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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 = \"Glucocorticoid_Sensitivity\"\n",
"cohort = \"GSE15820\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Glucocorticoid_Sensitivity\"\n",
"in_cohort_dir = \"../../input/GEO/Glucocorticoid_Sensitivity/GSE15820\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/GSE15820.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE15820.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE15820.csv\"\n",
"json_path = \"../../output/preprocess/Glucocorticoid_Sensitivity/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "db1af3cf",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"ZBTB16, a glucocorticoid response gene in acute lymphoblastic leukemia, interferes with glucocorticoid-induced apoptosis\"\n",
"!Series_summary\t\"Glucocorticoids (GCs) cause apoptosis in lymphoid lineage cells and are therefore widely used in the therapy of lymphoid malignancies. The molecular mechanisms of the anti-leukemic GC effects are, however, poorly understood. We have previously defined a list of GC-regulated candidate genes by Affymetrix-based whole genome comparative expression profiling in children with acute lymphoblastic leukemia (ALL) during systemic GC monotherapy and in experimental systems of GC-induced apoptosis. ZBTB16, a Zink finger and BOZ-domain containing transcriptional repressor, was one of the most promising candidates derived from this screen. To investigate its possible role in GC-induced apoptosis and cell cycle arrest, we performed conditional over-expression experiments in CCRF-CEM childhood ALL cells. Transgenic ZBTB16 alone had no detectable effect on survival, however, it reduced sensitivity to GC-induced apoptosis. This protective effect was not seen when apoptosis was induced by antibodies against Fas/CD95 or 3 different chemotherapeutics. To address the molecular mechanism underlying this protective effect, we performed whole genome expression profiling in cells with conditional ZBTB16 expression. Surprisingly, ZBTB16 induction did not significantly alter the expression profile, however, it interfered with the regulation of several GC response genes. One of them, BCL2L11/Bim, has previously been shown to be responsible for cell death induction in CCRF-CEM cells. Thus, ZBTB16´s protective effect can be attributed to interference with transcriptional regulation of apoptotic genes, at least in the investigated model system.\"\n",
"!Series_overall_design\t\"To determine ZBTB16 response genes, C7H2-2C8-ZBTB16#19 and #58 cells (expressing ZBTB16 in a doxycycline-dependent manner) were cultured in duplicates in the absence (treatment “none”) or presence of 400ng/ml doxycycline (treatment “Dox”) for 2h, 6h or 24h. Total RNA was prepared and 1.5 µg RNA subjected to expression profiling on Exon 1.0 microarrays (total of 24 arrays). To assess the effect of ZBTB16 on the GC response, the above cell lines were cultured for 24h in the absence (treatment “Dex”) or presence (treatment “DexDox”) of 200ng/ml doxycycline and subsequently exposed to 10-8M dexamethasone for 6h and 24h and expression-profiled as above resulting in a total of 15 arrays (one of the four 6h replicates (0997_001_58pp6S2_E01_MB_110708.CEL) had to be removed from the following analysis because it didn´t pass quality control).\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell ine: C7H2-2C8-ZBTB16#19', 'cell ine: C7H2-2C8-ZBTB16#58'], 1: ['treatment: none', 'treatment: Dox', 'treatment: Dex', 'treatment: DexDox'], 2: ['time [h]: 2', 'time [h]: 6', 'time [h]: 24'], 3: ['experiment nr: III', 'experiment nr: II', 'experiment nr: I'], 4: ['clone nr: 19', 'clone nr: 58']}\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": "4839f08c",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data file not found. Skipping clinical feature extraction.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this appears to be gene expression data\n",
