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{
"cells": [
{
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"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 = \"Rectal_Cancer\"\n",
"cohort = \"GSE123390\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Rectal_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Rectal_Cancer/GSE123390\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Rectal_Cancer/GSE123390.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Rectal_Cancer/gene_data/GSE123390.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Rectal_Cancer/clinical_data/GSE123390.csv\"\n",
"json_path = \"../../output/preprocess/Rectal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "56c1f385",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4693fc7d",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Locally advanced rectal cancer transcriptomic-based secretome analysis according to neoadjuvant chemoradiotherapy response\"\n",
"!Series_summary\t\"Most patients with locally advanced rectal cancer (LARC) present incomplete pathological response (pIR) to neoadjuvant chemoradiotherapy (nCRT). Despite the efforts to predict treatment response using tumor-molecular features, as differentially expressed genes, no molecule has proved to be a strong biomarker. The tumor secretome analysis is a promising strategy for biomarkers identification, which can be assessed using transcriptomic data. Here, we performed transcriptomic-based secretome analysis to select potentially secreted proteins using an in silico approach. The tumor expression profile of 28 LARC biopsies carefully selected and collected before nCRT was compared with normal rectal tissues (NT). The expression profile showed no significant differences between cases with complete (pCR) and incomplete response to nCRT. Genes with increased expression (pCR = 106 and pIR = 357) were used for secretome analysis based on public databases (Vesiclepedia, Human Cancer Secretome Database and Plasma and Proteome Database). Seventeen potentially secreted candidates (pCR=1, pIR=13 and 3 in both groups) were further investigated in two independent datasets (TCGA and GSE68204) confirming their over-expression in LARC. The potential secreted biomarkers were also confirmed as associated with the nCRT response (GSE68204). These putative proteins are candidates to be assessed in liquid biopsies aiming a personalized treatment in LARC patients.\"\n",
"!Series_overall_design\t\"Total RNA was extracted from 28 rectal cancer samples and 5 normal rectal tissue fixed in formaline and embedded in paraffin. Global gene expression was detected using the Affymetrix Human Transcriptome Array 2.0.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Rectum'], 1: ['disease: rectal cancer', 'disease: normal'], 2: ['response: pIR', 'response: pCR', 'response: -'], 3: ['trg: 3', 'trg: 2', 'trg: 1', 'trg: 0', 'trg: -']}\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": "6f3181e4",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1fc04ba6",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:46:36.055003Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data preview:\n",
"{'GSM3502511': [0.0], 'GSM3502512': [0.0], 'GSM3502513': [0.0], 'GSM3502514': [0.0], 'GSM3502515': [0.0], 'GSM3502516': [0.0], 'GSM3502517': [1.0], 'GSM3502518': [1.0], 'GSM3502519': [0.0], 'GSM3502520': [1.0], 'GSM3502521': [0.0], 'GSM3502522': [0.0], 'GSM3502523': [0.0], 'GSM3502524': [0.0], 'GSM3502525': [0.0], 'GSM3502526': [0.0], 'GSM3502527': [0.0], 'GSM3502528': [1.0], 'GSM3502529': [1.0], 'GSM3502530': [0.0], 'GSM3502531': [0.0], 'GSM3502532': [0.0], 'GSM3502533': [1.0], 'GSM3502534': [1.0], 'GSM3502535': [1.0], 'GSM3502536': [1.0], 'GSM3502537': [1.0], 'GSM3502538': [1.0], 'GSM3502539': [nan], 'GSM3502540': [nan], 'GSM3502541': [nan], 'GSM3502542': [nan], 'GSM3502543': [nan]}\n",
"Clinical data saved to ../../output/preprocess/Rectal_Cancer/clinical_data/GSE123390.csv\n"
]
}
],
"source": [
"#1. Check gene expression data availability\n",
"is_gene_available = True # This is Affymetrix expression data (Human Transcriptome Array 2.0)\n",
"\n",
"#2. Variable Availability and Data Type Conversion\n",
"#2.1 Identify rows containing trait, age, and gender data\n",
"trait_row = 2 # The trait is response to treatment (pCR/pIR) in row 2\n",
"age_row = None # Age is not available in the sample characteristics\n",
"gender_row = None # Gender is not available in the sample characteristics\n",
"\n",
"#2.2 Data Type Conversion functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert treatment response to binary values: \n",
" pCR (pathological Complete Response) = 1, \n",
" pIR (pathological Incomplete Response) = 0,\n",
" Other/unknown = None\"\"\"\n",
" if not value or not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert response values\n",
" if value == 'pCR':\n",
" return 1\n",
" elif value == 'pIR':\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Age data is not available, but function defined for completeness\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Gender data is not available, but function defined for completeness\n",
" return None\n",
"\n",
"#3. Save Metadata - Initial Filtering\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 (only if trait_row is not None)\n",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" 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 data\n",
