I created a dendrogram using the 'recluster.cons' function of the recluster package. I would like to know how to color the branches of the dendrogram by group resulting from this function.
tree <- recluster.cons(sp2, p=1)$cons # sp2 is a presence-absence matrix
plot(tree, direction="downwards")
Here is the current dendrogram:
You need to define how many clusters you want to get from the clustering (like cutree), and then using dendextend seems like an easier option. First I simulate a dataset that might look like yours:
library(recluster)
set.seed(222)
testdata = lapply(1:3,function(i){
truep = runif(200)
replicate(7,rbinom(200,size=1,prob=truep))
})
testdata = t(do.call(cbind,testdata))
rownames(testdata) = paste0(rep(letters[1:3],each=7),rep(1:7,3))
We plot it, 3 clusters of sites because it was simulated as such:
tree <- recluster.cons(sp2, p=1)$cons # sp2 is a presence-absence matrix
plot(tree,direction="downwards")
Then colour it:
dendextend
dend <- color_branches(as.dendrogram(tree),k=3)
plot(dend)
Related
I am running a function that returns a custom ggplot from an input data (it is in fact a plot with several layers on it). I run the function over several different input data and obtain a list of ggplots.
I want to create a grid with these plots to compare them but they all have different y axes.
I guess what I have to do is extract the maximum and minimum y axes limits from the ggplot list and apply those to each plot in the list.
How can I do that? I guess its through the use of ggbuild. Something like this:
test = ggplot_build(plot_list[[1]])
> test$layout$panel_scales_x
[[1]]
<ScaleContinuousPosition>
Range:
Limits: 0 -- 1
I am not familiar with the structure of a ggplot_build and maybe this one in particular is not a standard one as it comes from a "custom" ggplot.
For reference, these plots are created whit the gseaplot2 function from the enrichplot package.
I dont know how to "upload" an R object but if that would help, let me know how to do it.
Thanks!
edit after comments (thanks for your suggestions!)
Here is an example of the a gseaplot2 plot. GSEA stands for Gene Set Enrichment Analysis, it is a technique used in genomic studies. The gseaplot2 function calculates a running average and then plots it and another bar plot on the bottom.
and here is the grid I create to compare the plots generated from different data:
I would like to have a common scale for the "Running Enrichment Score" part.
I guess I could try to recreate the gseaplot2 function and input all of the datasets and then create the grid by facet_wrap, but I was wondering if there was an easy way of extracting parameters from a plot list.
As a reproducible example (from the enrichplot package):
library(clusterProfiler)
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
wpgmtfile <- system.file("extdata/wikipathways-20180810-gmt-Homo_sapiens.gmt", package="clusterProfiler")
wp2gene <- read.gmt(wpgmtfile)
wp2gene <- wp2gene %>% tidyr::separate(term, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
ewp2 <- GSEA(geneList, TERM2GENE = wpid2gene, TERM2NAME = wpid2name, verbose=FALSE)
gseaplot2(ewp2, geneSetID=1, subplots=1:2)
And this is how I generate the plot list (probably there is a much more elegant way):
plot_list = list()
for(i in 1:3) {
fig_i = gseaplot2(ewp2,
geneSetID=i,
subplots=1:2)
plot_list[[i]] = fig_i
}
ggarrange(plotlist=plot_list)
I am pretty new to R so I am struggling with the following:
I have a dataset where I am clustering several organ`s expresion values per patient. Like this I build 10 individual dendrograms. NOW, I would like to perform a consensus analysis and see how these individually built trees agree.
For the individual trees I have been using dendextend package. And I wanted to use the ape consensus function but I dont know how to transform my outcome from dendextend such that it is accepted in the consensus.
Like this I can plot the 10 individual trees
dend <- RawData[,4:ncol(RawData)] %>%
dist %>%
hclust(method = "average")%>%
as.dendrogram
labels(dend) <- TMA_TT
dev.new()
dend %>% plot(main="dend")
I d like to use this:
consensus(..., p = 1, check.labels = TRUE)
....either (i) a single object of class "phylo", (ii) a series of such objects separated by commas, or (iii) a list containing such objects.
But I am not sure how to get my dendextend results to this format.
