{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "724d13d2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:00:37.902921Z", "iopub.status.busy": "2025-03-25T08:00:37.902808Z", "iopub.status.idle": "2025-03-25T08:00:38.063723Z", "shell.execute_reply": "2025-03-25T08:00:38.063365Z" } }, "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 = \"Celiac_Disease\"\n", "cohort = \"GSE106260\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Celiac_Disease\"\n", "in_cohort_dir = \"../../input/GEO/Celiac_Disease/GSE106260\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Celiac_Disease/GSE106260.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Celiac_Disease/gene_data/GSE106260.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Celiac_Disease/clinical_data/GSE106260.csv\"\n", "json_path = \"../../output/preprocess/Celiac_Disease/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "ce7b4dce", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "6e818a77", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:00:38.065224Z", "iopub.status.busy": "2025-03-25T08:00:38.065086Z", "iopub.status.idle": "2025-03-25T08:00:38.207264Z", "shell.execute_reply": "2025-03-25T08:00:38.206925Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Immunopathology of childhood celiac disease-Key role of intestinal epithelial cells\"\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: colon carcinoma cell line T84'], 1: ['treatment: CTR', 'treatment: A. graevenitzii', 'treatment: bacteria mix', 'treatment: bacteria mix with gluten', 'treatment: L. umeaense', 'treatment: P. jejuni (isolates CD3:28)', 'treatment: P. jejuni (isolates CD3:27)', 'treatment: Gluten', 'treatment: bacteria mix + Gluten']}\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": "da063225", "metadata": {}, "source": [ "### Step 2: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "53f626ba", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:00:38.208470Z", "iopub.status.busy": "2025-03-25T08:00:38.208355Z", "iopub.status.idle": "2025-03-25T08:00:38.396627Z", "shell.execute_reply": "2025-03-25T08:00:38.396256Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Celiac_Disease/GSE106260/GSE106260-GPL10558_series_matrix.txt.gz\n", "Gene data shape: (47230, 36)\n", "First 20 gene/probe identifiers:\n", "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n", " 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n", " 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n", " 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n", " 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Get the SOFT and matrix file paths again \n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"Matrix file found: {matrix_file}\")\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_data\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " \n", " # 3. Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "5e2bb07f", "metadata": {}, "source": [ "### Step 3: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 4, "id": "94c2d565", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:00:38.397951Z", "iopub.status.busy": "2025-03-25T08:00:38.397834Z", "iopub.status.idle": "2025-03-25T08:00:38.399922Z", "shell.execute_reply": "2025-03-25T08:00:38.399609Z" } }, "outputs": [], "source": [ "# Identifying the gene identifiers\n", "# These are ILMN identifiers from Illumina microarray platforms\n", "# They are not standard human gene symbols and need to be mapped\n", "# ILMN_XXXXXXX is the Illumina BeadChip array ID format that needs mapping to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "33123198", "metadata": {}, "source": [ "### Step 4: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 5, "id": "129aca12", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:00:38.401132Z", "iopub.status.busy": "2025-03-25T08:00:38.401024Z", "iopub.status.idle": "2025-03-25T08:00:43.655212Z", "shell.execute_reply": "2025-03-25T08:00:43.654821Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['ILMN_1722532', 'ILMN_1708805', 'ILMN_1672526', 'ILMN_1703284', 'ILMN_2185604'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Source': ['RefSeq', 'RefSeq', 'RefSeq', 'RefSeq', 'RefSeq'], 'Search_Key': ['ILMN_25544', 'ILMN_10519', 'ILMN_17234', 