matrix - How to do multiple wilcox.test in R? -

i have matrix , aim wilxcon test in r (controls vs cases) not sure how put in matrix properly. thanks

gene.name  cont1 cont2  cont3  case1  case2  case3           10    2      3      21     18      8 b           14    8      7      12     34      22 c           16    9      19     21     2       8 d           32    81     17     29     43      25 .. 

you can try:

# load data  d <- read.table(text="gene.name  cont1 cont2  cont3  case1  case2  case3           10    2      3      21     18      8 b           14    8      7      12     34      22 c           16    9      19     21     2       8 b           32    81     17     29     43      25", header=t)  library(tidyverse) # transform long format using dplyr (included in tidyverse) dlong <- as.tbl(d) %>%    gather(key, value,-gene.name) %>%    mutate(group=ifelse(grepl("cont",key), "control", "case")) # plot data dlong %>%    ggplot(aes(x=group, y=value)) +    geom_boxplot() 

# run test dlong %>%    with(., wilcox.test(value ~ group))  wilcoxon rank sum test continuity correction  data:  value group w = 94.5, p-value = 0.2034 alternative hypothesis: true location shift not equal 0 

edits

# don't clarified how handle double occurence of b assume  # thats typo , fixed second b d library(ggpubr) dlong <- as.tbl(d) %>%   mutate(gene.name=letters[1:4]) %>%    gather(key, value,-gene.name) %>%    mutate(group=ifelse(grepl("cont",key), "control", "case"))  # plot boxplot wilcoxen p-values using ggpubr dlong %>%    ggplot(aes(x=gene.name, y=value, fill=group)) +   geom_boxplot() +   stat_compare_means(method= "wilcox.test") 

# pvalues dlong %>%    group_by(gene.name) %>%    summarise(p=wilcox.test(value~group)$p.value) # tibble: 4 x 2    gene.name     p        <chr> <dbl> 1           0.2 2         b   0.2 3         c   0.7 4         d   1.0 

or try base r using apply.

res <- apply(d[,-1], 1, function(x){   wilcox.test(x ~ c(1,1,1,2,2,2))$p.value }) cbind.data.frame(genes=as.character(d$gene.name), p=res, bh=p.adjust(res, method = "bh"))      genes   p        bh [1,]     1 0.2 0.4000000 [2,]     2 0.2 0.4000000 [3,]     3 0.7 0.9333333 [4,]     2 1.0 1.0000000