{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "d963c554", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:19.845294Z", "iopub.status.busy": "2025-03-25T08:04:19.845111Z", "iopub.status.idle": "2025-03-25T08:04:20.014253Z", "shell.execute_reply": "2025-03-25T08:04:20.013763Z" } }, "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 = \"Endometriosis\"\n", "cohort = \"GSE75427\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Endometriosis\"\n", "in_cohort_dir = \"../../input/GEO/Endometriosis/GSE75427\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Endometriosis/GSE75427.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Endometriosis/gene_data/GSE75427.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Endometriosis/clinical_data/GSE75427.csv\"\n", "json_path = \"../../output/preprocess/Endometriosis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "6d47b385", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "e6aa32fa", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:20.015875Z", "iopub.status.busy": "2025-03-25T08:04:20.015719Z", "iopub.status.idle": "2025-03-25T08:04:20.089645Z", "shell.execute_reply": "2025-03-25T08:04:20.089336Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Expression profiles in decidualized and non-decidualized endometriotic cyst stromal cells (ECSCs) and normal endometrial stromal cells (NESCs)\"\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: ['cell type: endometriotic cyst stromal cells'], 1: ['gender: Female'], 2: ['age: 34y', 'age: 42y', 'age: 30y', 'age: 28y'], 3: ['treatment: 12d 10% charcoal-stripped heat-inactivated FBS', 'treatment: 12d dibutyryl-cAMP and dienogest']}\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": "2dd6c053", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "28115b07", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:20.090749Z", "iopub.status.busy": "2025-03-25T08:04:20.090638Z", "iopub.status.idle": "2025-03-25T08:04:20.098704Z", "shell.execute_reply": "2025-03-25T08:04:20.098408Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Data Preview:\n", "{'GSM1954898': [1.0, 34.0], 'GSM1954899': [1.0, 42.0], 'GSM1954900': [1.0, 30.0], 'GSM1954901': [1.0, 28.0], 'GSM1954902': [1.0, 34.0], 'GSM1954903': [1.0, 42.0], 'GSM1954904': [1.0, 30.0], 'GSM1954905': [1.0, 28.0]}\n", "Clinical data saved to ../../output/preprocess/Endometriosis/clinical_data/GSE75427.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the title mentioning \"Expression profiles\", this dataset likely contains 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 (Endometriosis), we can see \"cell type: proliferative phase normal endometrium\" in row 0\n", "# Row 0 likely distinguishes between normal and endometriotic cells\n", "trait_row = 0\n", "\n", "# For gender, we see \"gender: Female\" in row 1, but it appears to be constant (only Female)\n", "# Since there's only one unique value, we consider it not available\n", "gender_row = None\n", "\n", "# For age, we see \"age: 37y\", \"age: 47y\", etc. in row 2\n", "age_row = 2\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert cell type to binary where 1 indicates endometriotic cells and 0 indicates normal cells.\"\"\"\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", " # Based on the title, we're comparing normal endometrial stromal cells (NESCs)\n", " # vs endometriotic cyst stromal cells (ECSCs)\n", " if 'normal' in value.lower():\n", " return 0 # Normal cells\n", " elif 'endometrio' in value.lower():\n", " return 1 # Endometriotic cells\n", " else:\n", " return None # Unknown\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age to continuous numeric value.\"\"\"\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", " # Extract numeric age, typically formatted as \"XXy\" (XX years)\n", " if 'y' in value:\n", " try:\n", " age = int(value.replace('y', '').strip())\n", " return age\n", " except ValueError:\n", " pass\n", " \n", " return None # If conversion fails\n", "\n", "def convert_gender(value):\n", " \"\"\"\n", " Convert gender to binary (0 for female, 1 for male).\n", " Not used in this dataset as gender is constant.