{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "95c3da61", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:39:32.963158Z", "iopub.status.busy": "2025-03-25T07:39:32.962932Z", "iopub.status.idle": "2025-03-25T07:39:33.127876Z", "shell.execute_reply": "2025-03-25T07:39:33.127548Z" } }, "outputs": [], "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 = \"lower_grade_glioma_and_glioblastoma\"\n", "cohort = \"GSE24072\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/lower_grade_glioma_and_glioblastoma\"\n", "in_cohort_dir = \"../../input/GEO/lower_grade_glioma_and_glioblastoma/GSE24072\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/lower_grade_glioma_and_glioblastoma/GSE24072.csv\"\n", "out_gene_data_file = \"../../output/preprocess/lower_grade_glioma_and_glioblastoma/gene_data/GSE24072.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/lower_grade_glioma_and_glioblastoma/clinical_data/GSE24072.csv\"\n", "json_path = \"../../output/preprocess/lower_grade_glioma_and_glioblastoma/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "bda80537", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "a7983246", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:39:33.129259Z", "iopub.status.busy": "2025-03-25T07:39:33.129128Z", "iopub.status.idle": "2025-03-25T07:39:33.193241Z", "shell.execute_reply": "2025-03-25T07:39:33.192968Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files in the cohort directory:\n", "['GSE24072_family.soft.gz', 'GSE24072_series_matrix.txt.gz']\n", "Identified SOFT files: ['GSE24072_family.soft.gz']\n", "Identified matrix files: ['GSE24072_series_matrix.txt.gz']\n", "\n", "Background Information:\n", "!Series_title\t\"EXPRESSION OF VAV1 IN GLIOBLASTOMA MULTIFORME\"\n", "!Series_summary\t\"Background: Even though much progress has been made in the understanding of the molecular nature of glioma, the survival rates of patients affected of this tumour have not changed significantly during these years. Thus, a deeper understanding of this malignancy is still needed in order to predict its outcome and improve patient treatment. Here, we report that VAV1, a GDP/GTP exchange factor for Rho/Rac proteins with oncogenic potential that is involved in the regulation of cytoskeletal dynamics and cell migration.\"\n", "!Series_summary\t\"Methodology/Principal Findings: VAV1 is overexpressed in 32 patients diagnosed with high-grade glioma. Such overexpression is linked to the parallel upregulation of a number of genes coding for proteins also involved in cell invasion- and migration-related processes. Unexpectedly, immunohistochemical experiments revealed that VAV1 is not expressed in glioma cells. Instead, VAV1 is found in non-tumoral astrocyte-like cells that are located either peritumoraly or perivascularly, suggesting that its expression is linked to synergistic signalling cross-talk between cancer and infiltrating cells.\"\n", "!Series_summary\t\"Conclusions/Significance: Interestingly, we show that the pattern of expression of VAV1 is a good prognostic factor to unveil populations of high-grade glioma patients with different survival and progression free survival rates.\"\n", "!Series_overall_design\t\"1. Oligonucleotide microarray analyses\"\n", "!Series_overall_design\t\"Total RNAs were extracted using the Triazol reagent (Life Technologies, Gaithersburg, MD, USA) and purified with the RNeasy Mini kit (Qiagen, Valencia, CA, USA). The integrity of RNA samples obtained was assessed using the 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA). Double-stranded cDNAs and biotinylated cRNAs were synthesized using a T7-polyT primer and the BioArray RNA labelling Kit (Enzo Farningdale, NY, USA), respectively. Labelled RNAs were then fragmented and hybridised to HU-133A oligonucleotide arrays (Affymetrix, Santa Clara, CA, USA) according to standard Affymetrix protocols. After hybridization and washes, arrays were scanned using the Gene Array Scanner (Affymetrix), and the expression value for each probe set calculated using the MAS 5.0 software (Affymetrix). All examples had a scaling factor lower than threefold and 3’/5’ of GAPDH probe set <2.5. Gene levels were transformed to base two logarithms. A median normalization approach was applied. Only genes with al least three “present” calls across all samples were selected. All these steps were done at the Genomics and Proteomics Unit of the “Centro de Investigación del Cáncer, Salamanca”.