{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "a276f199", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:14:25.391379Z", "iopub.status.busy": "2025-03-25T08:14:25.391278Z", "iopub.status.idle": "2025-03-25T08:14:25.548221Z", "shell.execute_reply": "2025-03-25T08:14:25.547879Z" } }, "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 = \"GSE163114\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Cervical_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Cervical_Cancer/GSE163114\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Cervical_Cancer/GSE163114.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Cervical_Cancer/gene_data/GSE163114.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Cervical_Cancer/clinical_data/GSE163114.csv\"\n", "json_path = \"../../output/preprocess/Cervical_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "81317870", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "c874ddcf", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:14:25.549640Z", "iopub.status.busy": "2025-03-25T08:14:25.549501Z", "iopub.status.idle": "2025-03-25T08:14:25.645536Z", "shell.execute_reply": "2025-03-25T08:14:25.645246Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Ki-67 promotes carcinogenesis by enabling global transcriptional programmes\"\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 line: HeLa'], 1: ['lentivirus: shRNA control', 'lentivirus: shRNA Ki-67']}\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": "ac59173d", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "b05204a7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:14:25.646617Z", "iopub.status.busy": "2025-03-25T08:14:25.646513Z", "iopub.status.idle": "2025-03-25T08:14:25.651240Z", "shell.execute_reply": "2025-03-25T08:14:25.650921Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data file not found at ../../input/GEO/Cervical_Cancer/GSE163114/clinical_data.csv\n", "This dataset may require manual inspection.\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# The dataset appears to be from a HeLa cell line with shRNA experiments\n", "# As a SuperSeries with SubSeries, it 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: Looking at row 1, it shows two different treatments (shRNA control vs shRNA Ki-67)\n", "# This is the experimental condition and should be our trait of interest\n", "trait_row = 1\n", "\n", "# Age and gender: Not available in this cell line dataset\n", "age_row = None\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert shRNA treatment information to a binary indicator.\n", " shRNA control = 0, shRNA Ki-67 = 1\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " value = value.lower()\n", " if 'control' in value:\n", " return 0\n", " elif 'ki-67' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " # Not applicable for this dataset\n", " return None\n", "\n", "def convert_gender(value):\n", " # Not applicable for this dataset\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", " try:\n", " # For GEO data, we need to load the clinical data file that was created in a previous step\n", " # Attempt to find the clinical data file in the expected location\n", " clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n", " \n", " if os.path.exists(clinical_data_path):\n", " clinical_data = pd.read_csv(clinical_data_path)\n", " \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 dataframe\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of selected clinical features:\")\n", " print(preview)\n", " \n", " # Ensure the output directory exists\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", " else:\n", " print(f\"Clinical data file not found at {clinical_data_path}\")\n", " print(\"This dataset may require manual inspection.\")\n", " except Exception as e:\n", " print(f\"Error processing clinical data: {e}\")\n", " print(\"This dataset may require special handling.\")\n" ] }, { "cell_type": "markdown", "id": "f28d5bed", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "3485509b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:14:25.652420Z", "iopub.status.busy": "2025-03-25T08:14:25.652319Z", "iopub.status.idle": "2025-03-25T08:14:25.753806Z", "shell.execute_reply": "2025-03-25T08:14:25.753429Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',\n", " '14', '15', '16', '17', '18', '19', '20'],\n", " dtype='object', name='ID')\n" ] } ], "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": "c1ee14a2", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "0e3585b8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:14:25.755100Z", "iopub.status.busy": "2025-03-25T08:14:25.754989Z", "iopub.status.idle": "2025-03-25T08:14:25.756818Z", "shell.execute_reply": "2025-03-25T08:14:25.756549Z" } }, "outputs": [], "source": [ "# The index values ['1', '2', '3', '4', ...] appear to be simple numeric identifiers,\n", "# not standard human gene symbols which typically follow formats like \"TP53\", \"BRCA1\", etc.\n", "# These are likely probe IDs or some other platform-specific identifiers that need to be\n", "# mapped to proper gene symbols for biological interpretation.