TCGAplot (v8.0.0)
(DO NOT INSTALL USING "devtools", PLEASE download the .zip file and install the package locally)
author: Xiong Wang
email: [wangxiong@tjh.tjmu.edu.cn]() or wangxiong@hust.edu.cn
affiliation: Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
1. Introduction
Pan-cancer analysis aimed to examine the commonalities and heterogeneity among the genomic and cellular alterations across diverse types of tumors. Pan-cancer analysis of gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TIME) became available based on the exome, transcriptome, and DNA methylome data from TCGA. Some online tools provided user-friendly analysis of gene and protein expression, mutation, methylation, and survival for TCGA data, such as GEPIA 2 (http://gepia2.cancer-pku.cn/#index), cBioPortal (http://www.cbioportal.org/), UALCAN (https://ualcan.path.uab.edu/index.html), and MethSurv (https://biit.cs.ut.ee/methsurv/). However, these online tools were either uni-functional or not able to perform analysis of user-defined functions. Therefore, TCGA pan-cancer multi-omics data were integrated and included in this package, including gene expression TPM (transcripts per million) matrix, TMB, MSI, immune cell ratio, immune score, promoter methylation, and clinical information. A number of functions were generated to perform pan-cancer paired/unpaired differential gene expression analysis, pan-cancer correlation analysis between gene expression and TMB, MSI, immune cell ratio, immune score,immune stimulator,immune inhibitor, and promoter methylation. Methods for visualization were provided, including paired/unpaired boxplot, survival plot, ROC curve, heatmap, scatter, radar chart, and forest plot,in order to easily perform integrative pan-cancer multi-omics analysis. Finally, these built-in data could be extracted and analyzed with user-defined functions, making the pan-cancer analysis much more convenient.
2. Installation
To install this package for Windows system, download TCGAplot R package at https://github.com/tjhwangxiong/TCGAplot/releases/download/v8.0.0/TCGAplot_8.0.0.zip
and install locally.
There were several dependent R packages, and users could install these dependent R packages using the following codes before the installation of TCGAplot.
if(!require("BiocManager")) install.packages("BiocManager",update = F,ask = F)
cran_packages=c("magrittr",
"dplyr",
"tibble",
"ggpubr",
"stringr",
"reshape2",
"psych",
"limma",
"circlize",
"grid",
"fmsb",
"survival",
"survminer",
"forestplot",
"pROC",
"tinyarray",
"ggplot2",
"patchwork",
"ggsci",
"RColorBrewer",
"pheatmap")
Biocductor_packages=c("edgeR",
"org.Hs.eg.db",
"clusterProfiler",
"enrichplot",
"ComplexHeatmap",
"GSVA")
# install packages in CRAN
for (pkg in cran_packages){
if (!require(pkg,character.only=T)){
install.packages(pkg,ask = F,update = F)
require(pkg,character.only=T)
}
}
# install packages in Biocductor
for (pkg in Biocductor_packages){
if (!require(pkg,character.only=T)) {
BiocManager::install(pkg,ask = F,update = F)
require(pkg,character.only=T)
}
}3. Pan-cancer analysis
3.1 Pan-cancer expression analysis
3.1.1 Pan-cancer tumor-normal boxplot
pan_boxplot
Create a pan-cancer box plot for a single gene with symbols indicating statistical significance.
pan_boxplot(gene,palette="jco",legend="right",method="wilcox.test")Arguments
gene
gene name likes "KLF7".
palette
the color palette to be used for filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
pan_boxplot("KLF7")
3.1.2 Pan-cancer paired tumor-normal boxplot
pan_paired_boxplot
Create a pan-cancer paired box plot for a single gene with symbols indicating statistical significance.
pan_paired_boxplot(gene,palette="jco",legend="right",method="wilcox.test")Arguments
gene
gene name likes "KLF7".
palette
the color palette to be used for filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
pan_paired_boxplot("KLF7",legend = "none")
3.1.3 Pan-tumor boxplot
pan_tumor_boxplot
Create a pan-cancer box plot for a single gene in tumor samples.
pan_tumor_boxplot(gene)Arguments
gene
gene name likes "KLF7".
