I have a dataset called data. The data is not that important, but every interaction has a name. I want to create a graph in iGraph with the following code:
tab <- count(data, B, S, K)
factors <- table(interaction(tab$B, tab$K),interaction(tab$S,tab$K))
graph1 <- graph_from_incidence_matrix(factors)
plot(graph1, vertex.size = 40, layout = layout.bipartite)
However, I get the following:
All the names of interactions are completely mixed together. I can make it a little more readable by lowering the vertex.size, but I want to find a solution to my problem.
I want to create more space between the verticies, but I cannot seem to find the right way.
I have tried creating a manual graph by using tkplot, but it is annoying that I manually have to sort them each time.
Best regards
Related
This is the function that is part of FactorMiner package
https://github.com/cran/FactoMineR/blob/master/R/plot.HCPC.R
As an example this is the code I ran
res.pca <- PCA(iris[, -5], scale = TRUE)
hc <- HCPC(res.pca, nb.clust=-1,)
plot.HCPC(hc, choice="3D.map", angle=60)
hc$call$X$clust <- factor(hc$call$X$clust, levels = unique(hc$call$X$clust))
plot(hc, choice="map")
The difference is when i run this hc$call$X$clust <- factor(hc$call$X$clust, levels = unique(hc$call$X$clust))
before plot.HCPC this doesn't change the annotation in the figure but when I do the same thing before I ran this plot(hc, choice="map") it is reflected in the final output.
When i see the plot.HCPC function this is the line of the code that does embed the cluster info into the figure
for(i in 1:nb.clust) leg=c(leg, paste("cluster",levs[i]," ", sep=" "))
legend("topleft", leg, text.col=as.numeric(levels(X$clust)),cex=0.8)
My question I have worked with small function where I understand when i edit or modify which one goes where and does what here in this case its a complicated function at least to me so Im not sure how do I modify that part and get what I would like to see.
I would like to see in case of my 3D dendrogram each of the cluster are labelled with group the way we can do in complexheatmap where we can annotate that are in row or column with a color code so it wont matter what the order in the data-frame we can still identify(it's just visual thing I know but I would like to learn how to modify these)
In order to create an Hasse Diagram like the following
One is using the following libraries
library(rPref)
library(Rgraphviz)
One is taking a small sample of one's data
df <- data[1:10,]
Then creating the preferences
pref <- low(time) * low(MAPE)
And the Better-Than-Graph (BTG)
btg <- get_btg(df, pref)
In order to display the labels for the nodes containing relevant values, one is creating the labels as following
labels <- paste0(df$time, "\n", df$MAPE)
However, when one builds the visualization with
plot_btg(df, pref, labels)
One can only see the first label, instead of the two. Here is what one is seeing
Passing use_dot=FALSE solved the problem
plot_btg(df, pref, labels, use_dot = FALSE)
I asked a number of different experts to sort 92 objects based on their similarity. Based on their answers, I constructed a 92 x 92 dissimilarity matrix. in R, I examined this matrix using the following commands:
cluster1 <- hclust(as.dist(DISS_MATRIX), method = "average")
plot(cluster1, cex=.55)
To highlight the clusters, I wanted to draw rectangles around them:
rect.hclust(cluster1, k = 3, border = "red")
The result is as follows:
However, when the objects have longer names ("AAAAAAAAAAAAAAAA43" instead of "A43") then the formating is off:
rownames(DISS_MATRIX) <- paste0(rep("AAAAAAAAAAAAAAAAAAAAAAAAAAAA",92),1:92)
colnames(DISS_MATRIX) <- paste0(rep("AAAAAAAAAAAAAAAAAAAAAAAAAAAA",92),1:92)
cluster1 <- hclust(as.dist(DISS_MATRIX), method = "average")
plot(cluster1, cex=.55)
rect.hclust(cluster1, k = 3, border = "red")
This can be seen by the resulting dendogram.
