geom_cor: Add correlation and p-value to a ggplot2 plot
In DEGreport: Report of DEG analysis

Description Usage Arguments Details See Also Examples

geom_cor will add the correlatin, method and p-value to the plot automatically guessing the position if nothing else specidfied. family font, size and colour can be used to change the format.

geom_cor(
  mapping = NULL,
  data = NULL,
  method = "spearman",
  xpos = NULL,
  ypos = NULL,
  inherit.aes = TRUE,
  ...
)

`mapping`	Set of aesthetic mappings created by `aes()` or `aes_()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame.`, and will be used as the layer data.
`method`	Method to calculate the correlation. Values are passed to `cor.test()`. (Spearman, Pearson, Kendall).
`xpos`	Locate text at that position on the x axis.
`ypos`	Locate text at that position on the y axis.
`inherit.aes`	If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.
`...`	other arguments passed on to `layer()`. These are often aesthetics, used to set an aesthetic to a fixed value, like `color = "red"` or `size = 3`. They may also be parameters to the paired geom/stat.

It was integrated after reading this tutorial to extend ggplot2 layers

ggplot2::layer()

data(humanGender)
library(SummarizedExperiment)
library(ggplot2)
ggplot(as.data.frame(assay(humanGender)[1:1000,]),
       aes(x = NA20502, y = NA20504)) +
  geom_point() +
  ylim(0,1.1e5) +
  geom_cor(method = "kendall", ypos = 1e5)