Example
pan_tumor_boxplot("KLF7")
3.2 Pan-cancer correlation analysis
3.2.1 Pan-cancer gene expression and TMB correlation radar chart
gene_TMB_radar
Create a pan-cancer radar chart for gene expression and TMB correlation.
gene_TMB_radar(gene,method = "pearson")Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
Example
gene_TMB_radar("KLF7")
3.2.2 Pan-cancer gene expression and MSI correlation radar chart
gene_MSI_radar
Create a pan-cancer radar chart for gene expression and MSI correlation.
gene_MSI_radar(gene,method = "pearson")Arguments
gene gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
Example
gene_MSI_radar("KLF7")
3.2.3 Pan-cancer gene expression and immune-related genes correlation
3.2.3.1 Pan-cancer gene expression and ICGs correlation
gene_checkpoint_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a single gene and ICGs (immune checkpoint genes).
ICGs geneset included "CD274","CTLA4","HAVCR2","LAG3","PDCD1","PDCD1LG2","SIGLEC15",and "TIGIT".
gene_checkpoint_heatmap(gene,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gene_checkpoint_heatmap("KLF7")
3.2.3.2 Pan-cancer gene expression and chemokine correlation
gene_chemokine_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a single gene and chemokine.
Chemokine geneset included "CCL1","CCL2","CCL3","CCL4","CCL5","CCL7","CCL8","CCL11","CCL13","CCL14","CCL15","CCL16","CCL17","CCL18","CCL19","CCL20","CCL21","CCL22","CCL23","CCL24","CCL25","CCL26","CCL28","CX3CL1","CXCL1","CXCL2","CXCL3","CXCL5","CXCL6","CXCL8","CXCL9","CXCL10","CXCL11","CXCL12","CXCL13","CXCL14","CXCL16", and "CXCL17".
gene_chemokine_heatmap(gene,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gene_chemokine_heatmap("KLF7")
3.2.3.3 Pan-cancer gene expression and chemokine receptor correlation
gene_receptor_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a single gene and chemokine receptors.
Chemokine receptor geneset included "CCR1","CCR2","CCR3","CCR4","CCR5","CCR6","CCR7","CCR8","CCR9","CCR10", "CXCR1","CXCR2","CXCR3","CXCR4","CXCR5","CXCR6","XCR1", and "CX3R1".
gene_receptor_heatmap(gene,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gene_receptor_heatmap("KLF7")
3.2.3.4 Pan-cancer gene expression and immune stimulator correlation
gene_immustimulator_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a single gene and immune stimulators.
Immune stimulator geneset included "CD27","CD276","CD28","CD40","CD40LG","CD48","CD70","CD80","CD86","CXCL12","CXCR4","ENTPD1","HHLA2","ICOS","ICOSLG","IL2RA","IL6","IL6R","KLRC1","KLRK1","LTA","MICB","NT5E","PVR","RAET1E","TMIGD2","TNFRSF13B","TNFRSF13C","TNFRSF14","TNFRSF17","TNFRSF18","TNFRSF25","TNFRSF4","TNFRSF8","TNFRSF9","TNFSF13","TNFSF13B","TNFSF14","TNFSF15","TNFSF18","TNFSF4","TNFSF9", and "ULBP1".
gene_immustimulator_heatmap(gene,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gene_immustimulator_heatmap("KLF7")
3.2.3.5 Pan-cancer gene expression and immune inhibitor correlation
gene_immuinhibitor_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a single gene and immune inhibitors.
Immune inhibitor geneset included "ADORA2A","BTLA","CD160","CD244","CD274","CD96","CSF1R","CTLA4","HAVCR2","IDO1","IL10","IL10RB","KDR","KIR2DL1","KIR2DL3","LAG3","LGALS9","PDCD1","PDCD1LG2","TGFB1","TGFBR1","TIGIT", and "VTCN1".
gene_immuinhibitor_heatmap(gene,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gene_immuinhibitor_heatmap("KLF7")
3.2.4 Pan-cancer gene expression and immune infiltration correlation
3.2.4.1 Pan-cancer gene expression and immune cell ratio correlation
gene_immucell_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a single gene and immune cell ratio.
gene_immucell_heatmap(gene,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gene_immucell_heatmap("KLF7")
3.2.4.2 Pan-cancer gene expression and immune score correlation
gene_immunescore_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a single gene and immune scores, including Stromal score, immune score, and ESTIMATE score.
