Spearman P-values inconsistency

1 Data

data <- data.frame(met = c(0.1073659,0.5130488, 0.0800250), 
                   exp = c(26.10,18.95,47.01)
)
data

2 Spearman

2.1 Correlation test

spearman.test <- cor.test(x = data$met,y = data$exp, method = "spearman")
spearman.test

## 
##  Spearman's rank correlation rho
## 
## data:  data$met and data$exp
## S = 8, p-value = 0.3333
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho 
##  -1

2.2 Plot - Correlation test

library(ggpubr)

## Loading required package: ggplot2

## Loading required package: magrittr

p <- ggscatter(data,
               x = "met",
               y = "exp",
               ylab = "log2(TPM + 1)",
               font.label = c(4),
               xlab = "DNA methylation",
               add = "reg.line",                                 # Add regression line
               conf.int = FALSE,                                  # Add confidence interval
               add.params = list(color = "blue",
                                 fill = "lightgray")
)  +    theme(legend.title = element_text(size=8),
          legend.text = element_text(size=8),
          legend.position="right",
          plot.title = element_text(size=8,face="bold"))
p + stat_cor(method = "spearman")

3 Pearson

pearson.test <- cor.test(x = data$met,y = data$exp, method = "pearson")
pearson.test

## 
##  Pearson's product-moment correlation
## 
## data:  data$met and data$exp
## t = -1.0884, df = 1, p-value = 0.4731
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##        cor 
## -0.7363781

p + stat_cor(method = "pearson")