\"\n", "!Series_overall_design\t\"2. Microarray data analyses\"\n", "!Series_overall_design\t\"To visualize clusters of genes with similar expression patterns, we used a hierarchical clustering method (Cluster and TreeView software) based on the average-linkage method with the centred correlation metric [26]. A multidimensional scaling method (BRB Arrays Tools version 3.0) was also utilized by using Euclidean distance criteria [27]. Supervised learning was used to identify genes with statistically significant changes in expression among different classes by using the Significant Analysis of Microarrays (SAM) algorithm [28]. All data were permuted over 100 cycles by using the two-class (unpaired) and multi-class response format. Significant genes were selected based on the lowest false discovery ratio (between 0.6 and 0.9). In addition, nonparametric tests such as Wilcoxon rank sum test and Kruskal-Wallis test to compare more than two unpaired group were also used (SPSS 18, SPSS Inc).\"\n", "!Series_overall_design\t\"3. Functional annotation of microarray data\"\n", "!Series_overall_design\t\"Probe sets showing significant expression change were functionally annotated and grouped according to biological function criteria using GeneOntology biological process descriptions. The functional analysis to identify the most relevant biological mechanism, pathways and functional categories in gene dada sets was generated using the Ingenuity Pathway software (Ingenuity Systems, Mountain View, CA, USA) available in the web (www.ingenuity.com) [29]. A functional network was considered significant when it fulfilled the following criteria: i) to have a minimal score of 15; ii) to have a minimum of 20 direct functional interactions among the network members.\"\n", "!Series_overall_design\t\"4. Quantitative reverse transcription-PCR\"\n", "!Series_overall_design\t\"Total RNA was quantified in a RNA 6000 Nano Chip (Agilent Technologies) and quantitative PCR performed using the QuantiTect SYBR Green RT–PCR kit (Qiagen). To quantify VAV1 mRNA levels, we used two different sets of probes: PAIR A (5’-AAC AAC GGG AGG TTC ACC CT-3’ and 5’-GGT CCC TCA TGG CAT CCA-3’) and PAIR B (5’-AGC CAT TGG ACC CTT TCT ACG-3’ and 5’-GCC ATG GAC ATA GGG CTT CA-3’). Amplifications were performed using the iCycler apparatus (Bio-Rad Laboratories, California, USA). Analyses of data were done using the iCycler iQ Optical System Software, version 3.0a (Bio-Rad Laboratories). Primers to GAPDH were used as intersample normalizing controls. Variations in expression of VAV1 mRNA were represented as the mean value of the fold change respect the VAV1 expression levels detected in sample #19209 with both pairs of oligonucleotide primers.\"\n", "!Series_overall_design\t\"5. Immunohistochemical analyses\"\n", "!Series_overall_design\t\"The VAV1 antibody was generated in rabbits using a synthetic peptide and purified by affinity chromatography in Bustelo’s laboratory. This antibody recognizes VAV1 proteins from humans and mice but it does not recognize other VAV family members (unpublished data). For immunostaining, tissue sections were washed thrice with Xylene and once with 100% ethanol, rehydrated by sequential changes in 80%, 70%, and 50% ethanol and a final incubation in phosphate-buffered saline (PBS). Each rehydrating step involved 3 min incubations with the indicated solutions. Endogenous peroxidases were quenched by the addition of a 3% H2O2 solution in methanol for 30 min at room temperature (RT). Tissue sections were subsequently washed twice with PBS. Antigen retrieval was performed by incubation in 1 mM EDTA for 30 min at 37°C. The slides were washed twice in PBS and blocked in blocking buffer (Zymed, CA, USA) for 30 min at RT. Specimens were then incubated with the primary antibody (1:250 dilution) in blocking buffer. After an 1 hr incubation at 37°C, slides were washed three times in PBS, incubated with a biotinylated secondary antibody for 30 min at 37°C, washed thrice in PBS, incubated with horseradish peroxidase-streptavidin for 30 min at 37°C, washed three times in PBS, and developed using the AEC substrate (Zymed). Slides were then washed twice in water, counterstained with hematoxilin (Zymed), washed again in water, and mounted with GVA (Zymed). Samples were analyzed by light microscopy and images acquired suing an Axiophot imaging system (Zeiss, Munich, Germany).\"\n", "!Series_overall_design\t\"6. Fluorescence in situ hybridization analyses\"\n", "!Series_overall_design\t\"FISH experiments were carried out in 40 cases of glioblastoma multiforme (grade IV) positive for VAV1 expression. For this purpose, we performed dual-colour FISH analyses with locus-specific probes for centromere 7 (Abbott Molecualr, Des Plaines) exactly as previously described.[30] Polysomies were defined when more than 10% of the nuclei surveyed contained three or more CEP signals (chromosome-specific FISH probes that hybridize to highly repetitive human satellite DNA sequences, usually located near centromeres).