{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "16181955", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:04.807950Z", "iopub.status.busy": "2025-03-25T04:08:04.807730Z", "iopub.status.idle": "2025-03-25T04:08:04.975951Z", "shell.execute_reply": "2025-03-25T04:08:04.975582Z" } }, "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 = \"Substance_Use_Disorder\"\n", "cohort = \"GSE161999\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Substance_Use_Disorder\"\n", "in_cohort_dir = \"../../input/GEO/Substance_Use_Disorder/GSE161999\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Substance_Use_Disorder/GSE161999.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Substance_Use_Disorder/gene_data/GSE161999.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Substance_Use_Disorder/clinical_data/GSE161999.csv\"\n", "json_path = \"../../output/preprocess/Substance_Use_Disorder/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "942e65b1", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "01b6cfb5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:04.977662Z", "iopub.status.busy": "2025-03-25T04:08:04.977485Z", "iopub.status.idle": "2025-03-25T04:08:04.987677Z", "shell.execute_reply": "2025-03-25T04:08:04.987377Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Network preservation reveals shared and unique biological processes associated with chronic alcohol abuse in the NAc and PFC\"\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: ['tissue: prefrontal cortex'], 1: ['diagnosis: Alcohol', 'diagnosis: Control'], 2: ['age: 61', 'age: 44', 'age: 62', 'age: 56', 'age: 63', 'age: 42', 'age: 46', 'age: 52', 'age: 43', 'age: 59', 'age: 54', 'age: 39', 'age: 73', 'age: 50', 'age: 51', 'age: 64', 'age: 55', 'age: 47', 'age: 53', 'age: 82', 'age: 57'], 3: ['Sex: Male'], 4: ['rin: 3.6', 'rin: 3.7', 'rin: 3.4', 'rin: 2.1', 'rin: 5.2', 'rin: 5.8', 'rin: 1.4', 'rin: 3.8', 'rin: 2.8', 'rin: 2.9', 'rin: 2.6', 'rin: 2.5', 'rin: 7.8', 'rin: 5', 'rin: 7.2', 'rin: 7.9', 'rin: 4.3', 'rin: 6.6', 'rin: 2.2', 'rin: 8.3', 'rin: 3.1', 'rin: 7.4', 'rin: 4.4', 'rin: 8', 'rin: 3.2'], 5: ['brain weight: 1340', 'brain weight: 1220', 'brain weight: 1480', 'brain weight: 1284', 'brain weight: 1570', 'brain weight: 1400', 'brain weight: 1490', 'brain weight: 1510', 'brain weight: 1380', 'brain weight: 1500', 'brain weight: 1520', 'brain weight: 1230', 'brain weight: 1200', 'brain weight: 1360', 'brain weight: 1300', 'brain weight: 1635', 'brain weight: 1616', 'brain weight: 1420', 'brain weight: 1460', 'brain weight: 1370', 'brain weight: 1362', 'brain weight: 1631', 'brain weight: 1534', 'brain weight: 1426', 'brain weight: 1560', 'brain weight: 1390', 'brain weight: 1188'], 6: ['ph: 6.93', 'ph: 6.6', 'ph: 6.56', 'ph: 6.51', 'ph: 6.94', 'ph: 6.5', 'ph: 6.65', 'ph: 6.76', 'ph: 6.78', 'ph: 6.43', 'ph: 6.57', 'ph: 6.52', 'ph: 6.41', 'ph: 6.3', 'ph: 6.53', 'ph: 6.26', 'ph: 6.21', 'ph: 6.59', 'ph: 6.35', 'ph: 7.02', 'ph: 6.39', 'ph: 6.74', 'ph: 6.37', 'ph: 6.89', 'ph: 6.75', 'ph: 6.24', 'ph: 6.84', 'ph: 6.8'], 7: ['pmi: 21', 'pmi: 50', 'pmi: 37.5', 'pmi: 45', 'pmi: 24', 'pmi: 41', 'pmi: 25', 'pmi: 37', 'pmi: 45.5', 'pmi: 13', 'pmi: 22', 'pmi: 17', 'pmi: 19', 'pmi: 25.5', 'pmi: 46', 'pmi: 39', 'pmi: 48', 'pmi: 12', 'pmi: 38', 'pmi: 30', 'pmi: 57', 'pmi: 36', 'pmi: 9.5', 'pmi: 18', 'pmi: 20'], 8: ['hemisphere: 0', 'hemisphere: 1'], 9: ['neuropathology: 0', 'neuropathology: 1'], 10: ['hepatology: 1', 'hepatology: 0', 'hepatology: 9'], 11: ['toxicology: 2', 'toxicology: 9', 'toxicology: 1', 'toxicology: 0'], 12: ['smoking: 1', 'smoking: 2', 'smoking: 9', 'smoking: 0']}\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": "44189083", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "65b9c5c2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:04.988768Z", "iopub.status.busy": "2025-03-25T04:08:04.988659Z", "iopub.status.idle": "2025-03-25T04:08:04.998071Z", "shell.execute_reply": "2025-03-25T04:08:04.997766Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Features Preview:\n", "{'GSM4929487': [1.0, 61.0], 'GSM4929488': [0.0, 44.0], 'GSM4929489': [0.0, 62.0], 'GSM4929490': [1.0, 56.0], 'GSM4929491': [0.0, 63.0], 'GSM4929492': [1.0, 42.0], 'GSM4929493': [0.0, 46.0], 'GSM4929494': [0.0, 56.0], 'GSM4929495': [1.0, 