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Base clusteriness on species count

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This commit is contained in:
Elias Projahn 2021-10-04 09:19:38 +02:00
parent 8b727a0329
commit 397b8d0ba2
2 changed files with 7 additions and 7 deletions
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12
clustering.R
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@ -7,9 +7,10 @@ library(rlog)
#' This function will cluster the data using `hclust` and `cutree` (with the
#' specified height). Every cluster with at least two members qualifies for
#' further analysis. Clusters are then ranked based on their size in relation
#' to the total number of values. The return value is a final score between
#' zero and one. Lower ranking clusters contribute less to this score.
clusteriness <- function(data, height = 1000000) {
#' to the total number of possible values (`n`). The return value is a final
#' score between zero and one. Lower ranking clusters contribute less to this
#' score.
clusteriness <- function(data, n, height = 1000000) {
# Cluster the data and compute the cluster sizes.
tree <- hclust(dist(data))
@ -19,7 +20,6 @@ clusteriness <- function(data, height = 1000000) {
# Compute the "cluteriness" score.
score <- 0.0
n <- length(data)
for (i in seq_along(cluster_sizes)) {
cluster_size <- cluster_sizes[i]
@ -70,11 +70,11 @@ process_clustering <- function(distances, species_ids, gene_ids) {
.(species, distance)
]
if (data[, .N] < 12) {
if (data[, .N] < 10) {
next
}
score <- clusteriness(data[, distance])
score <- clusteriness(data[, distance], length(species_ids))
results[
gene == gene_id,