The rectangles seem to have moved up to the end of the dendogram. Not nice. I assume this glitch must have been due to the long names of 92 objects in the dissimilarity matrix. It may also not seem very relevant. Just make sure your objects have names short enough.
However, due to different reasons I want my objects to have their original (i.e.admittedly long) names. This graph is for a presentation and thus I do not want to work with codes. I also do not want to use any other package since I generally find hclust quite easy to use. However, I do not find any way to position rectangles within the rect.hclust command. Hence, what can I do to position the rectangles into the dendogram even if object names are long? Thanks.
You wrote that "I also do not want to use any other package since I generally find hclust quite easy to use."
While hclust is great for creating the hierarchical clustering object it does not support much in terms of plotting. Once you have the hclust output, it is better to change it to dendrogram (using as.dendrogram) for visualizations (since it is better suited for that). There is no way to do what you want without using sophisticated code, which is packed in a package, this is the best route (IMHO) for you to move forward. (I know because I wrote rect.dendrogram, and it took a lot of work to get it to work the way you want it)
The dendextend R package allows many functions for manipulating and visualizing dendrograms (see the vignette here).
Specifically, the rect.dendrogram function can handle such cases as you asked about (with having long labels). For example (I've added color_branches and color_labels for the fun of it):
library(dendextend)
hc <- mtcars[, c("mpg", "disp")] %>% dist %>% hclust(method = "average")
dend <- hc %>% as.dendrogram %>% hang.dendrogram
# let's make the text longer
labels(dend)[1] <- "AAAAAAAAAAAAAAAAAAAAA"
par(mar = c(15,2,1,1))
dend %>% color_branches(k=3) %>% color_labels(k=3) %>% plot
dend %>% rect.dendrogram(k=3)
I am trying to create circular phylogenetic tree. I have this part of code:
fit<- hclust(dist(Data[,-4]), method = "complete", members = NULL)
nclus= 3
color=c('red','blue','green')
color_list=rep(color,nclus/length(color))
clus=cutree(fit,nclus)
plot(as.phylo(fit),type='fan',tip.color=color_list[clus],label.offset=0.2,no.margin=TRUE, cex=0.70, show.node.label = TRUE)
And this is result:
Also I am trying to show label for each node and to color branches. Any suggestion how to do that?
Thanks!
When you say "color branches" I assume you mean color the edges. This seems to work, but I have to think there's a better way.
Using the built-in mtcars dataset here, since you did not provide your data.
plot.fan <- function(hc, nclus=3) {
palette <- c('red','blue','green','orange','black')[1:nclus]
clus <-cutree(hc,nclus)
X <- as.phylo(hc)
edge.clus <- sapply(1:nclus,function(i)max(which(X$edge[,2] %in% which(clus==i))))
order <- order(edge.clus)
edge.clus <- c(min(edge.clus),diff(sort(edge.clus)))
edge.clus <- rep(order,edge.clus)
plot(X,type='fan',
tip.color=palette[clus],edge.color=palette[edge.clus],
label.offset=0.2,no.margin=TRUE, cex=0.70)
}
fit <- hclust(dist(mtcars[,c("mpg","hp","wt","disp")]))
plot.fan(fit,3); plot.fan(fit,5)
Regarding "label the nodes", if you mean label the tips, it looks like you've already done that. If you want different labels, unfortunately, unlike plot.hclust(...) the labels=... argument is rejected. You could experiment with the tiplabels(....) function, but it does not seem to work very well with type="fan". The labels come from the row names of Data, so your best bet IMO is to change the row names prior to clustering.
If you actually mean label the nodes (the connection points between the edges, have a look at nodelabels(...). I don't provide a working example because I can't imagine what labels you would put there.
I am trying to cluster a protein dna interaction dataset, and draw a heatmap using heatmap.2 from the R package gplots. My matrix is symmetrical.
