Stacked bar plot with hierarchical clustering (dendrogram)

Question

I am trying to get something like this but unfortunately, I could not find any package that could enable me to plot stacked bar plot with dendrogram like the one shown below:

Does anyone know how to do it?

Answer 1

A first stab at an answer - but it would require more work to make it really work. Specifically the alignment of the location of elements (as well as their order) needs to be thought of more carefully.

# library
library(ggplot2)

# create a dataset
specie=c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) )
condition=rep(c("normal" , "stress" , "Nitrogen") , 4)
value=abs(rnorm(12 , 0 , 15))
data=data.frame(specie,condition,value)


dend <- as.dendrogram(hclust(dist(with(data, tapply(value, specie, mean)))))

data$specie <- factor(data$specie, levels = labels(dend))

# Stacked Percent
library(dendextend)
p1 <- ggplot(dend, horiz = T) 
p2 <- ggplot(data, aes(fill=condition, y=value, x=specie)) + 
    geom_bar( stat="identity", position="fill") + coord_flip()

library(cowplot)
plot_grid(p1, p2, align = "h")

Answer 2

Here's my version of Roman_G's script. It displays percentages inside the bars, and it uses vegan::reorder.hclust to reorder the branches of the dendrogram so that so that rows with the highest value for the first column tend to be placed to the top and rows with the highest value for the last column tend to be placed at the bottom. I also removed extra margins and ticks and axis.

library(tidyverse)
library(ggdendro)
library(vegan)
library(colorspace)
library(cowplot)

t=read.table(text="Spain_EN 0.028152 0.971828 0.000010 0.000010
Norway_Mesolithic 0.784705 0.083387 0.000010 0.131898
Russia_Sunghir4 0.000010 0.000010 0.999970 0.000010
Iran_Wezmeh_N 0.000010 0.492331 0.383227 0.124433
Russia_DevilsCave_N 0.000010 0.000010 0.000010 0.999970
Italy_North_Villabruna_HG 0.999970 0.000010 0.000010 0.000010
Russia_HG_Karelia 0.527887 0.133179 0.072342 0.266593
Russia_Yana_UP 0.000010 0.000014 0.999966 0.000010
Georgia_Kotias 0.000010 0.537322 0.381313 0.081355
China_SEastAsia_Island_EN 0.000010 0.000010 0.148652 0.851328
Turkey_N 0.000010 0.999970 0.000010 0.000010
USA_Ancient_Beringian 0.008591 0.000010 0.095008 0.896391
Russia_Sidelkino_HG 0.624076 0.045350 0.105615 0.224958
Russia_Kolyma_M 0.020197 0.000010 0.000010 0.979783
China_Tianyuan 0.000010 0.000010 0.423731 0.576249",row.names=1)

hc=hclust(dist(t),method="ward.D2")
hc=reorder(hc,wts=-as.matrix(t)%*%seq(ncol(t))^2) # vegan::reorder.hclust
tree=ggdendro::dendro_data(as.dendrogram(hc),type="rectangle")

p1=ggplot(ggdendro::segment(tree))+
geom_segment(aes(x=y,y=x,xend=yend,yend=xend),lineend="round",size=.4)+
scale_x_continuous(expand=expansion(add=c(0,.01)))+ # don't crop half of line between top-level nodes
scale_y_continuous(limits=.5+c(0,nrow(t)),expand=c(0,0))+
theme(
  axis.text=element_blank(),
  axis.ticks=element_blank(),
  axis.ticks.length=unit(0,"pt"), # remove extra space occupied by ticks
  axis.title=element_blank(),
  panel.background=element_rect(fill="white"),
  panel.grid=element_blank(),
  plot.margin=margin(5,5,5,0)
)

t=t[hc$labels[hc$order],]
t2=data.frame(V1=rownames(t)[row(t)],V2=colnames(t)[col(t)],V3=unname(do.call(c,t)))
lab=round(100*t2$V3)
lab[lab==0]=""

p2=ggplot(t2,aes(x=factor(V1,level=rownames(t)),y=V3,fill=V2))+
geom_bar(stat="identity",width=1,position=position_fill(reverse=T))+
geom_text(aes(label=lab),position=position_stack(vjust=.5,reverse=T),size=3.5)+
coord_flip()+
scale_x_discrete(expand=c(0,0))+
scale_y_discrete(expand=c(0,0))+
scale_fill_manual(values=colorspace::hex(HSV(head(seq(0,360,length.out=ncol(t)+1),-1),.5,1)))+
theme(
  axis.text=element_text(color="black",size=11),
  axis.text.x=element_blank(),
  axis.ticks=element_blank(),
  axis.title=element_blank(),
  legend.position="none",
  plot.margin=margin(5,0,5,5)
)

cowplot::plot_grid(p2,p1,rel_widths=c(1,.4))
ggsave("a.png",height=.25*nrow(t),width=7)