" preview = preview_df(clinical_df)\n",
" print(\"Clinical data preview:\")\n",
" print(preview)\n",
" \n",
" # Save clinical data to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "c3b20fc2",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2d73571d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:46:36.056926Z",
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"shell.execute_reply": "2025-03-25T03:46:36.411924Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['2824546_st', '2824549_st', '2824551_st', '2824554_st', '2827992_st',\n",
" '2827995_st', '2827996_st', '2828010_st', '2828012_st', '2835442_st',\n",
" '2835447_st', '2835453_st', '2835456_st', '2835459_st', '2835461_st',\n",
" '2839509_st', '2839511_st', '2839513_st', '2839515_st', '2839517_st'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the file paths\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\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(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "e3c279c1",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8e2deeaf",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:46:36.414193Z",
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"shell.execute_reply": "2025-03-25T03:46:36.415987Z"
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},
"outputs": [],
"source": [
"# These gene identifiers (like \"2824546_st\") appear to be probe IDs from an Affymetrix microarray\n",
"# They are not standard human gene symbols and would need to be mapped to gene symbols\n",
"\n",
"# These look like Affymetrix probe IDs which need to be mapped to official gene symbols\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "28a4b977",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c18e6973",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T03:46:36.417846Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['TC01000001.hg.1', 'TC01000002.hg.1', 'TC01000003.hg.1', 'TC01000004.hg.1', 'TC01000005.hg.1'], 'probeset_id': ['TC01000001.hg.1', 'TC01000002.hg.1', 'TC01000003.hg.1', 'TC01000004.hg.1', 'TC01000005.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+', '+', '+'], 'start': ['11869', '29554', '69091', '160446', '317811'], 'stop': ['14409', '31109', '70008', '161525', '328581'], 'total_probes': [49.0, 60.0, 30.0, 30.0, 191.0], 'gene_assignment': ['NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000456328 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // 9p24.3 // 100287596 /// ENST00000456328 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102', 'ENST00000408384 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000408384 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000408384 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000408384 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000469289 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000469289 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000469289 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000469289 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000473358 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000473358 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000473358 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// OTTHUMT00000002841 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002841 // RP11-34P13.3 // NULL // --- // --- /// OTTHUMT00000002840 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002840 // RP11-34P13.3 // NULL // --- // ---', 'NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// OTTHUMT00000003223 // OR4F5 // NULL // --- // ---', 'OTTHUMT00000007169 // OTTHUMG00000002525 // NULL // --- // --- /// OTTHUMT00000007169 // RP11-34P13.9 // NULL // --- // ---', 'NR_028322 // LOC100132287 // uncharacterized LOC100132287 // 1p36.33 // 100132287 /// NR_028327 // LOC100133331 // uncharacterized LOC100133331 // 1p36.33 // 100133331 /// ENST00000425496 // LOC101060495 // uncharacterized LOC101060495 // --- // 101060495 /// ENST00000425496 // LOC101060494 // uncharacterized LOC101060494 // --- // 101060494 /// ENST00000425496 // LOC101059936 // uncharacterized LOC101059936 // --- // 101059936 /// ENST00000425496 // LOC100996502 // uncharacterized LOC100996502 // --- // 100996502 /// ENST00000425496 // LOC100996328 // uncharacterized LOC100996328 // --- // 100996328 /// ENST00000425496 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// NR_028325 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// OTTHUMT00000346878 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346878 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346879 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346879 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346880 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346880 // RP4-669L17.10 // NULL // --- // --- /// OTTHUMT00000346881 // OTTHUMG00000156968 // NULL // --- // --- /// OTTHUMT00000346881 // RP4-669L17.10 // NULL // --- // ---'], 'mrna_assignment': ['NR_046018 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 (DDX11L1), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aaa.3 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc010nxq.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc010nxr.