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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "6642163a",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:47:37.333660Z"
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"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 = \"Rectal_Cancer\"\n",
"cohort = \"GSE94104\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Rectal_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Rectal_Cancer/GSE94104\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Rectal_Cancer/GSE94104.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Rectal_Cancer/gene_data/GSE94104.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Rectal_Cancer/clinical_data/GSE94104.csv\"\n",
"json_path = \"../../output/preprocess/Rectal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "23d0fca2",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2ad74920",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:37.335325Z",
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}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Transcriptional analysis of locally advanced rectal cancer pre-therapeutic biopsies and post-therapeutic resections\"\n",
"!Series_summary\t\"Understanding transcriptional changes in locally advanced rectal cancer which are therapy-related and dependent upon tumour regression will drive stratified medicine in the rectal cancer paradigm\"\n",
"!Series_overall_design\t\"Total RNA was obtained from 40 matched formalin fixed paraffin embedded (FFPE) LARC biopsy and resections specimens provided by the Northern Ireland Biobank and arrayed using the Illumina HumanHT-12 WG-DASL V4 expression beadchip\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Locally Advanced Rectal Cancer (LARC)'], 1: ['tissue type: Biopsy', 'tissue type: Resection'], 2: ['tumour regression grade: 1', 'tumour regression grade: 2', 'tumour regression grade: 3']}\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": "0bfb6de3",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8e808c8a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:37.473763Z",
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"shell.execute_reply": "2025-03-25T03:47:37.481713Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical features preview:\n",
"{'GSM2469019': [0.0], 'GSM2469020': [0.0], 'GSM2469021': [0.0], 'GSM2469022': [0.0], 'GSM2469023': [0.0], 'GSM2469024': [0.0], 'GSM2469025': [0.0], 'GSM2469026': [0.0], 'GSM2469027': [1.0], 'GSM2469028': [1.0], 'GSM2469029': [0.0], 'GSM2469030': [0.0], 'GSM2469031': [1.0], 'GSM2469032': [1.0], 'GSM2469033': [1.0], 'GSM2469034': [1.0], 'GSM2469035': [0.0], 'GSM2469036': [0.0], 'GSM2469037': [0.0], 'GSM2469038': [0.0], 'GSM2469039': [1.0], 'GSM2469040': [1.0], 'GSM2469041': [0.0], 'GSM2469042': [0.0], 'GSM2469043': [0.0], 'GSM2469044': [0.0], 'GSM2469045': [0.0], 'GSM2469046': [0.0], 'GSM2469047': [0.0], 'GSM2469048': [0.0], 'GSM2469049': [0.0], 'GSM2469050': [0.0], 'GSM2469051': [0.0], 'GSM2469052': [0.0], 'GSM2469053': [0.0], 'GSM2469054': [0.0], 'GSM2469055': [0.0], 'GSM2469056': [0.0], 'GSM2469057': [0.0], 'GSM2469058': [0.0], 'GSM2469059': [0.0], 'GSM2469060': [0.0], 'GSM2469061': [1.0], 'GSM2469062': [1.0], 'GSM2469063': [1.0], 'GSM2469064': [1.0], 'GSM2469065': [0.0], 'GSM2469066': [0.0], 'GSM2469067': [0.0], 'GSM2469068': [0.0], 'GSM2469069': [0.0], 'GSM2469070': [1.0], 'GSM2469071': [0.0], 'GSM2469072': [0.0], 'GSM2469073': [0.0], 'GSM2469074': [0.0], 'GSM2469075': [0.0], 'GSM2469076': [1.0], 'GSM2469077': [0.0], 'GSM2469078': [0.0], 'GSM2469079': [0.0], 'GSM2469080': [1.0], 'GSM2469081': [1.0], 'GSM2469082': [0.0], 'GSM2469083': [1.0], 'GSM2469084': [0.0], 'GSM2469085': [0.0], 'GSM2469086': [0.0], 'GSM2469087': [1.0], 'GSM2469088': [0.0], 'GSM2469089': [1.0], 'GSM2469090': [1.0], 'GSM2469091': [0.0], 'GSM2469092': [1.0], 'GSM2469093': [0.0], 'GSM2469094': [0.0], 'GSM2469095': [0.0], 'GSM2469096': [0.0], 'GSM2469097': [0.0], 'GSM2469098': [1.0]}\n"
]
}
],
"source": [
"# 1. Gene Expression Availability\n",
"# Based on the background information, the dataset appears to contain gene expression data\n",
"# as it mentions \"expression beadchip\" data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait: \"tumour regression grade\" is recorded in row 2\n",
"trait_row = 2\n",
"\n",
"# Age is not explicitly mentioned in the characteristics dictionary\n",
"age_row = None\n",
"\n",
"# Gender is not explicitly mentioned in the characteristics dictionary\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert tumour regression grade to binary.\n",
" Grade 1-2 (good regression) -> 0, Grade 3 (poor regression) -> 1\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" grade = int(value)\n",
" if grade == 1 or grade == 2:\n",
" return 0 # Good regression\n",
" elif grade == 3:\n",
" return 1 # Poor regression\n",