52.0], 'GSM4929496': [0.0, 43.0], 'GSM4929497': [1.0, 59.0], 'GSM4929498': [1.0, 56.0], 'GSM4929499': [1.0, 54.0], 'GSM4929500': [1.0, 46.0], 'GSM4929501': [1.0, 39.0], 'GSM4929502': [1.0, 73.0], 'GSM4929503': [0.0, 56.0], 'GSM4929504': [0.0, 50.0], 'GSM4929505': [1.0, 63.0], 'GSM4929506': [1.0, 50.0], 'GSM4929507': [1.0, 50.0], 'GSM4929508': [1.0, 51.0], 'GSM4929509': [1.0, 64.0], 'GSM4929510': [1.0, 55.0], 'GSM4929511': [0.0, 55.0], 'GSM4929512': [0.0, 47.0], 'GSM4929513': [0.0, 50.0], 'GSM4929514': [0.0, 55.0], 'GSM4929515': [1.0, 53.0], 'GSM4929516': [0.0, 82.0], 'GSM4929517': [0.0, 64.0], 'GSM4929518': [1.0, 73.0], 'GSM4929519': [0.0, 73.0], 'GSM4929520': [0.0, 57.0], 'GSM4929521': [0.0, 59.0]}\n", "Clinical data saved to ../../output/preprocess/Substance_Use_Disorder/clinical_data/GSE161999.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Looking at the background info, this appears to be a gene expression study of alcohol abuse\n", "# in the prefrontal cortex, so gene data should be available\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# Trait: Substance Use Disorder - from row 1 (diagnosis: Alcohol/Control)\n", "trait_row = 1\n", "\n", "# Age: available in row 2\n", "age_row = 2\n", "\n", "# Gender: row 3 has Sex but it's only \"Male\" so it's a constant feature\n", "gender_row = None # Constant feature (all male)\n", "\n", "# 2.2 Data Type Conversion Functions\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary (0 for Control, 1 for Alcohol)\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if value.lower() == \"alcohol\":\n", " return 1\n", " elif value.lower() == \"control\":\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous numeric value\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"This function is defined but won't be used since gender is constant\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if value.lower() == \"male\":\n", " return 1\n", " elif value.lower() == \"female\":\n", " return 0\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", "# Save initial cohort info\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", " # The trait_row is not None, so we extract clinical features\n", " # Using the geo_select_clinical_features function from the library\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 features\n", " preview = preview_df(clinical_features)\n", " print(\"Clinical Features Preview:\")\n", " print(preview)\n", " \n", " # Save clinical data to CSV\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "9da5f5f6", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "3956ace8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:04.999057Z", "iopub.status.busy": "2025-03-25T04:08:04.998944Z", "iopub.status.idle": "2025-03-25T04:08:05.013460Z", "shell.execute_reply": "2025-03-25T04:08:05.013165Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 73\n", "Header line: \"ID_REF\"\t\"GSM4929487\"\t\"GSM4929488\"\t\"GSM4929489\"\t\"GSM4929490\"\t\"GSM4929491\"\t\"GSM4929492\"\t\"GSM4929493\"\t\"GSM4929494\"\t\"GSM4929495\"\t\"GSM4929496\"\t\"GSM4929497\"\t\"GSM4929498\"\t\"GSM4929499\"\t\"GSM4929500\"\t\"GSM4929501\"\t\"GSM4929502\"\t\"GSM4929503\"\t\"GSM4929504\"\t\"GSM4929505\"\t\"GSM4929506\"\t\"GSM4929507\"\t\"GSM4929508\"\t\"GSM4929509\"\t\"GSM4929510\"\t\"GSM4929511\"\t\"GSM4929512\"\t\"GSM4929513\"\t\"GSM4929514\"\t\"GSM4929515\"\t\"GSM4929516\"\t\"GSM4929517\"\t\"GSM4929518\"\t\"GSM4929519\"\t\"GSM4929520\"\t\"GSM4929521\"\n", "First data line: \"hsa-let-7a-2-star_st\"\t1.82741\t3.8846\t2.3203\t1.6715\t2.68131\t2.69626\t1.81954\t2.3203\t2.3203\t2.25006\t2.07315\t2.74054\t2.17867\t2.32175\t2.09534\t1.92895\t2.40712\t2.59145\t2.40434\t2.78593\t2.10078\t1.74555\t2.78988\t2.98177\t2.66927\t2.02203\t2.39759\t2.91484\t2.12533\t2.27246\t2.22079\t2.95433\t2.49163\t2.56559\t2.57982\n", "Index(['hsa-let-7a-2-star_st', 'hsa-let-7a-star_st', 'hsa-let-7a_st',\n", " 'hsa-let-7b-star_st', 'hsa-let-7b_st', 'hsa-let-7c_st',\n", " 'hsa-let-7d-star_st', 'hsa-let-7d_st', 'hsa-let-7e-star_st',\n", " 'hsa-let-7e_st', 'hsa-let-7f-1-star_st', 'hsa-let-7f-2-star_st',\n", " 'hsa-let-7f_st', 'hsa-let-7g-star_st', 'hsa-let-7g_st',\n", " 'hsa-let-7i-star_st', 'hsa-let-7i_st', 'hsa-miR-100-star_st',\n", " 'hsa-miR-100_st', 'hsa-miR-101-star_st'],\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": "e39cbcf3", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "fb1406dd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:05.014678Z", "iopub.status.busy": "2025-03-25T04:08:05.014576Z", "iopub.status.idle": "2025-03-25T04:08:05.016394Z", "shell.execute_reply": "2025-03-25T04:08:05.016103Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers from the output\n", "# The identifiers like \"1007_s_at\", \"1053_at\", etc. are probe IDs from Affymetrix microarrays\n", "# These are not standard human gene symbols (which would look like BRCA1, TP53, etc.)