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0', 'ENST00000408384 // ENSEMBL // ncrna:miRNA chromosome:GRCh37:1:30366:30503:1 gene:ENSG00000221311 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000469289 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:30267:31109:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000473358 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:29554:31097:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002841 // Havana transcript // cdna:all chromosome:VEGA52:1:30267:31109:1 Gene:OTTHUMG00000000959 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002840 // Havana transcript // cdna:all chromosome:VEGA52:1:29554:31097:1 Gene:OTTHUMG00000000959 // chr1 // 100 // 100 // 0 // --- // 0', 'NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // cdna:known chromosome:GRCh37:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // cdna:all chromosome:VEGA52:1:69091:70008:1 Gene:OTTHUMG00000001094 // chr1 // 100 // 100 // 0 // --- // 0', 'ENST00000496488 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:160446:161525:1 gene:ENSG00000241599 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000007169 // Havana transcript // cdna:all chromosome:VEGA52:1:160446:161525:1 Gene:OTTHUMG00000002525 // chr1 // 100 // 100 // 0 // --- // 0', 'NR_028322 // RefSeq // Homo sapiens uncharacterized LOC100132287 (LOC100132287), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// NR_028327 // RefSeq // Homo sapiens uncharacterized LOC100133331 (LOC100133331), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000425496 // ENSEMBL // ensembl:lincRNA chromosome:GRCh37:1:324756:328453:1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000426316 // ENSEMBL // [retired] cdna:known chromosome:GRCh37:1:317811:328455:1 gene:ENSG00000240876 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 0 // --- // 0 /// NR_028325 // RefSeq // Homo sapiens uncharacterized LOC100132062 (LOC100132062), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// uc009vjk.2 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc021oeh.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc021oei.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346906 // Havana transcript // [retired] cdna:all chromosome:VEGA50:1:317811:328455:1 Gene:OTTHUMG00000156972 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346878 // Havana transcript // cdna:all chromosome:VEGA52:1:320162:321056:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346879 // Havana transcript // cdna:all chromosome:VEGA52:1:320162:324461:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346880 // Havana transcript // cdna:all chromosome:VEGA52:1:317720:324873:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000346881 // Havana transcript // cdna:all chromosome:VEGA52:1:322672:324955:1 Gene:OTTHUMG00000156968 // chr1 // 100 // 100 // 0 // --- // 0'], 'swissprot': ['NR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 /// NR_046018 // B7ZGX7 /// NR_046018 // B7ZGX8 /// ENST00000456328 // B7ZGX0 /// ENST00000456328 // B7ZGX2 /// ENST00000456328 // B7ZGX3 /// ENST00000456328 // B7ZGX7 /// ENST00000456328 // B7ZGX8 /// ENST00000456328 // Q6ZU42', '---', 'NM_001005484 // Q8NH21 /// ENST00000335137 // Q8NH21', '---', 'NR_028325 // B4DYM5 /// NR_028325 // B4E0H4 /// NR_028325 // B4E3X0 /// NR_028325 // B4E3X2 /// NR_028325 // Q6ZQS4'], 'unigene': ['NR_046018 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.719844 // brain| testis| normal /// ENST00000456328 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.618434 // testis| normal', 'ENST00000469289 // Hs.622486 // eye| normal| adult /// ENST00000469289 // Hs.729632 // testis| normal /// ENST00000469289 // Hs.742718 // testis /// ENST00000473358 // Hs.622486 // eye| normal| adult /// ENST00000473358 // Hs.729632 // testis| normal /// ENST00000473358 // Hs.742718 // testis', 'NM_001005484 // Hs.554500 // --- /// ENST00000335137 // Hs.554500 // ---', '---', 'NR_028322 // Hs.446409 // adrenal gland| blood| bone| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| lymph node| mouth| pharynx| placenta| prostate| skin| testis| thymus| thyroid| uterus| bladder carcinoma| chondrosarcoma| colorectal tumor| germ cell tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| primitive neuroectodermal tumor of the CNS| uterine tumor|embryoid body| blastocyst| fetus| neonate| adult /// NR_028327 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000425496 // Hs.744556 // mammary gland| normal| adult /// ENST00000425496 // Hs.660700 // eye| placenta| testis| normal| adult /// ENST00000425496 // Hs.518952 // blood| brain| intestine| lung| mammary gland| mouth| muscle| pharynx| placenta| prostate| spleen| testis| thymus| thyroid| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| prostate cancer| fetus| adult /// ENST00000425496 // Hs.742131 // testis| normal| adult /// ENST00000425496 // Hs.636102 // uterus| uterine tumor /// ENST00000425496 // Hs.646112 // brain| intestine| larynx| lung| mouth| prostate| testis| thyroid| colorectal tumor| head and neck tumor| lung tumor| normal| prostate cancer| adult /// ENST00000425496 // Hs.647795 // brain| lung| lung tumor| adult /// ENST00000425496 // Hs.684307 // --- /// ENST00000425496 // Hs.720881 // testis| normal /// ENST00000425496 // Hs.729353 // brain| lung| placenta| testis| trachea| lung tumor| normal| fetus| adult /// ENST00000425496 // Hs.735014 // ovary| ovarian tumor /// NR_028325 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| kidney tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult'], 'category': ['main', 'main', 'main', 'main', 'main'], 'locus type': ['Coding', 'Coding', 'Coding', 'Coding', 'Coding'], 'notes': ['---', '---', '---', '---', '2 retired transcript(s) from ENSEMBL, Havana transcript'], 'SPOT_ID': ['chr1(+):11869-14409', 'chr1(+):29554-31109', 'chr1(+):69091-70008', 'chr1(+):160446-161525', 'chr1(+):317811-328581']}\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": "e4dc92db",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"id": "97a8d6b2",
