inst/doc/Correlations.R

In treestats: Phylogenetic Tree Statistics

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(tibble)
library(dplyr)
library(dendextend)
library(ggdendro)
library(pheatmap)

## ----load_data----------------------------------------------------------------
begin <- "https://raw.githubusercontent.com/thijsjanzen/treestats-scripts/main/datasets/phylogenies/fracced/" # nolint

tree_collection_files <- c("amphibia_fracced.rds",
                           "birds_fracced.rds",
                           "ferns_fracced.rds",
                           "mammals_fracced.rds",
                           "ray_finned_fish_fracced.rds",
                           "sharks_fracced.rds",
                           "vascular_plants_fracced.rds")

taxa_names <- c("Amphibians", "Birds", "Ferns", "Mammals",
                "Ray finned Fish", "Cartaliginous Fish", "Vascular Plants")

found_stats <- c()
for (i in seq_along(tree_collection_files)) {
  raw_url <- paste0(begin, tree_collection_files[i])
  con <- url(raw_url, open = "rb")
  on.exit(close(con))

  tree_collection <- readRDS(con)

  families <- names(tree_collection)
  cat(taxa_names[i], "\n")
  for (j in seq_along(tree_collection)) {
    focal_tree <- tree_collection[[j]]
    if (length(focal_tree$tip.label) >= 10) {
      if (!ape::is.ultrametric(focal_tree)) {
        testthat::expect_output(
          focal_tree <- phytools::force.ultrametric(focal_tree)
        )
      }
      all_stats <- treestats::calc_all_stats(focal_tree, FALSE)
      to_add <- unlist(all_stats)
      # we also need to add if they have polytomies
      is_binary <- treestats::check_binary(focal_tree)

      to_add <- c(families[j], is_binary, to_add)

      found_stats <- rbind(found_stats, to_add)
    }
  }
}

## ----add_random---------------------------------------------------------------
num_stats <- length(names(all_stats))
colnames(found_stats) <- c("Family", "is_binary", names(all_stats))
found_stats <- tibble::as_tibble(found_stats)
found_stats$random <- runif(n = length(found_stats$Family))

found_stats2 <- found_stats |>
  dplyr::mutate_at(3:(num_stats + 3), as.numeric)

## ----correct_for_tree_size----------------------------------------------------
get_cor <- function(local_stats) {

  local_stats2 <- local_stats |>
    dplyr::mutate_at(3:(num_stats + 3), as.numeric)

  local_stats2 <- as.data.frame(local_stats2[, 3:(num_stats + 3)])

  # remove NA statistics
  to_remove <- c()
  for (i in seq_len(ncol(local_stats2))) {
    if (any(is.na(local_stats2[, i]))) {
      to_remove <- c(to_remove, i)
    }
  }
  if (length(to_remove)) local_stats2 <- local_stats2[, -to_remove]

  res_cor <- stats::cor(as.data.frame(local_stats2), method = "pearson")

  res_cor2 <- res_cor

  for (i in seq_along(res_cor[1, ])) {
    for (j in seq_along(res_cor[1, ])) {
      stat1 <- colnames(res_cor)[i]
      stat2 <- colnames(res_cor)[j]

      if (stat1 != stat2) {
        if (stat1 != "number_of_lineages" && stat2 != "number_of_lineages") {
          x <- unlist(as.vector(local_stats2[stat1]))  # nolint
          y <- unlist(as.vector(local_stats2[stat2]))  # nolint
          z <- unlist(as.vector(local_stats2["number_of_lineages"])) # nolint

          a1 <- nlme::gls(y ~ z)
          a2 <- nlme::gls(x ~ z)

          found_cor <- cor(a1$residuals, a2$residuals)

          res_cor2[i, j] <- found_cor
        }
      }
    }
  }
  return(res_cor2)
}

## ----cor_binary---------------------------------------------------------------
found_stats_binary <- found_stats2 |>
  filter(is_binary == TRUE)
master_cor_binary <- get_cor(found_stats_binary)

## ----cor_poly-----------------------------------------------------------------
found_stats_poly <- found_stats2 |>
  filter(is_binary == FALSE)
master_cor_polytomy <- get_cor(found_stats_poly)

## ----plot_dendrogram, fig.width=25, fig.height=25-----------------------------

cor1 <- master_cor_binary

cor1 <- as.matrix(cor1)
to_remove <- which(colnames(cor1) == "number_of_lineages")

cor1 <- cor1[-to_remove, -to_remove]
cor1 <- as.data.frame(cor1)
cor1 <- tibble::as_tibble(cor1)
cor1 <- cor1 |>
  dplyr::mutate_at(seq_len(ncol(cor1)), as.numeric)

res_dist <- stats::as.dist(1 - abs(as.matrix(cor1)))

hc <- hclust(res_dist, method = "average")
dend0 <- stats::as.dendrogram(hc)
ddata <- ggdendro::dendro_data(hc, type = "rectangle")