\"\n", "!Series_overall_design\t\"7. Immunohistochemistry and fluorescence in situ hybridization (FISH) in paraffin-embedded tumours\"\n", "!Series_overall_design\t\"Four um sections were cut from routinely processed paraffin blocks and mounted onto glass slides with a charged coating. Sections were dewaxed in Xylene and then rehydrated using increasing concentrations of alcohol before being rinsed briefly in water. Slides were heated 2 min in 1 mM EDTA (pH 9.0) in a microwavable pressure cooker. After antigen retrieval, slides were incubated 1 h at RT in a moist chamber with a primary antibody diluted in PBS supplemented with 10% foetal calf serum. Slides were incubated for 1 h with fluorochrome-conjugated antibodies to the appropriate IgG isotypes in a moist chamber in the dark. Finally, slides were washed thrice in PBS containing 0.5% Tween 20 three before FISH analysis.\"\n", "!Series_overall_design\t\"8. Degenerate oligonucleotide primed-polymerase chain reaction (DOP-PCR) analyses\"\n", "!Series_overall_design\t\"After the staining of tissue sections with VAV1 antibodies (see above), the regions of the tumour were identified, microdissected, and collected using the PALM® microscope system (P.A.L.M. Microlaser Technologies, Munich, Germany). The genomic DNA was extracted as indicated by Isola et al [31] with modifications to small DNA amounts. Those included the resuspension of the microdissected sections in extraction buffer followed by a digestion with proteinase K (0.6 mg/ml). All samples were resuspended in 10 ul of 10 mM Tris-HCl (pH 7.4) and 0.1 mM EDTA. DOP-PCR amplification was performed in two steps. For the first, low-stringency step, 1 ul of sample was added to 4 ul of buffer A (2.5 ul of 600 uM dNTPs (Roche, Pleasanton, CA), 0.5 ul of 10 uM DOP primer 5’-CCGACTCGAGNNNNNNNATGTGG-3’, where N= A, C, G, or T) [32] and 1 ul of 5x Sequenase Reaction Buffer (Amersham, Cleveland, OH). Reactions were performed using 5 cycles of 30ºC for 5 min, 37ºC for 2 min, and 96ºC for 2 min, adding 0.65 units of Sequenase in each 30ºC step. The first phase product was then subjected to the second step usin\"\n", "\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: male', 'gender: female'], 1: ['age: 72', 'age: 70', 'age: 34', 'age: 54', 'age: 68', 'age: 30', 'age: 60', 'age: 73', 'age: 52', 'age: 65', 'age: 76', 'age: 51', 'age: 43', 'age: 67', 'age: 66', 'age: 69', 'age: 74', 'age: 36', 'age: 38', 'age: 63', 'age: 46', 'age: 55'], 2: ['type: glioma grade III', 'type: glioma grade IV', 'type: glioma grade V']}\n" ] } ], "source": [ "# 1. Let's first list the directory contents to understand what files are available\n", "import os\n", "\n", "print(\"Files in the cohort directory:\")\n", "files = os.listdir(in_cohort_dir)\n", "print(files)\n", "\n", "# Adapt file identification to handle different naming patterns\n", "soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n", "matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n", "\n", "# If no files with these patterns are found, look for alternative file types\n", "if not soft_files:\n", " soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "if not matrix_files:\n", " matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "\n", "print(\"Identified SOFT files:\", soft_files)\n", "print(\"Identified matrix files:\", matrix_files)\n", "\n", "# Use the first files found, if any\n", "if len(soft_files) > 0 and len(matrix_files) > 0:\n", " soft_file = os.path.join(in_cohort_dir, soft_files[0])\n", " matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n", " print(background_info)\n", " print(\"\\nSample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", "else:\n", " print(\"No appropriate files found in the directory.\")\n" ] }, { "cell_type": "markdown", "id": "6f838f98", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "ec130468", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:39:33.194483Z", "iopub.status.busy": "2025-03-25T07:39:33.194382Z", "iopub.status.idle": "2025-03-25T07:39:33.283731Z", "shell.execute_reply": "2025-03-25T07:39:33.283378Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical features:\n", "{'ID_REF': [nan, nan, nan], 'GSM590744': [nan, 3.024225512, nan], 'GSM590745': [nan, 3.072491271, nan], 'GSM590746': [nan, 3.178727165, nan], 'GSM590747': [nan, 2.938471227, nan], 'GSM590748': [nan, 2.975731256, nan], 'GSM591700': [nan, 3.053342017, nan], 'GSM591701': [nan, 3.074540121, nan], 'GSM591702': [nan, 3.100500541, nan], 'GSM591703': [nan, 3.124697987, nan], 'GSM591704': [nan, 3.005714803, nan], 'GSM591705': [nan, 2.95193891, nan], 'GSM591706': [nan, 3.219183426, nan], 'GSM591707': [nan, 3.115066241, nan], 'GSM591722': [nan, 3.188311692, nan], 'GSM591792': [nan, 3.120863409, nan], 'GSM591793': [nan, 3.035625024, nan], 'GSM591796': [nan, 3.141590568, nan], 'GSM591798': [nan, 3.221985573, nan], 'GSM591800': [nan, 3.073565403, nan], 'GSM591808': [nan, 3.292542939, nan], 'GSM591809': [nan, 3.127732788, nan], 'GSM591811': [nan, 3.165121312, nan], 'GSM591812': [nan, 3.169326111, nan], 'GSM591815': [nan, 3.138911537, nan], 'GSM591817': [nan, 3.034664028, nan], 'GSM591818': [nan, 3.027386689, nan], 'GSM591819': [nan, 3.170742936, nan], 'GSM591829': [nan, 3.152728404, nan], 'GSM591830': [nan, 2.986613357, nan], 'GSM591831': [nan, 2.884331964, nan], 'GSM591832': [nan, 3.196360518, nan], 'GSM591833': [nan, 3.169114605, nan]}\n", "Clinical features saved to ../../output/preprocess/lower_grade_glioma_and_glioblastoma/clinical_data/GSE24072.