R/diff_express.R

#' Differential gene expression analysis results
#'
#' @description Differential gene expression analysis results obtained from
#'   comparing the RNAseq data of two different cell populations using DESeq2
#' @name diff_express
#' @docType data
#' @usage data("diff_express")
#' @format A data frame with 36028 rows and 5 columns. \describe{
#'   \item{\code{name}}{gene names} \item{\code{baseMean}}{mean expression
#'   signal across all samples} \item{\code{log2FoldChange}}{log2 fold change}
#'   \item{\code{padj}}{Adjusted p-value}\item{\code{detection_call}}{a numeric
#'   vector specifying whether the genes is expressed (value = 1) or not (value = 0).}}
#'
#' @examples
#' data(diff_express)
#'
#' # Default plot
#'ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"),
#'    fdr = 0.05, fc = 2, size = 0.4,
#'    palette = c("#B31B21", "#1465AC", "darkgray"),
#'    genenames = as.vector(diff_express$name),
#'    legend = "top", top = 20,
#'    font.label = c("bold", 11),
#'    font.legend = "bold",
#'    font.main = "bold",
#'    ggtheme = ggplot2::theme_minimal())
#'
#' # Add rectangle around labesl
#'ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"),
#'    fdr = 0.05, fc = 2, size = 0.4,
#'    palette = c("#B31B21", "#1465AC", "darkgray"),
#'    genenames = as.vector(diff_express$name),
#'    legend = "top", top = 20,
#'    font.label = c("bold", 11), label.rectangle = TRUE,
#'    font.legend = "bold",
#'    font.main = "bold",
#'    ggtheme = ggplot2::theme_minimal())
#'
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ggpubr documentation built on Feb. 16, 2023, 7:18 p.m.