# Geotcgadata > GeoTcgaData - Skill: `biomate-ai/geotcgadata` (Agent Skill) - Install (CLI): `npx skillmds@latest add biomate-ai/geotcgadata` - Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/geotcgadata/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: biomate-ai (https://skillmd.com/u/biomate-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/biomate-ai/geotcgadata --- # GeoTcgaData ## Workflows ### Tcga Id Conversion And Normalization Convert TCGA ENSEMBL gene IDs to gene symbols and normalize raw counts to FPKM or TPM. ```r library(GeoTcgaData) # 1. Read user inputs data(profile) data(gene_cov) # 2. Convert TCGA ENSEMBL gene IDs to gene symbols result_sym <- id_conversion_TCGA(profile) # 3. Convert raw counts to FPKM lung_squ_count2 <- matrix(c(1,2,3,4,5,6,7,8,9), ncol=3) rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3") colnames(lung_squ_count2) <- c("sample1","sample2","sample3") result_fpkm <- countToFpkm(lung_squ_count2, keyType = "SYMBOL", gene_cov = gene_cov) # 4. Convert raw counts to TPM result_tpm <- countToTpm(lung_squ_count2, keyType = "SYMBOL", gene_cov = gene_cov) ``` *Input: A raw count matrix and a gene coverage data frame (`gene_cov`); Output: Converted gene symbols, FPKM, or TPM matrices.* ### Standard Workflow Perform differential expression analysis on RNA-seq count data using a Wilcoxon test or other methods. ```r library(GeoTcgaData) # 1. Prepare the count matrix and group vector df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) df <- as.data.frame(df) rownames(df) <- paste0("gene", 1:25) colnames(df) <- paste0("sample", 1:16) group <- sample(c("group1", "group2"), 16, replace = TRUE) # 2. Run differential_RNA to identify differentially expressed genes (Method 1: using data frame) result1 <- differential_RNA(counts = df, group = group, filte = FALSE, method = "Wilcoxon") # 3. Run differential_RNA to identify differentially expressed genes (Method 2: using SummarizedExperiment) colData <- S4Vectors::DataFrame(row.names = colnames(df), group = group) data <- SummarizedExperiment::SummarizedExperiment(assays=S4Vectors::SimpleList(counts=as.matrix(df)), colData = colData) result2 <- differential_RNA(counts = data, groupCol = "group", filte = FALSE, method = "Wilcoxon") ``` *Input: A count matrix (or `SummarizedExperiment`) and sample group annotations; Output: A data frame of differential expression results.* ### Cnv Differential Analysis Identify genes with differential copy number variations between groups. ```r library(GeoTcgaData) # 1. Prepare the copy number score matrix and sample groups aa <- matrix(sample(c(0, 1, -1), 200, replace = TRUE), 25, 8) rownames(aa) <- paste0("gene", 1:25) colnames(aa) <- paste0("sample", 1:8) sampleGroup <- sample(c("A", "B"), ncol(aa), replace = TRUE) # 2. Run differential_CNV to extract differential CNV genes diffCnv <- differential_CNV(aa, sampleGroup) ``` *Input: A gene-level copy number score matrix and a group vector; Output: A data frame of genes with differential copy number variations.* ### Dna Methylation Differential Analysis Identify differentially methylated genes from CpG methylation data. ```r library(GeoTcgaData) # 1. Prepare CpG methylation matrix, sample groups, and CpG-to-gene mapping cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) rownames(cpgData) <- paste0("cpg", seq_len(200)) colnames(cpgData) <- paste0("sample", seq_len(10)) sampleGroup <- c(rep("group1", 5), rep("group2", 5)) names(sampleGroup) <- colnames(cpgData) cpg2gene <- data.frame(cpg = rownames(cpgData), gene = rep(paste0("gene", seq_len(20)), 10)) # 2. Run differential_methy to identify differentially methylated genes result <- differential_methy(cpgData, sampleGroup, cpg2gene = cpg2gene, normMethod = NULL) ``` *Input: A CpG methylation beta-value matrix, sample group vector, and a CpG-to-gene mapping data frame; Output: A data frame of differentially methylated genes.