"# from Affymetrix Exon 1.0 microarrays studying ZBTB16 and glucocorticoid response\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# Looking at the sample characteristics dictionary\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait (Glucocorticoid Sensitivity):\n",
"# The \"treatment\" in row 1 indicates treatment conditions including \"Dex\" (dexamethasone, a glucocorticoid)\n",
"# We can use this to determine glucocorticoid sensitivity\n",
"trait_row = 1\n",
"\n",
"# For age: Not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# For gender: Not available - these appear to be cell lines, not individual patients\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert treatment information to glucocorticoid sensitivity binary indicator.\n",
" \n",
" The \"Dex\" and \"DexDox\" treatments indicate exposure to dexamethasone (a glucocorticoid)\n",
" which can be used as our trait indicator.\n",
" \"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Determine treatment type\n",
" if value == \"Dex\" or value == \"DexDox\":\n",
" return 1 # Exposed to glucocorticoid\n",
" elif value == \"none\" or value == \"Dox\":\n",
" return 0 # Not exposed to glucocorticoid\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age information to float.\"\"\"\n",
" # Age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender information to binary (0=female, 1=male).\"\"\"\n",
" # Gender data is not available\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save 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",
"# Only execute if trait_row is not None\n",
"if trait_row is not None:\n",
" # Assuming clinical_data is available from previous steps\n",
" try:\n",
" # Load clinical data if it exists\n",
" clinical_data = pd.read_csv(os.path.join(in_cohort_dir, \"clinical_data.csv\"))\n",
" \n",
" # Extract clinical features\n",
" selected_clinical_df = 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 dataframe\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview_df(selected_clinical_df))\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" \n",
" except FileNotFoundError:\n",
" print(\"Clinical data file not found. Skipping clinical feature extraction.\")\n",
" except Exception as e:\n",
" print(f\"Error during clinical feature extraction: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "deb141fe",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"Found data marker at line 69\n",
"Header line: \"ID_REF\"\t\"GSM397450\"\t\"GSM397451\"\t\"GSM397452\"\t\"GSM397453\"\t\"GSM397454\"\t\"GSM397455\"\t\"GSM397456\"\t\"GSM397457\"\t\"GSM397458\"\t\"GSM397459\"\t\"GSM397460\"\t\"GSM397461\"\t\"GSM397462\"\t\"GSM397463\"\t\"GSM397464\"\t\"GSM397465\"\t\"GSM397466\"\t\"GSM397467\"\t\"GSM397468\"\t\"GSM397469\"\t\"GSM397470\"\t\"GSM397471\"\t\"GSM397472\"\t\"GSM397473\"\t\"GSM397474\"\t\"GSM397475\"\t\"GSM397476\"\t\"GSM397477\"\t\"GSM397478\"\t\"GSM397479\"\t\"GSM397480\"\t\"GSM397481\"\t\"GSM397482\"\t\"GSM397483\"\t\"GSM397484\"\t\"GSM397485\"\t\"GSM397486\"\t\"GSM397487\"\t\"GSM397488\"\t\"GSM397489\"\n",
"First data line: \"52_36nbg_gc10\"\t1.36755972312661\t1.36703559676413\t1.35410230935632\t1.38059015791809\t1.42740938904254\t1.43124503160126\t1.41567377618281\t1.40124792419307\t1.35540529604859\t1.35799836513495\t1.38758634012505\t1.36094675480995\t1.35763165856656\t1.39344264875760\t1.36455661100291\t1.36552628300498\t1.36612594197734\t1.38366057436023\t1.40126778404146\t1.42285685315322\t1.33998005284175\t1.35451231570066\t1.32936586717567\t1.36449159029923\t1.36183473620910\t1.33019716685760\t1.41270901846183\t1.38224690964737\t1.3589714743535\t1.35338678279769\t1.36415166085043\t1.37797792254353\t1.36775135164149\t1.37647544213206\t1.3589271084623\t1.35838075985707\t1.35197707674852\t1.35591139709054\t1.35304570060391\t1.36392613290556\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['52_36nbg_gc10', '52_36nbg_gc11', '52_36nbg_gc12', '52_36nbg_gc13',\n",