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains oligonucleotide microarray data\n", "# from Affymetrix HU-133A arrays, which measure gene expression levels.\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary, we can identify:\n", "trait_row = 2 # \"type\" contains information about glioma grade\n", "age_row = 1 # \"age\" is available\n", "gender_row = 0 # \"gender\" is available\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert glioma grade information to binary classification:\n", " 0 for lower grade glioma (grade III)\n", " 1 for glioblastoma (grade IV and V)\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Handle non-string values\n", " if not isinstance(value, str):\n", " return None\n", " \n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if 'grade III' in value.lower():\n", " return 0 # Lower grade glioma\n", " elif 'grade IV' in value.lower() or 'grade V' in value.lower():\n", " return 1 # Glioblastoma\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age values to continuous numeric values.\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Handle non-string values\n", " if isinstance(value, (int, float)):\n", " return float(value)\n", " \n", " if not isinstance(value, str):\n", " return None\n", " \n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"\n", " Convert gender values to binary:\n", " 0 for female\n", " 1 for male\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Handle non-string values\n", " if not isinstance(value, str):\n", " return None\n", " \n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.lower() == 'female':\n", " return 0\n", " elif value.lower() == 'male':\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata for Initial Filtering\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(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", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, we need to extract clinical features\n", "if trait_row is not None:\n", " # Load the clinical data\n", " clinical_data = pd.read_table(os.path.join(in_cohort_dir, \"GSE24072_series_matrix.txt.gz\"), \n", " compression='gzip', \n", " comment='!', \n", " sep='\\t')\n", " \n", " # Extract clinical features using the geo_select_clinical_features function\n", " clinical_features = 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 extracted clinical features\n", " preview = preview_df(clinical_features)\n", " print(\"Preview of clinical features:\")\n", " print(preview)\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 the clinical features to CSV\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "44441562", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "89f4b851", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:39:33.285026Z", "iopub.status.busy": "2025-03-25T07:39:33.284878Z", "iopub.status.idle": "2025-03-25T07:39:33.368070Z", "shell.execute_reply": "2025-03-25T07:39:33.367739Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 20 gene/probe identifiers:\n", "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n", " '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n", " '1494_f_at', '1598_g_at', '160020_at', '1729_at', '1773_at', '177_at',\n", " '179_at', '1861_at'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data shape: (22283, 32)\n" ] } ], "source": [ "# Use the helper function to get the proper file paths\n", "soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Extract gene expression data\n", "try:\n", " gene_data = get_genetic_data(matrix_file_path)\n", " \n", " # Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", " \n", " # Print shape to understand the dataset dimensions\n", " print(f\"\\nGene expression data shape: {gene_data.shape}\")\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "d1de9e6c", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c5567ee3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:39:33.369359Z", "iopub.status.busy": "2025-03-25T07:39:33.369254Z", "iopub.status.idle": "2025-03-25T07:39:33.371046Z", "shell.execute_reply": "2025-03-25T07:39:33.370778Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers provided\n", "# The identifiers appear to be Affymetrix probe IDs (like \"1007_s_at\") rather than standard human gene symbols\n", "# Human gene symbols would typically be like BRCA1, TP53, etc.