Here is a copy of the data-set I am using after it is run through pearson:DataSet
Here is the complete process that I am following to generate these graphs: Generate a distance matrix using some correlation in my case pearson, then take that matrix and pass it to R and run the following code on it:
library(RColorBrewer);
library(gplots);
library(MASS);
args <- commandArgs(TRUE);
matrix_a <- read.table(args[1], sep='\t', header=T, row.names=1);
mtscaled <- as.matrix(scale(matrix_a))
# location <- args[2];
# setwd(args[2]);
pdf("result.pdf", pointsize = 15, width = 18, height = 18)
mycol <- c("blue","white","red")
my.breaks <- c(seq(-5, -.6, length.out=6),seq(-.5999999, .1, length.out=4),seq(.100009,5, length.out=7))
#colors <- colorpanel(75,"midnightblue","mediumseagreen","yellow")
result <- heatmap.2(mtscaled, Rowv=T, scale='none', dendrogram="row", symm = T, col=bluered(16), breaks=my.breaks)
dev.off()
The issue I am having is once I use breaks to help me control the color separation the heatmap no longer looks symmetrical.
Here is the heatmap before I use breaks, as you can see the heatmap looks symmetrical:
Here is the heatmap when breaks are used:
I have played with the cutoff's for the sequences to make sure for instance one sequence does not end exactly where the other begins, but I am not able to solve this problem. I would like to use the breaks to help bring out the clusters more.
Here is an example of what it should look like, this image was made using cluster maker:
I don't expect it to look identical to that, but I would like it if my heatmap is more symmetrical and I had better definition in terms of the clusters. The image was created using the same data.
After some investigating I noticed was that after running my matrix through heatmap, or heatmap.2 the values were changing, for example the interaction taken from the provided data set of
Pacdh-2
and
pegg-2
gave a value of 0.0250313 before the matrix was sent to heatmap.
After that I looked at the matrix values using result$carpet and the values were then
-0.224333135
-1.09805379
for the two interactions
So then I decided to reorder the original matrix based on the dendrogram from the clustered matrix so that I was sure that the values would be the same. I used the following stack overflow question for help:
Order of rows in heatmap?
Here is the code used for that:
rowInd <- rev(order.dendrogram(result$rowDendrogram))
colInd <- rowInd
data_ordered <- matrix_a[rowInd, colInd]
I then used another program "matrix2png" to draw the heatmap:
I still have to play around with the colors but at least now the heatmap is symmetrical and clustered.
Looking into it even more the issue seems to be that I was running scale(matrix_a) when I change my code to just be mtscaled <- as.matrix(matrix_a) the result now looks symmetrical.
I'm certainly not the person to attempt reproducing and testing this from that strange data object without code that would read it properly, but here's an idea:
..., col=bluered(20)[4:20], ...
Here's another though which should return the full rand of red which tha above strategy would not:
shift.BR<- colorRamp(c("blue","white", "red"), bias=0.5 )((1:16)/16)
heatmap.2( ...., col=rgb(shift.BR, maxColorValue=255), .... )
Or you can use this vector:
> rgb(shift.BR, maxColorValue=255)
[1] "#1616FF" "#2D2DFF" "#4343FF" "#5A5AFF" "#7070FF" "#8787FF" "#9D9DFF" "#B4B4FF" "#CACAFF" "#E1E1FF" "#F7F7FF"
[12] "#FFD9D9" "#FFA3A3" "#FF6C6C" "#FF3636" "#FF0000"
There was a somewhat similar question (also today) that was asking for a blue to red solution for a set of values from -1 to 3 with white at the center. This it the code and output for that question:
test <- seq(-1,3, len=20)
shift.BR <- colorRamp(c("blue","white", "red"), bias=2)((1:20)/20)
tpal <- rgb(shift.BR, maxColorValue=255)
barplot(test,col = tpal)
(But that would seem to be the wrong direction for the bias in your situation.)