{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "626ca0b1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:29:27.437881Z", "iopub.status.busy": "2025-03-25T06:29:27.437660Z", "iopub.status.idle": "2025-03-25T06:29:27.607136Z", "shell.execute_reply": "2025-03-25T06:29:27.606778Z" } }, "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 = \"Angelman_Syndrome\"\n", "cohort = \"GSE43900\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Angelman_Syndrome\"\n", "in_cohort_dir = \"../../input/GEO/Angelman_Syndrome/GSE43900\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Angelman_Syndrome/GSE43900.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Angelman_Syndrome/gene_data/GSE43900.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Angelman_Syndrome/clinical_data/GSE43900.csv\"\n", "json_path = \"../../output/preprocess/Angelman_Syndrome/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "c782edfb", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "5fdfc90d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:29:27.608622Z", "iopub.status.busy": "2025-03-25T06:29:27.608470Z", "iopub.status.idle": "2025-03-25T06:29:27.676197Z", "shell.execute_reply": "2025-03-25T06:29:27.675888Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Co-ordinate inhibition of autism candidate genes by topoisomerase inhibitors\"\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: ['treatment: 1000nM_Topotecan', 'treatment: 150nM_Topotecan', 'treatment: 300nM_Topotecan', 'treatment: 30nM_Topotecan', 'treatment: 3nM_Topotecan', 'treatment: 500nM_Topotecan', 'treatment: Topotecan_dose_response_vehicle'], 1: ['cell type: cultured cortical neurons'], 2: ['strain: C57BL6']}\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": "8027ecc4", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "436e2862", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:29:27.677278Z", "iopub.status.busy": "2025-03-25T06:29:27.677164Z", "iopub.status.idle": "2025-03-25T06:29:27.682738Z", "shell.execute_reply": "2025-03-25T06:29:27.682452Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A new JSON file was created at: ../../output/preprocess/Angelman_Syndrome/cohort_info.json\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "from typing import Callable, Dict, Any, Optional\n", "\n", "# Analysis of gene expression data availability\n", "# Based on the background information, this is a study on gene expression in cultured neurons\n", "# with various treatments. This suggests it likely contains gene expression data.\n", "is_gene_available = True\n", "\n", "# Analysis of trait data availability\n", "# From the characteristics, we don't see any Angelman Syndrome trait information.\n", "# The data shows only treatment types, cell type, and strain with no human subjects.\n", "trait_row = None # No trait data available\n", "\n", "# Since there's no human data, age and gender are not available\n", "age_row = None\n", "gender_row = None\n", "\n", "# Define conversion functions\n", "def convert_trait(value):\n", " # This function would extract and convert trait values if they were available\n", " # Since there's no trait data, this is a placeholder function\n", " if value is None:\n", " return None\n", " if ':' in str(value):\n", " value = value.split(':', 1)[1].strip()\n", " # Binary conversion would go here if data were available\n", " return None\n", "\n", "def convert_age(value):\n", " # Placeholder function since age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " # Placeholder function since gender data is not available\n", " return None\n", "\n", "# Save metadata about dataset usability\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", "# Skip clinical feature extraction since trait_row is None\n", "# If trait_row were not None, we would extract clinical features here\n" ] }, { "cell_type": "markdown", "id": "e5dcd2b3", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "a2a8d4fd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:29:27.683730Z", "iopub.status.busy": "2025-03-25T06:29:27.683623Z", "iopub.status.idle": "2025-03-25T06:29:27.731903Z", "shell.execute_reply": "2025-03-25T06:29:27.731594Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "First 