mp_cal_abundance-methods: Calculate the (relative) abundance of each taxonomy class for...

In YuLab-SMU/MicrobiotaProcess: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework

Calculate the (relative) abundance of each taxonomy class for each sample or group.

Description

Calculate the (relative) abundance of each taxonomy class for each sample or group.

Usage

mp_cal_abundance(
  .data,
  .abundance = NULL,
  .group = NULL,
  relative = TRUE,
  action = "add",
  force = FALSE,
  ...
)

## S4 method for signature 'MPSE'
mp_cal_abundance(
  .data,
  .abundance = NULL,
  .group = NULL,
  relative = TRUE,
  action = "add",
  force = FALSE,
  ...
)

## S4 method for signature 'tbl_mpse'
mp_cal_abundance(
  .data,
  .abundance = NULL,
  .group = NULL,
  relative = TRUE,
  action = "add",
  force = FALSE,
  ...
)

## S4 method for signature 'grouped_df_mpse'
mp_cal_abundance(
  .data,
  .abundance = NULL,
  .group = NULL,
  relative = TRUE,
  action = "add",
  force = FALSE,
  ...
)

Arguments

.data	MPSE or tbl_mpse object
.abundance	the name of otu abundance to be calculated
.group	the name of group to be calculated.
relative	logical whether calculate the relative abundance.
action	character, "add" joins the new information to the taxatree and otutree if they exists (default). In addition, All taxonomy class will be added the taxatree, and OTU (tip) information will be added to the otutree."only" return a non-redundant tibble with the just new information. "get" return 'taxatree' slot which is a treedata object.
force	logical whether calculate the relative abundance forcibly when the abundance is not be rarefied, default is FALSE.
...	additional parameters.

Value

update object or tibble according the 'action'

Author(s)

Shuangbin Xu

See Also

[mp_plot_abundance()] and [mp_extract_abundance()]

Examples

data(mouse.time.mpse)
mouse.time.mpse %<>%
  mp_rrarefy() 
mouse.time.mpse
mouse.time.mpse %<>%
  mp_cal_abundance(.abundance=RareAbundance, action="add") %>% 
  mp_cal_abundance(.abundance=RareAbundance, .group=time, action="add") 
mouse.time.mpse
library(ggplot2)
f <- mouse.time.mpse %>%
     mp_plot_abundance(
        .abundance=RelRareAbundanceBySample,
        .group = time,
        taxa.class = "Phylum",
        topn = 20,
        geom = "heatmap",
        feature.dist = "bray",
        feature.hclust = "average"
     ) %>%
     set_scale_theme(
        x = scale_fill_manual(values=c("orange", "deepskyblue")),
        aes_var = time
     )
f
p1 <- mouse.time.mpse %>% 
      mp_plot_abundance(.abundance=RelRareAbundanceBySample, 
                        .group=time, taxa.class="Phylum", 
                        topn=20, order.by.feature = "p__Firmicutes",
                        width = 4/5
                        )
p2 <- mouse.time.mpse %>% 
      mp_plot_abundance(.abundance = RareAbundance, 
                        .group = time, 
                        taxa.class = Phylum, 
                        topn = 20, 
                        relative = FALSE, 
                        force = TRUE,
                        order.by.feature = TRUE
                        )
p1 / p2
# Or you can also extract the result and visulize it with ggplot2 and ggplot2-extension
## Not run: 
tbl <- mouse.time.mpse %>%
       mp_extract_abundance(taxa.class="Class", topn=10)
tbl
library(ggplot2)
library(ggalluvial)
library(dplyr)
tbl %<>%
  tidyr::unnest(cols=RareAbundanceBySample) 
tbl
p <- ggplot(data=tbl,
            mapping=aes(x=Sample, 
                        y=RelRareAbundanceBySample, 
                        alluvium=label,
                        fill=label)
     ) + 
     geom_flow(stat="alluvium", lode.guidance = "frontback", color = "darkgray") +
     geom_stratum(stat="alluvium") +
     labs(x=NULL, y="Relative Abundance (%)") +
     scale_fill_brewer(name="Class", type = "qual", palette = "Paired") +
     facet_grid(cols=vars(time), scales="free_x", space="free") +
     theme(axis.text.x=element_text(angle=-45, hjust=0))
p

## End(Not run)

YuLab-SMU/MicrobiotaProcess documentation built on Nov. 8, 2024, 4:37 p.m.