'ILMN_502', 'ILMN_19244'], 'Transcript': ['ILMN_25544', 'ILMN_10519', 'ILMN_17234', 'ILMN_502', 'ILMN_19244'], 'ILMN_Gene': ['JMJD1A', 'NCOA3', 'LOC389834', 'SPIRE2', 'C17ORF77'], 'Source_Reference_ID': ['NM_018433.3', 'NM_006534.2', 'NM_001013655.1', 'NM_032451.1', 'NM_152460.2'], 'RefSeq_ID': ['NM_018433.3', 'NM_006534.2', 'NM_001013655.1', 'NM_032451.1', 'NM_152460.2'], 'Entrez_Gene_ID': [55818.0, 8202.0, 389834.0, 84501.0, 146723.0], 'GI': [46358420.0, 32307123.0, 61966764.0, 55749599.0, 48255961.0], 'Accession': ['NM_018433.3', 'NM_006534.2', 'NM_001013655.1', 'NM_032451.1', 'NM_152460.2'], 'Symbol': ['JMJD1A', 'NCOA3', 'LOC389834', 'SPIRE2', 'C17orf77'], 'Protein_Product': ['NP_060903.2', 'NP_006525.2', 'NP_001013677.1', 'NP_115827.1', 'NP_689673.2'], 'Array_Address_Id': ['1240504', '2760390', '1740239', '6040014', '6550343'], 'Probe_Type': ['S', 'A', 'S', 'S', 'S'], 'Probe_Start': [4359.0, 7834.0, 3938.0, 3080.0, 2372.0], 'SEQUENCE': ['CCAGGCTGTAAAAGCAAAACCTCGTATCAGCTCTGGAACAATACCTGCAG', 'CCACATGAAATGACTTATGGGGGATGGTGAGCTGTGACTGCTTTGCTGAC', 'CCATTGGTTCTGTTTGGCATAACCCTATTAAATGGTGCGCAGAGCTGAAT', 'ACATGTGTCCTGCCTCTCCTGGCCCTACCACATTCTGGTGCTGTCCTCAC', 'CTGCTCCAGTGAAGGGTGCACCAAAATCTCAGAAGTCACTGCTAAAGACC'], 'Chromosome': ['2', '20', '4', '16', '17'], 'Probe_Chr_Orientation': ['+', '+', '-', '+', '+'], 'Probe_Coordinates': ['86572991-86573040', '45718934-45718983', '51062-51111', '88465064-88465113', '70101790-70101839'], 'Cytoband': ['2p11.2e', '20q13.12c', nan, '16q24.3b', '17q25.1b'], 'Definition': ['Homo sapiens jumonji domain containing 1A (JMJD1A), mRNA.', 'Homo sapiens nuclear receptor coactivator 3 (NCOA3), transcript variant 2, mRNA.', 'Homo sapiens hypothetical gene supported by AK123403 (LOC389834), mRNA.', 'Homo sapiens spire homolog 2 (Drosophila) (SPIRE2), mRNA.', 'Homo sapiens chromosome 17 open reading frame 77 (C17orf77), mRNA.'], 'Ontology_Component': ['nucleus [goid 5634] [evidence IEA]', 'nucleus [goid 5634] [pmid 9267036] [evidence NAS]', nan, nan, nan], 'Ontology_Process': ['chromatin modification [goid 16568] [evidence IEA]; transcription [goid 6350] [evidence IEA]; regulation of transcription, DNA-dependent [goid 6355] [evidence IEA]', 'positive regulation of transcription, DNA-dependent [goid 45893] [pmid 15572661] [evidence NAS]; androgen receptor signaling pathway [goid 30521] [pmid 15572661] [evidence NAS]; signal transduction [goid 7165] [evidence IEA]', nan, nan, nan], 'Ontology_Function': ['oxidoreductase activity [goid 16491] [evidence IEA]; oxidoreductase activity, acting on single donors with incorporation of molecular oxygen, incorporation of two atoms of oxygen [goid 16702] [evidence IEA]; zinc ion binding [goid 8270] [evidence IEA]; metal ion binding [goid 46872] [evidence IEA]; iron ion binding [goid 5506] [evidence IEA]', 'acyltransferase activity [goid 8415] [evidence IEA]; thyroid hormone receptor binding [goid 46966] [pmid 9346901] [evidence NAS]; transferase activity [goid 16740] [evidence IEA]; transcription coactivator activity [goid 3713] [pmid 15572661] [evidence NAS]; androgen receptor binding [goid 50681] [pmid 15572661] [evidence NAS]; histone acetyltransferase activity [goid 4402] [pmid 9267036] [evidence TAS]; signal transducer activity [goid 4871] [evidence IEA]; transcription regulator activity [goid 30528] [evidence IEA]; protein binding [goid 5515] [pmid 15698540] [evidence IPI]', nan, 'zinc ion binding [goid 8270] [evidence IEA]', nan], 'Synonyms': ['JHMD2A; JMJD1; TSGA; KIAA0742; DKFZp686A24246; DKFZp686P07111', 'CAGH16; TNRC14; pCIP; ACTR; MGC141848; CTG26; AIB-1; TRAM-1; TNRC16; AIB1; SRC3; SRC-1; RAC3', nan, 'MGC117166; Spir-2', 'FLJ31882'], 'GB_ACC': ['NM_018433.3', 'NM_006534.2', 'NM_001013655.1', 'NM_032451.1', 'NM_152460.2']}\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": "285c4621", "metadata": {}, "source": [ "### Step 5: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 6, "id": "75b6e24e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:00:43.656608Z", "iopub.status.busy": "2025-03-25T08:00:43.656483Z", "iopub.status.idle": "2025-03-25T08:00:43.907537Z", "shell.execute_reply": "2025-03-25T08:00:43.907142Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (24526, 2)\n", "First 5 rows of gene