20 gene/probe identifiers:\n", "Index(['10338001', '10338002', '10338003', '10338004', '10338005', '10338006',\n", " '10338007', '10338008', '10338009', '10338010', '10338011', '10338012',\n", " '10338013', '10338014', '10338015', '10338016', '10338017', '10338018',\n", " '10338019', '10338020'],\n", " dtype='object', name='ID')\n", "\n", "Gene data dimensions: 35556 genes × 10 samples\n" ] } ], "source": [ "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Extract the gene expression data from the matrix file\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 3. Print the first 20 row IDs (gene or probe identifiers)\n", "print(\"\\nFirst 20 gene/probe identifiers:\")\n", "print(gene_data.index[:20])\n", "\n", "# 4. Print the dimensions of the gene expression data\n", "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n", "\n", "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n", "is_gene_available = True\n" ] }, { "cell_type": "markdown", "id": "5bfeca4c", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "9e7f3015", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:29:27.733099Z", "iopub.status.busy": "2025-03-25T06:29:27.732981Z", "iopub.status.idle": "2025-03-25T06:29:27.734774Z", "shell.execute_reply": "2025-03-25T06:29:27.734489Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers, these are numerical identifiers that appear to be probe IDs, \n", "# not standard human gene symbols. Human gene symbols would typically be alphabetical (like BRCA1, TP53, etc.)\n", "# or alphanumeric identifiers. These numerical identifiers likely need to be mapped to gene symbols.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "8541a68d", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "06bf0824", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:29:27.735878Z", "iopub.status.busy": "2025-03-25T06:29:27.735773Z", "iopub.status.idle": "2025-03-25T06:29:32.714301Z", "shell.execute_reply": "2025-03-25T06:29:32.713945Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['1415670_at', '1415671_at', '1415672_at', '1415673_at', '1415674_a_at'], 'GB_ACC': ['BC024686', 'NM_013477', 'NM_020585', 'NM_133900', 'NM_021789'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Mus musculus', 'Mus musculus', 'Mus musculus', 'Mus musculus', 'Mus musculus'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence'], 'Sequence Source': ['GenBank', 'GenBank', 'GenBank', 'GenBank', 'GenBank'], 'Target Description': ['gb:BC024686.1 /DB_XREF=gi:19354080 /FEA=FLmRNA /CNT=416 /TID=Mm.26422.1 /TIER=FL+Stack /STK=110 /UG=Mm.26422 /LL=54161 /UG_GENE=Copg1 /DEF=Mus musculus, coatomer protein complex, subunit gamma 1, clone MGC:30335 IMAGE:3992144, mRNA, complete cds. /PROD=coatomer protein complex, subunit gamma 1 /FL=gb:AF187079.1 gb:BC024686.1 gb:NM_017477.1 gb:BC024896.1', 'gb:NM_013477.1 /DB_XREF=gi:7304908 /GEN=Atp6v0d1 /FEA=FLmRNA /CNT=197 /TID=Mm.1081.1 /TIER=FL+Stack /STK=114 /UG=Mm.1081 /LL=11972 /DEF=Mus musculus ATPase, H+ transporting, lysosomal 38kDa, V0 subunit D isoform 1 (Atp6v0d1), mRNA. /PROD=ATPase, H+ transporting, lysosomal 38kDa, V0subunit D isoform 1 /FL=gb:U21549.1 gb:U13840.1 gb:BC011075.1 gb:NM_013477.1', 'gb:NM_020585.1 /DB_XREF=gi:10181207 /GEN=AB041568 /FEA=FLmRNA /CNT=213 /TID=Mm.17035.1 /TIER=FL+Stack /STK=102 /UG=Mm.17035 /LL=57437 /DEF=Mus musculus hypothetical protein, MNCb-1213 (AB041568), mRNA. /PROD=hypothetical protein, MNCb-1213 /FL=gb:BC016894.1 gb:NM_020585.1', 'gb:NM_133900.1 /DB_XREF=gi:19527115 /GEN=AI480570 /FEA=FLmRNA /CNT=139 /TID=Mm.10623.1 /TIER=FL+Stack /STK=96 /UG=Mm.10623 /LL=100678 /DEF=Mus musculus expressed sequence AI480570 (AI480570), mRNA. /PROD=expressed sequence AI480570 /FL=gb:BC002251.1 gb:NM_133900.1', 'gb:NM_021789.1 /DB_XREF=gi:11140824 /GEN=Sbdn /FEA=FLmRNA /CNT=163 /TID=Mm.29814.1 /TIER=FL+Stack /STK=95 /UG=Mm.29814 /LL=60409 /DEF=Mus musculus synbindin (Sbdn), mRNA. /PROD=synbindin /FL=gb:NM_021789.1 gb:AF233340.1'], 'Representative Public ID': ['BC024686', 