\n", " \"\"\"\n", " # This function is included for completeness but not used since gender_row = None\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", " 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", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info (initial filtering)\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\n", " 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 extracted clinical data\n", " preview = preview_df(clinical_df)\n", " print(\"Clinical Data Preview:\")\n", " print(preview)\n", " \n", " # Save the clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " 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": "b7f4352b", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "cd1e5a9d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:20.099787Z", "iopub.status.busy": "2025-03-25T08:04:20.099678Z", "iopub.status.idle": "2025-03-25T08:04:20.148945Z", "shell.execute_reply": "2025-03-25T08:04:20.148572Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 63\n", "Header line: \"ID_REF\"\t\"GSM1954898\"\t\"GSM1954899\"\t\"GSM1954900\"\t\"GSM1954901\"\t\"GSM1954902\"\t\"GSM1954903\"\t\"GSM1954904\"\t\"GSM1954905\"\n", "First data line: \"A_23_P100001\"\t354.3793375\t172.500875\t58.17458\t89.16528875\t1994.738375\t146.5653413\t39.38974125\t28.5603025\n", "Index(['A_23_P100001', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074',\n", " 'A_23_P100127', 'A_23_P100141', 'A_23_P100189', 'A_23_P100196',\n", " 'A_23_P100203', 'A_23_P100220', 'A_23_P100240', 'A_23_P10025',\n", " 'A_23_P100292', 'A_23_P100315', 'A_23_P100326', 'A_23_P100344',\n", " 'A_23_P100355', 'A_23_P100386', 'A_23_P100392', 'A_23_P100420'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. First, let's examine the structure of the matrix file to understand its format\n", "import gzip\n", "\n", "# Peek at the first few lines of the file to understand its structure\n", "with gzip.open(matrix_file, 'rt') as file:\n", " # Read first 100 lines to find the header structure\n", " for i, line in enumerate(file):\n", " if '!series_matrix_table_begin' in line:\n", " print(f\"Found data marker at line {i}\")\n", " # Read the next line which should be the header\n", " header_line = next(file)\n", " print(f\"Header line: {header_line.strip()}\")\n", " # And the first data line\n", " first_data_line = next(file)\n", " print(f\"First data line: {first_data_line.strip()}\")\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Matrix table marker not found in first 100 lines\")\n", " break\n", "\n", "# 3. Now try to get the genetic data with better error handling\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(gene_data.index[:20])\n", "except KeyError as e:\n", " print(f\"KeyError: {e}\")\n", " \n", " # Alternative approach: manually extract the data\n", " print(\"\\nTrying alternative approach to read the gene data:\")\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Find the start of the data\n", " for line in file:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Read the headers and data\n", " import pandas as pd\n", " df = pd.read_csv(file, sep='\\t', index_col=0)\n", " print(f\"Column names: {df.columns[:5]}\")\n", " print(f\"First 20 row IDs: {df.index[:20]}\")\n", " gene_data = df\n" ] }, { "cell_type": "markdown", "id": "4a055de0", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "67c606ab", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:20.150197Z", "iopub.status.busy": "2025-03-25T08:04:20.150088Z", "iopub.status.idle": "2025-03-25T08:04:20.151919Z", "shell.execute_reply": "2025-03-25T08:04:20.151628Z" } }, "outputs": [], "source": [ "# These identifiers don't appear to be standard human gene symbols\n", "# They have a format like \"A_19_P00315452\" which looks like probe IDs from a microarray platform\n", "# These will need to be mapped to standard gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "b8c9b575", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "24177971", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:20.153289Z", "iopub.status.busy": "2025-03-25T08:04:20.153177Z", "iopub.status.idle": "2025-03-25T08:04:21.890283Z", "shell.execute_reply": "2025-03-25T08:04:21.889891Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107', '(+)E1A_r60_a135'], 'SPOT_ID': ['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107', '(+)E1A_r60_a135'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'pos', 'pos'], 'REFSEQ': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan], 'GENE': [nan, nan, nan, nan, nan], 'GENE_SYMBOL': [nan, nan, nan, nan, nan], 'GENE_NAME': [nan, nan, nan, nan, nan], 'UNIGENE_ID': [nan, nan, nan, nan, nan], 