gene_immunescore_heatmap(gene,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gene_immunescore_heatmap("KLF7")
gene_immunescore_triangle
Create a pan-cancer triangle reveals the correlation between the expression of a single gene and immune scores, including Stromal score, immune score, and ESTIMATE score.
gene_immunescore_triangle(gene,method="pearson")Arguments
gene
gene name likes "KLF7".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
Example
gene_immunescore_triangle("KLF7")
3.3 Pan-cancer Cox regression analysis
3.3.1 Pan-cancer Cox regression forest plot
pan_forest
Create a pan-cancer Cox regression forest plot for a specific gene.
pan_forest(gene,adjust=F)Arguments
gene
gene name likes "KLF7".
adjust
adjust whether the Cox regression analysis was adjusted by age and stage. adjust=F is the default value.
Example
pan_forest("KLF7")
4. Cancer type specific analysis
4.1 Expression analysis
4.1.1 Expression analysis grouped by clinical information
4.1.1.1 Tumor-normal boxplot
tcga_boxplot
Create a tumor-normal box plot for a single gene with symbols indicating statistical significance in a specific type of cancer.
tcga_boxplot(cancer,gene,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "BRCA".
gene
gene name likes "KLF7".
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
tcga_boxplot("BRCA","KLF7")
4.1.1.2 Paired tumor-normal boxplot
paired_boxplot
Create a paired tumor-normal box plot for a single gene with symbols indicating statistical significance in a specific type of cancer.
Only cancers with more than 20 paired samples could be analyzed, including "BLCA","BRCA","COAD","ESCA","HNSC","KICH","KIRC","KIRP","LIHC","LUAD","LUSC","PRAD","STAD","THCA", and "UCEC".
paired_boxplot(cancer,gene,palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "BRCA".
gene
gene name likes "KLF7".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
paired_boxplot("BRCA","KLF7")
4.1.1.3 Age grouped boxplot
gene_age
Create a box plot for a single gene with symbols indicating statistical significance grouped by age in a specific type of cancer.
gene_age(cancer,gene,age=60,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "ACC".
gene
gene name likes "KLF7".
age
numeric number of age like 60.
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gene_age("ACC","KLF7")
gene_3age
Create a box plot for a single gene grouped by three age groups in a specific type of cancer.
gene_3age(cancer,gene,age1=40,age2=60,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "ACC".
gene
gene name likes "KLF7".
age
numeric number of age like 60.
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gene_3age("COAD","KLF7", age1=40, age2=60)
4.1.1.4 Gender grouped boxplot
gene_gender
Create a box plot for a single gene with symbols indicating statistical significance grouped by gender in a specific type of cancer.
gene_gender(cancer,gene,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "BLCA".
gene
gene name likes "KLF7".
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gene_gender("BLCA","KLF7")
4.1.1.5 Stage grouped boxplot
gene_stage
Create a box plot for a single gene with symbols indicating statistical significance grouped by stage in a specific type of cancer.
gene_stage(cancer,gene,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "COAD".
gene
gene name likes "KLF7".
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gene_stage("COAD","KLF7")
4.1.2 Expression analysis grouped by the expression of a spcecific gene
4.1.2.1 Differential expressed gene heatmap grouped by a specific gene
gene_deg_heatmap
Create a heatmap for differentially expressed genes grouped by the expression of a single gene in a specific type of cancer.
gene_deg_heatmap(cancer, gene,top_n=20)Arguments
cancer
cancer name likes "BLCA".
gene
gene name likes "KLF7".
top_n
the number of top DEGS to be shown in the heatmap.
Example
gene_deg_heatmap("BLCA","KLF7")
4.1.2.2 GSEA-GO grouped by the expression of a spcecific gene
gene_gsea_go
GSEA-GO analysis of DEGs grouped by the expression of a single gene in a specific type of cancer, and the top 5 GO BP pathways were shown.
gene_gsea_go(cancer,gene)Arguments
cancer
cancer name likes "BLCA".
gene
gene name likes "KLF7".
Example
gene_gsea_go("BLCA","KLF7")
4.1.2.3 GSEA-KEGG grouped by the expression of a spcecific gene
gene_gsea_kegg
GSEA-KEGG analysis of DEGs grouped by the expression of a single gene in a specific type of cancer, and the top 5 KEGG pathways were shown.
gene_gsea_kegg(cancer,gene)Arguments
cancer
cancer name likes "BLCA".
gene
gene name likes "KLF7".