" '52_36nbg_gc14', '52_36nbg_gc15', '52_36nbg_gc16', '52_36nbg_gc17',\n",
" '52_36nbg_gc18', '52_36nbg_gc19', '52_36nbg_gc20', '52_36nbg_gc21',\n",
" '52_36nbg_gc22', '52_36nbg_gc23', '52_36nbg_gc24', '52_36nbg_gc25',\n",
" '52_36nbg_gc3', '52_36nbg_gc4', '52_36nbg_gc5', '52_36nbg_gc6'],\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": "9e19df36",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
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"source": [
"# Looking at the gene identifiers in the dataset: '52_36nbg_gc10', '52_36nbg_gc11', etc.\n",
"# These are not standard human gene symbols, which are typically like BRCA1, TP53, etc.\n",
"# These appear to be probe IDs or custom identifiers that will need to be mapped to gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "bfaf887c",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
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"name": "stdout",
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"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 = [email protected]\n",
"Line 5: ^SERIES = GSE15820\n",
"Line 6: !Series_title = ZBTB16, a glucocorticoid response gene in acute lymphoblastic leukemia, interferes with glucocorticoid-induced apoptosis\n",
"Line 7: !Series_geo_accession = GSE15820\n",
"Line 8: !Series_status = Public on May 07 2010\n",
"Line 9: !Series_submission_date = Apr 24 2009\n",
"Line 10: !Series_last_update_date = Jun 26 2012\n",
"Line 11: !Series_pubmed_id = 20435142\n",
"Line 12: !Series_summary = Glucocorticoids (GCs) cause apoptosis in lymphoid lineage cells and are therefore widely used in the therapy of lymphoid malignancies. The molecular mechanisms of the anti-leukemic GC effects are, however, poorly understood. We have previously defined a list of GC-regulated candidate genes by Affymetrix-based whole genome comparative expression profiling in children with acute lymphoblastic leukemia (ALL) during systemic GC monotherapy and in experimental systems of GC-induced apoptosis. ZBTB16, a Zink finger and BOZ-domain containing transcriptional repressor, was one of the most promising candidates derived from this screen. To investigate its possible role in GC-induced apoptosis and cell cycle arrest, we performed conditional over-expression experiments in CCRF-CEM childhood ALL cells. Transgenic ZBTB16 alone had no detectable effect on survival, however, it reduced sensitivity to GC-induced apoptosis. This protective effect was not seen when apoptosis was induced by antibodies against Fas/CD95 or 3 different chemotherapeutics. To address the molecular mechanism underlying this protective effect, we performed whole genome expression profiling in cells with conditional ZBTB16 expression. Surprisingly, ZBTB16 induction did not significantly alter the expression profile, however, it interfered with the regulation of several GC response genes. One of them, BCL2L11/Bim, has previously been shown to be responsible for cell death induction in CCRF-CEM cells. Thus, ZBTB16´s protective effect can be attributed to interference with transcriptional regulation of apoptotic genes, at least in the investigated model system.\n",
