diff_express: Differential gene expression analysis results

In kassambara/ggpubr: 'ggplot2' Based Publication Ready Plots

Differential gene expression analysis results

Description

Differential gene expression analysis results obtained from comparing the RNAseq data of two different cell populations using DESeq2

Usage

data("diff_express")

Format

A data frame with 36028 rows and 5 columns.

name

gene names

baseMean

mean expression signal across all samples

log2FoldChange

log2 fold change

padj

Adjusted p-value

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())

kassambara/ggpubr documentation built on Feb. 15, 2023, 4:09 a.m.