells, which indicates it contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Identify rows in the sample characteristics dictionary for trait, age, and gender\n",
"\n",
"# For the trait (Psoriatic Arthritis), we can find it in the working_diagnosis field (key 6)\n",
"trait_row = 6\n",
"\n",
"# Age information is available in key 2\n",
"age_row = 2\n",
"\n",
"# Gender information is available in key 1\n",
"gender_row = 1\n",
"\n",
"# 2.2 Create conversion functions for each variable\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert the working_diagnosis value to a binary indicator for Psoriatic Arthritis.\n",
" 1 if the patient has Psoriatic Arthritis, 0 otherwise.\n",
" \"\"\"\n",
" if isinstance(value, str) and \":\" in value:\n",
" diagnosis = value.split(\":\", 1)[1].strip()\n",
" if diagnosis == \"Psoriatic Arthritis\":\n",
" return 1\n",
" else:\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"\n",
" Convert age string to a numerical value.\n",
" \"\"\"\n",
" if isinstance(value, str) and \":\" in value:\n",
" age_str = value.split(\":\", 1)[1].strip()\n",
" try:\n",
" return int(age_str)\n",
" except ValueError:\n",
" return None\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"\n",
" Convert gender string to binary: 0 for female, 1 for male.\n",
" \"\"\"\n",
" if isinstance(value, str) and \":\" in value:\n",
" gender = value.split(\":\", 1)[1].strip()\n",
" if gender.upper() == \"F\":\n",
" return 0\n",
" elif gender.upper() == \"M\":\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata - Initial Filtering\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",
" # Extract clinical features using the library function\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 extracted clinical features\n",
" preview = preview_df(clinical_features)\n",
" print(\"Preview of extracted clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical features to a CSV file\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "4dea5755",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d6ae7d7",
"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": "9b0fccbf",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fbc5de3a",
"metadata": {},
"outputs": [],
"source": [
"# Looking at the gene identifiers, I can see they are ILMN_ prefixed identifiers.\n",
"# These are Illumina BeadArray probe IDs, not standard human gene symbols.\n",
"# Illumina IDs like \"ILMN_1343291\" need to be mapped to human gene symbols.\n",
"# These IDs are specific to Illumina microarray platforms and require mapping.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "035e8f1b",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b827a264",
"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": "a47beafc",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdc931b3",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the relevant columns from the gene annotation dataframe\n",
"# The 'ID' column contains probe IDs (e.g., ILMN_...) which match the gene_data index\n",
"# The 'Symbol' column contains the human gene symbols we need to map to\n",
"\n",
"# 2. Get gene mapping dataframe by extracting the needed columns\n",
"mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(mapping_data))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
"print(\"After mapping to gene symbols, gene data shape:\", gene_data.shape)\n",
"print(\"First 10 gene symbols after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Store the gene data to a CSV file\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": "0f063d68",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "35687c8f",
"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",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Gene data saved to {out_gene_data_file}\")\n",
"\n",
"# Re-extract clinical features since we need it for linking\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",
"# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.\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",
"# 3. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\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=\"Dataset contains gene expression from endothelial cells derived from circulating progenitors of RA patients\"\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",
" print(f\"Data is usable. Saving to {out_data_file}\")\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
"else:\n",
" print(\"Data is not usable. Not saving linked data file.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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