R/gdcDEGVisulization.R
In Jialab-UCR/GDCRNATools: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC
Defines functions
gdcHeatmap
gdcVolcanoPlot
gdcBarPlot
Documented in
gdcBarPlot
gdcHeatmap
gdcVolcanoPlot
##' @title Bar plot of differentially expressed genes/miRNAs
##' @description A bar plot showing the number of down-regulated and
##' up-regulated DE genes/miRNAs of different biotypes
##' @param deg a dataframe generated from \code{\link{gdcDEReport}}
##' containing DE genes/miRNAs ids, logFC, etc.
##' @param angle a numeric value specifying the angle of text on x-axis.
##' Default is \code{0}
##' @param data.type one of \code{'RNAseq'} and \code{'miRNAs'}
##' @return A bar plot
##' @export
##' @author Ruidong Li and Han Qu
##' @examples
##' genes <- c('ENSG00000231806','ENSG00000261211','ENSG00000260920',
##' 'ENSG00000228594','ENSG00000125170','ENSG00000179909',
##' 'ENSG00000280012','ENSG00000134612','ENSG00000213071')
##' symbol <- c('PCAT7','AL031123.2','AL031985.3',
##' 'FNDC10','DOK4','ZNF154',
##' 'RPL23AP61','FOLH1B','LPAL2')
##' group <- rep(c('long_non_coding','protein_coding','pseudogene'), each=3)
##' logFC <- c(2.8,2.3,-1.1,1.9,-1.2,-1.6,1.5,2.1,-1.1)
##' FDR <- rep(c(0.1,0.00001,0.0002), each=3)
##' deg <- data.frame(symbol, group, logFC, FDR)
##' rownames(deg) <- genes
##' gdcBarPlot(deg, angle=45, data.type='RNAseq')
gdcBarPlot <- function(deg, angle=0, data.type) {
if (data.type=='miRNAs') {
down <- sum(deg$logFC < 0)
up <- sum(deg$logFC > 0)
d <- data.frame(geneClass = c('Up','Down'),
geneNums = c(up, down),
Regulation = factor(c('Up-regulated','Down-regulated'),
levels=c('Up-regulated','Down-regulated')))
if (angle==0) {
ggplot(data=d, aes(x=d$geneClass, y=d$geneNums,
fill=Regulation)) + geom_bar(stat = 'identity') +
scale_x_discrete(limits=d$geneClass) +
scale_fill_discrete(name = "") +
ylab('No. of Differentially Expressed miRNAs') +xlab('') +
#theme(axis.text.x = element_text(angle = angle)) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='white'),
panel.background = element_blank(),
axis.text.x = element_text(angle = angle,size=10),
axis.text.y=element_text(size=10))
} else {
ggplot(data=d, aes(x=d$geneClass, y=d$geneNums,
fill=Regulation)) + geom_bar(stat = 'identity') +
scale_x_discrete(limits=d$geneClass) +
scale_fill_discrete(name = "") +
ylab('No. of Differentially Expressed miRNAs') +xlab('') +
#theme(axis.text.x =
#element_text(angle = angle, vjust = 1, hjust=1)) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='white'),
panel.background = element_blank(),
axis.text.x = element_text(angle = angle, vjust = 1,
hjust=1,size=10),
axis.text.y=element_text(size=10))
}
} else if (data.type=='RNAseq') {
gr <- list()
for (tp in unique(deg$group)) {
gr[[tp]][['all']] <- sum(deg$group==tp)
gr[[tp]]['up'] <- sum(deg$group==tp & deg$logFC > 0)
gr[[tp]]['down'] <- sum(deg$group==tp & deg$logFC < 0)
}
names(gr)[names(gr) == 'protein_coding'] <- 'Protein coding'
names(gr)[names(gr) == 'long_non_coding'] <- 'Long non-coding'
names(gr)[names(gr) == 'pseudogene'] <- 'Pseudogene'
