UpSetVP

Visualization of variance partitioning analysis (VPA) and hierarchical partitioning (HP) with unlimited number of predictor variables (or matrices of predictors) using UpSet matrix layout (刘尧 等人. 2023; Lai et al. 2022).

Installation

Install the released version of UpSetVP from CRAN or GitHub with:

# from CRAN
install.packages('UpSetVP')

# or
# install.packages('devtools')
devtools::install_github('LiuXYh/UpSetVP', force = TRUE)

A Simple Example

Load packages.

library(rdacca.hp)
library(ggplot2)
library(patchwork)
library(UpSetVP)

Ectomycorrhizal (EcM) fungal community and environmental data were excerpted from Gong et al. (2022).

data(baima.fun)
data(baima.env)

Quantify the relative importance of individual soil properties (pH, TP, TK, AN, AP, AK) on the composition of EcM fungal community by using partial dbRDA.

# Bray-Curtis index was used to calculate community composition dissimilarity
baima.fun.bray <- vegdist(baima.fun, method = 'bray')

# VPA and HP by using rdacca.hp package (Lai et al. 2022)
soil <- baima.env[c('pH', 'TP', 'TK', 'AN', 'AP', 'AK')]
baima.soil.vp <- rdacca.hp(baima.fun.bray, soil, method = 'dbRDA', var.part = TRUE, type = 'adjR2')

# Plot unique, common, as well as individual effects
upset_vp(baima.soil.vp, plot.hp = TRUE)

# Only plot individual effects
barplot_hp(baima.soil.vp, col.fill = 'var')

The relative importance of groups of environmental factors on EcM fungal community composition.
Environmental factors including elevation, season, space (dbMEM1 and dbMEM2), host (em.GR and em.abun), climate (sea.MT), and soil (pH, TP, TK, AN, AP, and AK).

# Distance-based Moran's eigenvector maps (dbMEM) was used to extract spatial relationships
space.dbmem <- adespatial::dbmem(baima.env[c('latitude', 'lontitude')])

# VPA and HP by using rdacca.hp package
env.list <- list(
    elevation = baima.env['altitude'],
    season = baima.env['season'],
    space = data.frame(space.dbmem)[1:2],
    host = baima.env[c('em.GR', 'em.abun')],
    climate = baima.env['sea.MT'],
    soil = baima.env[c('pH', 'TP', 'TK', 'AN', 'AP', 'AK')]
)
baima.env.vp <- rdacca.hp(baima.fun.bray, env.list, method = 'dbRDA', var.part = TRUE, type = 'adjR2')

# Plot unique, common, as well as individual effects
upset_vp(baima.env.vp, plot.hp = TRUE, order.part = 'degree')

# Only plot individual effects
barplot_hp(baima.env.vp, col.fill = 'var', col.color = c('#8DD3C7', '#FFFFB3', '#BEBADA', '#FB8072', '#80B1D3', '#FDB462', '#B3DE69'))

References

刘尧, 于馨, 于洋, 胡文浩, 赖江山. rdacca.hp包在生态学数据分析中的应用: 案例与进展. 植物生态学报, 2023, 27:134-144.
Gong S, Feng B, Jian S P, et al. Elevation Matters More than Season in Shaping the Heterogeneity of Soil and Root Associated Ectomycorrhizal Fungal Community. Microbiology spectrum, 2022, 10(1): e01950-21.
Lai J, Zou Y, Zhang J, et al. Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca. hp R package. Methods in Ecology and Evolution, 2022, 13(4): 782-788.