'NM_013477', 'NM_020585', 'NM_133900', 'NM_021789'], 'Gene Title': ['coatomer protein complex, subunit gamma 1', 'ATPase, H+ transporting, lysosomal V0 subunit D1', 'golgi autoantigen, golgin subfamily a, 7', 'phosphoserine phosphatase', 'trafficking protein particle complex 4'], 'Gene Symbol': ['Copg1', 'Atp6v0d1', 'Golga7', 'Psph', 'Trappc4'], 'ENTREZ_GENE_ID': ['54161', '11972', '57437', '100678', '60409'], 'RefSeq Transcript ID': ['NM_017477 /// NM_201244 /// XM_006506386', 'NM_013477', 'NM_001042484 /// NM_020585 /// XM_006509179', 'NM_133900 /// XM_006504274 /// XM_006504275', 'NM_021789 /// XM_006510523'], 'Gene Ontology Biological Process': ['0006810 // transport // inferred from electronic annotation /// 0006886 // intracellular protein transport // inferred from electronic annotation /// 0015031 // protein transport // inferred from electronic annotation /// 0016192 // vesicle-mediated transport // inferred from electronic annotation /// 0051683 // establishment of Golgi localization // not recorded /// 0072384 // organelle transport along microtubule // not recorded', '0006200 // ATP catabolic process // inferred from direct assay /// 0006810 // transport // inferred from electronic annotation /// 0006811 // ion transport // inferred from electronic annotation /// 0007420 // brain development // inferred from electronic annotation /// 0015991 // ATP hydrolysis coupled proton transport // inferred from electronic annotation /// 0015992 // proton transport // inferred from electronic annotation /// 0030030 // cell projection organization // inferred from electronic annotation /// 0042384 // cilium assembly // inferred from sequence or structural similarity /// 1902600 // hydrogen ion transmembrane transport // inferred from direct assay', '0006893 // Golgi to plasma membrane transport // not recorded /// 0018230 // peptidyl-L-cysteine S-palmitoylation // not recorded /// 0043001 // Golgi to plasma membrane protein transport // not recorded /// 0050821 // protein stabilization // not recorded', '0006563 // L-serine metabolic process // not recorded /// 0006564 // L-serine biosynthetic process // not recorded /// 0008152 // metabolic process // inferred from electronic annotation /// 0008652 // cellular amino acid biosynthetic process // inferred from electronic annotation /// 0009612 // response to mechanical stimulus // inferred from electronic annotation /// 0016311 // dephosphorylation // not recorded /// 0031667 // response to nutrient levels // inferred from electronic annotation /// 0033574 // response to testosterone // inferred from electronic annotation', '0006810 // transport // inferred from electronic annotation /// 0006888 // ER to Golgi vesicle-mediated transport // inferred from electronic annotation /// 0016192 // vesicle-mediated transport // traceable author statement /// 0016358 // dendrite development // inferred from direct assay /// 0045212 // neurotransmitter receptor biosynthetic process // traceable author statement'], 'Gene Ontology Cellular Component': ['0000139 // Golgi membrane // not recorded /// 0005634 // nucleus // inferred from electronic annotation /// 0005737 // cytoplasm // inferred from electronic annotation /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0005829 // cytosol // inferred from electronic annotation /// 0016020 // membrane // inferred from electronic annotation /// 0030117 // membrane coat // inferred from electronic annotation /// 0030126 // COPI vesicle coat // inferred from electronic annotation /// 0030663 // COPI-coated vesicle membrane // inferred from electronic annotation /// 0031410 // cytoplasmic vesicle // inferred from electronic annotation', '0005765 // lysosomal membrane // not recorded /// 0005769 // early endosome // inferred from direct assay /// 0005813 // centrosome // not recorded /// 0008021 // synaptic vesicle // not recorded /// 0016020 // membrane // not recorded /// 0016324 // apical plasma membrane // not recorded /// 0016471 // vacuolar proton-transporting V-type ATPase complex // not recorded /// 0033179 // proton-transporting V-type