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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "93f80fd4",
"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 = \"Autism_spectrum_disorder_(ASD)\"\n",
"cohort = \"GSE113842\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)\"\n",
"in_cohort_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)/GSE113842\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE113842.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE113842.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE113842.csv\"\n",
"json_path = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "98cc760e",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d8abbb2e",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Autism-like phenotype and risk gene-RNA deadenylation by CPEB4 mis-splicing\"\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: ['genotype: WT', 'genotype: HD', 'genotype: CPEB4 GT/+', 'genotype: CPEB4 KO', 'genotype: CTRL', 'genotype: TgCPEB4{delta}4'], 1: ['condition: CPEB1 RIP', 'condition: none', 'condition: CPEB4 RIP', 'condition: IgG RIP', 'group: INPUT', 'group: WASH', 'group: ELUTED'], 2: [nan, 'replicate: 1', 'replicate: 2', 'replicate: 3'], 3: [nan, 'tissue: Cortex/Striatum'], 4: [nan, 'age: 6 week-old'], 5: [nan, 'rna fraction: total RNA', 'rna fraction: Enriched in short poly(A)-tail RNA', 'rna fraction: Enriched in long poly(A)-tail RNA']}\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": "a10dc099",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "19bacd37",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{0: [0.0, nan]}\n",
"Clinical data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE113842.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"from typing import Optional, Callable\n",
"\n",
"# Define the sample characteristics dictionary from the previous output\n",
"sample_characteristics_dict = {\n",
" 0: ['group: CTRL', 'group: ASD'], \n",
" 1: ['material: INPUT', 'material: WASH', 'material: ELUTED'], \n",
" 2: ['age: 7', 'age: 9', 'age: 5', 'age: 13', 'age: 20/21', 'age: 11', 'age: 12', 'age: 21/23'], \n",
" 3: ['postmorten interval (h): 16', 'postmorten interval (h): 12', 'postmorten interval (h): 21', 'postmorten interval (h): 3', 'postmorten interval (h): 28', 'postmorten interval (h): 11/19', 'postmorten interval (h): 27', 'postmorten interval (h): 22', 'postmorten interval (h): 14/18'], \n",
" 4: ['hybridization batch: A', 'hybridization batch: B'], \n",
" 5: ['tissue: Prefrontal Cortex BA9', 'tissue: Prefrontal Cortex BA8/9', 'tissue: Prefrontal Cortex BA8'], \n",
" 6: ['rna fraction: total RNA', 'rna fraction: Enriched in short poly(A)-tail RNA', 'rna fraction: Enriched in long poly(A)-tail RNA']\n",
"}\n",
"\n",
"# 1. Check Gene Expression Data Availability\n",
"# Based on series title and summary, it appears to be RNA expression data\n",
"is_gene_available = True # RNA data from deadenylation by CPEB4 mis-splicing\n",
"\n",
"# 2.1 Data Availability\n",
"trait_row = 0 # 'group: CTRL', 'group: ASD' indicates trait (ASD) status\n",
"age_row = 2 # 'age: 7', 'age: 9', etc. indicates age information\n",
"gender_row = None # Gender information is not provided in the sample characteristics\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait data to binary format (0 for control, 1 for ASD).\"\"\"\n",
" if value is None or ':' not in value:\n",
" return None\n",
" \n",
" value = value.split(':', 1)[1].strip().upper()\n",
" if value == 'CTRL' or value == 'CONTROL':\n",
" return 0\n",
" elif value == 'ASD':\n",
" return 1\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age data to continuous format.\"\"\"\n",
" if value is None or ':' not in value:\n",
" return None\n",
" \n",
" value = value.split(':', 1)[1].strip()\n",
" # Handle cases like \"20/21\" or \"21/23\" by averaging\n",
" if '/' in value:\n",
" ages = [int(age) for age in value.split('/')]\n",
" return sum(ages) / len(ages)\n",
" try:\n",
" return float(value)\n",
" except ValueError:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender data to binary format (0 for female, 1 for male).\"\"\"\n",
" # This function is defined but not used as gender data is not available\n",
" if value is None or ':' not in value:\n",
" return None\n",
" \n",
" value = value.split(':', 1)[1].strip().lower()\n",