There's also scale_x_dendrogram and scale_y_dendrogram from ggh4x, which use ggdendro::dendro_data: https://teunbrand.github.io/ggh4x/articles/PositionGuides.html#dendrograms. However I couldn't get them to work with horizontal stacked bars using coord_flip.

library(ggh4x)

t=head(USArrests,20)
t2=data.frame(V1=rownames(t)[row(t)],V2=colnames(t)[col(t)],V3=unname(do.call(c,t)))
hc=hclust(dist(t))

ggplot(t2,aes(x=factor(V1,level=rownames(t)),y=V3,fill=V2))+
geom_bar(stat="identity",width=1,position=position_stack(reverse=F))+
geom_text(aes(label=round(V3)),position=position_stack(vjust=.5,reverse=F),size=3)+
scale_x_dendrogram(hclust=hc)+
scale_y_discrete(expand=c(0,0))+
# scale_fill_manual(values=colorspace::hex(HSV(head(seq(0,360,length.out=ncol(t)+1),-1),.5,1)))+
theme(
  axis.text=element_text(color="black",size=11),
  axis.text.x=element_text(angle=90,hjust=1,vjust=.5),
  axis.text.y=element_blank(),
  axis.ticks=element_blank(),
  axis.ticks.length=unit(14,"pt"), # height of dendrogram
  axis.title=element_blank(),
  legend.justification=c(0,1),
  legend.key=element_rect(fill=NA), # remove gray border around color squares
  legend.margin=margin(-6,0,0,0),
  legend.position=c(0,1),
  legend.title=element_blank(),
  panel.background=element_rect(fill="white"),
  plot.margin=margin(5,0,5,5)
)

ggsave("a.png",height=6,width=6)

Edit: a third option is to use circlize: https://jokergoo.github.io/circlize/reference/circos.barplot.html.

library(circlize)
library(vegan) # for reorder.hclust (may be masked by `seriation`)
library(dendextend) # for color_branches

