I have generated a community network using iGraph and qGraph to determine the relatedness between individual's genetic codes above a threshold edge value. To further analyze this data, I used cluster_walktrap and then graphed this along with the network. I am wondering if there is a way label the individual communities, as well as plot a single community from the batch. I can provide extra details if need be. Thanks
Do you mean something using components, e.g.,
sapply(components(g), plot)
Related
I have a graph dataset at hand (3d mesh) and they have different sizes. I want to use hierarchical pooling but all the literature relies on fixed graph topology so they just use pooling for the one graph since all graphs have the same size. In my case, however, input graphs have different sizes. I want to use the spectral method and it is possible to use that but I have no idea how to handle pooling for each graph one by one.
before k-means clustering for consumer segmentation, I want to identify and delete outliers of my sample. I tried hierarchical clustering with single linkage algorithm. The problem is, I have a sample with more than 800 cases, and in my plot (single linkage dendrogram) the numbers are written across each other and therefore not readable, so it is impossible for me to clearly identify the outliers by just looking at the graph :-/
Here they say, you can create boxplots based on the branch distance to identify outliers in a more objective way. I thought that would be also a great way to just make the row numbers of the outliers in my dataset readable, however I am struggling with creating the boxplots..
https://link.springer.com/article/10.1186/s12859-017-1645-5/figures/3
Does anyone know, how to write the code to get the boxplots based on the height of the branches?
This is the code I use for clustering and attached you can see the plot
dr_dist<-dist(dr_ma_cluster[,c(148:154)])
hc_dr<-hclust(dr_dist,method = "single") #single linkage
plot(hc_dr,labels=(row.names(dr_ma_cluster)))
This is my failed trial to do the boxplot, as I don't know how to address the branch height
> boxplot(hc_dr)
Error in x[floor(d)] + x[ceiling(d)] :
non-numeric argument for binary operator
> boxplot(hc_dr[,c(148:154)])
Error in hc_dr[, c(148:154)] : Incorrect number of dimensions
And here another way to do the graph (and some automated outlier detection approach), but it makes the readability even worse with large datasets..
Another code to plot the tree, even less readable for large datasets:
Delete outliers automatically of a calculated agglomerative hierarchical clustering data
Thanks for any help!!
boxplot(hc_dr$height) as suggested by StupidWolf was the simple thing I was looking for.
Unfortunately I did not manage to label the outlier dots with the rownames from the original dataframe. Rownames from the branch height table were useless as they were assigned in ascending order.
hang = 0.0001 gave a better look to the dendrogram, but labels were still unreadable as still over eachother.
If anyone has a similar problem check R Shiny, zoomable dendrogram program
the code given there in the answer was super easy to adapt, resulting in a zoomable dendrogram, which makes it easy to identify the relevant cases (->outliers). for details search dendextendas proposed by csgroen.
Both together, the boxplot and this nice tool served to identify the rownames of the outliers after single linkage clustering in order to delete them before km means clustering
I have the following graph plot with 131 vertices made by using plot with an object of the igraph class. My question is whether there is any way to present this in a cleaner way so that nodes at least don't overlap each other and edges are more visible.
I'm not too familiar with these types of graphs, so I can't provide a specific answer. But this sounds like a good time to use the jitter() function which adds random noise between the data points, thus separating them out.
I am trying to find a way to plot a disease transmission tree that allows me to:
plot the tree over a timeline (a timeline spanning 2 months)
specify the shape and colour of the nodes in the tree (so that you can easily identify which nodes belong to the same household for example)
format the link between the nodes (dashed lines, two way arrows, solid lines...etc.)
plot "stray branches" that aren't linked to the root/parent node.
The dataset I am working with is relatively small (22 nodes) so I don't mind working with a package that is a bit fiddly!
I have thought about using phylogeny trees, but I'm uncertain whether they will allow me to plot stray nodes. Which package would be most suitable for this task?
Thanks!
Try DiagrammeR. I don't have much experience, but it did what I needed to do and I know I barely scratched the surface of what it can do.
I am starting a new project in python (to be used through jupyter-notebooks), where I'll need to visualise some hierarchically clustered graphs.
I have looked for existing packages, but so far I am not convinced by what I have seen.
I am not interested in the clustering process in itself, because this will be another part of the project and I know (roughly) how the graphs will be built up progressivelly.
What I am looking for are:
an appropriate data structure for storing hierarchically clustered graphs,
visualisation tools that would allow to represent the graph on a map (based on X and Y coordinates of the nodes) and either represent the subparts of the clusters, or simplify the clusters depending on their type or depth in the graph structure,
ideally, bring some interactivity, for example the ability to zoom-in or-out, or click on some clustered nodes to expand the nodes that were hidden in the cluster.
It looks pretty specific and despite some cool packages I have seen I am not sure which one would help without having too much to reimplement. So far, NetworkX looks like a cool starting point, especially with some D3.js (as shown here), but it is still far from what I have in mind.
Any advice about where to start digging?
Thanks a lot.
Gautier
For Python, Seaborn's clustermaps are nice. Seaborn is mainly meant to be used with Pandas dataframes; however, the documentation for clustermap says it can be rectangular data, and so I think it means other arrays will wor.
See also:
Dendrogram with heat map
SciPy Hierarchical Clustering and Dendrogram Tutorial
Hierarchical Clustering in Python