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{
"cells": [
{
"cell_type": "code",
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"id": "4fd343ca",
"metadata": {
"execution": {
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"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 = \"Adrenocortical_Cancer\"\n",
"cohort = \"GSE67766\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Adrenocortical_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Adrenocortical_Cancer/GSE67766\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Adrenocortical_Cancer/GSE67766.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE67766.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE67766.csv\"\n",
"json_path = \"../../output/preprocess/Adrenocortical_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "fba3c008",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a7477333",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Cancer Cells Hijack PRC2 to Modify Multiple Cytokine Pathways\"\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: SW-13']}\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": "b82f7e2a",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "68687736",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:21:44.936424Z",
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"shell.execute_reply": "2025-03-25T06:21:44.940741Z"
}
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"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the series title and summary, we cannot definitively determine if gene expression data is available\n",
"# The sample characteristics only mention \"cell line: SW-13\" which doesn't tell us about the type of data\n",
"# Since there's no clear indication that this is gene expression data (vs miRNA or methylation)\n",
"# and the series is described as a \"SuperSeries composed of the SubSeries\", \n",
"# we should err on the cautious side\n",
"is_gene_available = False\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# There's no trait data specific to Adrenocortical_Cancer in the sample characteristics\n",
"trait_row = None\n",
"\n",
"# Age is not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender is not available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"# Define conversion functions for completeness, though they won't be used in this case\n",
"def convert_trait(value):\n",
" # This won't be used as trait_row is None\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # This won't be used as age_row is None\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # This won't be used as gender_row is None\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Conduct initial filtering and save the metadata\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"# Since trait_row is None, we skip this substep\n"
]
},
{
"cell_type": "markdown",
"id": "7cfcf769",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cbbef164",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:21:45.059097Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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. First get the file paths again to access the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_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) for future observation\n",
"print(\"First 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "b7f57d1c",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "003b77d8",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:21:45.060757Z",
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"shell.execute_reply": "2025-03-25T06:21:45.062232Z"
}
},
"outputs": [],
"source": [
"# Examining the gene identifiers in the gene expression data\n",
"# The identifiers starting with \"ILMN_\" are Illumina BeadArray probe IDs, not human gene symbols\n",
"# These are proprietary identifiers used by Illumina microarray platforms\n",
"# They need to be mapped to standard human gene symbols for proper analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "d00ee47d",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "712ec6a1",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:22:00.396402Z"
}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['ILMN_1825594', 'ILMN_1810803', 'ILMN_1722532', 'ILMN_1884413', 'ILMN_1906034'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Source': ['Unigene', 'RefSeq', 'RefSeq', 'Unigene', 'Unigene'], 'Search_Key': ['ILMN_89282', 'ILMN_35826', 'ILMN_25544', 'ILMN_132331', 'ILMN_105017'], 'Transcript': ['ILMN_89282', 'ILMN_35826', 'ILMN_25544', 'ILMN_132331', 'ILMN_105017'], 'ILMN_Gene': ['HS.388528', 'LOC441782', 'JMJD1A', 'HS.580150', 'HS.540210'], 'Source_Reference_ID': ['Hs.388528', 'XM_497527.2', 'NM_018433.3', 'Hs.580150', 'Hs.540210'], 'RefSeq_ID': [nan, 'XM_497527.2', 'NM_018433.3', nan, nan], 'Unigene_ID': ['Hs.388528', nan, nan, 'Hs.580150', 'Hs.540210'], 'Entrez_Gene_ID': [nan, '441782', '55818', nan, nan], 'GI': ['23525203', '89042416', '46358420', '7376124', '5437312'], 'Accession': ['BU678343', 'XM_497527.2', 'NM_018433.3', 'AW629334', 'AI818233'], 'Symbol': [nan, 'LOC441782', 'JMJD1A', nan, nan], 'Protein_Product': [nan, 'XP_497527.2', 'NP_060903.2', nan, nan], 'Array_Address_Id': [1740241.0, 1850750.0, 1240504.0, 4050487.0, 2190598.0], 'Probe_Type': ['S', 'S', 'S', 'S', 'S'], 'Probe_Start': [349.0, 902.0, 4359.0, 117.0, 304.0], 'SEQUENCE': ['CTCTCTAAAGGGACAACAGAGTGGACAGTCAAGGAACTCCACATATTCAT', 'GGGGTCAAGCCCAGGTGAAATGTGGATTGGAAAAGTGCTTCCCTTGCCCC', 'CCAGGCTGTAAAAGCAAAACCTCGTATCAGCTCTGGAACAATACCTGCAG', 'CCAGACAGGAAGCATCAAGCCCTTCAGGAAAGAATATGCGAGAGTGCTGC', 'TGTGCAGAAAGCTGATGGAAGGGAGAAAGAATGGAAGTGGGTCACACAGC'], 'Chromosome': [nan, nan, '2', nan, nan], 'Probe_Chr_Orientation': [nan, nan, '+', nan, nan], 