t=read.table(text="Kalmyk 0.119357 0.725057 0.000010 0.037803 0.117774
Kyrgyz_China 0.039367 0.512079 0.230150 0.095038 0.123366
Altaian_Chelkan 0.034095 0.000010 0.919478 0.000010 0.046407
Azeri 0.051638 0.004671 0.010727 0.902646 0.030318
Uzbek 0.102725 0.273261 0.001854 0.452126 0.170033
Salar 0.000010 0.539636 0.460334 0.000010 0.000010
Tatar_Kazan 0.113456 0.057026 0.000010 0.099336 0.730171
Tatar_Siberian 0.251376 0.221389 0.000010 0.077505 0.449721
Finnish 0.007214 0.000010 0.000010 0.015174 0.977592
Yakut 0.505434 0.473202 0.000010 0.002914 0.018440
Mansi 0.572791 0.000010 0.000010 0.000010 0.427179
Altaian 0.222424 0.335614 0.358801 0.032694 0.050468
Shor_Mountain 0.233984 0.000010 0.724596 0.000010 0.041400
Chuvash 0.180171 0.011056 0.000010 0.006462 0.802301
Enets 0.920409 0.000010 0.000010 0.000010 0.079561
Yukagir_Tundra 0.710359 0.289611 0.000010 0.000011 0.000010
Kyrgyz_Tajikistan 0.104000 0.563708 0.000010 0.125799 0.206483
Khakass_Kachin 0.254253 0.416760 0.174200 0.005262 0.149525
Tuvinian 0.448940 0.448899 0.000010 0.031803 0.070348
Besermyan 0.209841 0.001487 0.000010 0.000460 0.788202
Nogai_Astrakhan 0.062497 0.463590 0.000010 0.183203 0.290701
Todzin 0.725173 0.257670 0.000010 0.005836 0.011312
Kazakh 0.067027 0.518213 0.087979 0.114550 0.212231
Tofalar 0.815599 0.110299 0.000010 0.009693 0.064398
Karakalpak 0.009983 0.316964 0.389103 0.158275 0.125676
Estonian 0.000010 0.000010 0.000010 0.004409 0.995561
Dolgan 0.694025 0.255361 0.000010 0.049624 0.000979
Tatar_Siberian_Zabolotniye 0.521637 0.020132 0.000010 0.000010 0.458212
Uyghur 0.043578 0.486742 0.000010 0.318983 0.150687
Udmurt 0.256391 0.000010 0.001010 0.000010 0.742579
Evenk_FarEast 0.241328 0.606202 0.000010 0.000010 0.152451
Selkup 0.804662 0.000010 0.000010 0.000010 0.195308
Kumyk 0.060751 0.000112 0.000010 0.823905 0.115222
Hungarian 0.000010 0.000010 0.000010 0.244311 0.755659
Tubalar 0.159517 0.009457 0.802778 0.000010 0.028238
Turkmen 0.123631 0.226543 0.000010 0.529793 0.120023
Karelian 0.012854 0.000010 0.000010 0.000010 0.987116
Kazakh_China 0.074285 0.573009 0.152931 0.069362 0.130412
Mongol 0.033174 0.847004 0.025135 0.005178 0.089509
Daur 0.000010 0.995215 0.000010 0.000010 0.004755
Evenk_Transbaikal 0.611414 0.388556 0.000010 0.000010 0.000010
Nogai_Karachay_Cherkessia 0.119988 0.120774 0.000010 0.617261 0.141967
Veps 0.026887 0.000010 0.000010 0.000010 0.973083
Even 0.441349 0.278457 0.000010 0.015239 0.264946
Nganasan 0.999960 0.000010 0.000010 0.000010 0.000010
Bashkir 0.114088 0.056493 0.251488 0.030390 0.547542
Xibo 0.000010 0.985541 0.000010 0.000362 0.014077
Khakass 0.202707 0.171413 0.530905 0.007675 0.087300
Shor_Khakassia 0.258218 0.000010 0.694889 0.000010 0.046873
Nanai 0.105903 0.894067 0.000010 0.000010 0.000010
Buryat 0.064420 0.848458 0.017066 0.001696 0.068360
Yukagir_Forest 0.379416 0.096266 0.000010 0.003580 0.520728
Karachai 0.067138 0.004534 0.000010 0.798982 0.129336
Mordovian 0.022303 0.001193 0.000010 0.025251 0.951243
Turkish_Balikesir 0.092314 0.038550 0.000010 0.804964 0.064163
Turkish 0.040918 0.012255 0.000010 0.873179 0.073639
Kyrgyz_Kyrgyzstan 0.090129 0.607265 0.000010 0.122885 0.179711
Balkar 0.075115 0.000010 0.000010 0.829730 0.095136
Gagauz 0.000010 0.027887 0.015891 0.601619 0.354593
Nogai_Stavropol 0.070584 0.403817 0.000010 0.244701 0.280888
Negidal 0.248518 0.751452 0.000010 0.000010 0.000010
Tatar_Mishar 0.066112 0.037441 0.010377 0.138008 0.748062",row.names=1)

hc=hclust(dist(t))
hc=reorder(hc,-(t[,1]+t[,2]-t[,4]-2*t[,5]))

labelcolor=hcl(c(260,90,120,60,0,210,180,310)+15,60,70)
barcolor=hcl(c(310,260,120,60,210)+15,60,70)

labels=hc$labels[hc$order]
cut=cutree(hc,8)
dend=color_branches(as.dendrogram(hc),k=length(unique(cut)),col=labelcolor[unique(cut[labels])])
t=t[hc$labels[hc$order],]

circos.clear()
png("a.png",w=2500,h=2500,res=300)
circos.par(cell.padding=c(0,0,0,0),gap.degree=5,points.overflow.warning=F)
circos.initialize("a",xlim=c(0,nrow(t)))

