I have a numeric matrix in R with 24 rows and 10,000 columns. The row names of this matrix are basically file names from which I have read the data corresponding to each of the 24 rows. Apart from this I have a separate factor list with 24 entires, specifying the group to which the 24 files belong. There are 3 groups - Alcohols, Hydrocarbon and Ester. The names and the corresponding group to which they belong look like this:
> MS.mz
[1] "int-354.19" "int-361.35" "int-368.35" "int-396.38" "int-408.41" "int-410.43" "int-422.43"
[8] "int-424.42" "int-436.44" "int-438.46" "int-452.00" "int-480.48" "int-648.64" "int-312.14"
[15] "int-676.68" "int-690.62" "int-704.75" "int-312.29" "int-326.09" "int-326.18" "int-326.31"
[22] "int-340.21" "int-340.32" "int-352.35"
> MS.groups
[1] Alcohol Alcohol Alcohol Alcohol Hydrocarbon Alcohol Hydrocarbon Alcohol
[9] Hydrocarbon Alcohol Alcohol Alcohol Ester Alcohol Ester Ester
[17] Ester Alcohol Alcohol Alcohol Alcohol Alcohol Alcohol Hydrocarbon
Levels: Alcohol Ester Hydrocarbon
I wanted to generate a dendrogram to look how the data in the matrix can be clustered. So, I used the following commands:
require(vegan)
dist.mat<-vegdist(MS.data.scaled.transposed,method="euclidean")
clust.res<-hclust(dist.mat)
plot(clust.res)
and I got a dendogram. Now I want to color the file names in the dendrogram according to the group they belong to i.e Alcohol, Hydrocarbon or Ester. I looked at different examples posted on the forum like
Label and color leaf dendrogram in r
Label and color leaf dendrogram in R using ape package
, but could not implement it for my data. I am not sure how to correlate row.names with the MS.groups to get the colored names in the dendrogram.
On generating the tree using dendextend (as explained in https://nycdatascience.com/wp-content/uploads/2013/09/dendextend-tutorial.pdf), I get the following tree
Here is the code used to generate it:
require(colorspace)
d_SIMS <- dist(firstpointsample5[,-1])
hc_SIMS <- hclust(d_SIMS)
labels(hc_SIMS)
dend_SIMS <- as.dendrogram(hc_SIMS)
SIMS_groups <- rev(levels(firstpointsample5[, 1]))
dend_SIMS <- color_branches(dend_SIMS, k = 3, groupLabels = SIMS_groups)
is.character(labels(dend_SIMS))
plot(dend_SIMS)
labels_colors(dend_SIMS) <- rainbow_hcl(3)[sort_levels_values(as.numeric(firstpointsample5[,1])[order.dendrogram(dend_SIMS)])]
labels(dend_SIMS) <- paste(as.character(firstpointsample5[, 1])[order.dendrogram(dend_SIMS)],"(", labels(dend_SIMS), ")", sep = "")
dend_SIMS <- hang.dendrogram(dend_SIMS, hang_height = 0.1)
dend_SIMS <- assign_values_to_leaves_nodePar(dend_SIMS, 0.5,"lab.cex")
par(mar = c(3, 3, 3, 7))
plot(dend_SIMS, main = "Clustered SIMS dataset\n (the labels give the true m/z groups)",horiz = TRUE, nodePar = list(cex = 0.007))
legend("topleft", legend = SIMS_groups, fill = rainbow_hcl(3))
You may try this solution, only change 'labs' with your 'MS.groups' and 'var' with your 'MS.groups' converted to numeric (maybe, with as.numeric). It comes from How to colour the labels of a dendrogram by an additional factor variable in R
I suspect the function you are looking for is either
color_labels
orget_leaves_branches_col
. The first color your labels based oncutree
(likecolor_branches
do) and the second allows you to get the colors of the branch of each leaf, and then use it to color the labels of the tree (if you use unusual methods for coloring the branches (as happens when usingbranches_attr_by_labels
). For example:Either way, you should always have a look at the
set
function, for ideas on what can be done to your dendrogram (this saves the hassle of remembering all the different functions names).You may take a look at this tutorial, which displays several solutions for visualizing dendograms in R by groups
https://rstudio-pubs-static.s3.amazonaws.com/1876_df0bf890dd54461f98719b461d987c3d.html
However, I think the best solution, suit for your data, is offered by the package 'dendextend'. See the tutorial (the example concerning the 'iris' dataset, which is similar to your problem): https://nycdatascience.com/wp-content/uploads/2013/09/dendextend-tutorial.pdf
See also the vignette: http://cran.r-project.org/web/packages/dendextend/vignettes/Cluster_Analysis.html