Cluster sets of genes based on Jaccard distance (mostly stolen from Biostars post)

jaccard_genes_cluster.R

# test Jaccard

# Create a list gene sets
char1 <- c("gene1", "gene2", "gene5", "gene9", "gene10")
char2 <- c("gene2", "gene3", "gene5", "gene7", "gene10")
char3 <- c("gene7", "gene9", "gene10", "gene11", "gene12", "gene1")

lst <- list(char1, char2, char3)

# Function to calculate Jaccard distance
siml  <- function(x,y) {
  1-length(intersect(lst[[x]],lst[[y]]))/length(union(lst[[x]],lst[[y]]))
}

# get a data frame of all factor combinations
z <- expand.grid(x=1:length(lst), y=1:length(lst))

# calculate Jaccard distances between each pair of gene sets
result <- mapply(siml,z$x,z$y)
# Turn into matrix
dim(result) <- c(length(lst),length(lst))
# Convert to distance
dists <- as.dist(result)
# Cluster a plot a dendrogram
plot(hclust(dists))

############ FUNCTION FOR GOstats on KEGG only ####################################
## The GOstats output has to have a last column with comma separated gene names
clusterJakkard <- function(res_table, plot_name)
{ 
  # res_table="Neuroblastoma_DOWN_SIG_GOstats_KEGG.txt" this has to be a file in my usual format
  # plot_name="Neuroblastoma_DOWN_SIG_KEGG_clusters.tiff" 
  keggtab <- read.table(res_table, sep = "\t", header = T, quote = "\"", 
                        stringsAsFactors = F)
  keggtab$KEGGID <- paste("0", keggtab$KEGGID, sep = "")
  keggtab <- data.frame(KEGGID=keggtab$KEGGID, Term=keggtab$Term, Genes=keggtab$Genes)
  keggtab$KEGGID <- paste(keggtab$KEGGID, keggtab$Term, sep="::")
  keggtab <- keggtab[,-2]
  
  # Get the list of vectors
  gene.list <- strsplit(as.character(keggtab$Genes), ";") 
  names(gene.list) <- keggtab$KEGGID
  
  lst <- gene.list
  siml  <- function(x,y) {
    1-length(intersect(lst[[x]],lst[[y]]))/length(union(lst[[x]],lst[[y]]))
  }
  
  z <- expand.grid(x=1:length(lst), y=1:length(lst))
  
  result <- mapply(siml,z$x,z$y)
  dim(result) <- c(length(lst),length(lst))
  colnames(result) <- names(lst)
  rownames(result) <- names(lst)
  dists <- as.dist(result)
  hc <- hclust(dists, method="ward.D2")
  tiff(plot_name, width=500, height = 800)
  par(cex = 0.8, mar=c(2,2,2,15))
  plot(as.dendrogram(hc), horiz=T, type = "triangle")
  dev.off()
}

(mostly stolen from Biostars post) lol
Amen to that !