\n", "# They need to be mapped to human gene symbols for meaningful biological interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "84e6abe7", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "723edebc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:05.017593Z", "iopub.status.busy": "2025-03-25T04:08:05.017493Z", "iopub.status.idle": "2025-03-25T04:08:05.515588Z", "shell.execute_reply": "2025-03-25T04:08:05.515176Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining SOFT file structure:\n", "Line 0: ^DATABASE = GeoMiame\n", "Line 1: !Database_name = Gene Expression Omnibus (GEO)\n", "Line 2: !Database_institute = NCBI NLM NIH\n", "Line 3: !Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n", "Line 4: !Database_email = geo@ncbi.nlm.nih.gov\n", "Line 5: ^SERIES = GSE161999\n", "Line 6: !Series_title = Network preservation reveals shared and unique biological processes associated with chronic alcohol abuse in the NAc and PFC\n", "Line 7: !Series_geo_accession = GSE161999\n", "Line 8: !Series_status = Public on Nov 24 2020\n", "Line 9: !Series_submission_date = Nov 23 2020\n", "Line 10: !Series_last_update_date = Nov 29 2022\n", "Line 11: !Series_pubmed_id = 33332381\n", "Line 12: !Series_summary = This SuperSeries is composed of the SubSeries listed below.\n", "Line 13: !Series_overall_design = Refer to individual Series\n", "Line 14: !Series_type = Expression profiling by array\n", "Line 15: !Series_type = Genome binding/occupancy profiling by high throughput sequencing\n", "Line 16: !Series_sample_id = GSM4929029\n", "Line 17: !Series_sample_id = GSM4929030\n", "Line 18: !Series_sample_id = GSM4929031\n", "Line 19: !Series_sample_id = GSM4929032\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n" ] } ], "source": [ "# 1. Let's first examine the structure of the SOFT file before trying to parse it\n", "import gzip\n", "\n", "# Look at the first few lines of the SOFT file to understand its structure\n", "print(\"Examining SOFT file structure:\")\n", "try:\n", " with gzip.open(soft_file, 'rt') as file:\n", " # Read first 20 lines to understand the file structure\n", " for i, line in enumerate(file):\n", " if i < 20:\n", " print(f\"Line {i}: {line.strip()}\")\n", " else:\n", " break\n", "except Exception as e:\n", " print(f\"Error reading SOFT file: {e}\")\n", "\n", "# 2. Now let's try a more robust approach to extract the gene annotation\n", "# Instead of using the library function which failed, we'll implement a custom approach\n", "try:\n", " # First, look for the platform section which contains gene annotation\n", " platform_data = []\n", " with gzip.open(soft_file, 'rt') as file:\n", " in_platform_section = False\n", " for line in file:\n", " if line.startswith('^PLATFORM'):\n", " in_platform_section = True\n", " continue\n", " if in_platform_section and line.startswith('!platform_table_begin'):\n", " # Next line should be the header\n", " header = next(file).strip()\n", " platform_data.append(header)\n", " # Read until the end of the platform table\n", " for table_line in file:\n", " if table_line.startswith('!platform_table_end'):\n", " break\n", " platform_data.append(table_line.strip())\n", " break\n", " \n", " # If we found platform data, convert it to a DataFrame\n", " if platform_data:\n", " import pandas as pd\n", " import io\n", " platform_text = '\\n'.join(platform_data)\n", " gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n", " low_memory=False, on_bad_lines='skip')\n", " print(\"\\nGene annotation preview:\")\n", " print(preview_df(gene_annotation))\n", " else:\n", " print(\"Could not find platform table in SOFT file\")\n", " \n", " # Try an alternative approach - extract