Example
gene_gsea_kegg("BLCA","KLF7")
4.2 Diagnostic ROC Curve
tcga_roc
Diagnostic ROC curve of a single gene in a specific type of cancer.
tcga_roc(cancer,gene)Arguments
cancer
cancer name likes "BRCA".
gene
gene name likes "KLF7".
Example
tcga_roc("BRCA","KLF7")
4.3 Cancer type specific correlation analysis
4.3.1 Gene-gene correlation scatter
gene_gene_scatter
Scatter plot of gene and gene correlation in a specific type cancer.
gene_gene_scatter(cancer,gene1,gene2,density="F")Arguments
cancer
cancer name likes "BLCA".
gene1
name of gene1 likes "CBX2".
gene2
name of gene1 likes "CBX3".
density
whether density of gene expression was shown.
Example
gene_gene_scatter("BLCA","CBX2","CBX3")
gene_gene_scatter("BLCA","CBX2","CBX3",density="T")
4.3.2 Gene-promoter methylation correlation scatter
gene_methylation_scatter
Scatter plot of gene expression and gene promoter methylation correlation in a specific type of cancer. A pdf file will be generated in the working directory.
gene_methylation_scatter(cancer,gene)Arguments
cancer
cancer name likes "BLCA".
gene
gene name likes "KLF7".
Example
gene_methylation_scatter("BLCA","KLF7")
4.3.3 Expression heatmap of significantly correlated genes and GO analysis
gene_coexp_heatmap
Heatmap and Go enrichment of the positive and negative co-expressed genes of a single gene in a specific type of cancer.
gene_coexp_heatmap(cancer,gene,top_n=20, method="pearson")Arguments
cancer
cancer name likes "STAD".
gene
gene name likes "KLF7".
top_n the number of co-expressed genes.
method method="pearson" is the default value. The alternatives to be passed to correlation were "spearman" and "kendall".
Example
gene_coexp_heatmap("STAD","KLF7")
4.4 Survavial analysis
4.4.1 Survavial analysis based on the expression of a single gene
tcga_kmplot
K_M survival plot for a single gene in a specific type of cancer.
tcga_kmplot(cancer,gene,palette='jco')Arguments
cancer
cancer name likes "COAD".
gene
gene name likes "KLF7".
palette the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
Example
tcga_kmplot("COAD","KLF7")
4.4.2 Survavial analysis based on the promoter methylation of a spcecific gene
methy_kmplot
Describes the K_M survival plot based on the promoter methylation of a single gene in a specific type of cancer. A pdf file will be generated in the working directory.
methy_kmplot(cancer,gene,palette='jco')Arguments
cancer
cancer name likes "COAD".
gene
gene name likes "KLF7".
palette the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
Example
methy_kmplot("COAD","KLF7")
5. Gene network analysis
This function is performed by the clusterProfiler and enrichplot R packages.
5.1 Gene network GO analysis
gene_network_go
Create a cnetplot to depict the linkages of gene(s) and GO terms as a network.
gene_network_go(gene)Arguments
gene
gene name likes "KLF7", or a vector of gene names like c("LAMA3","LAMC2","TNC","OSMR").
Example
gene_network_go(c("LAMA3","LAMC2","TNC","OSMR"))
5.2 Gene network KEGG analysis
gene_network_kegg
Create a cnetplot to depict the linkages of gene(s) and KEGG pathways as a network.
gene_network_kegg(gene)Arguments
gene
gene name likes "KLF7", or a vector of gene names like c("LAMA3","LAMC2","TNC","OSMR").
Example
gene_network_kegg(c("LAMA3","LAMC2","TNC","OSMR"))
6. Geneset based analysis
Both geneset listed in MSigDB and user defined geneset in the form of character vector were supported to perform geneset based pan-cancer and cancer type specific analysis. get_geneset() function could extract the whole built in geneset list from MSigDB.