names(gr)[names(gr) == 'ncRNA'] <- 'Other ncRNA'
#names(gr)[names(gr) == 'IG'] <- 'Immunoglobulin gene'
#names(gr)[names(gr) == 'TR'] <- 'T-cell receptor gene'
#names(gr)[names(gr) == 'TEC'] <- 'To be experimentally confirmed'
o <- order(unlist(lapply(gr, function(v) v[['all']])),
decreasing=TRUE)
d <- data.frame(geneClass = rep(names(gr)[o], 2),
geneNums = c(unlist(lapply(gr, function(v) v[['up']]))[o],
unlist(lapply(gr, function(v) v[['down']]))[o]),
Regulation = factor(rep(c('Up-regulated','Down-regulated'),
each=length(gr)), levels=c('Up-regulated','Down-regulated')))
if (angle==0) {
ggplot(data=d, aes(x=d$geneClass, y=d$geneNums,
fill=Regulation)) + geom_bar(stat = 'identity') +
scale_x_discrete(limits=d$geneClass[seq_len(nrow(d)/2)]) +
scale_fill_discrete(name = "") +
ylab('No. of Differentially Expressed Genes') +
xlab('') +
#theme(axis.text.x = element_text(angle = angle)) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='white'),
panel.background = element_blank(),
axis.text.x = element_text(angle = angle,size=14),
axis.text.y=element_text(size=14))
} else {
ggplot(data=d, aes(x=d$geneClass, y=d$geneNums,
fill=Regulation)) + geom_bar(stat = 'identity') +
scale_x_discrete(limits=d$geneClass[seq_len(nrow(d)/2)]) +
scale_fill_discrete(name = "") +
ylab('No. of Differentially Expressed Genes') +
xlab('') +
#theme(axis.text.x =
#element_text(angle = angle, vjust = 1, hjust=1)) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='white'),
panel.background = element_blank(),
axis.text.x = element_text(angle = angle, vjust = 1,
hjust=1,size=14),
axis.text.y=element_text(size=14)) +
theme(axis.title=element_text(size=16),
legend.text = element_text(size=14))
}
}
}
##' @title Volcano plot of differentially expressed genes/miRNAs
##' @description A volcano plot showing differentially expressed genes/miRNAs
##' @param deg.all a dataframe generated from \code{\link{gdcDEAnalysis}}
##' containing all genes of analysis no matter they are differentially
##' expressed or not
##' @param fc a numeric value specifying the threshold of fold change
##' @param pval a nuemric value specifying the threshold of p value
##' @return A volcano plot
##' @export
##' @author Ruidong Li and Han Qu
##' @examples
##' genes <- c('ENSG00000231806','ENSG00000261211','ENSG00000260920',
##' 'ENSG00000228594','ENSG00000125170','ENSG00000179909',
##' 'ENSG00000280012','ENSG00000134612','ENSG00000213071')
##' symbol <- c('PCAT7','AL031123.2','AL031985.3',
##' 'FNDC10','DOK4','ZNF154',
##' 'RPL23AP61','FOLH1B','LPAL2')
##' group <- rep(c('long_non_coding','protein_coding','pseudogene'), each=3)
##' logFC <- c(2.8,2.3,-1.1,1.9,-1.2,-1.6,1.5,2.1,-1.1)
##' FDR <- rep(c(0.1,0.00001,0.0002), each=3)
##' deg <- data.frame(symbol, group, logFC, FDR)
##' rownames(deg) <- genes
##' gdcVolcanoPlot(deg.all=deg)
gdcVolcanoPlot <- function(deg.all, fc=2, pval=0.01) {
geneList <- deg.all
geneList$threshold <- c()
geneList$threshold[geneList$logFC>log(fc,2) & geneList$FDR<pval] <- 1
geneList$threshold[geneList$logFC>=-log(fc,2) & geneList$logFC<=log(fc,2)
| geneList$FDR>=pval] <- 2
