mersmers comparison

Load the two analyses

savefile <- paste0("combined_", mers, ".rda")
old_data <- new.env()
load(file=file.path("small_previous", savefile), envir=old_data)
old_data <- old_data[["ret"]]
new_data <- new.env()
load(file=file.path("small_new", savefile), envir=new_data)
new_data <- new_data[["ret"]]

merged <- merge(x=old_data[["data"]][[1]][, c("limma_logfc", "deseq_logfc", "edger_logfc")],
                y=new_data[["data"]][[1]][, c("limma_logfc", "deseq_logfc", "edger_logfc")],
                by="row.names")
cor.test(x=merged[, "limma_logfc.x"], y=merged[, "limma_logfc.y"])

## 
##  Pearson's product-moment correlation
## 
## data:  merged[, "limma_logfc.x"] and merged[, "limma_logfc.y"]
## t = 9, df = 1600, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1739 0.2677
## sample estimates:
##    cor 
## 0.2213

cor.test(x=merged[, "deseq_logfc.x"], y=merged[, "deseq_logfc.y"])

## 
##  Pearson's product-moment correlation
## 
## data:  merged[, "deseq_logfc.x"] and merged[, "deseq_logfc.y"]
## t = 9.5, df = 1600, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1842 0.2774
## sample estimates:
##    cor 
## 0.2313

cor.test(x=merged[, "edger_logfc.x"], y=merged[, "edger_logfc.y"])

## 
##  Pearson's product-moment correlation
## 
## data:  merged[, "edger_logfc.x"] and merged[, "edger_logfc.y"]
## t = 9.6, df = 1600, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1879 0.2810
## sample estimates:
##   cor 
## 0.235

tt <- plot_linear_scatter(df=merged[, c(2,5)])

## Used Bon Ferroni corrected t test(s) between columns.

tt$scatter