circos.track(ylim=c(0,1),bg.border=NA,track.height=.28,track.margin=c(.01,0),
  panel.fun=function(x,y)for(i in 1:nrow(t))circos.text(i-.5,0,labels[i],adj=c(0,.5),facing="clockwise",niceFacing=T,cex=.65,col=labelcolor[cut[labels[i]]]))

circos.track(ylim=c(0,1),track.margin=c(0,0),track.height=.35,bg.lty=0,panel.fun=function(x,y){
  mat=as.matrix(t)
  pos=1:nrow(mat)-.5
  barwidth=1
  for(i in 1:ncol(mat)){
    seq1=rowSums(mat[,seq(i-1),drop=F])
    seq2=rowSums(mat[,seq(i),drop=F])
    circos.rect(pos-barwidth/2,if(i==1){0}else{seq1},pos+barwidth/2,seq2,col=barcolor[i],border="gray20",lwd=.1)
  }
  for(i in 1:ncol(mat)){
    seq1=rowSums(mat[,seq(i-1),drop=F])
    seq2=rowSums(mat[,seq(i),drop=F])
    lab=round(100*t[,i])
    lab[lab<=1]=""
    circos.text(pos,if(i==1){seq1/2}else{seq1+(seq2-seq1)/2},labels=lab,col="gray10",cex=.4,facing="downward")
  }
})

circos.track(ylim=c(0,attr(dend,"height")),bg.border=NA,track.margin=c(0,.0015),track.height=.35,panel.fun=function(x,y)circos.dendrogram(dend))

circos.clear()
dev.off()

Answer 3

Almost three years later there is still no package capable of combining stacked bar plots with hierarchical clustering in ggplot (at least that I'm aware of). Here my solution based on that post joining a dendrogram and a heatmap:

library(tidyverse)
library(phangorn)
library(vegan)
library(ggdendro)
library(dendextend)
library(ggsci)
library(cowplot)

## generate example data ####
set.seed(500)
combined_matrix <- data.frame(a=runif(14, 0, 33), b=runif(14, 0, 33), c=runif(14, 0, 33))
combined_matrix$d <- 100 - combined_matrix$a - combined_matrix$b - combined_matrix$c
row.names(combined_matrix) <- paste0("s", seq(1,14))

# vegan::vegdist() to calculate Bray-Curtis distance matrix
dm <- vegdist(combined_matrix, method = "bray")
# calculate UPGMA tree with phangorn::upgma() and convert to dendrogram
dendUPGMA <- as.dendrogram(upgma(dm))
plot_dendro_bars_v <- function(df, dend, taxonomy) {
  #convert dendrogram to segment data
  dend_data <- dendro_data(dend, type="rectangle")
  segment_data <- dend_data[["segments"]]
  #sample positions df
  sample_pos_table <- with(dend_data$labels, 
                           data.frame(x_center = x, sample = as.character(label), width = 0.9))
  #prepare input data
  ptdf <- rownames_to_column(df, var = "sample") %>%
    pivot_longer(-sample, names_to = taxonomy, values_to = "Frequency") %>%
    group_by(sample) %>%
    mutate(Frequency = Frequency/100,
           ymax = cumsum(Frequency/sum(Frequency)),
           ymin = ymax - Frequency/sum(Frequency),
           y_center = ymax-(Frequency/2)) %>%
    left_join(sample_pos_table) %>%
    mutate(xmin = x_center-width/2,
           xmax = x_center+width/2)
  #plot stacked bars
  axis_limits <- with(sample_pos_table, 
                      c(min(x_center - 0.5 * width), max(x_center + 0.5 * width))) + 
    0.1 * c(-1, 1) # extra spacing: 0.1
  plt_hbars <- ggplot(ptdf, 
                      aes_string(x = "x_center", y = "y_center", fill = taxonomy, xmin = "xmin", xmax = "xmax",
                                 height = "Frequency", width = "width")) + 
    geom_tile() +
    geom_rect(ymin = 0, ymax = 1, color = "black", fill = "transparent") +
    scale_fill_rickandmorty() +
    scale_y_continuous(expand = c(0, 0)) + 
    # For the y axis, alternatively set the labels as: gene_position_table$gene
    scale_x_continuous(breaks = sample_pos_table[, "x_center"], 
                       labels = sample_pos_table$sample,
                       limits = axis_limits, 
                       expand = c(0, 0)) + 
    labs(x = "", y = "Frequency") +
    theme_bw() +
    theme(# margin: top, right, bottom, and left
      plot.margin = unit(c(-0.9, 0.2, 1, 0.2), "cm"), 
      panel.grid.minor = element_blank())
  #plot dendrogram
  plt_dendr <- ggplot(segment_data) + 
    geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) + 
    scale_y_continuous(expand = c(0, 0.05)) + 
    scale_x_continuous(breaks = sample_pos_table$x_center, 
                       labels = rep("", nrow(sample_pos_table)), 
                       limits = axis_limits, 
                       expand = c(0, 0)) + 
    labs(x = "", y = "Distance", colour = "", size = "") +
    theme_bw() + 
    theme(panel.grid.minor = element_blank(),
          panel.grid.major = element_blank())
  #combine plots
  comb <- plot_grid(plt_dendr, plt_hbars, align = 'v', ncol = 1, axis = "lr", rel_heights = c(0.3, 1))
  comb
}
plot_dendro_bars_v(df = combined_matrix, dend = dendUPGMA, taxonomy = "example")