mapping from other sections\n", " with gzip.open(soft_file, 'rt') as file:\n", " for line in file:\n", " if 'ANNOTATION information' in line or 'annotation information' in line:\n", " print(f\"Found annotation information: {line.strip()}\")\n", " if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n", " print(f\"Platform title: {line.strip()}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation: {e}\")\n" ] }, { "cell_type": "markdown", "id": "8ec3a18f", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "8dd8de66", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:05.516988Z", "iopub.status.busy": "2025-03-25T04:08:05.516881Z", "iopub.status.idle": "2025-03-25T04:08:05.532610Z", "shell.execute_reply": "2025-03-25T04:08:05.532326Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview (first 5 rows):\n", " ID Gene\n", "0 1007_s_at DDR1 /// MIR4640\n", "1 1053_at RFC2\n", "2 117_at HSPA6\n", "3 121_at PAX8\n", "4 1255_g_at GUCA1A\n", "\n", "Gene expression data preview (first 5 genes, first 5 samples):\n", "Empty DataFrame\n", "Columns: [GSM4929487, GSM4929488, GSM4929489, GSM4929490, GSM4929491]\n", "Index: []\n", "\n", "Gene expression data saved to ../../output/preprocess/Substance_Use_Disorder/gene_data/GSE161999.csv\n" ] } ], "source": [ "# 1. Identify which columns in the gene annotation dataframe contain probe IDs and gene symbols\n", "# From examining the gene annotation preview, we can see:\n", "# - 'ID' column contains probe IDs like \"1007_s_at\" that match the gene expression data\n", "# - 'Gene Symbol' column contains the human gene symbols we need\n", "\n", "# 2. Create a gene mapping dataframe by extracting these two columns\n", "mapping_data = gene_annotation.loc[:, ['ID', 'Gene Symbol']]\n", "mapping_data = mapping_data.dropna() # Remove rows with missing values\n", "mapping_data = mapping_data.rename(columns={'Gene Symbol': 'Gene'}).astype({'ID': 'str'})\n", "\n", "# Preview the mapping data\n", "print(\"Gene mapping preview (first 5 rows):\")\n", "print(mapping_data.head())\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n", "# Use the library function to handle the many-to-many relation between probes and genes\n", "gene_data = apply_gene_mapping(gene_data, mapping_data)\n", "\n", "# Preview the gene expression data\n", "print(\"\\nGene expression data preview (first 5 genes, first 5 samples):\")\n", "print(gene_data.iloc[:5, :5])\n", "\n", "# Save the gene expression data to a CSV 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\"\\nGene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "dfb2983b", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "84fc3127", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:08:05.533871Z", "iopub.status.busy": "2025-03-25T04:08:05.533770Z", "iopub.status.idle": "2025-03-25T04:08:05.635564Z", "shell.execute_reply": "2025-03-25T04:08:05.635235Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (0, 35)\n", "Gene data shape after normalization: (0, 35)\n", "Normalized gene data saved to ../../output/preprocess/Substance_Use_Disorder/gene_data/GSE161999.csv\n", "Raw clinical data shape: (13, 36)\n", "Clinical features:\n", " GSM4929487 GSM4929488 GSM4929489 GSM4929490 \\\n", "Substance_Use_Disorder 1.0 0.0 0.0 1.0 \n", "Age 61.0 44.0 62.0 56.0 \n", "\n", " GSM4929491 GSM4929492 GSM4929493 GSM4929494 \\\n", "Substance_Use_Disorder 0.0 1.0 0.0 0.0 \n", "Age 63.0 42.0 46.0 56.0 \n", "\n", " GSM4929495 GSM4929496 ... GSM4929512 GSM4929513 \\\n", "Substance_Use_Disorder 1.0 0.0 ... 0.0 0.0 \n", "Age 52.0 43.0 ... 47.0 50.0 \n", "\n", " GSM4929514 GSM4929515 GSM4929516 GSM4929517 \\\n", "Substance_Use_Disorder 0.0 1.0 0.0 0.0 \n", "Age 55.0 53.0 82.0 64.0 \n", "\n", " GSM4929518 GSM4929519 GSM4929520 GSM4929521 \n", "Substance_Use_Disorder 1.0 0.0 0.0 0.0 \n", "Age 73.0 73.0 57.0 59.0 \n", "\n", "[2 rows x 35 columns]\n", "Clinical features saved to ../../output/preprocess/Substance_Use_Disorder/clinical_data/GSE161999.csv\n", "Linked data shape: (35, 2)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Substance_Use_Disorder Age\n", "GSM4929487 1.0 61.0\n", "GSM4929488 0.0 44.0\n", "GSM4929489 0.0 62.0\n", "GSM4929490 1.0 56.0\n", "GSM4929491 0.0 63.0\n", "Missing values before handling:\n", " Trait (Substance_Use_Disorder) missing: 0 out of 35\n", " Age missing: 0 out of 35\n", " Genes with >20% missing: 0\n", " Samples with >5% missing genes: 0\n", "Data shape after handling missing values: (0, 2)\n", "No data remains after handling missing values.