" if value in ['female', 'f']:\n",
" return 0\n",
" elif value in ['male', 'm']:\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata (Initial Filtering)\n",
"# Determine is_trait_available based on whether trait_row is None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort information\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 (if trait_row is not None)\n",
"if trait_row is not None:\n",
" # Create a DataFrame with sample characteristics in a row-wise format\n",
" data = []\n",
" for key, values in sample_characteristics_dict.items():\n",
" for value in values:\n",
" data.append([value])\n",
" clinical_data = pd.DataFrame(data)\n",
" \n",
" # Use geo_select_clinical_features to 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",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Ensure the directory exists\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the clinical data 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"
]
},
{
"cell_type": "markdown",
"id": "f7352292",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "62a6b707",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1415670_PM_at', '1415671_PM_at', '1415672_PM_at', '1415673_PM_at',\n",
" '1415674_PM_a_at', '1415675_PM_at', '1415676_PM_a_at', '1415677_PM_at',\n",
" '1415678_PM_at', '1415679_PM_at', '1415680_PM_at', '1415681_PM_at',\n",
" '1415682_PM_at', '1415683_PM_at', '1415684_PM_at', '1415685_PM_at',\n",
" '1415686_PM_at', '1415687_PM_a_at', '1415688_PM_at', '1415689_PM_s_at'],\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": "eec2ef4f",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f47f99c4",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# These identifiers appear to be Affymetrix probeset IDs (format with \"_at\", \"_s_at\", \"_x_at\" suffix patterns)\n",
"# rather than standard human gene symbols like BRCA1, TP53, etc.\n",
"# These probesets would need to be mapped to gene symbols for biological interpretation.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "d28ea07b",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f5df1f1b",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1415670_PM_at', '1415671_PM_at', '1415672_PM_at', '1415673_PM_at', '1415674_PM_a_at'], 'GB_ACC': ['BC024686', 'NM_013477', 'NM_020585', 'NM_133900', 'NM_021789'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Mus musculus', 'Mus musculus', 'Mus musculus', 'Mus musculus', 'Mus musculus'], 'Annotation Date': ['Aug 10, 2010', 'Aug 10, 2010', 'Aug 10, 2010', 'Aug 10, 2010', 'Aug 10, 2010'], 'Sequence Type': ['Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence'], 'Sequence Source': ['GenBank', 'GenBank', 'GenBank', 'GenBank', 'GenBank'], 'Target Description': ['gb:BC024686.1 /DB_XREF=gi:19354080 /FEA=FLmRNA /CNT=416 /TID=Mm.26422.1 /TIER=FL+Stack /STK=110 /UG=Mm.26422 /LL=54161 /UG_GENE=Copg1 /DEF=Mus musculus, coatomer protein complex, subunit gamma 1, clone MGC:30335 IMAGE:3992144, mRNA, complete cds. /PROD=coatomer protein complex, subunit gamma 1 /FL=gb:AF187079.1 gb:BC024686.1 gb:NM_017477.1 gb:BC024896.1', 'gb:NM_013477.1 /DB_XREF=gi:7304908 /GEN=Atp6v0d1 /FEA=FLmRNA /CNT=197 /TID=Mm.1081.1 /TIER=FL+Stack /STK=114 /UG=Mm.1081 /LL=11972 /DEF=Mus musculus ATPase, H+ transporting, lysosomal 38kDa, V0 subunit D isoform 1 (Atp6v0d1), mRNA. /PROD=ATPase, H+ transporting, lysosomal 38kDa, V0subunit D isoform 1 /FL=gb:U21549.1 gb:U13840.1 gb:BC011075.1 gb:NM_013477.1', 'gb:NM_020585.1 /DB_XREF=gi:10181207 /GEN=AB041568 /FEA=FLmRNA /CNT=213 /TID=Mm.17035.1 /TIER=FL+Stack /STK=102 /UG=Mm.17035 /LL=57437 /DEF=Mus musculus hypothetical protein, MNCb-1213 (AB041568), mRNA. /PROD=hypothetical protein, MNCb-1213 /FL=gb:BC016894.1 gb:NM_020585.1', 'gb:NM_133900.1 /DB_XREF=gi:19527115 /GEN=AI480570 /FEA=FLmRNA /CNT=139 /TID=Mm.10623.1 /TIER=FL+Stack /STK=96 /UG=Mm.10623 /LL=100678 /DEF=Mus musculus expressed sequence AI480570 (AI480570), mRNA. /PROD=expressed sequence AI480570 /FL=gb:BC002251.1 gb:NM_133900.1', 'gb:NM_021789.1 /DB_XREF=gi:11140824 /GEN=Sbdn /FEA=FLmRNA /CNT=163 /TID=Mm.29814.1 /TIER=FL+Stack /STK=95 /UG=Mm.29814 /LL=60409 /DEF=Mus musculus synbindin (Sbdn), mRNA. /PROD=synbindin /FL=gb:NM_021789.1 gb:AF233340.1'], 'Representative Public ID': ['BC024686', 'NM_013477', 'NM_020585', 'NM_133900', 'NM_021789'], 'Gene Title': ['coatomer