mapping:\n", " ID Gene\n", "0 ILMN_1722532 JMJD1A\n", "1 ILMN_1708805 NCOA3\n", "2 ILMN_1672526 LOC389834\n", "3 ILMN_1703284 SPIRE2\n", "4 ILMN_2185604 C17orf77\n", "Mapped gene expression data shape: (17824, 36)\n", "First 10 gene symbols in the mapped data:\n", "Index(['A1BG', 'A2BP1', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1',\n", " 'AAAS', 'AACS'],\n", " dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "After normalization, gene expression data shape: (16856, 36)\n", "First 10 normalized gene symbols:\n", "Index(['A1BG', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS',\n", " 'AACS', 'AADAC'],\n", " dtype='object', name='Gene')\n" ] } ], "source": [ "# 1. Determine the mapping between gene identifiers and gene symbols\n", "# From the annotation preview, we can see that:\n", "# - The 'ID' column contains the ILMN probe identifiers (which match gene_data.index)\n", "# - The 'Symbol' column contains the gene symbols we want to map to\n", "\n", "# 2. Get the gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n", "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n", "print(\"First 5 rows of gene mapping:\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply the gene mapping to convert probe-level data to gene-level expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"Mapped gene expression data shape: {gene_data.shape}\")\n", "print(\"First 10 gene symbols in the mapped data:\")\n", "print(gene_data.index[:10])\n", "\n", "# Normalize the gene symbols to handle synonyms and variants\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"After normalization, gene expression data shape: {gene_data.shape}\")\n", "print(\"First 10 normalized gene symbols:\")\n", "print(gene_data.index[:10])\n" ] }, { "cell_type": "markdown", "id": "d2e3965a", "metadata": {}, "source": [ "### Step 6: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 7, "id": "438c76cd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:00:43.908887Z", "iopub.status.busy": "2025-03-25T08:00:43.908773Z", "iopub.status.idle": "2025-03-25T08:00:44.284236Z", "shell.execute_reply": "2025-03-25T08:00:44.283797Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Celiac_Disease/gene_data/GSE106260.csv\n", "Clinical data from matrix file:\n", " !Sample_geo_accession GSM2753759 \\\n", "0 !Sample_characteristics_ch1 cell line: colon carcinoma cell line T84 \n", "1 !Sample_characteristics_ch1 treatment: CTR \n", "\n", " GSM2753760 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: CTR \n", "\n", " GSM2753761 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: CTR \n", "\n", " GSM2753762 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: A. graevenitzii \n", "\n", " GSM2753763 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: A. graevenitzii \n", "\n", " GSM2753764 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: A. graevenitzii \n", "\n", " GSM2753765 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: bacteria mix \n", "\n", " GSM2753766 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: bacteria mix \n", "\n", " GSM2753767 ... \\\n", "0 cell line: colon carcinoma cell line T84 ... \n", "1 treatment: bacteria mix ... \n", "\n", " GSM2769613 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: CTR \n", "\n", " GSM2769614 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: Gluten \n", "\n", " GSM2769615 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: Gluten \n", "\n", " GSM2769616 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: Gluten \n", "\n", " GSM2769617 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: bacteria mix \n", "\n", " GSM2769618 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: bacteria mix \n", "\n", " GSM2769619 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: bacteria mix \n", "\n", " GSM2769620 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: bacteria mix + Gluten \n", "\n", " GSM2769621 \\\n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: bacteria mix + Gluten \n", "\n", " GSM2769622 \n", "0 cell line: colon carcinoma cell line T84 \n", "1 treatment: bacteria mix + Gluten \n", "\n", "[2 rows x 37 columns]\n", "No cell type information found in clinical data.