6.1 Geneset based pan-cancer expression analysis
6.1.1 Geneset based pan-cancer tumor-normal boxplot
gs_pan_boxplot
Create a pan-cancer box plot for a geneset with symbols indicating statistical significance.
gs_pan_boxplot(geneset,geneset_alias,palette="jco",legend="right",method="wilcox.test")Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
palette
the color palette to be used for filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gs_pan_boxplot("ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.1.2 Geneset based pan-cancer paired tumor-normal boxplot
gs_pan_paired_boxplot
Create a pan-cancer paired box plot for a geneset with symbols indicating statistical significance.
gs_pan_paired_boxplot(geneset,geneset_alias,palette="jco",legend="right",method="wilcox.test")Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
palette
the color palette to be used for filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gs_pan_paired_boxplot("ALONSO_METASTASIS_EMT_DN","ALONSO_METASTASIS_EMT_DN")
6.1.3 Geneset based pan-tumor boxplot
gs_pan_tumor_boxplot
Create a pan-cancer box plot for a single gene in tumor samples.
gs_pan_tumor_boxplot(geneset,geneset_alias)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
Example
gs_pan_tumor_boxplot("ALONSO_METASTASIS_EMT_DN","ALONSO_METASTASIS_EMT_DN")
6.2 Geneset based pan-cancer correlation analysis
6.2.1 Geneset based pan-cancer correlation with TMB
gs_TMB_radar
Create a pan-cancer radar chart for geneset and TMB correlation.
gs_TMB_radar(geneset,geneset_alias,method = "pearson")Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
Example
# We defined a geneset.
klf=c("KLF4","KLF7")
gs_TMB_radar(geneset=klf,geneset_alias="KLF family")
6.2.2 Geneset based pan-cancer correlation with MSI
gs_MSI_radar
Create a pan-cancer radar chart for geneset and MSI correlation.
gs_MSI_radar(geneset,geneset_alias,method = "pearson")Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
Example
gs_MSI_radar("ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.2.3 Genset based pan-cancer correlation with immune-related genes
6.2.3.1 Genset based pan-cancer correlation with ICGs
gs_checkpoint_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a geneset and ICGs (immune checkpoint genes)..
ICGs geneset included "CD274","CTLA4","HAVCR2","LAG3","PDCD1","PDCD1LG2","SIGLEC15",and "TIGIT".
gs_checkpoint_heatmap(geneset,method="pearson",lowcol="blue",highcol="red",
cluster_row=T,cluster_col=T,legend=T)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gs_checkpoint_heatmap("ALONSO_METASTASIS_EMT_DN")
6.2.3.2 Genset based pan-cancer correlation with chemokine
gs_chemokine_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a geneset and chemokine.
Chemokine geneset included "CCL1","CCL2","CCL3","CCL4","CCL5","CCL7","CCL8","CCL11","CCL13","CCL14","CCL15","CCL16","CCL17","CCL18","CCL19","CCL20","CCL21","CCL22","CCL23","CCL24","CCL25","CCL26","CCL28","CX3CL1","CXCL1","CXCL2","CXCL3","CXCL5","CXCL6","CXCL8","CXCL9","CXCL10","CXCL11","CXCL12","CXCL13","CXCL14","CXCL16", and "CXCL17".
gs_chemokine_heatmap(geneset,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gs_chemokine_heatmap("ALONSO_METASTASIS_EMT_DN")
6.2.3.3 Genset based pan-cancer correlation with chemokine receptor
gs_receptor_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a geneset and chemokine receptors.
Chemokine receptor geneset included "CCR1","CCR2","CCR3","CCR4","CCR5","CCR6","CCR7","CCR8","CCR9","CCR10", "CXCR1","CXCR2","CXCR3","CXCR4","CXCR5","CXCR6","XCR1", and "CX3R1".
gs_receptor_heatmap(geneset,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gs_receptor_heatmap("ALONSO_METASTASIS_EMT_DN")
6.2.3.4 Genset based pan-cancer correlation with immune stimulator
gs_immustimulator_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a geneset and immune stimulators.
Immune stimulator geneset included "CD27","CD276","CD28","CD40","CD40LG","CD48","CD70","CD80","CD86","CXCL12","CXCR4","ENTPD1","HHLA2","ICOS","ICOSLG","IL2RA","IL6","IL6R","KLRC1","KLRK1","LTA","MICB","NT5E","PVR","RAET1E","TMIGD2","TNFRSF13B","TNFRSF13C","TNFRSF14","TNFRSF17","TNFRSF18","TNFRSF25","TNFRSF4","TNFRSF8","TNFRSF9","TNFSF13","TNFSF13B","TNFSF14","TNFSF15","TNFSF18","TNFSF4","TNFSF9", and "ULBP1".
gs_immustimulator_heatmap(geneset,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gs_immustimulator_heatmap("ALONSO_METASTASIS_EMT_DN")
6.2.3.5 Genset based pan-cancer correlation with immune inhibitor
gs_immuinhibitor_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a geneset and immune inhibitors.