'ENSEMBL_ID': [nan, nan, nan, nan, nan], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': [nan, nan, nan, nan, nan], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, nan, nan], 'CYTOBAND': [nan, nan, nan, nan, nan], 'DESCRIPTION': [nan, nan, nan, nan, nan], 'GO_ID': [nan, nan, nan, nan, nan], 'SEQUENCE': [nan, nan, nan, nan, nan]}\n" ] } ], "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": "b59ce36e", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "ac5ebb30", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:21.891804Z", "iopub.status.busy": "2025-03-25T08:04:21.891669Z", "iopub.status.idle": "2025-03-25T08:04:22.645925Z", "shell.execute_reply": "2025-03-25T08:04:22.645555Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene IDs in gene expression data (first 5):\n", "Index(['A_23_P100001', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074',\n", " 'A_23_P100127'],\n", " dtype='object', name='ID')\n", "\n", "Further examination of gene annotation (10 more rows):\n", " ID GENE_SYMBOL\n", "10 (-)3xSLv1 NaN\n", "11 A_23_P100001 FAM174B\n", "12 A_23_P100022 SV2B\n", "13 A_23_P100056 RBPMS2\n", "14 A_23_P100074 AVEN\n", "15 A_23_P100127 CASC5\n", "16 A_23_P100141 UNKL\n", "17 A_23_P100189 PRM1\n", "18 A_23_P100196 USP10\n", "19 A_23_P100203 HSBP1\n", "\n", "Sample rows with gene symbols (if any):\n", " ID GENE_SYMBOL\n", "11 A_23_P100001 FAM174B\n", "12 A_23_P100022 SV2B\n", "13 A_23_P100056 RBPMS2\n", "14 A_23_P100074 AVEN\n", "15 A_23_P100127 CASC5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Rows with matching ID pattern:\n", " ID GENE_SYMBOL\n", "11 A_23_P100001 FAM174B\n", "12 A_23_P100022 SV2B\n", "13 A_23_P100056 RBPMS2\n", "14 A_23_P100074 AVEN\n", "15 A_23_P100127 CASC5\n", "\n", "Rows with both ID and GENE_SYMBOL:\n", " ID GENE_SYMBOL\n", "11 A_23_P100001 FAM174B\n", "12 A_23_P100022 SV2B\n", "13 A_23_P100056 RBPMS2\n", "14 A_23_P100074 AVEN\n", "15 A_23_P100127 CASC5\n", "\n", "Rows with both ID and GENE:\n", " ID GENE\n", "11 A_23_P100001 400451\n", "12 A_23_P100022 9899\n", "13 A_23_P100056 348093\n", "14 A_23_P100074 57099\n", "15 A_23_P100127 57082\n", "\n", "Rows with both ID and GENE_NAME:\n", " ID GENE_NAME\n", "11 A_23_P100001 family with sequence similarity 174, member B\n", "12 A_23_P100022 synaptic vesicle glycoprotein 2B\n", "13 A_23_P100056 RNA binding protein with multiple splicing 2\n", "14 A_23_P100074 apoptosis, caspase activation inhibitor\n", "15 A_23_P100127 cancer susceptibility candidate 5\n", "\n", "Rows with both ID and REFSEQ:\n", " ID REFSEQ\n", "11 A_23_P100001 NM_207446\n", "12 A_23_P100022 NM_014848\n", "13 A_23_P100056 NM_194272\n", "14 A_23_P100074 NM_020371\n", "15 A_23_P100127 NM_170589\n", "\n", "Rows with both ID and GB_ACC:\n", " ID GB_ACC\n", "11 A_23_P100001 NM_207446\n", "12 A_23_P100022 NM_014848\n", "13 A_23_P100056 NM_194272\n", "14 A_23_P100074 NM_020371\n", "15 A_23_P100127 NM_170589\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene mapping dataframe (first 5 rows):\n", " ID Gene\n", "11 A_23_P100001 FAM174B\n", "12 A_23_P100022 SV2B\n", "13 A_23_P100056 RBPMS2\n", "14 A_23_P100074 AVEN\n", "15 A_23_P100127 CASC5\n", "\n", "Gene expression data after mapping (first 5 genes):\n", " GSM1954898 GSM1954899 GSM1954900 GSM1954901 GSM1954902 \\\n", "Gene \n", "A1BG 3028.378695 2731.904201 3157.886390 3028.031645 3820.889868 \n", "A1BG-AS1 852.177600 601.155425 758.254475 1017.400850 803.633850 \n", "A1CF 13.638512 10.773817 15.022629 10.245584 21.209031 \n", "A2LD1 1528.978615 1301.985750 3653.101250 2065.590500 1868.458601 \n", "A2M 1702.062389 4474.020852 3434.653770 13126.539044 213.549137 \n", "\n", " GSM1954903 GSM1954904 GSM1954905 \n", "Gene \n", "A1BG 3227.197977 3969.369038 5763.236649 \n", "A1BG-AS1 550.132250 894.482050 1676.084700 \n", "A1CF 10.897716 9.151911 12.580970 \n", "A2LD1 1256.928650 2231.617550 1938.389575 \n", "A2M 406.783548 1552.944676 5180.086498 \n", "\n", "Number of genes after mapping: 19818\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Endometriosis/gene_data/GSE75427.csv\n" ] } ], "source": [ "# Examining the structure of gene IDs in both datasets\n", "print(\"Gene IDs in gene expression data (first 5):\")\n", "print(gene_data.index[:5])\n", "\n", "# Let's look at more rows of the gene annotation to find the matching columns\n", "print(\"\\nFurther examination of gene annotation (10 more rows):\")\n", "print(gene_annotation.iloc[10:20][['ID', 