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MversusA plot for visualizing differentially expressed genes.

Source: R/ma_plot.R
ma_plot.Rd

MversusA plot for visualizing differentially expressed genes.

Usage

ma_plot(
  data,
  foldchange = 1,
  fdr_value = 0.05,
  point_size = 3,
  color_up = "#FF0000",
  color_down = "#008800",
  color_alpha = 0.5,
  top_method = "fc",
  top_num = 20,
  label_size = 8,
  label_box = TRUE,
  title = "CT-vs-LT12",
  xlab = "Log2 mean expression",
  ylab = "Log2 fold change",
  ggTheme = "theme_light"
)

Arguments

data

Dataframe: differentially expressed genes (DEGs) stats 2 (1st-col: Gene, 2nd-col: baseMean, 3rd-col: Log2FoldChange, 4th-col: FDR).

foldchange

Numeric: fold change value. Default: 1.0, min: 0.0, max: null.

fdr_value

Numeric: false discovery rate. Default: 0.05, min: 0.00, max: 1.00.

point_size

Numeric: point size. Default: 1.0, min: 0.0, max: null.

color_up

Character: up-regulated genes color (color name or hex value). Default: "#FF0000".

color_down

Character: down-regulated genes color (color name or hex value). Default: "#008800".

color_alpha

Numeric: point color alpha. Default: 0.50, min: 0.00, max: 1.00.

top_method

Character: top genes select method. Default: "fc" (fold change), options: "padj" (p-adjust), "fc".

top_num

Numeric: top genes number. Default: 20, min: 0, max: null.

label_size

Numeric: label font size. Default: 8.00, min: 0.00, max: null.

label_box

Logical: add box to label. Default: TRUE, options: TRUE, FALSE.

title

Character: plot title. Default: "CT-vs-Trait1".

xlab

Character: x label. Default: "Log2 mean expression".

ylab

Character: y label. Default: "Log2 fold change".

ggTheme

Character: ggplot2 themes. Default: "theme_light", options: "theme_default", "theme_bw", "theme_gray", "theme_light", "theme_linedraw", "theme_dark", "theme_minimal", "theme_classic", "theme_void"

Value

Plot: MversusA plot for visualizing differentially expressed genes.

Author

benben-miao

Examples

# 1. Library TOmicsVis package
library(TOmicsVis)

# 2. Use example dataset
data(degs_stats2)
head(degs_stats2)
#>    name     baseMean log2FoldChange         padj
#> 1  A1I3    0.1184475      0.0000000           NA
#> 2   A1M 1654.4618140      0.6789538 5.280802e-02
#> 3   A2M  681.0463277      1.5263838 3.920000e-07
#> 4 A2ML1  389.7226640      3.8933573 1.180000e-14
#> 5  ABAT  364.7810090     -2.3554014 1.559230e-04
#> 6 ABCC3    1.1346239      1.2932740 4.491812e-01

# 3. Default parameters
ma_plot(degs_stats2)


# 4. Set color_up = "#FF8800"
ma_plot(degs_stats2, color_up = "#FF8800")


# 5. Set top_num = 10
ma_plot(degs_stats2, top_num = 10)