使用R优雅的批量计算相关性

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❝本节来介绍如何通过R来批量做相关性分析，将通过两个小例子来进行介绍，1个for循环与另一个tidyverse体系；

加载R包

代码语言：javascript
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library(tidyverse)
library(magrittr)
library(ggstatsplot)

案例一

导入数据

代码语言：javascript
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Bats <- read.csv(file = "Bats_data.csv", header = T, stringsAsFactors = F)

Bats_subset <- select(Bats, Activity, Area.thinned:Distance.creek.water)

构建容器

代码语言：javascript
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rows <- ncol(Bats_subset) - 1


Correlations <- data.frame(

variable = character(length = rows),

correlation = numeric(length = rows),

stringsAsFactors = F

)

循环计算相关性

代码语言：javascript
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for (i in 1:rows) {

temp1 <- colnames(Bats_subset[i + 1])

temp2 <- cor(Bats_subset[, 1], Bats_subset[, i + 1], method = "pearson")

Correlations[i, 1] <- temp1

Correlations[i, 2] <- temp2

}
代码语言：javascript
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 variable correlation

1          Area.thinned -0.40890389

2    Time.since.thinned -0.02135752

3     Exclusion.thinned  0.17562438

4 Distance.murray.water -0.18071570

5  Distance.creek.water -0.09130258

案例二

❝此处计算单个基因与其余全部基因的相关性，小编在此介绍如何不使用循环用tidyverse体系函数来进行计算
❞

代码语言：javascript
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read_tsv("data.xls") %>% column_to_rownames(var="TCGA_id") %>%

pivot_longer(-B2M) %>%

pivot_longer(names_to = "name_2", values_to = "value_2",B2M) %>%

group_by(name_2,name) %>%

summarise(cor= cor.test(value_2,value,method="spearman")$estimate,

p.value = cor.test(value_2,value,method="spearman")$p.value) %>% as.data.frame() %>%

set_colnames(c("gene_1","gene_2","cor","pvalue")) %>%

filter(pvalue < 0.05) %>%

arrange(desc(abs(cor)))%>%

dplyr::slice(1:500)

❝可以看到与B2M相关性最高的为APOBEC3H基因
❞

代码语言：javascript
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   gene_1 gene_2     cor   pvalue

<chr>  <chr>    <dbl>    <dbl>

1 B2M    APOBEC3H 0.577 1.48e-25

2 B2M    XCL2     0.577 1.51e-25

3 B2M    KIR2DL4  0.565 2.31e-24

4 B2M    TIFAB    0.565 2.63e-24

5 B2M    XCL1     0.561 5.92e-24

6 B2M    FUT7     0.558 1.21e-23

7 B2M    ZBED2    0.557 1.57e-23

8 B2M    IFNG     0.526 8.71e-21

9 B2M    NCR3     0.524 1.39e-20

10 B2M    SSTR3    0.506 4.22e-19

数据可视化

❝此处用ggstatsplot包来进行结果的展示真是方便至极
❞

代码语言：javascript
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df2 <- read_tsv("data.xls")


ggscatterstats(data = df2,y = B2M,x=APOBEC3H,

centrality.para = "mean",

margins = "both",

xfill = "#CC79A7",

yfill = "#009E73",

marginal.type = "histogram")

#容器#数据可视化#for循环#tidyverse