linkET | 完美解决ggcor安装失败方案（附教程） Science神图
linkET | 完美解决ggcor安装失败方案（附教程）
Science神图
最近看了一个出自Science的神图，在网上搜遍教程，踩了好多坑，在这里分享一下完美解决方案~ (•‿•)
1. 方法一
这个包最难的不是使用，而是安装，通过git已经无法安装，这里提供一个通过gitee完美安装ggcor的方法
通过Gitee地址安装（原github地址已不可用）
# install.packages("devtools")
devtools::install_git("https://gitee.com/dr_yingli/ggcor")
2. 方法二
安装linkET, 新版ggcor ~
还是熟悉的配方，熟悉的味道~
…ᴅᴜᴅᴜ！
# install.packages("devtools")
devtools::install_github("Hy4m/linkET", force = TRUE)
packageVersion("linkET")
3. linkET教程
由于
ggcor和linkET语法几乎一样，所以在这仅介绍linkET的用法示例数据使用
mtcars
3.1 加载包和数据
library(linkET)
## matrix_data
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
3.2 转换格式
## md_tbl
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
#> 6 Valiant mpg 18.1
#> 7 Duster 360 mpg 14.3
#> 8 Merc 240D mpg 24.4
#> 9 Merc 230 mpg 22.8
#> 10 Merc 280 mpg 19.2
#> # … with 342 more rows
## as method
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
#> 6 Valiant mpg 18.1
#> 7 Duster 360 mpg 14.3
#> 8 Merc 240D mpg 24.4
#> 9 Merc 230 mpg 22.8
#> 10 Merc 280 mpg 19.2
#> # … with 342 more rows
3.3 计算相关性矩阵
## 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
#> 6 wt mpg -0.868 1.29e-10 -0.934 -0.744
#> 7 qsec mpg 0.419 1.71e- 2 0.0820 0.670
#> 8 vs mpg 0.664 3.42e- 5 0.410 0.822
#> 9 am mpg 0.600 2.85e- 4 0.318 0.784
#> 10 gear mpg 0.480 5.40e- 3 0.158 0.710
#> # … with 111 more rows
3.4 Heatmap展示数据
library(ggplot2)
matrix_data(list(mtcars = mtcars)) %>%
hyplot(aes(fill = mtcars)) +
geom_tile()
3.5 以dot的形式展示数据
as_md_tbl(mtcars) %>%
hyplot(aes(size = mtcars)) +
geom_point(shape = 21, fill = NA)
3.6 渐变方格展示相关性~
(๑ ꒪ꌂ꒪๑)
correlate(mtcars) %>%
as_md_tbl() %>%
qcorrplot() +
geom_square()
4. 两矩阵相关性计算
4.1 加载包和示例数据
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")
示例数据1 - Varechem
示例数据2 - varespec
4.2 快速绘制相关性矩阵图
correlate(varespec[1:30], varechem) %>%
qcorrplot() +
geom_square() +
scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu"))
4.3 只展示左下
qcorrplot(varespec[1:30], type = "lower") +
geom_square() +
scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu"))
#> The input data is not a correlation matrix,
#> you can override this behavior by setting the `is_corr` parameter.
5. Mantel test
Mantel test 计算的是两个不相似矩阵之间的相关性。 生态学上的意义是验证环境相似的地方是否物种也相似；环境不相似的地方物种是否不相似。
也可以应用到两个基因表达矩阵之间，如
mRNA表达矩阵和miRNA表达矩阵
5.1 加载包和数据
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")
5.2 进行mantel test并新增连线数据
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'.
5.3 开始画图啦 ！~ •̀ᴗ• •̀ᴗ•́
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))
作为
ggcor的接班人，linkET真是颜值担当啊！~
6. 其他的图
linkET包还提供了其他的可视化方式，大家有兴趣继续探索吧
6.1 pairs plot
qpairs(iris) + geom_pairs()
6.2 network
correlate(mtcars) %>%
as.igraph() %>%
plot(layout = layout_with_circular)
参考资料
- Sunagawa, S., Coelho, L. P., Chaffron, S., Kultima, J.R., Labadie, K., Salazar, G., … & Bork, P. (2015). Structure and function of theglobal ocean microbiome. Science, 348(6237), 1261359-1261359.
- Houyun Huang(2021). linkET: Everything is Linkable. R package version 0.0.3.
点个在看吧各位~ ✐.ɴɪᴄᴇ ᴅᴀʏ 〰