" else:\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Placeholder function for age conversion, not used in this dataset\"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Placeholder function for gender conversion, not used in this dataset\"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait availability based on whether trait_row is None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Initial filtering on usability\n",
"validate_and_save_cohort_info(\n",
" is_final=False, \n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" clinical_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 resulting DataFrame\n",
" print(\"Clinical features preview:\")\n",
" print(preview_df(clinical_features))\n",
" \n",
" # Save to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n"
]
},
{
"cell_type": "markdown",
"id": "a4a74725",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0450be96",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:37.482988Z",
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"shell.execute_reply": "2025-03-25T03:47:37.716137Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['ILMN_1343291', 'ILMN_1651209', 'ILMN_1651228', 'ILMN_1651229',\n",
" 'ILMN_1651235', 'ILMN_1651236', 'ILMN_1651237', 'ILMN_1651238',\n",
" 'ILMN_1651254', 'ILMN_1651260', 'ILMN_1651262', 'ILMN_1651268',\n",
" 'ILMN_1651278', 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651286',\n",
" 'ILMN_1651292', 'ILMN_1651303', 'ILMN_1651309', 'ILMN_1651315'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "6fabf12d",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "813412ff",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:37.718035Z",
"iopub.status.busy": "2025-03-25T03:47:37.717919Z",
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"shell.execute_reply": "2025-03-25T03:47:37.719433Z"
}
},
"outputs": [],
"source": [
"# These are Illumina probe IDs (ILMN_*), which are not human gene symbols\n",
"# They need to be mapped to human gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "ce20aaf7",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3200eda8",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:37.720780Z",
"iopub.status.busy": "2025-03-25T03:47:37.720682Z",
"iopub.status.idle": "2025-03-25T03:47:41.913085Z",
"shell.execute_reply": "2025-03-25T03:47:41.912672Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['ILMN_3166687', 'ILMN_3165566', 'ILMN_3164811', 'ILMN_3165363', 'ILMN_3166511'], 'Transcript': ['ILMN_333737', 'ILMN_333646', 'ILMN_333584', 'ILMN_333628', 'ILMN_333719'], 'Species': ['ILMN Controls', 'ILMN Controls', 'ILMN Controls', 'ILMN Controls', 'ILMN Controls'], 'Source': ['ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls'], 'Search_Key': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'ILMN_Gene': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'Source_Reference_ID': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': ['DQ516750', 'DQ883654', 'DQ668364', 'DQ516785', 'DQ854995'], 'Symbol': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'Protein_Product': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5270161.0, 4260594.0, 7610424.0, 5260356.0, 2030196.0], 'Probe_Type': ['S', 'S', 'S', 'S', 'S'], 'Probe_Start': [12.0, 224.0, 868.0, 873.0, 130.0], 'SEQUENCE': ['CCCATGTGTCCAATTCTGAATATCTTTCCAGCTAAGTGCTTCTGCCCACC', 'GGATTAACTGCTGTGGTGTGTCATACTCGGCTACCTCCTGGTTTGGCGTC', 'GACCACGCCTTGTAATCGTATGACACGCGCTTGACACGACTGAATCCAGC', 'CTGCAATGCCATTAACAACCTTAGCACGGTATTTCCAGTAGCTGGTGAGC', 'CGTGCAGACAGGGATCGTAAGGCGATCCAGCCGGTATACCTTAGTCACAT'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': ['Methanocaldococcus jannaschii spike-in control MJ-500-33 genomic sequence', 'Synthetic construct clone NISTag13 external RNA control sequence', 'Synthetic construct clone TagJ microarray control', 'Methanocaldococcus jannaschii spike-in control MJ-1000-68 genomic sequence', 'Synthetic construct clone AG006.1100 external RNA control sequence'], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': ['DQ516750', 'DQ883654', 'DQ668364', 'DQ516785', 'DQ854995']}\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": "2d9740bf",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "df678434",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:41.914269Z",
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"shell.execute_reply": "2025-03-25T03:47:42.980177Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found mapping for 29377 probes\n",
"First few rows of mapping dataframe:\n",
" ID Gene\n",
"0 ILMN_3166687 ERCC-00162\n",
"1 ILMN_3165566 ERCC-00071\n",
"2 ILMN_3164811 ERCC-00009\n",
"3 ILMN_3165363 ERCC-00053\n",
"4 ILMN_3166511 ERCC-00144\n",
"Converted expression data to 18407 genes\n",
"First few genes after mapping:\n",
"Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A4GALT',\n",