\n", "Abnormality detected in the cohort: GSE161999. Preprocessing failed.\n", "Data was determined to be unusable or empty and was not saved\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/media/techt/DATA/GenoAgent/tools/preprocess.py:400: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " linked_data = pd.concat([clinical_df, genetic_df], axis=0).T\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", "\n", "# Normalize gene symbols using NCBI Gene database\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "\n", "# Save the normalized 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", "# 2. Check if trait data is available before proceeding with clinical data extraction\n", "if trait_row is None:\n", " print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n", " # Create an empty dataframe for clinical features\n", " clinical_features = pd.DataFrame()\n", " \n", " # Create an empty dataframe for linked data\n", " linked_data = pd.DataFrame()\n", " \n", " # Validate and save cohort info\n", " 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, # Trait data is not available\n", " is_biased=True, # Not applicable but required\n", " df=pd.DataFrame(), # Empty dataframe\n", " note=f\"Dataset contains gene expression data but lacks clear trait indicators for {trait} status.\"\n", " )\n", " print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n", "else:\n", " try:\n", " # Get the file paths for the matrix file to extract clinical data\n", " _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " \n", " # Get raw clinical data from the matrix file\n", " _, clinical_raw = get_background_and_clinical_data(matrix_file)\n", " \n", " # Verify clinical data structure\n", " print(\"Raw clinical data shape:\", clinical_raw.shape)\n", " \n", " # Extract clinical features using the defined conversion functions\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_raw,\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", " print(\"Clinical features:\")\n", " print(clinical_features)\n", " \n", " # Save clinical features to file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n", " \n", " # 3. Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # 4. Handle missing values\n", " print(\"Missing values before handling:\")\n", " print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n", " if 'Age' in linked_data.columns:\n", " print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n", " if 'Gender' in linked_data.columns:\n", " print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n", " \n", " gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n", " print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n", " print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n", " \n", " cleaned_data = handle_missing_values(linked_data, trait)\n", " print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n", " \n", " # 5. Evaluate bias in trait and demographic features\n", " is_trait_biased = False\n", " if len(cleaned_data) > 0:\n", " trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n", " is_trait_biased = trait_biased\n", " else:\n", " print(\"No data remains after handling missing values.\")\n", " is_trait_biased = True\n", " \n", " # 6. Final validation and save\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=cleaned_data,\n", " note=f\"Dataset contains gene expression data for {trait} analysis.\"\n", " )\n", " \n", " # 7. Save if usable\n", " if is_usable and len(cleaned_data) > 0:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " cleaned_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 or empty and was not saved\")\n", " \n", " except Exception as e:\n", " print(f\"Error processing data: {e}\")\n", " # Handle the error case by still recording cohort info\n", " 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, # Mark as not available due to processing issues\n", " is_biased=True, \n", " df=pd.DataFrame(), # Empty dataframe\n", " note=f\"Error processing data for {trait}: {str(e)}\"\n", " )\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 }