"Line 13: !Series_overall_design = To determine ZBTB16 response genes, C7H2-2C8-ZBTB16#19 and #58 cells (expressing ZBTB16 in a doxycycline-dependent manner) were cultured in duplicates in the absence (treatment “none”) or presence of 400ng/ml doxycycline (treatment “Dox”) for 2h, 6h or 24h. Total RNA was prepared and 1.5 µg RNA subjected to expression profiling on Exon 1.0 microarrays (total of 24 arrays). To assess the effect of ZBTB16 on the GC response, the above cell lines were cultured for 24h in the absence (treatment “Dex”) or presence (treatment “DexDox”) of 200ng/ml doxycycline and subsequently exposed to 10-8M dexamethasone for 6h and 24h and expression-profiled as above resulting in a total of 15 arrays (one of the four 6h replicates (0997_001_58pp6S2_E01_MB_110708.CEL) had to be removed from the following analysis because it didn´t pass quality control).\n",
"Line 14: !Series_type = Expression profiling by array\n",
"Line 15: !Series_contributor = Muhammad,,Wasim\n",
"Line 16: !Series_contributor = Muhammad,,Mansha\n",
"Line 17: !Series_contributor = Michela,,Carlet\n",
"Line 18: !Series_contributor = Christian,,Ploner\n",
"Line 19: !Series_contributor = Alexander,,Trockenbacher\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"{'ID': ['52_36nENST00000343609', '52_36nTRAN00000088867', '52_36nENST00000361444', '52_36nENST00000401567', '52_36nENST00000332556'], 'transcript_id': ['ENST00000343609', 'TRAN00000088867', 'ENST00000361444;ENST00000335791;TRAN00000088866', 'ENST00000401567', 'ENST00000332556'], 'gene_id': ['ENSG00000136840', 'ENSG00000136840', 'ENSG00000136840', 'ENSG00000218830', 'ENSG00000185896'], 'nr_probes': [45, 25, 49, 8, 50], 'probes': ['1000013;1821570;3542467;5826253;2703268;372673;939345;3829429;3465732;4704754;2926002;4384017;3619759;4707366;4988334;3744432;1489412;3923300;3488760;5449937;5498240;1104695;4952605;1066297;2631422;2795348;3778075;3701190;2556339;4473678;1271165;3013026;5982170;5971292;455529;1263516;1532691;2502837;6540389;6204608;3314611;3836325;3053558;2448962;4057529', '1000013;1821570;5492779;4126240;29898;866976;3542467;2703268;372673;939345;3465732;4704754;2926002;3619759;4707366;3923300;4952605;2631422;3701190;4473678;1271165;5982170;5971292;2502837;4057529', '1000013;1821570;5492779;4126240;29898;866976;3542467;5826253;2703268;372673;939345;3829429;3465732;4704754;2926002;4384017;3619759;4707366;4988334;3744432;1489412;3923300;3488760;5449937;5498240;1104695;4952605;1066297;2631422;2795348;3778075;3701190;2556339;4473678;1271165;3013026;5982170;5971292;455529;1263516;1532691;2502837;6540389;6204608;3314611;3836325;3053558;2448962;4057529', '1000038;3183931;5216729;4868450;4804549;4085423;4284782;2080417', '1000058;3365945;3749277;4988641;5761327;85887;403374;2617651;65288;5383310;3202013;3691402;4054688;1074813;6430176;4123373;6107467;1901462;452232;189358;2372175;1962913;5311082;2579237;5048074;1241376;3462843;4482269;3414403;3471163;4880780;3298746;547167;429389;5812919;6309474;4093105;55840;5193353;4315785;4130628;3652963;1986178;1725325;798011;6104549;6378967;5100924;1213422;2902552'], 'indices': ['1000013;1821570;3542467;5826253;2703268;372673;939345;3829429;3465732;4704754;2926002;4384017;3619759;4707366;4988334;3744432;1489412;3923300;3488760;5449937;5498240;1104695;4952605;1066297;2631422;2795348;3778075;3701190;2556339;4473678;1271165;3013026;5982170;5971292;455529;1263516;1532691;2502837;6540389;6204608;3314611;3836325;3053558;2448962;4057529', '1000013;1821570;5492779;4126240;29898;866976;3542467;2703268;372673;939345;3465732;4704754;2926002;3619759;4707366;3923300;4952605;2631422;3701190;4473678;1271165;5982170;5971292;2502837;4057529', '1000013;1821570;5492779;4126240;29898;866976;3542467;5826253;2703268;372673;939345;3829429;3465732;4704754;2926002;4384017;3619759;4707366;4988334;3744432;1489412;3923300;3488760;5449937;5498240;1104695;4952605;1066297;2631422;2795348;3778075;3701190;2556339;4473678;1271165;3013026;5982170;5971292;455529;1263516;1532691;2502837;6540389;6204608;3314611;3836325;3053558;2448962;4057529', '1000038;3183931;5216729;4868450;4804549;4085423;4284782;2080417', '1000058;3365945;3749277;4988641;5761327;85887;403374;2617651;65288;5383310;3202013;3691402;4054688;1074813;6430176;4123373;6107467;1901462;452232;189358;2372175;1962913;5311082;2579237;5048074;1241376;3462843;4482269;3414403;3471163;4880780;3298746;547167;429389;5812919;6309474;4093105;55840;5193353;4315785;4130628;3652963;1986178;1725325;798011;6104549;6378967;5100924;1213422;2902552'], 