ATPase, V0 domain // inferred from electronic annotation /// 0043005 // neuron projection // not recorded /// 0043234 // protein complex // not recorded /// 0043679 // axon terminus // not recorded /// 0070062 // extracellular vesicular exosome // not recorded', '0000139 // Golgi membrane // not recorded /// 0002178 // palmitoyltransferase complex // not recorded /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0005795 // Golgi stack // not recorded /// 0016020 // membrane // inferred from electronic annotation /// 0031228 // intrinsic component of Golgi membrane // not recorded /// 0070062 // extracellular vesicular exosome // not recorded', '0005737 // cytoplasm // not recorded /// 0043005 // neuron projection // not recorded', '0005622 // intracellular // inferred from electronic annotation /// 0005783 // endoplasmic reticulum // inferred from electronic annotation /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0005795 // Golgi stack // inferred from direct assay /// 0005801 // cis-Golgi network // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from electronic annotation /// 0008021 // synaptic vesicle // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0030008 // TRAPP complex // inferred from direct assay /// 0030054 // cell junction // inferred from electronic annotation /// 0030425 // dendrite // inferred from direct assay /// 0045202 // synapse // inferred from direct assay /// 0045211 // postsynaptic membrane // inferred from electronic annotation'], 'Gene Ontology Molecular Function': ['0005198 // structural molecule activity // inferred from electronic annotation /// 0005488 // binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from electronic annotation', '0005515 // protein binding // inferred from electronic annotation /// 0008553 // hydrogen-exporting ATPase activity, phosphorylative mechanism // inferred from direct assay /// 0015078 // hydrogen ion transmembrane transporter activity // inferred from electronic annotation /// 0032403 // protein complex binding // not recorded', nan, \"0000287 // magnesium ion binding // not recorded /// 0004647 // phosphoserine phosphatase activity // not recorded /// 0005509 // calcium ion binding // not recorded /// 0008253 // 5'-nucleotidase activity // inferred from electronic annotation /// 0016787 // hydrolase activity // inferred from electronic annotation /// 0016791 // phosphatase activity // inferred from electronic annotation /// 0042803 // protein homodimerization activity // not recorded /// 0046872 // metal ion binding // inferred from electronic annotation\", '0005515 // protein binding // inferred from physical interaction']}\n" ] } ], "source": [ "# 1. First get the file paths using geo_get_relevant_filepaths function\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. 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", "# 3. 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": "9567fd64", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "415ffd90", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:29:32.715696Z", "iopub.status.busy": "2025-03-25T06:29:32.715571Z", "iopub.status.idle": "2025-03-25T06:29:32.910965Z", "shell.execute_reply": "2025-03-25T06:29:32.910582Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data index preview:\n", "['10338001', '10338002', '10338003', '10338004', '10338005']\n", "\n", "Gene annotation ID preview:\n", "['1415670_at', '1415671_at', '1415672_at', '1415673_at', '1415674_a_at']\n", "\n", "This appears to be a mouse dataset with platform mismatch between expression data and annotation.\n", "Will save the original probe-level data for further analysis.\n", "\n", "Saved gene expression data to ../../output/preprocess/Angelman_Syndrome/gene_data/GSE43900.csv\n", "Gene data shape (using original probe IDs): (35556, 10)\n", "\n", "Preserving original probe-level data for downstream analysis.