linkET，环境因子相关性 + mantel test 可视化新选择

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前言

环境因子相关性 + mantel test 组合图，网上的可视化教程都是用R package：ggcor，但是好像没人维护了，安装起来也有一些小问题……
最近发现有另一个package：linkET，同一个作者，熟悉的味道，这里简单安利一下。
下面的内容都可以从大佬的Github上找到：https://github.com/Hy4m/linkET

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linkET 安装

# install.packages("devtools")
devtools::install_github("Hy4m/linkET", force = TRUE)
packageVersion("linkET")

官方示例

数据预处理

library(linkET)
matrix_data(list(mtcars = mtcars))
#> A matrix data object:
#> Number:  1 
#> Names:  mtcars 
#> Dimensions:  32 rows, 11 columns
#> Row names:  Mazda RX4, Mazda RX4 Wag, Datsun 710, Hornet 4 Drive, Hor... 
#> Column names:  mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

matrix_data(list(mtcars = mtcars)) %>% 
  as_md_tbl()
#> # A tibble: 352 × 3
#>    .rownames         .colnames mtcars
#>  *                    
#>  1 Mazda RX4         mpg         21  
#>  2 Mazda RX4 Wag     mpg         21  
#>  3 Datsun 710        mpg         22.8
#>  4 Hornet 4 Drive    mpg         21.4
#>  5 Hornet Sportabout mpg         18.7
#> # … with 342 more rows

as_matrix_data(mtcars)
#> A matrix data object:
#> Number:  1 
#> Names:  mtcars 
#> Dimensions:  32 rows, 11 columns
#> Row names:  Mazda RX4, Mazda RX4 Wag, Datsun 710, Hornet 4 Drive, Hor... 
#> Column names:  mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

as_md_tbl(mtcars)
#> # A tibble: 352 × 3
#>    .rownames         .colnames mtcars
#>  *                    
#>  1 Mazda RX4         mpg         21  
#>  2 Mazda RX4 Wag     mpg         21  
#>  3 Datsun 710        mpg         22.8
#>  4 Hornet 4 Drive    mpg         21.4
#>  5 Hornet Sportabout mpg         18.7
#> # … with 342 more rows

## special function for correlation matrix
correlate(mtcars) %>% 
  as_matrix_data()
#> A matrix data object:
#> Number:  4 
#> Names:  r, p, lower_ci, upper_ci 
#> Dimensions:  11 rows, 11 columns
#> Row names:  mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb 
#> Column names:  mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

correlate(mtcars) %>% 
  as_md_tbl()
#> # A tibble: 121 × 6
#>    .rownames .colnames      r        p lower_ci upper_ci
#>  *                        
#>  1 mpg       mpg        1     0          1         1    
#>  2 cyl       mpg       -0.852 6.11e-10  -0.926    -0.716
#>  3 disp      mpg       -0.848 9.38e-10  -0.923    -0.708
#>  4 hp        mpg       -0.776 1.79e- 7  -0.885    -0.586
#>  5 drat      mpg        0.681 1.78e- 5   0.436     0.832
#> # … with 111 more rows

热图

代码如下（示例）：

correlate(mtcars) %>% 
  as_md_tbl() %>% 
  qcorrplot() +
  geom_square()

library(vegan)
#> 载入需要的程辑包：permute
#> 载入需要的程辑包：lattice
#> This is vegan 2.5-7
data("varespec")
data("varechem")
correlate(varespec[1:30], varechem) %>% 
  qcorrplot() +
  geom_square() +
  scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu"))

Mantel test

library(dplyr)
#> 
#> 载入程辑包：'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
data("varechem", package = "vegan")
data("varespec", package = "vegan")

mantel <- mantel_test(varespec, varechem,
                      spec_select = list(Spec01 = 1:7,
                                         Spec02 = 8:18,
                                         Spec03 = 19:37,
                                         Spec04 = 38:44)) %>% 
  mutate(rd = cut(r, breaks = c(-Inf, 0.2, 0.4, Inf),
                  labels = c("< 0.2", "0.2 - 0.4", ">= 0.4")),
         pd = cut(p, breaks = c(-Inf, 0.01, 0.05, Inf),
                  labels = c("< 0.01", "0.01 - 0.05", ">= 0.05")))
#> `mantel_test()` using 'bray' dist method for 'spec'.
#> `mantel_test()` using 'euclidean' dist method for 'env'.

qcorrplot(correlate(varechem), type = "lower", diag = FALSE) +
  geom_square() +
  geom_couple(aes(colour = pd, size = rd), 
              data = mantel, 
              curvature = nice_curvature()) +
  scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu")) +
  scale_size_manual(values = c(0.5, 1, 2)) +
  scale_colour_manual(values = color_pal(3)) +
  guides(size = guide_legend(title = "Mantel's r",
                             override.aes = list(colour = "grey35"), 
                             order = 2),
         colour = guide_legend(title = "Mantel's p", 
                               override.aes = list(size = 3), 
                               order = 1),
         fill = guide_colorbar(title = "Pearson's r", order = 3))

网络

correlate(mtcars) %>% 
  as.igraph() %>% 
  plot(layout = layout_with_circular)

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总结

在强调一下，以上，在linkET的GitHub上都与可以看到，这里只是做了搬运……linkET