\n", "\n", "# Based on my biomedical knowledge, these are not human gene symbols and require mapping\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "10ab462a", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "c98399e9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:14:25.757902Z", "iopub.status.busy": "2025-03-25T08:14:25.757806Z", "iopub.status.idle": "2025-03-25T08:14:27.521243Z", "shell.execute_reply": "2025-03-25T08:14:27.520870Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['1', '2', '3', '4', '5'], 'COL': ['192', '192', '192', '192', '192'], 'ROW': [328.0, 326.0, 324.0, 322.0, 320.0], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_23_P117082', 'A_33_P3246448'], 'SPOT_ID': ['CONTROL', 'CONTROL', 'CONTROL', 'A_23_P117082', 'A_33_P3246448'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'GB_ACC': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'LOCUSLINK_ID': [nan, nan, nan, 50865.0, 23704.0], 'GENE_SYMBOL': [nan, nan, nan, 'HEBP1', 'KCNE4'], 'GENE_NAME': [nan, nan, nan, 'heme binding protein 1', 'potassium voltage-gated channel, Isk-related family, member 4'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.642618', 'Hs.348522'], 'ENSEMBL_ID': [nan, nan, nan, 'ENST00000014930', 'ENST00000281830'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_015987|ens|ENST00000014930|gb|AF117615|gb|BC016277', 'ref|NM_080671|ens|ENST00000281830|tc|THC2655788'], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, 'chr12:13127906-13127847', 'chr2:223920197-223920256'], 'CYTOBAND': [nan, nan, nan, 'hs|12p13.1', 'hs|2q36.1'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens heme binding protein 1 (HEBP1), mRNA [NM_015987]', 'Homo sapiens potassium voltage-gated channel, Isk-related family, member 4 (KCNE4), mRNA [NM_080671]'], 'GO_ID': [nan, nan, nan, 'GO:0005488(binding)|GO:0005576(extracellular region)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0007623(circadian rhythm)|GO:0020037(heme binding)', 'GO:0005244(voltage-gated ion channel activity)|GO:0005249(voltage-gated potassium channel activity)|GO:0006811(ion transport)|GO:0006813(potassium ion transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0016324(apical plasma membrane)'], 'SEQUENCE': [nan, nan, nan, 'AAGGGGGAAAATGTGATTTGTGCCTGATCTTTCATCTGTGATTCTTATAAGAGCTTTGTC', 'GCAAGTCTCTCTGCACCTATTAAAAAGTGATGTATATACTTCCTTCTTATTCTGTTGAGT']}\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": "8dcf59c3", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "731cf145", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:14:27.522691Z", "iopub.status.busy": "2025-03-25T08:14:27.522479Z", "iopub.status.idle": "2025-03-25T08:14:27.669376Z", "shell.execute_reply": "2025-03-25T08:14:27.669009Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data after mapping (first 5 rows):\n", " GSM4950455 GSM4950456 GSM4950457 GSM4950458 GSM4950459 \\\n", "Gene \n", "A1BG 132.083288 129.833098 95.850230 100.615695 124.773355 \n", "A1BG-AS1 16.155110 14.861680 4.716991 11.106840 8.644419 \n", "A1CF 212.139998 173.847205 54.092575 156.444192 106.723514 \n", "A2LD1 244.871530 180.816300 109.013836 173.861860 247.707170 \n", "A2M 133.594765 128.883865 62.684670 123.532323 81.277947 \n", "\n", " GSM4950460 GSM4950461 GSM4950462 GSM4950463 GSM4950464 \\\n", "Gene \n", "A1BG 109.751668 108.931347 92.967957 127.500770 106.803252 \n", "A1BG-AS1 4.350618 10.533510 7.061245 13.553190 10.343080 \n", "A1CF 76.701249 157.344856 122.121485 154.139703 196.274230 \n", "A2LD1 145.602781 206.909400 220.527440 229.859650 200.153210 \n", "A2M 55.676736 104.865621 83.508082 146.794847 133.055046 \n", "\n", " GSM4950465 GSM4950466 \n", "Gene \n", "A1BG 94.078066 122.938080 \n", "A1BG-AS1 13.383460 15.715330 \n", "A1CF 105.685073 173.692618 \n", "A2LD1 177.095571 185.686890 \n", "A2M 66.040354 151.910271 \n" ] } ], "source": [ "# 1. Based on the output, we need to identify which columns to use for gene mapping\n", "# Looking at the gene annotation preview, we can see:\n", "# - 'ID' contains the numeric identifiers matching the gene expression data index\n", "# - 'GENE_SYMBOL' contains the actual gene symbols (e.g., HEBP1, KCNE4)\n", "\n", "# 2. Extract the gene mapping dataframe from gene annotation\n", "gene_mapping = get_gene_mapping(\n", " annotation=gene_annotation,\n", " prob_col='ID', # Gene identifiers column\n", " gene_col='GENE_SYMBOL' # Gene symbols column\n", ")\n", "\n", "# 3. Convert probe-level measurements to gene expression data by applying gene mapping\n", "gene_data = apply_gene_mapping(\n", " expression_df=gene_data,\n", " mapping_df=gene_mapping\n", ")\n", "\n", "# Print first few rows of the gene data after mapping to verify\n", "print(\"Gene expression data after mapping (first 5 rows):\")\n", "print(gene_data.head(5))\n" ] }, { "cell_type": "markdown", "id": "b5eaf74d", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "923e2e1d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:14:27.670752Z", "iopub.status.busy": "2025-03-25T08:14:27.670646Z", "iopub.status.idle": "2025-03-25T08:14:34.767139Z", "shell.execute_reply": "2025-03-25T08:14:34.766559Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Cervical_Cancer/gene_data/GSE163114.csv\n", "Clinical data saved to ../../output/preprocess/Cervical_Cancer/clinical_data/GSE163114.csv\n", "Linked data shape: (12, 19848)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "For the feature 'Cervical_Cancer', the least common label is '0.0' with 6 occurrences. This represents 50.00% of the dataset.\n", "The distribution of the feature 'Cervical_Cancer' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Cervical_Cancer/GSE163114.csv\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", "# Extract clinical features directly from the matrix file data (reuse the data from previous steps)\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", "\n", "# Use the correct trait conversion function from Step 2\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert shRNA treatment information to a binary indicator.\n", " shRNA control = 0, shRNA Ki-67 = 1\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " value = value.lower()\n", " if 'control' in value:\n", " return 0\n", " elif 'ki-67' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# Extract clinical features using the trait_row identified in Step 2\n", "selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=1, # Using row 1 which contains shRNA treatment information\n", " convert_trait=convert_trait\n", ")\n", "\n", "# Save clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "selected_clinical_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(selected_clinical_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=\"HeLa cell line samples treated with shRNA control vs. shRNA Ki-67 knockdown.\"\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 }