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{
"cells": [
{
"cell_type": "code",
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"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 = \"Cervical_Cancer\"\n",
"cohort = \"GSE163114\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Cervical_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Cervical_Cancer/GSE163114\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Cervical_Cancer/GSE163114.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Cervical_Cancer/gene_data/GSE163114.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Cervical_Cancer/clinical_data/GSE163114.csv\"\n",
"json_path = \"../../output/preprocess/Cervical_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "81317870",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c874ddcf",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Ki-67 promotes carcinogenesis by enabling global transcriptional programmes\"\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: HeLa'], 1: ['lentivirus: shRNA control', 'lentivirus: shRNA Ki-67']}\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": "ac59173d",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b05204a7",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:14:25.646617Z",
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"shell.execute_reply": "2025-03-25T08:14:25.650921Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data file not found at ../../input/GEO/Cervical_Cancer/GSE163114/clinical_data.csv\n",
"This dataset may require manual inspection.\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# The dataset appears to be from a HeLa cell line with shRNA experiments\n",
"# As a SuperSeries with SubSeries, it likely contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait: Looking at row 1, it shows two different treatments (shRNA control vs shRNA Ki-67)\n",
"# This is the experimental condition and should be our trait of interest\n",
"trait_row = 1\n",
"\n",
"# Age and gender: Not available in this cell line dataset\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert shRNA treatment information to a binary indicator.\n",
" shRNA control = 0, shRNA Ki-67 = 1\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" value = value.lower()\n",
" if 'control' in value:\n",
" return 0\n",
" elif 'ki-67' in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Not applicable for this dataset\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Not applicable for this dataset\n",
" return None\n",
"\n",
"# 3. Save 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",
"if trait_row is not None:\n",
" try:\n",
" # For GEO data, we need to load the clinical data file that was created in a previous step\n",
" # Attempt to find the clinical data file in the expected location\n",
" clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
" \n",
" if os.path.exists(clinical_data_path):\n",
" clinical_data = pd.read_csv(clinical_data_path)\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",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Ensure the output directory exists\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",
" else:\n",
" print(f\"Clinical data file not found at {clinical_data_path}\")\n",
" print(\"This dataset may require manual inspection.\")\n",
" except Exception as e:\n",
" print(f\"Error processing clinical data: {e}\")\n",
" print(\"This dataset may require special handling.\")\n"
]
},
{
"cell_type": "markdown",
"id": "f28d5bed",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3485509b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:14:25.652420Z",
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"shell.execute_reply": "2025-03-25T08:14:25.753429Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',\n",
" '14', '15', '16', '17', '18', '19', '20'],\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": "c1ee14a2",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0e3585b8",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:14:25.755100Z",
"iopub.status.busy": "2025-03-25T08:14:25.754989Z",
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"shell.execute_reply": "2025-03-25T08:14:25.756549Z"
}
},
"outputs": [],
"source": [
"# The index values ['1', '2', '3', '4', ...] appear to be simple numeric identifiers,\n",
"# not standard human gene symbols which typically follow formats like \"TP53\", \"BRCA1\", etc.\n",
"# These are likely probe IDs or some other platform-specific identifiers that need to be\n",
"# mapped to proper gene symbols for biological interpretation.\n",
"\n",
"# Based on my biomedical knowledge, these are not human gene symbols and require mapping\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "10ab462a",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c98399e9",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:14:25.757902Z",
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"shell.execute_reply": "2025-03-25T08:14:27.520870Z"