protein complex, subunit gamma', 'ATPase, H+ transporting, lysosomal V0 subunit D1', 'golgi autoantigen, golgin subfamily a, 7', 'phosphoserine phosphatase', 'trafficking protein particle complex 4'], 'Gene Symbol': ['Copg', 'Atp6v0d1', 'Golga7', 'Psph', 'Trappc4'], 'Entrez Gene': ['54161', '11972', '57437', '100678', '60409'], 'RefSeq Transcript ID': ['NM_017477 /// NM_201244', 'NM_013477', 'NM_001042484 /// NM_020585', 'NM_133900', 'NM_021789'], 'Gene Ontology Biological Process': ['0006810 // transport // inferred from electronic annotation /// 0006886 // intracellular protein transport // inferred from electronic annotation /// 0015031 // protein transport // inferred from electronic annotation /// 0016192 // vesicle-mediated transport // inferred from electronic annotation', '0006810 // transport // inferred from electronic annotation /// 0006811 // ion transport // inferred from electronic annotation /// 0007420 // brain development // inferred from electronic annotation /// 0015986 // ATP synthesis coupled proton transport // inferred from electronic annotation /// 0015992 // proton transport // inferred from electronic annotation', '0006893 // Golgi to plasma membrane transport // not recorded', '0006564 // L-serine biosynthetic process // inferred from electronic annotation /// 0008152 // metabolic process // inferred from electronic annotation /// 0008652 // cellular amino acid biosynthetic process // inferred from electronic annotation /// 0009612 // response to mechanical stimulus // inferred from electronic annotation /// 0031667 // response to nutrient levels // inferred from electronic annotation /// 0033574 // response to testosterone stimulus // inferred from electronic annotation', '0006810 // transport // inferred from electronic annotation /// 0006888 // ER to Golgi vesicle-mediated transport // inferred from electronic annotation /// 0016192 // vesicle-mediated transport // traceable author statement /// 0016192 // vesicle-mediated transport // inferred from electronic annotation /// 0016358 // dendrite development // inferred from direct assay /// 0045212 // neurotransmitter receptor biosynthetic process // traceable author statement'], 'Gene Ontology Cellular Component': ['0000139 // Golgi membrane // inferred from electronic annotation /// 0005737 // cytoplasm // inferred from electronic annotation /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0005798 // Golgi-associated vesicle // inferred from electronic annotation /// 0016020 // membrane // inferred from electronic annotation /// 0030117 // membrane coat // inferred from electronic annotation /// 0030126 // COPI vesicle coat // inferred from electronic annotation /// 0030663 // COPI coated vesicle membrane // inferred from electronic annotation /// 0031410 // cytoplasmic vesicle // inferred from electronic annotation', '0005769 // early endosome // inferred from direct assay /// 0008021 // synaptic vesicle // not recorded /// 0008021 // synaptic vesicle // inferred from electronic annotation /// 0016020 // membrane // inferred from electronic annotation /// 0016324 // apical plasma membrane // not recorded /// 0016324 // apical plasma membrane // inferred from electronic annotation /// 0019717 // synaptosome // not recorded /// 0019717 // synaptosome // inferred from electronic annotation /// 0033177 // proton-transporting two-sector ATPase complex, proton-transporting domain // inferred from electronic annotation /// 0033179 // proton-transporting V-type ATPase, V0 domain // inferred from electronic annotation /// 0043234 // protein complex // not recorded /// 0043679 // axon terminus // not recorded /// 0043679 // axon terminus // inferred from electronic annotation', '0000139 // Golgi membrane // not recorded /// 0000139 // Golgi membrane // inferred from electronic annotation /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0016020 // membrane // inferred from electronic annotation', '0019717 // synaptosome // not recorded /// 0019717 // synaptosome // inferred from electronic annotation', '0005783 // endoplasmic reticulum // inferred from electronic annotation /// 0005794 // Golgi apparatus // inferred from electronic annotation /// 0005795 // Golgi stack // inferred from direct assay /// 0005801 // cis-Golgi network // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from