Immune inhibitor geneset included "ADORA2A","BTLA","CD160","CD244","CD274","CD96","CSF1R","CTLA4","HAVCR2","IDO1","IL10","IL10RB","KDR","KIR2DL1","KIR2DL3","LAG3","LGALS9","PDCD1","PDCD1LG2","TGFB1","TGFBR1","TIGIT", and "VTCN1".
gs_immuinhibitor_heatmap(geneset,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gs_immuinhibitor_heatmap("ALONSO_METASTASIS_EMT_DN")
6.2.4 Genset based pan-cancer correlation with immune infiltration
5.2.4.1 Genset based pan-cancer correlation with immune cell ratio
gs_immucell_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a geneset and immune cell ratio.
gs_immucell_heatmap(geneset,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
klf=c("KLF4","KLF7")
gs_immucell_heatmap(geneset=klf)
6.2.4.2 Genset based pan-cancer correlation with immune score
gs_immunescore_heatmap
Create a pan-cancer heatmap with symbols indicating statistical significance to reveal the correlation between the expression of a geneset and immune scores, including Stromal score, immune score, and ESTIMATE score.
gs_immunescore_heatmap(geneset,method="pearson",lowcol="blue",highcol="red",cluster_row=T,cluster_col=T,legend=T)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
method
method="pearson" is the default value. The alternatives to be passed to correlation are "spearman" and "kendall".
lowcol
color for low point.
highcol
color for high point.
cluster_row
boolean values determining if rows should be clustered or hclust object.
cluster_col
boolean values determining if columns should be clustered or hclust object.
legend
logical to determine if legend should be drawn or not.
Example
gs_immunescore_heatmap("ALONSO_METASTASIS_EMT_DN")
6.3 Genset based pan-cancer Cox regression analysis
6.3.1 Genset based pan-cancer Cox regression forest plot
gs_pan_forest
Create a pan-cancer Cox regression forest plot for a geneset.
gs_pan_forest(geneset,geneset_alias,adjust=F)Arguments
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
adjust
adjust whether the Cox regression analysis was adjusted by age and stage. adjust=F is the default value.
Example
gs_pan_forest("ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.4 Genset based cancer type specific analysis
6.4.1 Genset based cancer type specific expression analysis
6.4.1.1 Genset based expression analysis grouped by clinical information
5.4.1.1.1 Genset based tumor-normal boxplot
gs_boxplot
Create a tumor-normal box plot for a geneset in a specific type of cancer.
gs_boxplot(cancer,geneset,geneset_alias,add = "jitter",
palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "BRCA".
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gs_boxplot("BRCA","ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.4.1.1.2 Genset based paired tumor-normal boxplot
gs_paired_boxplot
Create a paired tumor-normal box plot for a geneset in a specific type of cancer. Only cancers with more than 20 paired samples could be analyzed, including "BLCA","BRCA","COAD","ESCA","HNSC","KICH","KIRC","KIRP","LIHC","LUAD","LUSC","PRAD","STAD","THCA", and "UCEC".
gs_paired_boxplot(cancer,geneset,geneset_alias, palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "BRCA".
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gs_paired_boxplot("BRCA","ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.4.1.1.3 Age grouped boxplot
gs_age
Create a box plot for a geneset grouped by age in a specific type of cancer.
gs_age(cancer,geneset,geneset_alias,age=60,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "ACC".
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
age
numeric number of age like 60.
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gs_age("COAD","ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
gs_3age
Create a box plot for a geneset grouped by three age groups in a specific type of cancer.
gs_3age(cancer,geneset,geneset_alias,age1=40,age2=60,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "ACC".
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
age1
numeric number of young age like 40.
age2
numeric number of old age like 60.
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gs_3age("COAD","ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.4.1.1.4 Genset based gender grouped boxplot
gs_gender
Create a box plot for a single gene with symbols indicating statistical significance grouped by gender in a specific type of cancer.
gs_gender(cancer,geneset,geneset_alias,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "BLCA".