geneList$threshold[geneList$logFC < -log(fc,2) & geneList$FDR<pval] <- 3
geneList$threshold <- as.factor(geneList$threshold)
lim <- max(max(geneList$logFC), abs(min(geneList$logFC)))+0.5
volcano <- ggplot(data=geneList, aes(x=geneList$logFC,
y = -log10(geneList$FDR)))
volcano+geom_point(aes(color=geneList$threshold), alpha=1, size=0.8) +
xlab("log2(Fold Change)") + ylab("-log10(FDR)") +
scale_colour_manual(breaks = geneList$threshold,
values = c('red','black','green3')) + xlim(c(-lim,lim)) +
geom_vline(xintercept = c(-log(fc,2),log(fc,2)),
color='darkgreen', linetype=3) +
geom_hline(yintercept = -log(pval,10), color='darkgreen',linetype=3)+
theme_bw() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='black'),
panel.background = element_blank()) +
theme(legend.position="none") +
theme(axis.text=element_text(size=14),
axis.title=element_text(size=16))
}
##' @title Heatmap of differentially expressed genes/miRNAs
##' @description A heatmap showing unsupervised hierarchical clustering of
##' DE genes/miRNAs by \code{\link[gplots]{heatmap.2}} in the
##' \pkg{gplots} package
##' @param deg.id a vector of Ensembl gene ids or miRBase v21 mature miRNA ids
##' @param metadata metadata parsed from \code{\link{gdcParseMetadata}}
##' @param rna.expr \code{\link[limma]{voom}} transformed expression data
##' @importFrom gplots heatmap.2
##' @importFrom gplots colorpanel
##' @importFrom gplots bluered
##' @return A heatmap with rows are DE genes/miRNAs and columns are samples.
##' \emph{Solid Tissue Normal} samples are labeled with blue and
##' \emph{Primary Tumor} samples are labeled with red
##' @export
##' @author Ruidong Li and Han Qu
##' @examples
##' genes <- c('ENSG00000000938','ENSG00000000971','ENSG00000001036',
##' 'ENSG00000001084','ENSG00000001167','ENSG00000001460')
##'
##' samples <- c('TCGA-2F-A9KO-01', 'TCGA-2F-A9KP-01',
##' 'TCGA-2F-A9KQ-01', 'TCGA-2F-A9KR-01',
##' 'TCGA-2F-A9KT-01', 'TCGA-2F-A9KW-01')
##'
##' metaMatrix <- data.frame(sample_type=rep('PrimaryTumor',6),
##' sample=samples,
##' days_to_death=seq(100,600,100),
##' days_to_last_follow_up=rep(NA,6))
##' rnaExpr <- matrix(c(2.7,7.0,4.9,6.9,4.6,2.5,
##' 0.5,2.5,5.7,6.5,4.9,3.8,
##' 2.1,2.9,5.9,5.7,4.5,3.5,
##' 2.7,5.9,4.5,5.8,5.2,3.0,
##' 2.5,2.2,5.3,4.4,4.4,2.9,
##' 2.4,3.8,6.2,3.8,3.8,4.2),6,6)
##' rownames(rnaExpr) <- genes
##' colnames(rnaExpr) <- samples
##' gdcHeatmap(deg.id=genes, metadata=metaMatrix, rna.expr=rnaExpr)
gdcHeatmap <- function(deg.id, metadata, rna.expr) {
degDa <- rna.expr[deg.id,]
sampleCol <- ifelse(metadata$sample_type=='SolidTissueNormal',
'blue', 'red')
#col=colorpanel(75,"darkblue","white","orangered")
lmat = rbind(c(4,3),c(2,1))
lwid = c(2,4)
lhei = c(1,5)
heatmap.2(as.matrix(degDa), col=bluered(75), trace='none',
cexCol=0.32, cexRow=0.1,dendrogram='both', srtCol=90,
adjCol=c(0.8,0.15), density.info="none", labRow=NA,
key.title=NA,na.color=NA,lwid=lwid, lhei=lhei,
margins =c(3,3), labCol=NA, key.xlab='Normalized intensity',
scale='row', ColSideColors = sampleCol)
}
Jialab-UCR/GDCRNATools documentation built on Nov. 28, 2020, 11:23 a.m.