electronic annotation /// 0008021 // synaptic vesicle // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0030008 // TRAPP complex // inferred from direct assay /// 0030054 // cell junction // inferred from electronic annotation /// 0030425 // dendrite // inferred from direct assay /// 0045202 // synapse // inferred from direct assay /// 0045202 // synapse // inferred from electronic annotation /// 0045211 // postsynaptic membrane // inferred from electronic annotation'], 'Gene Ontology Molecular Function': ['0005198 // structural molecule activity // inferred from electronic annotation /// 0005488 // binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from electronic annotation', '0008553 // hydrogen-exporting ATPase activity, phosphorylative mechanism // inferred from direct assay /// 0015078 // hydrogen ion transmembrane transporter activity // inferred from electronic annotation /// 0032403 // protein complex binding // not recorded /// 0032403 // protein complex binding // inferred from electronic annotation', nan, '0003824 // catalytic activity // inferred from electronic annotation /// 0004647 // phosphoserine phosphatase activity // inferred from electronic annotation /// 0005515 // protein binding // inferred from electronic annotation /// 0016787 // hydrolase activity // inferred from electronic annotation /// 0016791 // phosphatase activity // inferred from electronic annotation', '0005515 // protein binding // inferred from physical interaction /// 0005515 // protein binding // inferred from electronic annotation']}\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": "ed43878a",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a2744369",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data after mapping to gene symbols:\n",
" GSM3120892 GSM3120893 GSM3120894 GSM3120895 GSM3120896 \\\n",
"Gene \n",
"A130033P14 4.039066 4.192930 4.128634 3.988778 3.336130 \n",
"A430075N02 4.264285 4.319175 4.591880 4.228277 3.879057 \n",
"A630043P06 3.667454 4.484825 4.563515 4.573879 3.933827 \n",
"A730034C02 12.692358 11.214867 9.627281 8.557741 15.380640 \n",
"A830091E24 3.606027 3.780010 4.225027 3.949768 3.420589 \n",
"\n",
" GSM3120897 GSM3120898 GSM3120899 GSM3120964 GSM3120965 ... \\\n",
"Gene ... \n",
"A130033P14 3.814148 4.979083 4.338055 4.626518 4.631608 ... \n",
"A430075N02 3.999499 4.272288 3.856207 4.751322 4.695923 ... \n",
"A630043P06 4.060068 4.100830 4.268562 6.168188 6.319480 ... \n",
"A730034C02 12.898844 10.628091 11.304317 19.737081 19.225753 ... \n",
"A830091E24 3.749105 3.702691 3.922714 7.779632 6.323384 ... \n",
"\n",
" GSM3121096 GSM3121097 GSM3121098 GSM3121099 GSM3121100 \\\n",
"Gene \n",
"A130033P14 5.072450 4.603705 4.603528 4.934665 4.713400 \n",
"A430075N02 5.162419 4.674936 4.596325 4.824838 4.605254 \n",
"A630043P06 6.300151 6.366614 6.018211 6.077133 6.134430 \n",
"A730034C02 19.154467 20.151631 19.415156 18.511080 20.518194 \n",
"A830091E24 7.309921 7.182640 5.956179 7.284793 7.121327 \n",
"\n",
" GSM3121101 GSM3121102 GSM3121103 GSM3121104 GSM3121105 \n",
"Gene \n",
"A130033P14 4.574106 5.004884 4.785717 4.524453 4.823034 \n",
"A430075N02 4.753700 5.095664 4.854732 4.816047 4.762050 \n",
"A630043P06 6.025884 6.209419 6.137426 6.138736 6.081797 \n",
"A730034C02 19.852528 18.760226 19.691923 19.047606 18.295848 \n",
"A830091E24 5.895475 7.338838 7.184301 6.041828 7.085319 \n",
"\n",
"[5 rows x 44 columns]\n"
]
}
],
"source": [
"# 1. Identify the columns in the annotation data that contain probe IDs and gene symbols\n",
"# Looking at the gene_annotation preview, I can see:\n",
"# - 'ID' column contains probe IDs (like '1415670_PM_at')\n",
"# - 'Gene Symbol' column contains the corresponding gene symbols (like 'Copg')\n",
"\n",
"# 2. Extract these two columns from the gene annotation dataframe to create the mapping\n",
"mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression\n",
"# This function will split expression values for probes that map to multiple genes\n",
"# and then sum values for each gene from all contributing probes\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Preview the first few rows of the mapped gene data\n",
"print(\"Gene data after mapping to gene symbols:\")\n",
"print(gene_data.head())"
]
}
],
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