Just say I have some unlabeled data which I know should be clustered into six catergories, like for example this dataset:
library(tidyverse)
ts <- read_table(url("http://kdd.ics.uci.edu/databases/synthetic_control/synthetic_control.data"), col_names = FALSE)
If I create an hclust object with a sample of 60 from the original dataset like so:
n <- 10
s <- sample(1:100, n)
idx <- c(s, 100+s, 200+s, 300+s, 400+s, 500+s)
ts.samp <- ts[idx,]
observedLabels <- c(rep(1,n), rep(2,n), rep(3,n), rep(4,n), rep(5,n), rep(6,n))
# compute DTW distances
library(dtw)#Dynamic Time Warping (DTW)
distMatrix <- dist(ts.samp, method= 'DTW')
# hierarchical clustering
hc <- hclust(distMatrix, method='average')
I know that I can then add the labels to the dendrogram for viewing like this:
observedLabels <- c(rep(1,), rep(2,n), rep(3,n), rep(4,n), rep(5,n), rep(6,n))
plot(hc, labels=observedLabels, main="")
However, I would like to the correct labels to the initial data frame that was clustered. So for ts.samp I would like to add a extra column with the correct label that each observation has been clustered into.
It would seems that ts.samp$cluster <- hc$label should add the cluster to the data frame, however hc$label returns NULL.
Can anyone help with extracting this information?
You need to define a level where you cut your dendrogram, this will form the groups.
Use:
labels <- cutree(hc, k = 3) # you set the number of k that's more appropriate, see how to read a dendrogram
ts.samp$grouping <- labels
Let's look at the dendrogram in order to find the best number for k:
plot(hc, main="")
abline(h=500, col = "red") # cut at height 500 forms 2 groups
abline(h=300, col = "blue") # cut at height 300 forms 3/4 groups
It looks like either 2 or 3 might be good. You need to find the highest jump in the vertical lines (Height).
Use the horizontal lines at that height and count the cluster "formed".
Okay so I'm sure this has been asked before but I can't find a nice answer anywhere after many hours of searching.
I have some data, I run a classification then I make a dendrogram.
The problem has to do with aesthetics, specifically; (1) how to cut according to the number of groups (in this example I want 3), (2) make the group labels aligned with the branches of the trees, (2) Re-scale so that there aren't any huge gaps between the groups
More on (3). I have dataset which is very species rich and there would be ~1000 groups without cutting. If I cut at say 3, the tree has some branches on the right and one 'miles' off to the right which I would want to re-scale so that its closer. All of this is possible via external programs but I want to do it all in r!
Bonus points if you can put an average silhouette width plot nested into the top right of this plot
Here is example using iris data
library(ggplot2)
data(iris)
df = data.frame(iris)
df$Species = NULL
ED10 = vegdist(df,method="euclidean")
EucWard_10 = hclust(ED10,method="ward.D2")
hcd_ward10 = as.dendrogram(EucWard_10)
plot(hcd_ward10)
plot(cut(hcd_ward10, h = 10)$upper, main = "Upper tree of cut at h=75")
I suspect what you would want to look at is the dendextend R package (it also has a paper in bioinformatics).
I am not fully sure about your question on (3), since I am not sure I understand what rescaling means. What I can tell you is that you can do quite a lot of dendextend. Here is a quick example for coloring the branches and labels for 3 groups.
library(ggplot2)
library(vegan)
data(iris)
df = data.frame(iris)
df$Species = NULL
library(vegan)
ED10 = vegdist(df,method="euclidean")
EucWard_10 = hclust(ED10,method="ward.D2")
hcd_ward10 = as.dendrogram(EucWard_10)
plot(hcd_ward10)
install.packages("dendextend")
library(dendextend)
dend <- hcd_ward10
dend <- color_branches(dend, k = 3)
dend <- color_labels(dend, k = 3)
plot(dend)
You can also get an interactive dendrogram by using plotly (ggplot method is available through dendextend):
library(plotly)
library(ggplot2)
p <- ggplot(dend)
ggplotly(p)
I am using R to do a hierarchical cluster analysis using the Ward's squared euclidean distance. I have a matrix of x columns(stations) and y rows(numbers in float), the first row contain the header(stations' names). I want to have a good dendrogram where the name of the station appear at the bottom of the tree as i am not able to interprete my result. My aim is to find those stations which are similar. However using the following codes i am having numbers (100,101,102,...) for the lower branches.
Yu<-read.table("yu_s.txt",header = T, dec=",")
library(cluster)
agn1 <- agnes(Yu, metric = "euclidean", method="ward", stand = TRUE)
hcd<-as.dendrogram(agn1)
par(mfrow=c(3,1))
plot(hcd, main="Main")
plot(cut(hcd, h=25)$upper,
main="Upper tree of cut at h=25")
plot(cut(hcd, h=25)$lower[[2]],
main="Second branch of lower tree with cut at h=25")
A nice collection of examples are present here (http://gastonsanchez.com/blog/how-to/2012/10/03/Dendrograms.html)
Two methods:
with hclust from base R
hc<-hclust(dist(mtcars),method="ward")
plot(hc)
Default plot
ggplot
with ggplot and ggdendro
library(ggplot2)
library(ggdendro)
# basic option
ggdendrogram(hc, rotate = TRUE, size = 4, theme_dendro = FALSE)