TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data

Background Pan-cancer analysis examines both the commonalities and heterogeneity among genomic and cellular alterations across numerous types of tumors. Pan-cancer analysis of gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TIME),...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 24; no. 1; pp. 483 - 15
Main Authors: Liao, Chenqi, Wang, Xiong
Format: Journal Article
Language:English
Published: London BioMed Central 17.12.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Analysis
Bioinformatics
Biological analysis
Biomedical and Life Sciences
Building codes
Cancer
Chemokines
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Correlation analysis
Diagnosis
DNA methylation
DNA sequencing
Functionals
Gene expression
Genetic aspects
Genomes
Genomics
Heterogeneity
Life Sciences
Medical research
Methylation
Microarrays
Microenvironments
Microsatellite instability
Mutation
Nucleotide sequencing
Ontology
Pan-cancer analysis
Regression analysis
Software
Source code
Stability analysis
Survival
Survival analysis
TCGA
TCGAplot
Tumors
User-defined function
Visualization
China
Visualization
Pan-cancer analysis
User-defined function
TCGAplot
TCGA
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Description
Summary:Background Pan-cancer analysis examines both the commonalities and heterogeneity among genomic and cellular alterations across numerous types of tumors. Pan-cancer analysis of gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TIME), and methylation becomes available based on the multi-omics data from The Cancer Genome Atlas Program (TCGA). Some online tools provide analysis of gene and protein expression, mutation, methylation, and survival for TCGA data. However, these online tools were either Uni-functional or were not able to perform analysis of user-defined functions. Therefore, we created the TCGAplot R package to facilitate perform pan-cancer analysis and visualization of the built-in multi-omic TCGA data. Results TCGAplot provides several functions to perform pan-cancer paired/unpaired differential gene expression analysis, pan-cancer correlation analysis between gene expression and TMB, MSI, TIME, and promoter methylation. Functions for visualization include paired/unpaired boxplot, survival plot, ROC curve, heatmap, scatter, radar chart, and forest plot. Moreover, gene set based pan-cancer and tumor specific analyses were also available. Finally, all these built-in multi-omic data could be extracted for implementation for user-defined functions, making the pan-cancer analysis much more convenient.\ Conclusions We developed an R-package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. The source code and pre-built package are available at GitHub ( https://github.com/tjhwangxiong/TCGAplot ).
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-023-05615-3