R Package Documentation
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Defines functions gdcHeatmap gdcVolcanoPlot gdcBarPlot
Documented in gdcBarPlot gdcHeatmap gdcVolcanoPlot
##' @title Bar plot of differentially expressed genes/miRNAs
##' @description A bar plot showing the number of down-regulated and
##' up-regulated DE genes/miRNAs of different biotypes
##' @param deg a dataframe generated from \code{\link{gdcDEReport}}
##' containing DE genes/miRNAs ids, logFC, etc.
##' @param angle a numeric value specifying the angle of text on x-axis.
##' Default is \code{0}
##' @param data.type one of \code{'RNAseq'} and \code{'miRNAs'}
##' @return A bar plot
##' @export
##' @author Ruidong Li and Han Qu
##' @examples
##' genes <- c('ENSG00000231806','ENSG00000261211','ENSG00000260920',
##' 'ENSG00000228594','ENSG00000125170','ENSG00000179909',
##' 'ENSG00000280012','ENSG00000134612','ENSG00000213071')
##' symbol <- c('PCAT7','AL031123.2','AL031985.3',
##' 'FNDC10','DOK4','ZNF154',
##' 'RPL23AP61','FOLH1B','LPAL2')
##' group <- rep(c('long_non_coding','protein_coding','pseudogene'), each=3)
##' logFC <- c(2.8,2.3,-1.1,1.9,-1.2,-1.6,1.5,2.1,-1.1)
##' FDR <- rep(c(0.1,0.00001,0.0002), each=3)
##' deg <- data.frame(symbol, group, logFC, FDR)
##' rownames(deg) <- genes
##' gdcBarPlot(deg, angle=45, data.type='RNAseq')
gdcBarPlot <- function(deg, angle=0, data.type) {
if (data.type=='miRNAs') {
down <- sum(deg$logFC < 0)
up <- sum(deg$logFC > 0)
d <- data.frame(geneClass = c('Up','Down'),
geneNums = c(up, down),
Regulation = factor(c('Up-regulated','Down-regulated'),
levels=c('Up-regulated','Down-regulated')))
if (angle==0) {
ggplot(data=d, aes(x=d$geneClass, y=d$geneNums,
fill=Regulation)) + geom_bar(stat = 'identity') +
scale_x_discrete(limits=d$geneClass) +
scale_fill_discrete(name = "") +
ylab('No. of Differentially Expressed miRNAs') +xlab('') +
#theme(axis.text.x = element_text(angle = angle)) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='white'),
panel.background = element_blank(),
axis.text.x = element_text(angle = angle,size=10),
axis.text.y=element_text(size=10))
} else {
ggplot(data=d, aes(x=d$geneClass, y=d$geneNums,
fill=Regulation)) + geom_bar(stat = 'identity') +
scale_x_discrete(limits=d$geneClass) +
scale_fill_discrete(name = "") +
ylab('No. of Differentially Expressed miRNAs') +xlab('') +
#theme(axis.text.x =
#element_text(angle = angle, vjust = 1, hjust=1)) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='white'),
panel.background = element_blank(),
axis.text.x = element_text(angle = angle, vjust = 1,
hjust=1,size=10),
axis.text.y=element_text(size=10))
}
} else if (data.type=='RNAseq') {
gr <- list()
for (tp in unique(deg$group)) {
gr[[tp]][['all']] <- sum(deg$group==tp)
gr[[tp]]['up'] <- sum(deg$group==tp & deg$logFC > 0)
gr[[tp]]['down'] <- sum(deg$group==tp & deg$logFC < 0)
}
names(gr)[names(gr) == 'protein_coding'] <- 'Protein coding'
names(gr)[names(gr) == 'long_non_coding'] <- 'Long non-coding'
names(gr)[names(gr) == 'pseudogene'] <- 'Pseudogene'
names(gr)[names(gr) == 'ncRNA'] <- 'Other ncRNA'
