bioconductor-geotcgadata

name	bioconductor-geotcgadata
description	Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.

GeoTcgaData

Workflows

Tcga Id Conversion And Normalization

Convert TCGA ENSEMBL gene IDs to gene symbols and normalize raw counts to FPKM or TPM.

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.

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_RNAcounts  df group  group filte   method  


colData  S4VectorsDataFramerow.names  colnamesdf group  group
data  SummarizedExperimentSummarizedExperimentassaysS4VectorsSimpleListcountsas.matrixdf colData  colData
result2  differential_RNAcounts  data groupCol   filte   method  

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.

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 = 


diffCnv  differential_CNVaa 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.

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) <- colnamescpgData
cpg2gene  data.framecpg  rownamescpgData gene  paste0  


result  differential_methycpgData sampleGroup cpg2gene  cpg2gene normMethod  

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.

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"  cc   
repAssign_result  repAssigninput_file 
repRemove_result  repRemoveinput_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