I'm using the Complexheatmap function (or "Heatmap") in R and was wondering if there was a way to use the Bray-Curtis method in calculating col/row distance (with ward.D2 clustering method) since it's not a supported method in Complexheatmap. I need to use this function instead of heatmap.2 and pheatmap, unfortunately.
Here is some made-up fish count data (My actual data has 47 sites (rows) and 32 seasons, but I wasn't sure how to recreate that here):
data<-matrix(rpois(30,0.9),ncol=6, nrow=5)
colnames(data) <- c("2004W", "2004D", "2005W", "2005D", "2006W", "2006D")
# I tried assigning the method this way:
d1 <- vegdist(log(data+1), method = "bray")
d2 <- vegdist(t(log(data+1)), method = "bray")
Heatmap(data,
row_names_side = "left",
row_dend_side = "left",
column_names_side = "bottom",
row_names_gp = gpar(cex=fontsize, fontface = "bold"),
column_names_gp = gpar(cex=0.9, fontface = "bold"),
row_dend_width = unit(4, "cm"),
column_dend_height = unit(3, "cm"),
rect_gp = gpar(col = "grey"),
column_title = "Year/Season",
column_names_rot = 35,
row_title = "Site",
clustering_distance_rows = d1,
clustering_distance_columns = d2,
clustering_method_rows = "ward.D2",
clustering_method_columns = "ward.D2",
row_km = 3,
column_km = 4
)
You should first define a function for Bray-Curtis distance calculation (bray_dist
).
Then, you set clustering_distance_rows=bray_dist
and
clustering_distance_rows=bray_dist
in Heatmap
.
library(vegan)
library(ComplexHeatmap)
set.seed(1234)
data <- matrix(rpois(30,0.9),ncol=6, nrow=5)
colnames(data) <- c("2004W", "2004D", "2005W", "2005D", "2006W", "2006D")
fontsize <- 8
bray_dist <- function(x) {
vegdist(log(x+1), method = "bray")
}
Heatmap(data, row_names_side = "left", column_names_side = "bottom",
row_dend_side = "left", rect_gp = gpar(col = "grey"),
row_names_gp = gpar(cex=fontsize, fontface = "bold"),
column_names_gp = gpar(cex=0.9, fontface = "bold"),
row_dend_width = unit(4, "cm"), column_dend_height = unit(3, "cm"),
column_title = "Year/Season", column_names_rot = 35, row_title = "Site",
clustering_distance_rows = bray_dist, clustering_distance_columns = bray_dist,
clustering_method_rows = "ward.D2", clustering_method_columns = "ward.D2",
row_km = 3, column_km = 4)