mp_cal_dist-methods: Calculate the distances between the samples or features with...

In xiangpin/MicrobitaProcess: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework

Calculate the distances between the samples or features with specified abundance.

Description

Calculate the distances between the samples or features with specified abundance.

Usage

mp_cal_dist(
  .data,
  .abundance,
  .env = NULL,
  distmethod = "bray",
  action = "add",
  scale = FALSE,
  cal.feature.dist = FALSE,
  ...
)

## S4 method for signature 'MPSE'
mp_cal_dist(
  .data,
  .abundance,
  .env = NULL,
  distmethod = "bray",
  action = "add",
  scale = FALSE,
  cal.feature.dist = FALSE,
  ...
)

## S4 method for signature 'tbl_mpse'
mp_cal_dist(
  .data,
  .abundance,
  .env = NULL,
  distmethod = "bray",
  action = "add",
  scale = FALSE,
  cal.feature.dist = FALSE,
  ...
)

## S4 method for signature 'grouped_df_mpse'
mp_cal_dist(
  .data,
  .abundance,
  .env = NULL,
  distmethod = "bray",
  action = "add",
  scale = FALSE,
  cal.feature.dist = FALSE,
  ...
)

Arguments

.data	MPSE or tbl_mpse object
.abundance	the name of otu abundance to be calculated
.env	the column names of continuous environment factors, default is NULL.
distmethod	character the method to calculate distance. option is "manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao", "mahalanobis", "chisq", "chord", "aitchison", "robust.aitchison" (implemented in vegdist of vegan), and "w", "-1", "c", "wb", "r", "I", "e", "t", "me", "j", "sor", "m", "-2", "co", "cc", "g", "-3", "l", "19", "hk", "rlb", "sim", "gl", "z" (implemented in betadiver of vegan), "maximum", "binary", "minkowski" (implemented in dist of stats), "unifrac", "weighted unifrac" (implemented in phyloseq), "cor", "abscor", "cosangle", "abscosangle" (implemented in hopach), or other customized distance function.
action	character, "add" joins the distance data to the object, "only" return a non-redundant tibble with the distance information. "get" return 'dist' object.
scale	logical whether scale the metric of environment (.env is provided) before the distance was calculated, default is FALSE. The environment matrix can be processed when it was joined to the MPSE or tbl_mpse object.
cal.feature.dist	logical whether to calculate the distance between the features. default is FALSE, meaning calculate the distance between the samples.
...	additional parameters. some dot arguments if distmethod is unifrac or weighted unifrac: weighted logical, whether to use weighted-UniFrac calculation, which considers the relative abundance of taxa, default is FALSE, meaning unweightrd-UniFrac, which only considers presence/absence of taxa. normalized logical, whether normaized the branch length of tree to the range between 0 and 1 when the weighted=TRUE. parallel logical, whether to execute the calculation in parallel, default is FALSE.

Value

update object or tibble according the 'action'

Author(s)

Shuangbin Xu

See Also

[mp_extract_dist()] and [mp_plot_dist()]

Examples

data(mouse.time.mpse)
mouse.time.mpse %<>%
    mp_decostand(.abundance=Abundance) %>% 
    mp_cal_dist(.abundance=hellinger, distmethod="bray")
mouse.time.mpse
p1 <- mouse.time.mpse %>%
        mp_plot_dist(.distmethod = bray)
p2 <- mouse.time.mpse %>%
        mp_plot_dist(.distmethod = bray, .group = time, group.test = TRUE)
p3 <- mouse.time.mpse %>%
        mp_plot_dist(.distmethod = bray, .group = time)
# adjust the legend of heatmap of distance between the samples.
# the p3 is a aplot object, we define set_scale_theme to adjust the 
# character (color, size or legend size) of figure with specified 
# 'aes_var' according to legend title. 
library(ggplot2)
p3 %>% 
   set_scale_theme(
     x = scale_size_continuous(
       range = c(0.1, 4), 
       guide = guide_legend(keywidth = 0.5, keyheight = 1)), 
     aes_var = bray
   ) %>% 
   set_scale_theme(
     x = scale_colour_gradient(
       guide = guide_legend(keywidth = 0.5, keyheight = 1)), 
     aes_var = bray
   ) %>% 
   set_scale_theme(
     x = scale_fill_manual(values = c("orangered", "deepskyblue"), 
       guide = guide_legend(keywidth = 0.5, keyheight = 0.5, label.theme = element_text(size=6))), 
     aes_var = time) %>% 
   set_scale_theme(
     x = theme(axis.text=element_text(size=6), panel.background=element_blank()), 
     aes_var = bray
   )
## Not run: 
# Visualization manual
library(ggplot2)
tbl <- mouse.time.mpse %>%
       mp_extract_dist(distmethod="bray", .group=time)
tbl
tbl %>% 
  ggplot(aes(x=GroupsComparison, y=bray)) + 
  geom_boxplot(aes(fill=GroupsComparison)) + 
  geom_jitter(width=0.1) + 
  xlab(NULL) +
  theme(legend.position="none")

## End(Not run)

xiangpin/MicrobitaProcess documentation built on Nov. 6, 2024, 1:15 a.m.