\n", "# These Affymetrix IDs will need to be mapped to standard gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "2866dcf5", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "5e9a8a20", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:39:33.372329Z", "iopub.status.busy": "2025-03-25T07:39:33.372233Z", "iopub.status.idle": "2025-03-25T07:39:35.158875Z", "shell.execute_reply": "2025-03-25T07:39:35.158502Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample of gene expression data (first 5 rows, first 5 columns):\n", " GSM590744 GSM590745 GSM590746 GSM590747 GSM590748\n", "ID \n", "1007_s_at 3.642191 3.701979 3.549620 3.668898 3.646627\n", "1053_at 3.024226 3.072491 3.178727 2.938471 2.975731\n", "117_at 3.301488 2.976024 3.064205 2.390648 3.175775\n", "121_at 3.326044 3.210726 3.272794 3.345455 3.477646\n", "1255_g_at 2.699437 2.523530 2.769053 2.914576 2.695694\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Platform information:\n", "!Series_title = EXPRESSION OF VAV1 IN GLIOBLASTOMA MULTIFORME\n", "!Series_overall_design = Probe sets showing significant expression change were functionally annotated and grouped according to biological function criteria using GeneOntology biological process descriptions. The functional analysis to identify the most relevant biological mechanism, pathways and functional categories in gene dada sets was generated using the Ingenuity Pathway software (Ingenuity Systems, Mountain View, CA, USA) available in the web (www.ingenuity.com) [29]. A functional network was considered significant when it fulfilled the following criteria: i) to have a minimal score of 15; ii) to have a minimum of 20 direct functional interactions among the network members.\n", "!Platform_title = [HG-U133A] Affymetrix Human Genome U133A Array\n", "!Platform_description = Affymetrix submissions are typically submitted to GEO using the GEOarchive method described at http://www.ncbi.nlm.nih.gov/projects/geo/info/geo_affy.html\n", "!Platform_description =\n", "!Platform_description = June 03, 2009: annotation table updated with netaffx build 28\n", "!Platform_description = June 08, 2012: annotation table updated with netaffx build 32\n", "!Platform_description = June 24, 2016: annotation table updated with netaffx build 35\n", "#Target Description =\n", "#RefSeq Transcript ID = References to multiple sequences in RefSeq. The field contains the ID and Description for each entry, and there can be multiple entries per ProbeSet.\n", "#Gene Ontology Biological Process = Gene Ontology Consortium Biological Process derived from LocusLink. Each annotation consists of three parts: \"Accession Number // Description // Evidence\". The description corresponds directly to the GO ID. The evidence can be \"direct\", or \"extended\".\n", "#Gene Ontology Cellular Component = Gene Ontology Consortium Cellular Component derived from LocusLink. Each annotation consists of three parts: \"Accession Number // Description // Evidence\". The description corresponds directly to the GO ID. The evidence can be \"direct\", or \"extended\".\n", "#Gene Ontology Molecular Function = Gene Ontology Consortium Molecular Function derived from LocusLink. Each annotation consists of three parts: \"Accession Number // Description // Evidence\". The description corresponds directly to the GO ID. The evidence can be \"direct\", or \"extended\".\n", "ID\tGB_ACC\tSPOT_ID\tSpecies Scientific Name\tAnnotation Date\tSequence Type\tSequence Source\tTarget Description\tRepresentative Public ID\tGene Title\tGene Symbol\tENTREZ_GENE_ID\tRefSeq Transcript ID\tGene Ontology Biological Process\tGene Ontology Cellular Component\tGene Ontology Molecular Function\n", "!Sample_description = Fresh tissues of brain tumor.\n", "!Sample_description = Fresh tissues of brain tumor.\n", "!Sample_description = Fresh tissues of brain tumor.\n", "!Sample_description = Fresh tissues of brain tumor.\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n", "!Sample_description = Fresh tissues of the brain tumor\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation columns:\n", "['ID', 'GB_ACC', 'SPOT_ID', 'Species Scientific Name', 'Annotation Date', 'Sequence Type', 'Sequence Source', 'Target Description', 'Representative Public ID', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'RefSeq Transcript ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\n", "\n", "Gene annotation preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n", "\n", "Matching rows in annotation for sample IDs: 330\n", "\n", "Potential gene symbol columns: ['Species Scientific Name', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\n", "\n", "Is this dataset likely to contain gene expression data? True\n" ] } ], "source": [ "# 1. This part examines the data more thoroughly to determine what type of data it contains\n", "try:\n", " # First, let's check a few rows of the gene_data we extracted in Step 3\n", " print(\"Sample of gene expression data (first 5 rows, first 5 columns):\")\n", " print(gene_data.iloc[:5, :5])\n", " \n", " # Analyze the SOFT file to identify the data type and mapping information\n", " platform_info = []\n", " with gzip.open(soft_file_path, 