'symbol': ['ST6GALNAC4', 'ST6GALNAC4', 'ST6GALNAC4', nan, 'LAMP1'], 'entrezgene': ['27090', '27090', '27090', nan, '3916'], 'mirbase': [nan, nan, nan, nan, nan], 'SPOT_ID': ['ENST00000343609', 'TRAN00000088867', 'ENST00000361444;ENST00000335791;TRAN00000088866', 'ENST00000401567', 'ENST00000332556']}\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": "e82b3f69",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ee1ceec2",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:24:40.394724Z",
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"shell.execute_reply": "2025-03-25T05:24:41.396184Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample gene expression IDs: ['52_36nbg_gc13', '52_36nbg_gc10', '52_36nbg_gc11', '52_36nbg_gc14', '52_36nbg_gc12']\n",
"Sample annotation IDs: ['52_36nENST00000401567', '52_36nENST00000361444', '52_36nENST00000332556', '52_36nTRAN00000088867', '52_36nENST00000343609']\n",
"Gene mapping created with 118098 rows\n",
"Sample mapping entries:\n",
" ID Gene\n",
"0 52_36nENST00000343609 ST6GALNAC4\n",
"1 52_36nTRAN00000088867 ST6GALNAC4\n",
"2 52_36nENST00000361444 ST6GALNAC4\n",
"4 52_36nENST00000332556 LAMP1\n",
"5 52_36nTRAN00000107877 LAMP1\n",
"Number of common IDs between expression data and mapping: 118098\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data created with 18151 genes and 40 samples\n",
"Sample gene expression data:\n",
" GSM397450 GSM397451 GSM397452 GSM397453 GSM397454 GSM397455 \\\n",
"Gene \n",
"A1BG 8.510088 8.778496 8.191638 9.090064 9.387474 9.093869 \n",
"A1CF 19.023268 20.432171 20.078757 21.372999 21.347969 23.153673 \n",
"A26A1 5.568398 5.678820 5.527850 5.642854 5.836115 5.679008 \n",
"A2LD1 13.905637 13.912146 12.940048 15.369641 16.542266 14.013720 \n",
"A2M 27.717106 27.142966 26.976324 28.835185 29.746781 31.796226 \n",
"\n",
" GSM397456 GSM397457 GSM397458 GSM397459 ... GSM397480 GSM397481 \\\n",
"Gene ... \n",
"A1BG 9.241304 8.718551 9.075634 8.368584 ... 8.267192 8.349470 \n",
"A1CF 20.842212 21.226769 20.845009 19.277951 ... 21.011489 20.391923 \n",
"A26A1 5.793950 6.203565 5.674946 5.659406 ... 5.209453 5.572318 \n",
"A2LD1 15.661127 15.476094 14.129344 14.675561 ... 15.465957 13.519326 \n",
"A2M 29.980280 30.078266 30.294065 28.925444 ... 28.225792 28.148947 \n",
"\n",
" GSM397482 GSM397483 GSM397484 GSM397485 GSM397486 GSM397487 \\\n",
"Gene \n",
"A1BG 8.609079 9.027214 8.319996 7.920551 8.703216 8.777528 \n",
"A1CF 20.668052 21.937723 20.234471 20.378771 20.239224 20.802530 \n",
"A26A1 5.457108 5.591031 5.429368 5.171567 5.252621 5.766174 \n",
"A2LD1 14.446946 15.645423 13.834515 13.525970 13.790216 13.304547 \n",
"A2M 27.549746 28.882444 26.205307 26.390324 27.615895 27.230797 \n",
"\n",
" GSM397488 GSM397489 \n",
"Gene \n",
"A1BG 8.230826 8.060867 \n",
"A1CF 21.018104 21.105185 \n",
"A26A1 5.570421 5.471707 \n",
"A2LD1 14.055442 13.762268 \n",
"A2M 28.624573 28.681745 \n",
"\n",
"[5 rows x 40 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE15820.csv\n"
]
}
],
"source": [
"# 1. Determine the right columns for probe IDs and gene symbols from the gene annotation data\n",
"# Looking at the preview, we can see:\n",
"# - 'ID' column contains identifiers like '52_36nENST00000343609' which match the format of the gene expression data\n",
"# - 'symbol' column contains gene symbols like 'ST6GALNAC4', 'LAMP1'\n",
"\n",
"# Let's extract these two columns for our mapping\n",
"prob_col = 'ID'\n",
"gene_col = 'symbol'\n",
"\n",
