如何在R数据帧中对多对向量运行多对Wilcoxon符号秩检验?

Question

我有161个免疫标记的数据集，每个都是数据框架中的载体。使用R，我想使用Wilcoxon有符号秩(配对)检验来比较这些向量的78对。免疫标记物在名称上用"_ mom_"来区分。或"_CB !">

这里有一个"玩具"。具有示例变量名称的数据集:

# Create toy data frame
toydata = data.frame(CCBB_dyad_number=c(1,2,3,4,5,6,7,8,9,10),
cCMV_status = c("cCMV+", "cCMV-", "cCMV-", 
"cCMV+", "cCMV+", "cCMV-",
"cCMV-", "cCMV+", "cCMV+",
"cCMV+"),
maternal_CMV_IgM_status = c("negative", "negative", "positive", 
"negative", "positive", "negative",
"positive", "positive", "positive",
"negative"),
TB40E_conc_CB = c(1.954727, NA, 1.992956,
1.831331, 1.905936, 2.053446,
2.055809, 1.739377, 2.052576,
1.961838),
AD169r_conc_CB = c(5.86714, 6.469020, 9.387268,
5.733174, 6.480673, 5.176167,
7.548077, 7.209173, 4.944089,
9.667219),
TB40E_conc_MOM = c(7.389400, 5.917861, 7.022016,
8.017846, 10.046830, 7.503896,
6.427719, 9.498801, 7.351678,
6.050478),
AD169r_conc_MOM = c(7.011906, 6.506734, 9.986478,
5.673412, 3.825439, 5.795331,
7.082124, 6.810222, 5.54213,
8.271366)
)

在一些帮助下，我编写了代码来循环遍历所有161个向量，并使用lapply生成一个具有p值和测试类型的新数据帧:

# Pull actual names of variables, not just numbers
excluded_vars <- toydata %>%
select(., c(CCBB_dyad_number,
cCMV_status,
maternal_CMV_IgM_status)) %>%
names(.)
var_list <- toydata %>%
select(., -any_of(excluded_vars)) %>%
names(.)

out = lapply(var_list, function(v){
#cat(paste0("Wilcox: ", v, "n")) #Loop message for checking
fmla <- formula(paste(v, " ~ cCMV_status"))
wilcox.test(fmla, data = toydata, paired = FALSE) %>%
purrr::flatten() %>% #Unnest/convert to plain list
as.data.frame(stringsAsFactors=FALSE) %>% #Set as data frame
mutate(Variable = v) %>% #add new variable column (could also get it from data.name)
select(Variable, W.statistic=W, P.value=p.value, Method=method) %>%
mutate(P.value=scientific(P.value, digits=2, format="e"))
}) %>% #%T>% { names(out) <- var_list } %>%  #Didn't actually need this, but could if wanted a named list
purrr::compact() %>% #Remove any empty data frames/list elements (NULL)
dplyr::bind_rows() #Bind list of data frames into single data frame
out$FDR_P.value <- p.adjust(out$P.value, method="fdr", n=length(out$P.value)) %>%
scientific(., digits = 2, format = "e")
col_order <- c("Variable", "W.statistic", "P.value", # Reorder columns for tabling
"FDR_P.value", "Method")
out <- out[, col_order]

kable(out, "html", booktabs = T) %>%
kable_styling(latex_options = c("striped", "scale_down")) # Print output as a nice table

然而，我在思考如何编写代码来通过多个不同的向量对循环有符号秩测试时遇到了麻烦。我想我会拉向量(或只是向量名称?)，像这样:

toy_cCMV_pos <- toydata %>%
filter(cCMV_status == 'cCMV+') %>%
select(., -any_of(excluded_vars))

variable.set1 <- toy_cCMV_pos %>%
select(., ends_with("_MOM"))

variable.set2 <- toy_cCMV_pos %>%
select(., ends_with("_CB"))

有人建议像这样循环遍历向量。然而，我总是得到一个"未定义的列"被选中。错误，因为我不太明白下面的代码在做什么，所以我无法排除故障。

for (a in variable.set1) {
groups = unique(toy_cCMV_pos[,a])
for (b in variable.set2) {
wilcox.test(x=toy_cCMV_pos[which(toy_cCMV_pos[a]==groups[1]),b], 
y=toy_cCMV_pos[which(toy_cCMV_pos[a]==groups[2]),b], 
paired=TRUE)
}
}
# Keep getting error "undefined columns selected"

我希望能够像秩和测试一样将结果(包括p值)拉到新的数据帧中。

谁能帮我想想如何运行这些配对测试?