\n" ] } ], "source": [ "# 1. Look at what's in our gene expression data versus annotation data\n", "print(\"Gene expression data index preview:\")\n", "print(gene_data.index[:5].tolist())\n", "\n", "print(\"\\nGene annotation ID preview:\")\n", "print(gene_annotation['ID'][:5].tolist())\n", "\n", "# The gene expression data uses numerical IDs while the annotation uses different format IDs\n", "# Since there's a platform mismatch, we'll use the original probe data but normalize it\n", "print(\"\\nThis appears to be a mouse dataset with platform mismatch between expression data and annotation.\")\n", "print(\"Will save the original probe-level data for further analysis.\")\n", "\n", "# Check if the gene expression data has any rows\n", "if gene_data.shape[0] > 0:\n", " # Save the gene expression data with the original probe IDs\n", " # Ensure the output directory exists\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " gene_data.to_csv(out_gene_data_file)\n", " \n", " print(f\"\\nSaved gene expression data to {out_gene_data_file}\")\n", " print(f\"Gene data shape (using original probe IDs): {gene_data.shape}\")\n", " \n", " # Set the flag to indicate gene data is available\n", " is_gene_available = True\n", "else:\n", " print(\"\\nNo gene expression data was found.\")\n", " is_gene_available = False\n", "\n", "# Keep the gene data in its original form for further steps\n", "mapped_gene_data = gene_data.copy()\n", "print(\"\\nPreserving original probe-level data for downstream analysis.\")\n" ] }, { "cell_type": "markdown", "id": "ddb2dc65", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "b6a7f679", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:29:32.912269Z", "iopub.status.busy": "2025-03-25T06:29:32.912151Z", "iopub.status.idle": "2025-03-25T06:29:33.093943Z", "shell.execute_reply": "2025-03-25T06:29:33.093617Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalization returned empty results - keeping original gene data.\n", "Gene data shape to be saved: (35556, 10)\n", "First 5 gene/probe IDs: ['10338001', '10338002', '10338003', '10338004', '10338005']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to ../../output/preprocess/Angelman_Syndrome/gene_data/GSE43900.csv\n", "Trait data is unavailable in this dataset.\n", "Abnormality detected in the cohort: GSE43900. Preprocessing failed.\n", "Dataset deemed not usable for human trait associational studies.\n" ] } ], "source": [ "# 1. The normalized gene data was empty, which suggests that the normalization was unsuccessful.\n", "# Let's keep the original gene data instead of attempting to normalize it.\n", "print(\"Normalization returned empty results - keeping original gene data.\")\n", "gene_data_to_save = gene_data\n", "print(f\"Gene data shape to be saved: {gene_data_to_save.shape}\")\n", "print(f\"First 5 gene/probe IDs: {gene_data_to_save.index[:5].tolist()}\")\n", "\n", "# Save the gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data_to_save.to_csv(out_gene_data_file)\n", "print(f\"Gene data saved to {out_gene_data_file}\")\n", "\n", "# Since we previously determined that trait data is not available in this dataset,\n", "# we won't attempt to load or create clinical data that doesn't exist\n", "is_trait_available = False\n", "print(\"Trait data is unavailable in this dataset.\")\n", "\n", "# Since this is mouse data and doesn't contain human trait (Angelman Syndrome) information,\n", "# the dataset is not usable for our human trait association studies\n", "is_biased = True # Set to True to indicate dataset is not suitable\n", "\n", "# Create a minimal valid DataFrame for metadata using gene data sample IDs\n", "sample_df = pd.DataFrame(index=gene_data.columns)\n", "\n", "# Validate and save cohort info with appropriate values\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=is_trait_available,\n", " is_biased=is_biased, # Providing a value even when trait is not available\n", " df=sample_df,\n", " note=\"This dataset contains mouse neuron gene expression data with various treatments, but no human Angelman Syndrome trait information.\"\n", ")\n", "\n", "# No linked data to save since trait data is not available\n", "print(\"Dataset deemed not usable for human trait associational studies.\")" ] } ], "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 }