'Probe_Coordinates': [nan, nan, '86572991-86573040', nan, nan], 'Cytoband': [nan, nan, '2p11.2e', nan, nan], 'Definition': ['UI-CF-EC0-abi-c-12-0-UI.s1 UI-CF-EC0 Homo sapiens cDNA clone UI-CF-EC0-abi-c-12-0-UI 3, mRNA sequence', 'PREDICTED: Homo sapiens similar to spectrin domain with coiled-coils 1 (LOC441782), mRNA.', 'Homo sapiens jumonji domain containing 1A (JMJD1A), mRNA.', 'hi56g05.x1 Soares_NFL_T_GBC_S1 Homo sapiens cDNA clone IMAGE:2976344 3, mRNA sequence', 'wk77d04.x1 NCI_CGAP_Pan1 Homo sapiens cDNA clone IMAGE:2421415 3, mRNA sequence'], 'Ontology_Component': [nan, nan, 'nucleus [goid 5634] [evidence IEA]', nan, nan], 'Ontology_Process': [nan, nan, 'chromatin modification [goid 16568] [evidence IEA]; transcription [goid 6350] [evidence IEA]; regulation of transcription, DNA-dependent [goid 6355] [evidence IEA]', nan, nan], 'Ontology_Function': [nan, nan, '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]', nan, nan], 'Synonyms': [nan, nan, 'JHMD2A; JMJD1; TSGA; KIAA0742; DKFZp686A24246; DKFZp686P07111', nan, nan], 'GB_ACC': ['BU678343', 'XM_497527.2', 'NM_018433.3', 'AW629334', 'AI818233']}\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": "28d7a2ac",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e835bebf",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview (first 5 rows):\n",
" ID Gene\n",
"1 ILMN_1810803 LOC441782\n",
"2 ILMN_1722532 JMJD1A\n",
"6 ILMN_1708805 NCOA3\n",
"8 ILMN_1672526 LOC389834\n",
"9 ILMN_2185604 C17orf77\n",
"\n",
"Gene expression data after mapping (first 5 genes):\n",
" GSM1652385 GSM1652386 GSM1652387 GSM1652388 GSM1652389 \\\n",
"Gene \n",
"A1BG 229.98830 248.54450 237.17420 248.39860 249.26820 \n",
"A2BP1 318.77866 332.46035 345.21173 338.50507 351.49917 \n",
"A2M 70.80764 70.58915 63.49310 75.62191 72.35401 \n",
"A2ML1 73.61642 70.96689 79.69693 71.65809 74.31523 \n",
"A3GALT2 206.22774 193.16380 218.59780 188.89192 202.47758 \n",
"\n",
" GSM1652390 GSM1652391 GSM1652392 GSM1652393 GSM1652394 ... \\\n",
"Gene ... \n",
"A1BG 244.07600 258.36630 263.38710 258.61440 258.33540 ... \n",
"A2BP1 345.69635 346.11921 361.47327 354.68587 359.69274 ... \n",
"A2M 108.32530 72.16235 135.00630 79.80496 82.38654 ... \n",
"A2ML1 73.23978 77.67924 72.64681 70.11669 69.30971 ... \n",
"A3GALT2 207.16097 206.89650 197.30278 205.58321 204.26970 ... \n",
"\n",
" GSM1652399 GSM1652400 GSM1652401 GSM1652402 GSM1652403 \\\n",
"Gene \n",
"A1BG 220.35480 219.74660 218.51810 237.50740 224.25190 \n",
"A2BP1 336.92718 318.65626 341.50960 333.50831 320.01791 \n",
"A2M 71.92281 123.33600 73.92870 94.54202 70.73442 \n",
"A2ML1 73.53131 66.09079 64.53247 69.09312 69.25777 \n",
"A3GALT2 196.39571 199.43877 179.84575 179.21808 188.58534 \n",
"\n",
" GSM1652404 GSM1652405 GSM1652406 GSM1652407 GSM1652408 \n",
"Gene \n",
"A1BG 256.08970 243.24950 202.08701 223.68940 212.66030 \n",
"A2BP1 323.68906 367.63241 314.59370 347.27794 304.38977 \n",
"A2M 84.44023 75.40449 118.87620 68.69892 108.61290 \n",
"A2ML1 72.47518 74.04777 81.68905 70.02788 70.21660 \n",
"A3GALT2 212.61490 176.39538 173.74357 151.66942 190.38029 \n",
"\n",
"[5 rows x 24 columns]\n",
"\n",
"Shape of gene expression data: (18838, 24)\n"
]
}
],
"source": [
"# 1. Identifying the key columns for gene mapping\n",
"# From the annotation preview, we can see that:\n",
"# - 'ID' column contains ILMN identifiers that match our gene expression data\n",
"# - 'Symbol' column contains the gene symbols we want to map to\n",
"\n",
"# 2. Create the gene mapping dataframe\n",
"prob_col = 'ID'\n",
"gene_col = 'Symbol'\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"\n",
"# Print a preview of the mapping\n",
"print(\"Gene mapping preview (first 5 rows):\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Print the first few gene symbols and their data\n",
"print(\"\\nGene expression data after mapping (first 5 genes):\")\n",
"print(gene_data.head())\n",
"\n",
"# Print the shape of the gene expression data\n",
"print(f\"\\nShape of gene expression data: {gene_data.shape}\")\n"
]
},
{
"cell_type": "markdown",
"id": "25fb85c8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "eaf507b6",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:22:00.786038Z",
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"shell.execute_reply": "2025-03-25T06:22:01.112346Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing gene symbols using NCBI Gene database...\n",
"After normalization, gene data shape: (17551, 24)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE67766.csv\n",
"Clinical data not available, dataset marked as unusable for trait-gene association studies.\n",
"Dataset is not usable for trait-gene association studies.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"print(\"Normalizing gene symbols using NCBI Gene database...\")\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"After normalization, gene data shape: {gene_data.shape}\")\n",
"\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 expression data saved to {out_gene_data_file}\")\n",
"\n",
"# Since we already determined that trait data is not available (is_trait_available=False in step 2),\n",
"# we should not attempt to link clinical and genetic data or process them further\n",
"print(\"Clinical data not available, dataset marked as unusable for trait-gene association studies.\")\n",
"\n",
"# Since we cannot perform final validation without clinical data, we need to use is_final=False\n",
"# We're recording information about gene data availability but not performing full validation\n",
"is_usable = 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",
"print(\"Dataset is not usable for trait-gene association studies.\")"
]
}
],
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