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"shell.execute_reply": "2025-03-25T03:46:42.814371Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data after mapping to gene symbols:\n",
"Number of genes: 71528\n",
"Number of samples: 33\n",
"First few gene symbols:\n",
"Index(['A-', 'A-2', 'A-52', 'A-575C2', 'A-E', 'A-I', 'A-II', 'A-IV', 'A-V',\n",
" 'A0'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# Looking at the gene expression identifiers and comparing with the annotation data,\n",
"# we need to map from probe IDs in the gene expression data to gene symbols\n",
"\n",
"# After examining both dataframes, it seems \"ID\" in the annotation dataframe is our key \n",
"# for gene identifiers and \"gene_assignment\" contains gene symbols\n",
"\n",
"# 1. Extract the mapping from the gene annotation dataframe\n",
"gene_mapping = get_gene_mapping(\n",
" annotation=gene_annotation,\n",
" prob_col='ID',\n",
" gene_col='gene_assignment'\n",
")\n",
"\n",
"# 2. Apply the gene mapping to convert probe-level measurements to gene expression\n",
"# This will handle the many-to-many mapping by properly distributing signal\n",
"gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)\n",
"\n",
"# Preview the mapped gene data\n",
"print(\"Gene expression data after mapping to gene symbols:\")\n",
"print(f\"Number of genes: {len(gene_data)}\")\n",
"print(f\"Number of samples: {gene_data.shape[1]}\")\n",
"print(\"First few gene symbols:\")\n",
"print(gene_data.index[:10])\n"
]
},
{
"cell_type": "markdown",
"id": "b7fa2708",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
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"id": "1ffb4243",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (24018, 33)\n",
"First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2ML1-AS1', 'A2ML1-AS2', 'A2MP1', 'A4GALT']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Rectal_Cancer/gene_data/GSE123390.csv\n",
"Clinical features loaded from ../../output/preprocess/Rectal_Cancer/clinical_data/GSE123390.csv\n",
"Clinical features shape: (1, 33)\n",
"Linked data shape: (33, 24019)\n",
"First few columns: ['Rectal_Cancer', 'A1BG', 'A1BG-AS1', 'A1CF', 'A2M']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape after handling missing values: (28, 24019)\n",
"For the feature 'Rectal_Cancer', the least common label is '1.0' with 11 occurrences. This represents 39.29% of the dataset.\n",
"The distribution of the feature 'Rectal_Cancer' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Rectal_Cancer/GSE123390.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\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# Get clinical data from previous step\n",
"# Use the clinical data that was already extracted and saved in Step 2\n",
"clinical_file_path = out_clinical_data_file\n",
"if os.path.exists(clinical_file_path):\n",
" clinical_features = pd.read_csv(clinical_file_path, index_col=0)\n",
" print(f\"Clinical features loaded from {clinical_file_path}\")\n",
" print(f\"Clinical features shape: {clinical_features.shape}\")\n",
"else:\n",
" # If file doesn't exist, we need to extract it again\n",
" # Get trait data as described in step 2, where trait_row=2 and age/gender are not available\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=2, # 'response: pIR/pCR' is in row 2\n",
" convert_trait=convert_trait, # Use the previously defined function\n",
" age_row=None, # No age data available\n",
" convert_age=None,\n",
" gender_row=None, # No gender data available\n",
" convert_gender=None\n",
" )\n",
" print(f\"Clinical features re-extracted\")\n",
" print(f\"Clinical features shape: {clinical_features.shape}\")\n",
"\n",
"# 2. Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(f\"First few columns: {list(linked_data.columns[:5])}\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data_processed = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape after handling missing values: {linked_data_processed.shape}\")\n",
"\n",
"# Add validation check - if no samples remain, note the issue\n",
"if linked_data_processed.shape[0] == 0:\n",
" print(\"No samples remain after handling missing values. The dataset cannot be processed further.\")\n",
" is_trait_biased = True # Mark as biased since we can't use it\n",
" unbiased_linked_data = linked_data_processed\n",
"else:\n",
" # 4. Determine whether the trait and demographic features are severely biased\n",
" is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_processed, 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=\"Dataset contains gene expression data from rectal cancer patients with treatment response data (pCR/pIR).\"\n",
")\n",
"\n",
"# 6. Save the data if it's usable\n",
"if is_usable:\n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" # Save the data\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")"
]
}
],
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|