* ### Geo Chip Data Preprocessing Preprocess GEO microarray data by averaging duplicate gene symbols and resolving multi-mapped probe IDs. ```r library(GeoTcgaData) # 1. Average expression values of different IDs for the same gene using gene_ave file_gene_ave <- data.frame(Gene = c("MARCH1","MARC1","MARCH1"), GSM1 = c(2.9, 4.7, 8.1), GSM2 = c(3.9, 5.7, 7.1)) result_ave <- gene_ave(file_gene_ave, 1) # 2. Resolve multi-mapped probe IDs by either assigning the expression to each gene using repAssign or deleting them using repRemove input_file <- data.frame(aa = c("MARCH1 /// MMA", "MARC1"), bb = c("2.9", "4.7"), cc = c("3.9", "5.7")) repAssign_result <- repAssign(input_file, " /// ") repRemove_result <- repRemove(input_file, " /// ") ``` *Input: A data frame of probe/gene expression values with potential duplicate or multi-mapped IDs; Output: A cleaned data frame with unique gene symbols.* ## When to Use - To perform differential expression analysis on RNA-seq count data using Wilcoxon rank-sum tests via `differential_RNA()`. - To convert TCGA ENSEMBL gene IDs to gene symbols using `id_conversion_TCGA()`. - To normalize raw RNA-seq counts to FPKM or TPM using `countToFpkm()` and `countToTpm()`. - To identify differentially methylated genes from CpG methylation matrices using `differential_methy()`. - To preprocess GEO microarray data by averaging duplicate gene symbols with `gene_ave()` and resolving multi-mapped probe IDs with `repAssign()` or `repRemove()`. ## When NOT to Use - For complex multi-factor experimental designs in RNA-seq differential expression, use `DESeq2` or `edgeR` because `differential_RNA()` is designed for simple two-group comparisons. - For advanced microarray normalization and linear modeling, use `limma` because `GeoTcgaData` focuses on basic ID averaging and multi-mapping resolution. ## Data Requirements - **Input Format**: Standard R `matrix` or `data.frame`, or a `SummarizedExperiment` object. - **Structure**: Gene expression matrices should have genes/probes as rows and samples as columns. - **Normalization State**: Raw counts are required for `countToFpkm()` and `countToTpm()`. Methylation data should be beta-values or similar continuous matrices. ## Key Parameters - **counts** (NULL): Input count matrix or `SummarizedExperiment` object in `differential_RNA()`. - **group** (NULL): Vector of sample group annotations in `differential_RNA()`. - **filte** (TRUE): Logical indicating whether to filter low-expression genes in `differential_RNA()`. - **method** ("Wilcoxon"): Statistical method for differential analysis in `differential_RNA()`. - **keyType** ("SYMBOL"): Type of gene identifier used in `countToFpkm()` or `countToTpm()`. - **gene_cov** (NULL): Gene coverage data frame used for length normalization in FPKM/TPM conversion. - **normMethod** (NULL): Normalization method for methylation data in `differential_methy()`. - **cpg2gene** (NULL): Data frame mapping CpG probe IDs to gene symbols in `differential_methy()`. ## Best Practices - Perform quality control on SNP data using `SNP_QC()` before running differential SNP analysis with `differential_SNP()`. - Use `model = "gene"` in methylation analysis to avoid bias related to the number of CpGs per gene. - Ensure the gene coverage dataset `gene_cov` is loaded before running `countToFpkm()` or `countToTpm()`. ## Common Pitfalls - **Using raw counts directly in downstream analyses without normalization**: Fix by converting counts to FPKM or TPM using `countToFpkm()` or `countToTpm()`. - **Multi-mapped probe IDs causing duplicate rows**: Fix by using `repAssign()` or `repRemove()` to handle probe IDs mapped to multiple genes. - **CpG number bias in methylation differential analysis**: Fix by setting the model to `"gene"` instead of `"cpg"` in `differential_methy()`. ## Alternatives - `DESeq2` for robust parametric differential expression analysis of RNA-seq data. - `edgeR` for differential expression analysis using empirical Bayes estimation. - `limma` for linear modeling of microarray and RNA-seq data. - `ChAMP` for comprehensive Illumina methylation integration and analysis. ## Citations - Young et al. 2010, Genome Biol (for gene ontology selection bias). - Oshlack and Wakefield 2009, Biol Direct (for transcript length bias). ## References - Homepage: bioconductor.org/packages/GeoTcgaData - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GeoTcgaData/inst/doc/GeoTcgaData.html