xmax <- 0.8

clust_ref <- dendextend::cutree(dend0, h = xmax)
xmin <- 0
all_rect <- c() # xmin, xmax, ymin, ymax

for (a in unique(clust_ref)) {
  b <- clust_ref[clust_ref == a]
  in_plot <- subset(ddata$labels, ddata$labels$label %in% names(b))
  ymin <- min(in_plot$x) - 0.5
  ymax <- max(in_plot$x) + 0.5
  to_add <- c(xmin, xmax, ymin, ymax)
  all_rect <- rbind(all_rect, to_add)
}

rect_plot <- data.frame(xmin = all_rect[, 1],
                        xmax = all_rect[, 2],
                        ymin = all_rect[, 3],
                        ymax = all_rect[, 4],
                        categ = seq_along(all_rect[, 1]))

lvls <- sort(unique(as.numeric(rect_plot$categ)))
rect_plot$categ <- factor(rect_plot$categ, levels = lvls)

ggplot() +
  geom_rect(data = rect_plot,
            aes(xmin = ymin, xmax = ymax, ymin = xmin, ymax = xmax,
                fill = categ), alpha = 1) +
  scale_fill_brewer(type = "qual", palette = 3) +
  geom_segment(data = ggdendro::segment(ddata),
               aes(x = x, y = y, xend = xend, yend = yend)
  ) +
  geom_text(data = ggdendro::label(ddata),
            aes(x = x, y = y, label = label, hjust = 0),
            size = 10
  ) +
  coord_flip() +
  ylim(1, -0.3) +
  theme_classic() +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = "none") +
  ylab("1 - abs(correlation)") +
  xlab("") +
  ggtitle("On Binary Trees")

## ----heatmap, , fig.width=25, fig.height=25-----------------------------------
breakz <- seq(-1, 1, length.out = 99)
cor2 <- as.matrix(cor1)
rownames(cor2) <- colnames(cor2)
hm1 <- pheatmap::pheatmap(mat = cor2,
                          breaks = breakz,
                          clustering_method = "average",
                          fontsize_col = 18,
                          fontsize_row = 18)

## ----plot_dendrogram_poly, fig.width=25, fig.height=25------------------------

cor1 <- master_cor_polytomy

cor1 <- as.matrix(cor1)
to_remove <- which(colnames(cor1) == "number_of_lineages")

cor1 <- cor1[-to_remove, -to_remove]
cor1 <- as.data.frame(cor1)
cor1 <- tibble::as_tibble(cor1)
cor1 <- cor1 |>
  dplyr::mutate_at(seq_len(ncol(cor1)), as.numeric)

res_dist <- stats::as.dist(1 - abs(as.matrix(cor1)))

hc <- hclust(res_dist, method = "average")
dend0 <- stats::as.dendrogram(hc)
ddata <- ggdendro::dendro_data(hc, type = "rectangle")

xmax <- 0.8

clust_ref <- dendextend::cutree(dend0, h = xmax)
xmin <- 0
all_rect <- c() # xmin, xmax, ymin, ymax

for (a in unique(clust_ref)) {
  b <- clust_ref[clust_ref == a]
  in_plot <- subset(ddata$labels, ddata$labels$label %in% names(b))
  ymin <- min(in_plot$x) - 0.5
  ymax <- max(in_plot$x) + 0.5
  to_add <- c(xmin, xmax, ymin, ymax)
  all_rect <- rbind(all_rect, to_add)
}

rect_plot <- data.frame(xmin = all_rect[, 1],
                        xmax = all_rect[, 2],
                        ymin = all_rect[, 3],
                        ymax = all_rect[, 4],
                        categ = seq_along(all_rect[, 1]))

lvls <- sort(unique(as.numeric(rect_plot$categ)))
rect_plot$categ <- factor(rect_plot$categ, levels = lvls)

ggplot() +
  geom_rect(data = rect_plot,
            aes(xmin = ymin, xmax = ymax, ymin = xmin, ymax = xmax,
                fill = categ), alpha = 1) +
  scale_fill_brewer(type = "qual", palette = 3) +
  geom_segment(data = ggdendro::segment(ddata),
               aes(x = x, y = y, xend = xend, yend = yend)
  ) +
  geom_text(data = ggdendro::label(ddata),
            aes(x = x, y = y, label = label, hjust = 0),
            size = 10
  ) +
  coord_flip() +
  ylim(1, -0.3) +
  theme_classic() +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = "none") +
  ylab("1 - abs(correlation)") +
  xlab("") +
  ggtitle("On Trees with Polytomies")

## ----heatmap poly, , fig.width=25, fig.height=25------------------------------
breakz <- seq(-1, 1, length.out = 99)
cor2 <- as.matrix(cor1)
rownames(cor2) <- colnames(cor2)
hm1 <- pheatmap::pheatmap(mat = cor2,
                          breaks = breakz,
                          clustering_method = "average",
                          fontsize_col = 18,
                          fontsize_row = 18)