Calculate distances between sequential samples in ps_extra/phyloseq object

Calculate distances between sequential samples in ps_extra/phyloseq object

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"
  )
#> Warning: ps_split may drop different taxa per group with ps_filter

pseq %>%
  samdat_tbl() %>%
  dplyr::select(1, subject, timepoint, dplyr::last_col())
#> # A tibble: 222 × 4
#>    .sample_name subject timepoint aitchison_DistFromLast
#>    <chr>        <fct>       <int>                  <dbl>
#>  1 Sample-32    azh             1                 NaN   
#>  2 Sample-109   azh             2                   9.27
#>  3 Sample-119   azh             3                  11.2 
#>  4 Sample-130   azh             4                   9.19
#>  5 Sample-140   azh             5                   5.12
#>  6 Sample-41    azh             6                   5.30
#>  7 Sample-61    azl             1                 NaN   
#>  8 Sample-93    azl             2                   7.99
#>  9 Sample-106   azl             3                   8.44
#> 10 Sample-78    azl             4                   8.24
#> # ℹ 212 more rows

# 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