}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1', '2', '3', '4', '5'], 'COL': ['192', '192', '192', '192', '192'], 'ROW': [328.0, 326.0, 324.0, 322.0, 320.0], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_23_P117082', 'A_33_P3246448'], 'SPOT_ID': ['CONTROL', 'CONTROL', 'CONTROL', 'A_23_P117082', 'A_33_P3246448'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'GB_ACC': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'LOCUSLINK_ID': [nan, nan, nan, 50865.0, 23704.0], 'GENE_SYMBOL': [nan, nan, nan, 'HEBP1', 'KCNE4'], 'GENE_NAME': [nan, nan, nan, 'heme binding protein 1', 'potassium voltage-gated channel, Isk-related family, member 4'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.642618', 'Hs.348522'], 'ENSEMBL_ID': [nan, nan, nan, 'ENST00000014930', 'ENST00000281830'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_015987|ens|ENST00000014930|gb|AF117615|gb|BC016277', 'ref|NM_080671|ens|ENST00000281830|tc|THC2655788'], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, 'chr12:13127906-13127847', 'chr2:223920197-223920256'], 'CYTOBAND': [nan, nan, nan, 'hs|12p13.1', 'hs|2q36.1'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens heme binding protein 1 (HEBP1), mRNA [NM_015987]', 'Homo sapiens potassium voltage-gated channel, Isk-related family, member 4 (KCNE4), mRNA [NM_080671]'], 'GO_ID': [nan, nan, nan, 'GO:0005488(binding)|GO:0005576(extracellular region)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0007623(circadian rhythm)|GO:0020037(heme binding)', 'GO:0005244(voltage-gated ion channel activity)|GO:0005249(voltage-gated potassium channel activity)|GO:0006811(ion transport)|GO:0006813(potassium ion transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0016324(apical plasma membrane)'], 'SEQUENCE': [nan, nan, nan, 'AAGGGGGAAAATGTGATTTGTGCCTGATCTTTCATCTGTGATTCTTATAAGAGCTTTGTC', 'GCAAGTCTCTCTGCACCTATTAAAAAGTGATGTATATACTTCCTTCTTATTCTGTTGAGT']}\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": "8dcf59c3",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "731cf145",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:14:27.522691Z",
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"shell.execute_reply": "2025-03-25T08:14:27.669009Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data after mapping (first 5 rows):\n",
" GSM4950455 GSM4950456 GSM4950457 GSM4950458 GSM4950459 \\\n",
"Gene \n",
"A1BG 132.083288 129.833098 95.850230 100.615695 124.773355 \n",
"A1BG-AS1 16.155110 14.861680 4.716991 11.106840 8.644419 \n",
"A1CF 212.139998 173.847205 54.092575 156.444192 106.723514 \n",
"A2LD1 244.871530 180.816300 109.013836 173.861860 247.707170 \n",
"A2M 133.594765 128.883865 62.684670 123.532323 81.277947 \n",
"\n",
" GSM4950460 GSM4950461 GSM4950462 GSM4950463 GSM4950464 \\\n",
"Gene \n",
"A1BG 109.751668 108.931347 92.967957 127.500770 106.803252 \n",
"A1BG-AS1 4.350618 10.533510 7.061245 13.553190 10.343080 \n",
"A1CF 76.701249 157.344856 122.121485 154.139703 196.274230 \n",
"A2LD1 145.602781 206.909400 220.527440 229.859650 200.153210 \n",
"A2M 55.676736 104.865621 83.508082 146.794847 133.055046 \n",
"\n",
" GSM4950465 GSM4950466 \n",
"Gene \n",
"A1BG 94.078066 122.938080 \n",
"A1BG-AS1 13.383460 15.715330 \n",
"A1CF 105.685073 173.692618 \n",
"A2LD1 177.095571 185.686890 \n",
"A2M 66.040354 151.910271 \n"
]
}
],
"source": [
"# 1. Based on the output, we need to identify which columns to use for gene mapping\n",
"# Looking at the gene annotation preview, we can see:\n",
"# - 'ID' contains the numeric identifiers matching the gene expression data index\n",
"# - 'GENE_SYMBOL' contains the actual gene symbols (e.g., HEBP1, KCNE4)\n",
"\n",
"# 2. Extract the gene mapping dataframe from gene annotation\n",
"gene_mapping = get_gene_mapping(\n",
" annotation=gene_annotation,\n",
" prob_col='ID', # Gene identifiers column\n",
" gene_col='GENE_SYMBOL' # Gene symbols column\n",
")\n",
"\n",
"# 3. Convert probe-level measurements to gene expression data by applying gene mapping\n",
"gene_data = apply_gene_mapping(\n",
" expression_df=gene_data,\n",
" mapping_df=gene_mapping\n",
")\n",
"\n",
"# Print first few rows of the gene data after mapping to verify\n",
"print(\"Gene expression data after mapping (first 5 rows):\")\n",
"print(gene_data.head(5))\n"
]
},
{
"cell_type": "markdown",
"id": "b5eaf74d",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "923e2e1d",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:14:34.766559Z"
}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Cervical_Cancer/gene_data/GSE163114.csv\n",
"Clinical data saved to ../../output/preprocess/Cervical_Cancer/clinical_data/GSE163114.csv\n",
"Linked data shape: (12, 19848)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"For the feature 'Cervical_Cancer', the least common label is '0.0' with 6 occurrences. This represents 50.00% of the dataset.\n",
"The distribution of the feature 'Cervical_Cancer' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Cervical_Cancer/GSE163114.csv\n"
]
}
],
"source": [
"# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
"normalized_gene_data = normalize_gene_symbols_in_index(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",
"# Extract clinical features directly from the matrix file data (reuse the data from previous steps)\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Use the correct trait conversion function from Step 2\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert shRNA treatment information to a binary indicator.\n",
" shRNA control = 0, shRNA Ki-67 = 1\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" value = value.lower()\n",
" if 'control' in value:\n",
" return 0\n",
" elif 'ki-67' in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Extract clinical features using the trait_row identified in Step 2\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=1, # Using row 1 which contains shRNA treatment information\n",
" convert_trait=convert_trait\n",
")\n",
"\n",
"# Save clinical data\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",
"# Now link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(\"Linked data shape:\", linked_data.shape)\n",
"\n",
"# Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"\n",
"# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\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=\"HeLa cell line samples treated with shRNA control vs. shRNA Ki-67 knockdown.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_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 and was not saved\")"
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|