"# First, let's check if the gene expression data's identifiers match the annotation IDs\n",
"gene_expression_ids = set(gene_data.index.tolist()[:5]) # Take a small sample\n",
"annotation_ids = set(gene_annotation[prob_col].tolist()[:5])\n",
"\n",
"print(f\"Sample gene expression IDs: {list(gene_expression_ids)[:5]}\")\n",
"print(f\"Sample annotation IDs: {list(annotation_ids)[:5]}\")\n",
"\n",
"# 2. Create the gene mapping dataframe\n",
"try:\n",
" mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
" print(f\"Gene mapping created with {len(mapping_df)} rows\")\n",
" print(\"Sample mapping entries:\")\n",
" print(mapping_df.head())\n",
"except Exception as e:\n",
" print(f\"Error creating gene mapping: {e}\")\n",
" \n",
" # Manual approach if library function fails\n",
" mapping_df = gene_annotation[[prob_col, gene_col]].copy()\n",
" mapping_df = mapping_df.dropna(subset=[gene_col])\n",
" mapping_df = mapping_df.rename(columns={gene_col: 'Gene'})\n",
" mapping_df = mapping_df.astype({prob_col: 'str'})\n",
" print(f\"Gene mapping created manually with {len(mapping_df)} rows\")\n",
" print(\"Sample mapping entries:\")\n",
" print(mapping_df.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe expression to gene expression\n",
"try:\n",
" # Check if our IDs in gene_data match those in the mapping\n",
" common_ids = set(gene_data.index).intersection(set(mapping_df[prob_col]))\n",
" print(f\"Number of common IDs between expression data and mapping: {len(common_ids)}\")\n",
" \n",
" # Apply the mapping to get gene expression data\n",
" gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
" print(f\"Gene expression data created with {gene_data.shape[0]} genes and {gene_data.shape[1]} samples\")\n",
" print(\"Sample gene expression data:\")\n",
" print(gene_data.head())\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",
" print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error applying gene mapping: {e}\")\n",
" \n",
" # Print more diagnostic information\n",
" print(\"\\nDiagnostic information:\")\n",
" print(f\"gene_data index type: {type(gene_data.index[0])}\")\n",
" if 'ID' in mapping_df.columns:\n",
" print(f\"mapping_df ID column type: {type(mapping_df['ID'].iloc[0])}\")\n"
]
},
{
"cell_type": "markdown",
"id": "d7ab8d3c",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3c81ac1e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:24:41.397814Z",
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"shell.execute_reply": "2025-03-25T05:24:50.446445Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after normalization: (18042, 40)\n",
"Sample gene symbols after normalization: ['A1BG', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE15820.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected clinical features:\n",
" GSM397450 GSM397451 GSM397452 GSM397453 \\\n",
"Glucocorticoid_Sensitivity 0.0 0.0 0.0 0.0 \n",
"\n",
" GSM397454 GSM397455 GSM397456 GSM397457 \\\n",
"Glucocorticoid_Sensitivity 0.0 0.0 0.0 0.0 \n",
"\n",
" GSM397458 GSM397459 ... GSM397480 GSM397481 \\\n",
"Glucocorticoid_Sensitivity 0.0 0.0 ... 1.0 1.0 \n",
"\n",
" GSM397482 GSM397483 GSM397484 GSM397485 \\\n",
"Glucocorticoid_Sensitivity 1.0 0.0 1.0 1.0 \n",
"\n",
" GSM397486 GSM397487 GSM397488 GSM397489 \n",
"Glucocorticoid_Sensitivity 1.0 1.0 1.0 1.0 \n",
"\n",
"[1 rows x 40 columns]\n",
"Clinical data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE15820.csv\n",
"Linked data shape: (40, 18043)\n",
"Linked data columns preview:\n",
"['Glucocorticoid_Sensitivity', 'A1BG', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAAS', 'AACS']\n",
"\n",
"Missing values before handling:\n",
" Trait (Glucocorticoid_Sensitivity) missing: 0 out of 40\n",
" Genes with >20% missing: 0\n",
" Samples with >5% missing genes: 0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (40, 18043)\n",