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gs_gender("COAD","ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.4.1.1.5 Geneset based stage grouped boxplot
gs_stage
Create a box plot for a single gene with symbols indicating statistical significance grouped by stage in a specific type of cancer.
gs_stage(cancer,geneset,geneset_alias,add = "jitter",palette="jco",legend="none",label="p.signif",method="wilcox.test")Arguments
cancer
cancer name likes "COAD".
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
add
character vector for adding another plot element likes "none", "dotplot", "jitter".
palette
the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
legend
legend position. Allowed values include "top","bottom","left","right" and "none".
label
character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). label="p.signif" is default.
method
a character string indicating which method to be used for comparing means including "wilcox.text" and "t.test". method="wilcox.test" is default.
Example
gs_stage("COAD","ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.4.2 Geneset based diagnostic ROC Curve
gs_roc
Diagnostic ROC curve of a geneset in a specific type of cancer.
gs_roc(cancer,geneset,geneset_alias)Arguments
cancer
cancer name likes "BRCA".
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
add
Example
gs_roc("BRCA","ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
6.4.3 Geneset based survavial analysis
gs_kmplot
K_M survival plot for a geneset in a specific type of cancer.
gs_kmplot(cancer,geneset,geneset_alias,palette='jco')Arguments
cancer
cancer name likes "COAD".
geneset
geneset name likes "ALONSO_METASTASIS_EMT_DN" or a character vector like c("KLF4","KLF7").
geneset_alias
geneset alias name for plotting likes "METASTASIS EMT".
palette the color palette to be used for coloring or filling by groups. Allowed values include scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco".
Example
gs_kmplot("COAD","ALONSO_METASTASIS_EMT_DN","METASTASIS EMT")
7. Built-in data extraction
7.1 TPM matrix extraction
get_all_tpm
Extract the whole TPM matrix of all types of cancer in TCGA.
get_all_tpm()get_tpm
Extract the TPM matrix of a specific type of cancer in TCGA.
get_tpm(cancer)Arguments
cancer
cancer name likes "COAD".
Example
get_tpm("COAD")
#> Cancer Group TSPAN6 TNMD DPM1 SCYL3 C1orf112 FGR CFH FUCA2
#> TCGA-CM-4743-01A COAD Tumor 4.83 0.00 6.54 1.92 1.50 2.72 3.82 6.05
#> TCGA-D5-6931-01A COAD Tumor 6.58 1.73 6.70 3.26 3.42 3.11 3.97 6.31
#> TCGA-AA-A00A-01A COAD Tumor 5.93 1.03 6.20 2.90 2.12 2.99 3.24 6.82
#> TCGA-AD-A5EK-01A COAD Tumor 7.36 0.47 8.03 2.75 2.75 1.51 2.26 6.35
#> TCGA-A6-2680-01A COAD Tumor 6.90 1.73 6.66 2.55 3.01 2.79 2.88 6.027.2 Paired TPM matrix extraction
get_all_paired_tpm
Extract the whole TPM matrix of all types of cancer with paired samples (n>20) in TCGA.
get_all_paired_tpm()get_paired_tpm
Extract the TPM matrix of a specific type of cancer with paired samples (n>20) in TCGA.
get_paired_tpm(cancer)Arguments
cancer
cancer name likes "COAD".
Example
get_paired_tpm("COAD")
#> Cancer Group TSPAN6 TNMD DPM1 SCYL3 C1orf112 FGR CFH FUCA2
#> TCGA-CM-4743-01A COAD Tumor 4.83 0.00 6.54 1.92 1.50 2.72 3.82 6.05
#> TCGA-D5-6931-01A COAD Tumor 6.58 1.73 6.70 3.26 3.42 3.11 3.97 6.31
#> TCGA-AA-A00A-01A COAD Tumor 5.93 1.03 6.20 2.90 2.12 2.99 3.24 6.82
#> TCGA-AD-A5EK-01A COAD Tumor 7.36 0.47 8.03 2.75 2.75 1.51 2.26 6.35
#> TCGA-A6-2680-01A COAD Tumor 6.90 1.73 6.66 2.55 3.01 2.79 2.88 6.027.3 Clinical information extraction
get_all_meta
Extract the clinical information of all types of cancer in TCGA.
get_all_meta()get_meta
Extract the clinical information of a specific type of cancer in TCGA.
get_meta(cancer)Arguments
cancer
cancer name likes "COAD".