#names(gr)[names(gr) == 'IG'] <- 'Immunoglobulin gene'
#names(gr)[names(gr) == 'TR'] <- 'T-cell receptor gene'
#names(gr)[names(gr) == 'TEC'] <- 'To be experimentally confirmed'
o <- order(unlist(lapply(gr, function(v) v[['all']])),
decreasing=TRUE)
d <- data.frame(geneClass = rep(names(gr)[o], 2),
geneNums = c(unlist(lapply(gr, function(v) v[['up']]))[o],
unlist(lapply(gr, function(v) v[['down']]))[o]),
Regulation = factor(rep(c('Up-regulated','Down-regulated'),
each=length(gr)), levels=c('Up-regulated','Down-regulated')))
if (angle==0) {
ggplot(data=d, aes(x=d$geneClass, y=d$geneNums,
fill=Regulation)) + geom_bar(stat = 'identity') +
scale_x_discrete(limits=d$geneClass[seq_len(nrow(d)/2)]) +
scale_fill_discrete(name = "") +
ylab('No. of Differentially Expressed Genes') +
xlab('') +
#theme(axis.text.x = element_text(angle = angle)) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='white'),
panel.background = element_blank(),
axis.text.x = element_text(angle = angle,size=14),
axis.text.y=element_text(size=14))
} else {
ggplot(data=d, aes(x=d$geneClass, y=d$geneNums,
fill=Regulation)) + geom_bar(stat = 'identity') +
scale_x_discrete(limits=d$geneClass[seq_len(nrow(d)/2)]) +
scale_fill_discrete(name = "") +
ylab('No. of Differentially Expressed Genes') +
xlab('') +
#theme(axis.text.x =
#element_text(angle = angle, vjust = 1, hjust=1)) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='white'),
panel.background = element_blank(),
axis.text.x = element_text(angle = angle, vjust = 1,
hjust=1,size=14),
axis.text.y=element_text(size=14)) +
theme(axis.title=element_text(size=16),
legend.text = element_text(size=14))
}
}
}
##' @title Volcano plot of differentially expressed genes/miRNAs
##' @description A volcano plot showing differentially expressed genes/miRNAs
##' @param deg.all a dataframe generated from \code{\link{gdcDEAnalysis}}
##' containing all genes of analysis no matter they are differentially
##' expressed or not
##' @param fc a numeric value specifying the threshold of fold change
##' @param pval a nuemric value specifying the threshold of p value
##' @return A volcano plot
##' @export
##' @author Ruidong Li and Han Qu
##' @examples
##' genes <- c('ENSG00000231806','ENSG00000261211','ENSG00000260920',
##' 'ENSG00000228594','ENSG00000125170','ENSG00000179909',
##' 'ENSG00000280012','ENSG00000134612','ENSG00000213071')
##' symbol <- c('PCAT7','AL031123.2','AL031985.3',
##' 'FNDC10','DOK4','ZNF154',
##' 'RPL23AP61','FOLH1B','LPAL2')
##' group <- rep(c('long_non_coding','protein_coding','pseudogene'), each=3)
##' logFC <- c(2.8,2.3,-1.1,1.9,-1.2,-1.6,1.5,2.1,-1.1)
##' FDR <- rep(c(0.1,0.00001,0.0002), each=3)
##' deg <- data.frame(symbol, group, logFC, FDR)
##' rownames(deg) <- genes
##' gdcVolcanoPlot(deg.all=deg)
gdcVolcanoPlot <- function(deg.all, fc=2, pval=0.01) {
geneList <- deg.all
geneList$threshold <- c()
geneList$threshold[geneList$logFC>log(fc,2) & geneList$FDR<pval] <- 1
geneList$threshold[geneList$logFC>=-log(fc,2) & geneList$logFC<=log(fc,2)
| geneList$FDR>=pval] <- 2
geneList$threshold[geneList$logFC < -log(fc,2) & geneList$FDR<pval] <- 3