Answer 1

编辑:原来的解决方案是按行删除缺失的值，所以一些有效的数据被删除了，导致结果与其他方法不一致。

这是一个更正确的方法:

library(tidyr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
toydata = data.frame(CCBB_dyad_number=c(1,2,3,4,5,6,7,8,9,10),
cCMV_status = c("cCMV+", "cCMV-", "cCMV-", 
"cCMV+", "cCMV+", "cCMV-",
"cCMV-", "cCMV+", "cCMV+",
"cCMV+"),
maternal_CMV_IgM_status = c("negative", "negative", "positive", 
"negative", "positive", "negative",
"positive", "positive", "positive",
"negative"),
TB40E_conc_CB = c(1.954727, NA, 1.992956,
1.831331, 1.905936, 2.053446,
2.055809, 1.739377, 2.052576,
1.961838),
AD169r_conc_CB = c(5.86714, 6.469020, 9.387268,
5.733174, 6.480673, 5.176167,
7.548077, 7.209173, 4.944089,
9.667219),
TB40E_conc_MOM = c(7.389400, 5.917861, 7.022016,
8.017846, 10.046830, 7.503896,
6.427719, 9.498801, 7.351678,
6.050478),
AD169r_conc_MOM = c(7.011906, 6.506734, 9.986478,
5.673412, 3.825439, 5.795331,
7.082124, 6.810222, 5.54213,
8.271366))
toydata |> 
select(ends_with("MOM"), ends_with("CB")) |> 
pivot_longer(everything(),
names_to=c(".value", "group"),
names_sep="_(?!.*_)") |> 
pivot_longer(-group,
names_to="variable",
values_to="value") |>  
group_by(variable) |> 
do(broom::tidy(wilcox.test(.$value ~ .$group, paired=TRUE, na.action=na.pass)))
#> # A tibble: 2 × 5
#> # Groups:   variable [2]
#>   variable    statistic p.value method                          alternative
#>   <chr>           <dbl>   <dbl> <chr>                           <chr>      
#> 1 AD169r_conc        28 1       Wilcoxon signed rank exact test two.sided  
#> 2 TB40E_conc          0 0.00391 Wilcoxon signed rank exact test two.sided

^{由reprex包(v2.0.1)在2021-09-09创建}

结果与单独计算的结果相匹配:

> wilcox.test(toydata$TB40E_conc_CB, toydata$TB40E_conc_MOM, paired=TRUE)
Wilcoxon signed rank exact test
data:  toydata$TB40E_conc_CB and toydata$TB40E_conc_MOM
V = 0, p-value = 0.003906
alternative hypothesis: true location shift is not equal to 0

和

> wilcox.test(toydata$AD169r_conc_CB, toydata$AD169r_conc_MOM, paired=TRUE)
Wilcoxon signed rank exact test
data:  toydata$AD169r_conc_CB and toydata$AD169r_conc_MOM
V = 28, p-value = 1
alternative hypothesis: true location shift is not equal to 0

建议的解决方案的结果是一个标题/数据框架，因此您可以只选择所需的列来修改它。

Answer 2

不确定这是否是您正在寻找的，但在这里，我为每个前缀组执行CB和MOM之间的Wilcoxon测试。

library(tidyverse)
library(broom)
toydata = data.frame(CCBB_dyad_number=c(1,2,3,4,5,6,7,8,9,10), cCMV_status = c("cCMV+", "cCMV-", "cCMV-",  "cCMV+", "cCMV+", "cCMV-", "cCMV-", "cCMV+", "cCMV+", "cCMV+"), maternal_CMV_IgM_status = c("negative", "negative", "positive",  "negative", "positive", "negative", "positive", "positive", "positive", "negative"), TB40E_conc_CB = c(1.954727, NA, 1.992956, 1.831331, 1.905936, 2.053446, 2.055809, 1.739377, 2.052576, 1.961838), AD169r_conc_CB = c(5.86714, 6.469020, 9.387268, 5.733174, 6.480673, 5.176167, 7.548077, 7.209173, 4.944089, 9.667219), TB40E_conc_MOM = c(7.389400, 5.917861, 7.022016, 8.017846, 10.046830, 7.503896, 6.427719, 9.498801, 7.351678, 6.050478), AD169r_conc_MOM = c(7.011906, 6.506734, 9.986478, 5.673412, 3.825439, 5.795331, 7.082124, 6.810222, 5.54213, 8.271366))
toydata %>% 
as_tibble() %>% 
gather("var", "val", -1:-3) %>% 
separate(var, c("marker", "conc", "type")) %>% 
spread(type, val) %>% 
group_by(marker) %>% 
summarize(wilcox = tidy(wilcox.test(MOM, CB)))
#> # A tibble: 2 × 2
#>   marker wilcox$statistic  $p.value $method                      $alternative
#>   <chr>             <dbl>     <dbl> <chr>                        <chr>       
#> 1 AD169r               49 0.971     Wilcoxon rank sum exact test two.sided   
#> 2 TB40E                90 0.0000217 Wilcoxon rank sum exact test two.sided