Example
get_meta("COAD")
#> Cancer event time age gender stage
#> TCGA-3L-AA1B COAD 0 5.13 61 F I
#> TCGA-4N-A93T COAD 0 0.27 67 M III
#> TCGA-4T-AA8H COAD 0 5.33 42 F II
#> TCGA-5M-AAT4 COAD 1 1.63 74 M IV
#> TCGA-5M-AAT6 COAD 1 9.67 41 F IV
#> TCGA-5M-AATE COAD 0 40.00 76 M II
#> TCGA-A6-2671 COAD 0 21.60 86 M IV
7.4 TMB extraction
get_tmb
Extract the TMB matrix of all samples in TCGA.
get_tmb()Example
get_tmb()
#> TMB
#> TCGA-OR-A5J1-01A 0.70
#> TCGA-OR-A5J2-01A 0.83
#> TCGA-OR-A5J3-01A 0.27
#> TCGA-OR-A5J5-01A 8.53
#> TCGA-OR-A5J6-01A 0.777.5 MSI extraction
get_msi
Extract the MSI matrix of all samples in TCGA.
get_msi()Example
get_msi()
#> MSI
#> TCGA-OR-A5J1 0.275
#> TCGA-OR-A5J2 0.324
#> TCGA-OR-A5J3 0.343
#> TCGA-OR-A5J5 0.522
#> TCGA-OR-A5J6 0.2897.6 Methylation extraction
get_methy
Show the download link of the whole methylation mtrix with 8Gb.
get_methy()get_promoter_methy
Extract promoter methylation information of a specific type of cancer.
get_promoter_methy(cancer)Arguments
cancer
cancer name likes "UVM".
Example
uvm=get_promoter_methy("UVM")
uvm$probe[1:4,1:2]
# probe gene
# 47 cg03586879 A2BP1
# 111 cg19378133 A2BP1
# 121 cg00336946 A2LD1
# 125 cg02923162 A2LD1
uvm$methy[1:4,1:4]
# Cancer cg18147296 cg13897241 cg13176867
# TCGA-WC-A87W-01A UVM 0.872 0.465 0.357
# TCGA-V4-A9F8-01A UVM 0.915 0.844 0.640
# TCGA-V4-A9F7-01A UVM 0.862 0.767 0.744
# TCGA-WC-A888-01A UVM 0.791 0.858 0.7987.7 Immune cell ratio extraction
get_immu_ratio
Extract the immune cell ratio of all samples in TCGA.
get_immu_ratio()Example
get_immu_ratio()
#> B cells memory B cells naive Dendritic cells activated
#> TCGA-OR-A5LD-01A 0.0069 0.0000 0.0000
#> TCGA-OR-A5KO-01A 0.0685 0.0000 0.0844
#> TCGA-OR-A5LA-01A 0.0000 0.0117 0.0000
#> TCGA-OR-A5JW-01A 0.0133 0.0000 0.0258
#> TCGA-PA-A5YG-01A 0.0085 0.0056 0.0100
#> TCGA-OR-A5JD-01A 0.0146 0.0000 0.0093
7.8 Immune score extraction
get_immuscore
Extract the immune score of all samples in TCGA.
get_immuscore()Example
get_immuscore()
#> B cells memory B cells naive Dendritic cells activated
#> TCGA-OR-A5LD-01A 0.0069 0.0000 0.0000
#> TCGA-OR-A5KO-01A 0.0685 0.0000 0.0844
#> TCGA-OR-A5LA-01A 0.0000 0.0117 0.0000
#> TCGA-OR-A5JW-01A 0.0133 0.0000 0.0258
#> TCGA-PA-A5YG-01A 0.0085 0.0056 0.0100
7.9 Geneset list extraction
get_geneset
Extract the whole built in geneset list from MSigDB.
get_geneset()7.10 Built-in data summary
get_cancers
Return the sample summary of 33 types of cancer in TCGA.
get_cancers()
get_paired_cancers
Return the sample summary of 15 types of cancer containing more than 20 paired samples in TCGA.
get_paired_cancers()
END
2024.10.17