Character Partitioning

1. Generate distance matrix

Generate a Gower distance matrix with get_gower_dist() by supplying the file path of a .nex file containing a character data matrix:

#Load a character data matrix and produce a Gower distance matrix
dist_matrix <- get_gower_dist("DataMatrix.nex", numeric = FALSE)

Below, we use the example data matrix characters that accompanies EvoPhylo.

data(characters)

dist_matrix <- get_gower_dist(characters, numeric = FALSE)

2. Estimate the optimal number of partitions

The optimal number of partitions (clusters) will be first determined using partitioning around medoids (PAM) with Silhouette widths index (Si) using get_sil_widths(). The latter will estimate the quality of each PAM cluster proposal relative to other potential clusters.

## Estimate and plot number of cluster against silhouette width
sw <- get_sil_widths(dist_matrix, max.k = 10)
plot(sw, color = "blue", size = 1)

Decide on number of clusters based on plot; here, \(k = 3\) partitions appears optimal.

3. Simple Workflow

3.1. Analyze clusters with PAM under chosen \(k\) value (from Si) with make_clusters().

3.2. Produce simple cluster graph

3.3. Export clusters/partitions to Nexus file with cluster_to_nexus() or write_partitioned_alignments().

## Generate and vizualize clusters with PAM under chosen k value. 
clusters <- make_clusters(dist_matrix, k = 3)

plot(clusters)

## Write clusters to Nexus file for Mr. Bayes
cluster_to_nexus(clusters, file = "Clusters_MB.txt")

## Write partitioned alignments to separate Nexus files for BEAUTi
write_partitioned_alignments(characters, clusters, file = "Clusters_BEAUTi.nex")

4. Complete Workflow

4.1. Analyze clusters with PAM under chosen \(k\) value (from Si) with make_clusters().

4.2. Produce a graphic clustering (tSNEs), coloring data points according to PAM clusters, to independently verify PAM clustering. This is set with the tsne argument within make_clusters().

4.3. Export clusters/partitions to Nexus file with cluster_to_nexus(). This can be copied and pasted into the Mr. Bayes command block. Alternatively, write the partitioned alignments as separate Nexus files using write_partitioned_alignments(). This will allow you to import the partitions separately into BEAUti for analyses with BEAST2.

#User may also generate clusters with PAM and produce a graphic clustering (tSNEs)
clusters <- make_clusters(dist_matrix, k = 3, tsne = TRUE, tsne_dim = 3)

plot(clusters, nrow = 2, max.overlaps = 5)

## Write clusters to Nexus file for Mr. Bayes
cluster_to_nexus(clusters, file = "Clusters_MB.txt")

## Write partitioned alignments to separate Nexus files for BEAUTi
write_partitioned_alignments(characters, clusters, file = "Clusters_BEAUTi.nex")