dist_calc_seq: Calculate distances between sequential samples in...

In david-barnett/microViz: Microbiome Data Analysis and Visualization

Calculate distances between sequential samples in ps_extra/phyloseq object

Description

Calculate distances between sequential samples in ps_extra/phyloseq object

Usage

dist_calc_seq(
  data,
  dist,
  group,
  seq,
  unequal = "warn",
  start_value = NaN,
  return = "data",
  var_name = paste0(dist, "_DistFromLast")
)

Arguments

data	psExtra object, e.g. output from tax_transform()
dist	name of distance to calculate between pairs of sequential samples
group	name of variable in phyloseq sample_data used to define groups of samples
seq	name of variable in phyloseq sample_data used to define order of samples within groups
unequal	"error" or "warn" or "ignore" if groups of samples, defined by group argument, are of unequal size
start_value	value returned for the first sample in each group, which has no preceding sample in the group's sequence, and so has no obvious value
return	format of return object: "data" returns psExtra with sorted samples and additional variable. "vector" returns only named vector of sequential distances.
var_name	name of variable created in psExtra if return arg = "data"

Value

psExtra object sorted and with new sequential distance variable or a named vector of that variable

See Also

dist_calc

Examples

library(ggplot2)
library(dplyr)
data("dietswap", package = "microbiome")

pseq <- dietswap %>%
  tax_transform("identity", rank = "Genus") %>%
  dist_calc_seq(
    dist = "aitchison", group = "subject", seq = "timepoint",
    # group sizes are unequal because some subjects are missing a timepoint
    unequal = "ignore"
  )

pseq %>%
  samdat_tbl() %>%
  dplyr::select(1, subject, timepoint, dplyr::last_col())

# ggplot heatmap - unsorted
pseq %>%
  samdat_tbl() %>%
  filter(timepoint != 1) %>%
  ggplot(aes(x = timepoint, y = subject)) +
  geom_tile(aes(fill = aitchison_DistFromLast)) +
  scale_fill_viridis_c(na.value = NA, name = "dist") +
  theme_minimal(base_line_size = NA) +
  scale_y_discrete(limits = rev(levels(samdat_tbl(pseq)$subject)))

# ComplexHeatmap plotting with clustering #
library(tidyr)
library(ComplexHeatmap)

# make data matrix
heatmat <- pseq %>%
  samdat_tbl() %>%
  filter(timepoint != 1) %>%
  pivot_wider(
    id_cols = subject,
    names_from = timepoint, names_prefix = "t",
    values_from = aitchison_DistFromLast
  ) %>%
  tibble::column_to_rownames("subject")

heatmat <- as.matrix.data.frame(heatmat)

heatmap <- Heatmap(
  name = "dist",
  matrix = heatmat, col = viridisLite::viridis(12), na_col = "white",
  cluster_columns = FALSE,
  cluster_rows = hclust(dist(heatmat), method = "ward.D"),
  width = unit(1.5, "in"), rect_gp = gpar(col = "black"),
  row_names_side = "left", row_names_gp = gpar(fontsize = 8)
)
heatmap

# comparison with subject tracking on PCA
pca <- pseq %>%
  # already sorted data
  dist_calc("aitchison") %>%
  ord_calc("PCoA") %>%
  ord_plot(alpha = 0.1, shape = "nationality", size = 2) %>%
  add_paths(
    mapping = aes(colour = subject, alpha = timepoint, size = timepoint),
    id_var = "subject", id_values = c(
      "eve", "hsf", # low variation
      "vem", # medium
      "ufm", # high variation
      "pku" # starts high
    )
  ) +
  scale_alpha_continuous(range = c(0.3, 1), breaks = c(2, 4, 6)) +
  scale_size_continuous(range = c(1, 2), breaks = c(2, 4, 6))

heatmap
pca

david-barnett/microViz documentation built on April 17, 2025, 4:25 a.m.