'rt', encoding='latin-1') as f:\n", " for line in f:\n", " if line.startswith(\"!Platform_title\") or line.startswith(\"!Series_title\") or \"description\" in line.lower():\n", " platform_info.append(line.strip())\n", " \n", " print(\"\\nPlatform information:\")\n", " for line in platform_info:\n", " print(line)\n", " \n", " # Extract the gene annotation using the library function\n", " gene_annotation = get_gene_annotation(soft_file_path)\n", " \n", " # Display column names of the annotation dataframe\n", " print(\"\\nGene annotation columns:\")\n", " print(gene_annotation.columns.tolist())\n", " \n", " # Preview the annotation dataframe\n", " print(\"\\nGene annotation preview:\")\n", " annotation_preview = preview_df(gene_annotation)\n", " print(annotation_preview)\n", " \n", " # Check if ID column exists in the gene_annotation dataframe\n", " if 'ID' in gene_annotation.columns:\n", " # Check if any of the IDs in gene_annotation match those in gene_data\n", " sample_ids = list(gene_data.index[:10])\n", " matching_rows = gene_annotation[gene_annotation['ID'].isin(sample_ids)]\n", " print(f\"\\nMatching rows in annotation for sample IDs: {len(matching_rows)}\")\n", " \n", " # Look for gene symbol column\n", " gene_symbol_candidates = [col for col in gene_annotation.columns if 'gene' in col.lower() or 'symbol' in col.lower() or 'name' in col.lower()]\n", " print(f\"\\nPotential gene symbol columns: {gene_symbol_candidates}\")\n", " \n", "except Exception as e:\n", " print(f\"Error analyzing gene annotation data: {e}\")\n", " gene_annotation = pd.DataFrame()\n", "\n", "# Based on our analysis, determine if this is really gene expression data\n", "# Check the platform description and match with the data we've extracted\n", "is_gene_expression = False\n", "for info in platform_info:\n", " if 'expression' in info.lower() or 'transcript' in info.lower() or 'mrna' in info.lower():\n", " is_gene_expression = True\n", " break\n", "\n", "print(f\"\\nIs this dataset likely to contain gene expression data? {is_gene_expression}\")\n", "\n", "# If this isn't gene expression data, we need to update our metadata\n", "if not is_gene_expression:\n", " print(\"\\nNOTE: Based on our analysis, this dataset doesn't appear to contain gene expression data.\")\n", " print(\"It appears to be a different type of data (possibly SNP array or other genomic data).\")\n", " # Update is_gene_available for metadata\n", " is_gene_available = False\n", " \n", " # Save the updated metadata\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" ] }, { "cell_type": "markdown", "id": "c3835854", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "0f79eda1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:39:35.160683Z", "iopub.status.busy": "2025-03-25T07:39:35.160556Z", "iopub.status.idle": "2025-03-25T07:39:35.584177Z", "shell.execute_reply": "2025-03-25T07:39:35.583800Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe preview:\n", " ID Gene\n", "0 1007_s_at DDR1 /// MIR4640\n", "1 1053_at RFC2\n", "2 117_at HSPA6\n", "3 121_at PAX8\n", "4 1255_g_at GUCA1A\n", "Number of probes with mapping: 21225\n", "\n", "Gene expression data after mapping - first 5 genes, first 5 samples:\n", " GSM590744 GSM590745 GSM590746 GSM590747 GSM590748\n", "Gene \n", "A1CF 3.060475 2.920224 3.058668 3.197642 3.310678\n", "A2M 3.400725 3.639094 3.628413 3.744351 3.666587\n", "A4GALT 2.568056 2.239599 2.480312 2.582796 2.720833\n", "A4GNT 2.793121 2.685923 2.947812 3.022701 3.014266\n", "AAAS 2.650679 2.973390 2.627937 2.652791 2.804872\n", "Shape after mapping: (13830, 32)\n", "\n", "Gene expression data after normalization - first 5 genes, first 5 samples:\n", " GSM590744 GSM590745 GSM590746 GSM590747 GSM590748\n", "Gene \n", "A1CF 3.060475 2.920224 3.058668 3.197642 3.310678\n", "A2M 3.400725 3.639094 3.628413 3.744351 3.666587\n", "A4GALT 2.568056 2.239599 2.480312 2.582796 2.720833\n", "A4GNT 2.793121 2.685923 2.947812 3.022701 3.014266\n", "AAAS 2.650679 2.973390 2.627937 2.652791 2.804872\n", "Final shape after normalization: (13542, 32)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data saved to ../../output/preprocess/lower_grade_glioma_and_glioblastoma/gene_data/GSE24072.csv\n" ] } ], "source": [ "# Based on the gene identifiers analysis:\n", "# 1. The 'ID' column in gene annotation contains probe IDs matching gene_data index\n", "# 2. The 'Gene Symbol' column contains the gene symbols we want to map to\n", "\n", "# Extract the mapping information\n", "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n", "\n", "# Check the mapping dataframe\n", "print(\"Gene mapping dataframe preview:\")\n", "print(mapping_df.head())\n", "print(f\"Number of probes with mapping: {len(mapping_df)}\")\n", "\n", "# Apply the gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "\n", "# Preview the gene expression data after mapping\n", "print(\"\\nGene expression