"For the feature 'Glucocorticoid_Sensitivity', the least common label is '1.0' with 15 occurrences. This represents 37.50% of the dataset.\n",
"The distribution of the feature 'Glucocorticoid_Sensitivity' in this dataset is fine.\n",
"\n",
"A new JSON file was created at: ../../output/preprocess/Glucocorticoid_Sensitivity/cohort_info.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Glucocorticoid_Sensitivity/GSE15820.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the obtained gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"print(f\"Sample gene symbols after normalization: {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\"Gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Since we didn't successfully save clinical data in previous steps, let's extract it again\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_info, clinical_df = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Define conversion functions (from Step 2)\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert treatment information to glucocorticoid sensitivity binary indicator.\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Determine treatment type\n",
" if value == \"Dex\" or value == \"DexDox\":\n",
" return 1 # Exposed to glucocorticoid\n",
" elif value == \"none\" or value == \"Dox\":\n",
" return 0 # Not exposed to glucocorticoid\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age information to float.\"\"\"\n",
" # Age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender information to binary (0=female, 1=male).\"\"\"\n",
" # Gender data is not available\n",
" return None\n",
"\n",
"# Extract clinical features with the identified row indices\n",
"trait_row = 1 # From Step 2\n",
"age_row = None # From Step 2\n",
"gender_row = None # From Step 2\n",
"\n",
"# Extract clinical features\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_df,\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(\"Selected clinical features:\")\n",
"print(selected_clinical_df)\n",
"\n",
"# Save clinical data for future reference\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.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_df, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data columns preview:\")\n",
"print(list(linked_data.columns[:10])) # Show first 10 column names\n",
"\n",
"# 4. Handle missing values\n",
"print(\"\\nMissing values before handling:\")\n",
"print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
"gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
"if gene_cols:\n",
" missing_genes_pct = linked_data[gene_cols].isna().mean()\n",
" genes_with_high_missing = sum(missing_genes_pct > 0.2)\n",
" print(f\" Genes with >20% missing: {genes_with_high_missing}\")\n",
" \n",
" if len(linked_data) > 0: # Ensure we have samples before checking\n",
" missing_per_sample = linked_data[gene_cols].isna().mean(axis=1)\n",
" samples_with_high_missing = sum(missing_per_sample > 0.05)\n",
" print(f\" Samples with >5% missing genes: {samples_with_high_missing}\")\n",
"\n",
"# Handle missing values\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",
"trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
"\n",
"# 6. Final validation and save\n",
"note = \"Dataset contains gene expression data from glucocorticoid sensitivity studies. \"\n",
"if 'Age' in cleaned_data.columns:\n",
" note += \"Age data is available. \"\n",
"if 'Gender' in cleaned_data.columns:\n",
" note += \"Gender data is available. \"\n",
"\n",
"is_gene_available = len(normalized_gene_data) > 0\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=is_gene_available, \n",
" is_trait_available=True, \n",
" is_biased=trait_biased, \n",
" df=cleaned_data,\n",
" note=note\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\")"
]
}
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