Mercurial > repos > ecology > ecoregion_clara_cluster

annotate claraguess.R @ 2:a7269e5eed27 draft default tip

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author ecology
date Wed, 28 May 2025 10:11:06 +0000
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2
a7269e5eed27 planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/Ecoregionalization_workflow commit ced658540f05bb07e1e687af30a3fa4ea8e4803c
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1 ##30/04/2025
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2 ##Jean Le Cras
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3 ### Clustering with Clara algorithm with an option to determine the optimal number of clusters based on SIH index
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5 #load libraries
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6 library(cluster)
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7 library(dplyr)
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8 library(tidyverse)
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9
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10 #load arguments
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11 args <- commandArgs(trailingOnly = TRUE)
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12 if (length(args)==0) {
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13 stop("This tool needs at least one argument")
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14 }
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16 #load data
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17 enviro_path <- args[1]
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18 preds_path <- args[2]
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19 taxas_path <- args[3]
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20 type <- args[4]
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21 k <- as.integer(args[5])
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22 metric <- args[6]
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23 samples <- as.integer(args[7])
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24 env.data <- read.table(enviro_path, sep = "\t", header = TRUE, dec = ".", na.strings = "-9999")
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26 data_split = str_split(preds_path, ",")
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27 preds.data = NULL
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29 for (i in 1:length(data_split[[1]])) {
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30 df <- read.table(data_split[[1]][i], dec=".", sep="\t", header=T, na.strings="NA")
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31 preds.data <- rbind(preds.data, df)
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32 remove(df)
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33 }
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35 names(preds.data) <- c("lat", "long", "pred", "taxa")
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37 development_traits <- str_split(readLines(taxas_path), "\t")
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39 #select the clara model with the optimal number of clusters
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40 model <- NULL
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42 if (type == "auto") {
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43 sih_scores <- c()
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44 models <- list()
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46 for (i in 2:k) {
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47 models[[i]] <- clara(preds.data$pred, i, metric = metric, samples = samples, stand = TRUE)
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48 sih_scores[i] <- models[[i]]$silinfo$avg.width
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49 }
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50 png("sih_scores.png")
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51 plot(2:k, sih_scores[2:k], type = "b", xlab = "Number of clusters", ylab = "SIH index")
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52 dev.off()
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53
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54 best_k <- which.max(sih_scores[3:k]) + 2
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55 model <- models[[best_k]]
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56 k <- best_k
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57 } else {
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58 model <- clara(preds.data$pred, k, metric = metric, samples = samples, stand = TRUE)
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59 }
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61 #saving results
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62 png("silhouette_plot.png")
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63 plot(silhouette(model), main = paste("Silhouette plot for k =", k))
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64 dev.off()
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65
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66 data.test <- matrix(preds.data$pred, nrow = nrow(env.data), ncol = nrow(preds.data) / nrow(env.data))
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67 data.test <- data.frame(data.test)
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68 names(data.test) <- unique(preds.data$development)
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69
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70 full.data <- cbind(preds.data[1:nrow(data.test), 1:2], model$clustering)
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71 names(full.data) <- c("lat", "long", "cluster")
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72 full.data <- cbind(full.data, data.test, env.data[, 3:ncol(env.data)])
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73
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74 write.table(full.data[1:3], file = "data_cluster.tabular", quote = FALSE, sep = "\t", row.names = FALSE)
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75 write.table(full.data, file = "clustered_taxas_env.tabular", quote = FALSE, sep = "\t", row.names = FALSE)