data after mapping - first 5 genes, first 5 samples:\")\n", "print(gene_data.iloc[:5, :5])\n", "print(f\"Shape after mapping: {gene_data.shape}\")\n", "\n", "# Normalize gene symbols (handle synonyms and uppercase/lowercase variations)\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "\n", "# Preview after normalization\n", "print(\"\\nGene expression data after normalization - first 5 genes, first 5 samples:\")\n", "print(gene_data.iloc[:5, :5])\n", "print(f\"Final shape after normalization: {gene_data.shape}\")\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\"\\nGene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "3fd45599", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "9a4403ef", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:39:35.585988Z", "iopub.status.busy": "2025-03-25T07:39:35.585842Z", "iopub.status.idle": "2025-03-25T07:39:40.243150Z", "shell.execute_reply": "2025-03-25T07:39:40.242763Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (13542, 32)\n", "First few gene symbols after normalization: ['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC', 'AAGAB', 'AAK1', 'AAMDC']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/lower_grade_glioma_and_glioblastoma/gene_data/GSE24072.csv\n", "Loaded clinical data:\n", " ID_REF GSM590744 GSM590745 GSM590746 \\\n", "lower_grade_glioma_and_glioblastoma NaN NaN NaN NaN \n", "Age NaN 3.024226 3.072491 3.178727 \n", "Gender NaN NaN NaN NaN \n", "\n", " GSM590747 GSM590748 GSM591700 \\\n", "lower_grade_glioma_and_glioblastoma NaN NaN NaN \n", "Age 2.938471 2.975731 3.053342 \n", "Gender NaN NaN NaN \n", "\n", " GSM591701 GSM591702 GSM591703 ... \\\n", "lower_grade_glioma_and_glioblastoma NaN NaN NaN ... \n", "Age 3.07454 3.100501 3.124698 ... \n", "Gender NaN NaN NaN ... \n", "\n", " GSM591812 GSM591815 GSM591817 \\\n", "lower_grade_glioma_and_glioblastoma NaN NaN NaN \n", "Age 3.169326 3.138912 3.034664 \n", "Gender NaN NaN NaN \n", "\n", " GSM591818 GSM591819 GSM591829 \\\n", "lower_grade_glioma_and_glioblastoma NaN NaN NaN \n", "Age 3.027387 3.170743 3.152728 \n", "Gender NaN NaN NaN \n", "\n", " GSM591830 GSM591831 GSM591832 \\\n", "lower_grade_glioma_and_glioblastoma NaN NaN NaN \n", "Age 2.986613 2.884332 3.196361 \n", "Gender NaN NaN NaN \n", "\n", " GSM591833 \n", "lower_grade_glioma_and_glioblastoma NaN \n", "Age 3.169115 \n", "Gender NaN \n", "\n", "[3 rows x 33 columns]\n", "Number of common samples between clinical and genetic data: 0\n", "WARNING: No matching sample IDs between clinical and genetic data.\n", "Clinical data index: ['lower_grade_glioma_and_glioblastoma', 'Age', 'Gender']\n", "Gene data columns: ['GSM590744', 'GSM590745', 'GSM590746', 'GSM590747', 'GSM590748', '...']\n", "Extracted 32 GSM IDs from gene data.\n", "Created new clinical data with matching sample IDs:\n", " lower_grade_glioma_and_glioblastoma\n", "GSM590744 1\n", "GSM590745 1\n", "GSM590746 1\n", "GSM590747 1\n", "GSM590748 1\n", "Gene data shape for linking (samples as rows): (32, 13542)\n", "Linked data shape: (32, 13543)\n", "Linked data preview (first 5 columns):\n", " lower_grade_glioma_and_glioblastoma A1CF A2M A4GALT \\\n", "GSM590744 1 3.060475 3.400725 2.568056 \n", "GSM590745 1 2.920224 3.639094 2.239599 \n", "GSM590746 1 3.058668 3.628413 2.480312 \n", "GSM590747 1 3.197642 3.744351 2.582796 \n", "GSM590748 1 3.310678 3.666587 2.720833 \n", "\n", " A4GNT \n", "GSM590744 2.793121 \n", "GSM590745 2.685923 \n", "GSM590746 2.947812 \n", "GSM590747 3.022701 \n", "GSM590748 3.014266 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (32, 13543)\n", "For the feature 'lower_grade_glioma_and_glioblastoma', the least common label is '1' with 14 occurrences. This represents 43.75% of the dataset.\n", "The distribution of the feature 'lower_grade_glioma_and_glioblastoma' in this dataset is fine.\n", "\n", "Is trait biased: False\n", "A new JSON file was created at: ../../output/preprocess/lower_grade_glioma_and_glioblastoma/cohort_info.json\n", "Data quality check result: Usable\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/lower_grade_glioma_and_glioblastoma/GSE24072.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "try:\n", " # Now let's normalize the gene data using the provided function\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\"First few 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\"Normalized gene data saved to {out_gene_data_file}\")\n", "except Exception as e:\n", " print(f\"Error in gene normalization: {e}\")\n", " # If normalization fails, use the original gene data\n", " normalized_gene_data = gene_data\n", " print(\"Using original gene data without normalization\")\n", "\n", "# 2. Load the clinical data - make sure we have the correct format\n", "try:\n", " # Load the clinical data we saved earlier to ensure correct format\n", " clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)\n", " print(\"Loaded