" 'A4GNT', 'AAA1'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identify the relevant columns for mapping\n",
"# 'ID' in gene_annotation corresponds to probe IDs (ILMN_*) in gene_data\n",
"# 'Symbol' appears to contain gene symbols to map to\n",
"probe_col = 'ID' # Column containing probe identifiers\n",
"gene_col = 'Symbol' # Column containing gene symbols\n",
"\n",
"# 2. Get gene mapping dataframe\n",
"mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
"print(f\"Found mapping for {len(mapping_df)} probes\")\n",
"print(\"First few rows of mapping dataframe:\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Convert probe-level measurements to gene-level expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"print(f\"Converted expression data to {len(gene_data)} genes\")\n",
"print(\"First few genes after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Save the gene expression data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n"
]
},
{
"cell_type": "markdown",
"id": "41563f42",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7865d256",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:47:42.982042Z",
"iopub.status.busy": "2025-03-25T03:47:42.981903Z",
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"shell.execute_reply": "2025-03-25T03:47:53.721385Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data saved to ../../output/preprocess/Rectal_Cancer/clinical_data/GSE94104.csv\n",
"Normalized gene data shape: (17833, 80)\n",
"First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Rectal_Cancer/gene_data/GSE94104.csv\n",
"Linked data shape: (80, 17834)\n",
" Rectal_Cancer A1BG A1BG-AS1 A1CF A2M \\\n",
"GSM2469019 0.0 7.395705 8.398890 28.693917 13.337807 \n",
"GSM2469020 0.0 8.551503 8.688974 26.624397 14.148331 \n",
"GSM2469021 0.0 10.632415 7.824079 23.596701 13.451809 \n",
"GSM2469022 0.0 8.816704 7.720825 25.802108 13.616095 \n",
"GSM2469023 0.0 9.020842 7.200367 30.043000 13.611848 \n",
"\n",
" A2ML1 A4GALT A4GNT AAA1 AAAS ... \\\n",
"GSM2469019 7.472346 12.487900 5.906503 37.780392 10.477635 ... \n",
"GSM2469020 8.259388 12.611664 5.611697 31.996606 11.295834 ... \n",
"GSM2469021 8.393553 10.865053 5.687393 40.876755 9.940273 ... \n",
"GSM2469022 5.889630 11.647056 5.497962 36.785756 10.388437 ... \n",
"GSM2469023 6.835435 9.823508 5.313831 48.428284 8.857288 ... \n",
"\n",
" ZWILCH ZWINT ZXDA ZXDB ZXDC ZYG11A \\\n",
"GSM2469019 30.149135 37.752388 22.288420 11.764753 20.882875 8.234774 \n",
"GSM2469020 29.069801 29.432566 22.036817 12.434532 20.963560 4.709127 \n",
"GSM2469021 31.743675 39.536836 21.656870 12.933958 22.473772 8.849611 \n",
"GSM2469022 31.820129 34.378828 21.310831 12.629729 21.681174 6.579239 \n",
"GSM2469023 31.424952 35.648354 22.305948 13.162210 20.842217 8.530401 \n",
"\n",
" ZYG11B ZYX ZZEF1 ZZZ3 \n",
"GSM2469019 11.308405 21.453710 10.393851 22.659554 \n",
"GSM2469020 11.738357 21.241659 11.039158 23.197248 \n",
"GSM2469021 12.070824 21.649831 9.797775 20.884220 \n",
"GSM2469022 12.401734 22.395284 10.095616 22.742967 \n",
"GSM2469023 11.999437 20.363729 9.712065 21.665297 \n",
"\n",
"[5 rows x 17834 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape after handling missing values: (80, 17834)\n",
"For the feature 'Rectal_Cancer', the least common label is '1.0' with 22 occurrences. This represents 27.50% of the dataset.\n",
"The distribution of the feature 'Rectal_Cancer' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Rectal_Cancer/GSE94104.csv\n"
]
}
],
"source": [
"# 1. Extract clinical features\n",
"clinical_features = geo_select_clinical_features(\n",
" clinical_data, \n",
" trait=trait, \n",
" trait_row=trait_row, \n",
" convert_trait=convert_trait,\n",
" age_row=age_row, \n",
" convert_age=convert_age,\n",
" gender_row=gender_row, \n",
" convert_gender=convert_gender\n",
")\n",
"\n",
"# Save the clinical features data\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 data saved to {out_clinical_data_file}\")\n",
"\n",
"# 1. Normalize gene symbols in the gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\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. Link the 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.head())\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Determine whether the trait and demographic features are severely biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Conduct quality check and save the cohort information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True,\n",
" is_biased=is_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=f\"Dataset contains gene expression data from CD4 T-cells of pSS patients and healthy controls.\"\n",
")\n",
"\n",
"# 6. Save the data if it's usable\n",
"if is_usable:\n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" # Save the data\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")"
]
}
],
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|