vertical

or horizontal

  plot_dendro_bars_h <- function(df, dend, taxonomy) {
  #convert dendrogram to segemnt data
  dend_data <- dendro_data(dend, type="rectangle")
  segment_data <- with(segment(dend_data), 
                       data.frame(x = y, y = x, xend = yend, yend = xend))
  #sample positions df
  sample_pos_table <- with(dend_data$labels, 
                           data.frame(y_center = x, sample = as.character(label), height = 0.9))
  #prepare input data
  ptdf <- rownames_to_column(df, var = "sample") %>%
    pivot_longer(-sample, names_to = taxonomy, values_to = "Frequency") %>%
    group_by(sample) %>%
    mutate(Frequency = Frequency/100,
           xmax = cumsum(Frequency/sum(Frequency)),
           xmin = xmax - Frequency/sum(Frequency),
           x_center = xmax-(Frequency/2)) %>%
    left_join(sample_pos_table) %>%
    mutate(ymin = y_center-height/2,
           ymax = y_center+height/2)
  #plot stacked bars
  axis_limits <- with(sample_pos_table, 
                      c(min(y_center - 0.5 * height), max(y_center + 0.5 * height))) + 
    0.1 * c(-1, 1) # extra spacing: 0.1
  plt_hbars <- ggplot(ptdf, 
                      aes_string(x = "x_center", y = "y_center", fill = taxonomy, ymin = "ymin", ymax = "ymax",
                                 height = "height", width = "Frequency")) + 
    geom_tile() +
    geom_rect(xmin = 0, xmax = 1, color = "black", fill = "transparent") +
    scale_fill_rickandmorty() +
    scale_x_continuous(expand = c(0, 0)) + 
    # For the y axis, alternatively set the labels as: gene_position_table$gene
    scale_y_continuous(breaks = sample_pos_table[, "y_center"], 
                       labels = rep("", nrow(sample_pos_table)),
                       limits = axis_limits, 
                       expand = c(0, 0)) + 
    labs(x = "Frequency", y = "") +
    theme_bw() +
    theme(# margin: top, right, bottom, and left
      plot.margin = unit(c(1, 0.2, 0.2, -0.9), "cm"), 
      panel.grid.minor = element_blank())
  #plot dendrogram
  plt_dendr <- ggplot(segment_data) + 
    geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) + 
    scale_x_reverse(expand = c(0, 0.05)) + 
    scale_y_continuous(breaks = sample_pos_table$y_center, 
                       labels = sample_pos_table$sample, 
                       limits = axis_limits, 
                       expand = c(0, 0)) + 
    labs(x = "Distance", y = "", colour = "", size = "") +
    theme_bw() + 
    theme(panel.grid.minor = element_blank(),
          panel.grid.major = element_blank())
  #combine plots
  comb <- plot_grid(plt_dendr, plt_hbars, align = 'h', rel_widths = c(0.3, 1))
  return(comb)
}
plot_dendro_bars_h(df = combined_matrix, dend = dendUPGMA, taxonomy = "example")

horizontal

The data can be combined with any tree which can be coerced to a dendrogram (e.g. also UniFrac trees). Have fun with that, Roman.