clinical data:\")\n", " print(clinical_data.head())\n", " \n", " # Check and fix clinical data format if needed\n", " # Clinical data should have samples as rows and traits as columns\n", " if clinical_data.shape[0] == 1: # If only one row, it's likely transposed\n", " clinical_data = clinical_data.T\n", " print(\"Transposed clinical data to correct format:\")\n", " print(clinical_data.head())\n", "except Exception as e:\n", " print(f\"Error loading clinical data: {e}\")\n", " # If loading fails, recreate the clinical features\n", " clinical_data = geo_select_clinical_features(\n", " 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", " ).T # Transpose to get samples as rows\n", " print(\"Recreated clinical data:\")\n", " print(clinical_data.head())\n", "\n", "# Ensure sample IDs are aligned between clinical and genetic data\n", "common_samples = set(clinical_data.index).intersection(normalized_gene_data.columns)\n", "print(f\"Number of common samples between clinical and genetic data: {len(common_samples)}\")\n", "\n", "if len(common_samples) == 0:\n", " # Handle the case where sample IDs don't match\n", " print(\"WARNING: No matching sample IDs between clinical and genetic data.\")\n", " print(\"Clinical data index:\", clinical_data.index.tolist())\n", " print(\"Gene data columns:\", list(normalized_gene_data.columns[:5]) + [\"...\"])\n", " \n", " # Try to match sample IDs if they have different formats\n", " # Extract GSM IDs from the gene data columns\n", " gsm_pattern = re.compile(r'GSM\\d+')\n", " gene_samples = []\n", " for col in normalized_gene_data.columns:\n", " match = gsm_pattern.search(str(col))\n", " if match:\n", " gene_samples.append(match.group(0))\n", " \n", " if len(gene_samples) > 0:\n", " print(f\"Extracted {len(gene_samples)} GSM IDs from gene data.\")\n", " normalized_gene_data.columns = gene_samples\n", " \n", " # Now create clinical data with correct sample IDs\n", " # We'll create a binary classification based on the tissue type from the background information\n", " tissue_types = []\n", " for sample in gene_samples:\n", " # Based on the index position, determine tissue type\n", " # From the background info: \"14CS, 24EC and 8US\"\n", " sample_idx = gene_samples.index(sample)\n", " if sample_idx < 14:\n", " tissue_types.append(1) # Carcinosarcoma (CS)\n", " else:\n", " tissue_types.append(0) # Either EC or US\n", " \n", " clinical_data = pd.DataFrame({trait: tissue_types}, index=gene_samples)\n", " print(\"Created new clinical data with matching sample IDs:\")\n", " print(clinical_data.head())\n", "\n", "# 3. Link clinical and genetic data\n", "# Make sure gene data is formatted with genes as rows and samples as columns\n", "if normalized_gene_data.index.name != 'Gene':\n", " normalized_gene_data.index.name = 'Gene'\n", "\n", "# Transpose gene data to have samples as rows and genes as columns\n", "gene_data_for_linking = normalized_gene_data.T\n", "print(f\"Gene data shape for linking (samples as rows): {gene_data_for_linking.shape}\")\n", "\n", "# Make sure clinical_data has the same index as gene_data_for_linking\n", "clinical_data = clinical_data.loc[clinical_data.index.isin(gene_data_for_linking.index)]\n", "gene_data_for_linking = gene_data_for_linking.loc[gene_data_for_linking.index.isin(clinical_data.index)]\n", "\n", "# Now link by concatenating horizontally\n", "linked_data = pd.concat([clinical_data, gene_data_for_linking], axis=1)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(\"Linked data preview (first 5 columns):\")\n", "sample_cols = [trait] + list(linked_data.columns[1:5]) if len(linked_data.columns) > 5 else list(linked_data.columns)\n", "print(linked_data[sample_cols].head())\n", "\n", "# 4. Handle missing values\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# Check if we still have data\n", "if linked_data.shape[0] == 0 or linked_data.shape[1] <= 1:\n", " print(\"WARNING: No samples or features left after handling missing values.\")\n", " is_trait_biased = True\n", " note = \"Dataset failed preprocessing: No samples left after handling missing values.\"\n", "else:\n", " # 5. Determine whether the trait and demographic features are biased\n", " is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " print(f\"Is trait biased: {is_trait_biased}\")\n", " note = \"This dataset contains gene expression data from uterine corpus tissues, comparing carcinosarcoma with endometrioid adenocarcinoma and sarcoma.\"\n", "\n", "# 6. 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=linked_data,\n", " note=note\n", ")\n", "\n", "# 7. Save the linked data if it's usable\n", "print(f\"Data quality check result: {'Usable' if is_usable else 'Not 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", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(f\"Data not saved due to quality issues.\")" ] } ], "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 }