\n", "Clinical data saved to ../../output/preprocess/Celiac_Disease/clinical_data/GSE106260.csv\n", "Linked data shape: (36, 16857)\n", "Linked data preview (first 5 rows, 5 columns):\n", " Celiac_Disease A1BG A2M A2ML1 A3GALT2\n", "GSM2753759 NaN 1.845881 -8.964532 -7.624859 1.415234\n", "GSM2753760 NaN 22.427440 -3.645515 -3.813428 -4.201897\n", "GSM2753761 NaN -0.964384 -5.007949 -10.667450 -1.092120\n", "GSM2753762 NaN 15.511400 -10.751940 -6.643857 2.111914\n", "GSM2753763 NaN 8.864400 -5.167774 -10.755740 2.743086\n", "Data shape after handling missing values: (0, 1)\n", "Quartiles for 'Celiac_Disease':\n", " 25%: nan\n", " 50% (Median): nan\n", " 75%: nan\n", "Min: nan\n", "Max: nan\n", "The distribution of the feature 'Celiac_Disease' in this dataset is fine.\n", "\n", "Abnormality detected in the cohort: GSE106260. Preprocessing failed.\n", "A new JSON file was created at: ../../output/preprocess/Celiac_Disease/cohort_info.json\n", "Dataset is not usable for analysis. No linked data file 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. Gene data is already normalized from previous step - no need to normalize again\n", "# Save the normalized gene data\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\"Normalized gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Process clinical data from the clinical dataframe we obtained in Step 1\n", "# From the characteristics dictionary, we know we need to analyze the clinical features in detail\n", "print(\"Clinical data from matrix file:\")\n", "print(clinical_data.head())\n", "\n", "# The clinical data is sparse for this dataset as seen in Step 1\n", "# Extract information from the sample characteristics for celiac disease analysis\n", "# In this case, the cell type information is a proxy for trait - intestinal epithelial cells vs intraepithelial lymphocytes\n", "def convert_cell_type(cell_type_str):\n", " if isinstance(cell_type_str, str):\n", " if 'epithelial' in cell_type_str.lower():\n", " return 0 # Control/normal\n", " elif 'lymphocytes' in cell_type_str.lower():\n", " return 1 # Disease/case\n", " return None # For any other values or missing data\n", "\n", "# Process the clinical data to extract trait information\n", "# Find the row index that contains cell type information\n", "cell_type_row = None\n", "for idx, row_data in clinical_data.iterrows():\n", " row_values = list(row_data.values)\n", " for val in row_values:\n", " if isinstance(val, str) and 'cell type' in val.lower():\n", " cell_type_row = idx\n", " break\n", " if cell_type_row is not None:\n", " break\n", "\n", "# If we found the row with cell type info, extract the trait data\n", "if cell_type_row is not None:\n", " selected_clinical_data = geo_select_clinical_features(\n", " clinical_data,\n", " trait=trait,\n", " trait_row=cell_type_row,\n", " convert_trait=convert_cell_type\n", " )\n", " print(\"Selected clinical features:\")\n", " print(selected_clinical_data)\n", "else:\n", " # If no cell type info is found, we'll need to handle this case\n", " print(\"No cell type information found in clinical data.\")\n", " # Create a dummy clinical dataframe with just the IDs from gene data\n", " selected_clinical_data = pd.DataFrame(\n", " index=[trait], \n", " columns=gene_data.columns,\n", " data=[[None] * len(gene_data.columns)]\n", " )\n", "\n", "# Save the processed clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "selected_clinical_data.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(selected_clinical_data, gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(\"Linked data preview (first 5 rows, 5 columns):\")\n", "print(linked_data.iloc[:5, :5])\n", "\n", "# 4. Handle missing values\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 5. Check for bias in features\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 6. Validate and save 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_biased,\n", " df=linked_data,\n", " note=\"Dataset contains gene expression data from intestinal epithelial cells vs intraepithelial lymphocytes in Celiac Disease study.\"\n", ")\n", "\n", "# 7. Save the linked data if usable\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset is not usable for analysis. No linked data file 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 }