geneList$threshold <- as.factor(geneList$threshold)
lim <- max(max(geneList$logFC), abs(min(geneList$logFC)))+0.5
volcano <- ggplot(data=geneList, aes(x=geneList$logFC,
y = -log10(geneList$FDR)))
volcano+geom_point(aes(color=geneList$threshold), alpha=1, size=0.8) +
xlab("log2(Fold Change)") + ylab("-log10(FDR)") +
scale_colour_manual(breaks = geneList$threshold,
values = c('red','black','green3')) + xlim(c(-lim,lim)) +
geom_vline(xintercept = c(-log(fc,2),log(fc,2)),
color='darkgreen', linetype=3) +
geom_hline(yintercept = -log(pval,10), color='darkgreen',linetype=3)+
theme_bw() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour='black'),
panel.background = element_blank()) +
theme(legend.position="none") +
theme(axis.text=element_text(size=14),
axis.title=element_text(size=16))
}
##' @title Heatmap of differentially expressed genes/miRNAs
##' @description A heatmap showing unsupervised hierarchical clustering of
##' DE genes/miRNAs by \code{\link[gplots]{heatmap.2}} in the
##' \pkg{gplots} package
##' @param deg.id a vector of Ensembl gene ids or miRBase v21 mature miRNA ids
##' @param metadata metadata parsed from \code{\link{gdcParseMetadata}}
##' @param rna.expr \code{\link[limma]{voom}} transformed expression data
##' @importFrom gplots heatmap.2
##' @importFrom gplots colorpanel
##' @importFrom gplots bluered
##' @return A heatmap with rows are DE genes/miRNAs and columns are samples.
##' \emph{Solid Tissue Normal} samples are labeled with blue and
##' \emph{Primary Tumor} samples are labeled with red
##' @export
##' @author Ruidong Li and Han Qu
##' @examples
##' genes <- c('ENSG00000000938','ENSG00000000971','ENSG00000001036',
##' 'ENSG00000001084','ENSG00000001167','ENSG00000001460')
##'
##' samples <- c('TCGA-2F-A9KO-01', 'TCGA-2F-A9KP-01',
##' 'TCGA-2F-A9KQ-01', 'TCGA-2F-A9KR-01',
##' 'TCGA-2F-A9KT-01', 'TCGA-2F-A9KW-01')
##'
##' metaMatrix <- data.frame(sample_type=rep('PrimaryTumor',6),
##' sample=samples,
##' days_to_death=seq(100,600,100),
##' days_to_last_follow_up=rep(NA,6))
##' rnaExpr <- matrix(c(2.7,7.0,4.9,6.9,4.6,2.5,
##' 0.5,2.5,5.7,6.5,4.9,3.8,
##' 2.1,2.9,5.9,5.7,4.5,3.5,
##' 2.7,5.9,4.5,5.8,5.2,3.0,
##' 2.5,2.2,5.3,4.4,4.4,2.9,
##' 2.4,3.8,6.2,3.8,3.8,4.2),6,6)
##' rownames(rnaExpr) <- genes
##' colnames(rnaExpr) <- samples
##' gdcHeatmap(deg.id=genes, metadata=metaMatrix, rna.expr=rnaExpr)
gdcHeatmap <- function(deg.id, metadata, rna.expr) {
degDa <- rna.expr[deg.id,]
sampleCol <- ifelse(metadata$sample_type=='SolidTissueNormal',
'blue', 'red')
#col=colorpanel(75,"darkblue","white","orangered")
lmat = rbind(c(4,3),c(2,1))
lwid = c(2,4)
lhei = c(1,5)
heatmap.2(as.matrix(degDa), col=bluered(75), trace='none',
cexCol=0.32, cexRow=0.1,dendrogram='both', srtCol=90,
adjCol=c(0.8,0.15), density.info="none", labRow=NA,
key.title=NA,na.color=NA,lwid=lwid, lhei=lhei,
margins =c(3,3), labCol=NA, key.xlab='Normalized intensity',
scale='row', ColSideColors = sampleCol)
}
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Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.