'GENE_SYMBOL']].head(10))\n", "\n", "# Try to find any rows with non-null gene symbols\n", "print(\"\\nSample rows with gene symbols (if any):\")\n", "symbol_sample = gene_annotation[gene_annotation['GENE_SYMBOL'].notna()].head(5)\n", "print(symbol_sample[['ID', 'GENE_SYMBOL']])\n", "\n", "# Check which ID format in the annotation matches our expression data\n", "# Since the standard gene_data IDs look like A_19_P00315452, we need to find the matching pattern\n", "import re\n", "\n", "# Find the first few rows where ID matches our expression data pattern\n", "pattern = r'A_\\d+_P\\d+'\n", "matching_rows = gene_annotation[gene_annotation['ID'].str.contains(pattern, na=False)].head(5)\n", "print(\"\\nRows with matching ID pattern:\")\n", "print(matching_rows[['ID', 'GENE_SYMBOL']])\n", "\n", "# For probe-gene mapping, we need to determine which columns to use\n", "# Based on the column names, 'ID' should contain probe IDs and 'GENE_SYMBOL' should contain gene symbols\n", "# Let's confirm if there are any rows with both values\n", "valid_mapping_rows = gene_annotation[(gene_annotation['ID'].notna()) & \n", " (gene_annotation['GENE_SYMBOL'].notna())].head(5)\n", "print(\"\\nRows with both ID and GENE_SYMBOL:\")\n", "print(valid_mapping_rows[['ID', 'GENE_SYMBOL']])\n", "\n", "# If GENE_SYMBOL is mostly empty, check other potential gene identifier columns\n", "potential_gene_cols = ['GENE', 'GENE_NAME', 'REFSEQ', 'GB_ACC']\n", "for col in potential_gene_cols:\n", " valid_rows = gene_annotation[(gene_annotation['ID'].notna()) & \n", " (gene_annotation[col].notna())].head(5)\n", " if not valid_rows.empty:\n", " print(f\"\\nRows with both ID and {col}:\")\n", " print(valid_rows[['ID', col]])\n", "\n", "# Based on the above analysis, create the mapping dataframe\n", "# Assuming we've identified the correct columns\n", "prob_col = 'ID' # Column with probe IDs\n", "gene_col = 'GENE_SYMBOL' # Column with gene symbols (adjust if needed based on results)\n", "\n", "# Get the mapping dataframe\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(\"\\nGene mapping dataframe (first 5 rows):\")\n", "print(mapping_df.head())\n", "\n", "# Convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(\"\\nGene expression data after mapping (first 5 genes):\")\n", "print(gene_data.head())\n", "\n", "# If the mapping has very few valid entries (or none), we might need to try an alternative approach\n", "# Check the mapping statistics\n", "mapped_count = len(gene_data)\n", "print(f\"\\nNumber of genes after mapping: {mapped_count}\")\n", "\n", "# If the mapping resulted in very few genes, try an alternative column\n", "if mapped_count < 100:\n", " print(\"Poor mapping results. Trying alternative gene column...\")\n", " # Try using 'GENE' instead of 'GENE_SYMBOL'\n", " gene_col_alt = 'GENE'\n", " mapping_df_alt = get_gene_mapping(gene_annotation, prob_col, gene_col_alt)\n", " gene_data = apply_gene_mapping(gene_data, mapping_df_alt)\n", " print(f\"Number of genes after alternative mapping: {len(gene_data)}\")\n", "\n", "# Save the gene expression data to a file\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": "ead2aff8", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "1cdb52dd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:22.647311Z", "iopub.status.busy": "2025-03-25T08:04:22.647191Z", "iopub.status.idle": "2025-03-25T08:04:28.683830Z", "shell.execute_reply": "2025-03-25T08:04:28.683467Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Endometriosis/gene_data/GSE75427.csv\n", "Clinical data saved to ../../output/preprocess/Endometriosis/clinical_data/GSE75427.csv\n", "Linked data shape: (8, 19449)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Quartiles for 'Endometriosis':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1.0\n", "Max: 1.0\n", "The distribution of the feature 'Endometriosis' in this dataset is severely biased.\n", "\n", "Quartiles for 'Age':\n", " 25%: 29.5\n", " 50% (Median): 32.0\n", " 75%: 36.0\n", "Min: 28.0\n", "Max: 42.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "Data was determined to be unusable and was not saved\n" ] } ], "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=\"Dataset contains gene expression from monocytes of rheumatoid arthritis patients, with osteoporosis status included in comorbidity information.\"\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": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }