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Trend plot for visualizing gene expression trend profile in multiple traits.

Source: R/trend_plot.R
trend_plot.Rd

Trend plot for visualizing gene expression trend profile in multiple traits.

Usage

trend_plot(
  data,
  scale_method = "centerObs",
  miss_value = "exclude",
  line_alpha = 0.5,
  show_points = TRUE,
  show_boxplot = TRUE,
  num_column = 1,
  xlab = "Traits",
  ylab = "Genes Expression",
  sci_fill_color = "Sci_AAAS",
  sci_fill_alpha = 0.8,
  sci_color_alpha = 0.8,
  legend_pos = "right",
  legend_dir = "vertical",
  ggTheme = "theme_light"
)

Arguments

data

Dataframe: Shared degs of all paired comparisons in all groups expression dataframe of RNA-Seq. (1st-col: Genes, 2nd-col~n-1-col: Groups, n-col: Pathways).

scale_method

Character: data scale methods. Default: "globalminmax" (global min and max values), options: "std" (standard), "robust", "uniminmax" (unique min and max values), "globalminmax", "center", "centerObs" (center observes).

miss_value

Character: deal method for missing values. Default: "exclude", options: "exclude", "mean", "median", "min10", "random".

line_alpha

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

show_points

Logical: show points at trait node. Default: TRUE, options: TRUE, FALSE.

show_boxplot

Logical: show boxplot at trait node. Default: TRUE, options: TRUE, FALSE.

num_column

Logical: column number. Default: 2, min: 1, max: null.

xlab

Character: x label. Default: "Traits".

ylab

Character: y label. Default: "Genes Expression".

sci_fill_color

Character: ggsci color pallet. Default: "Sci_AAAS", options: "Sci_AAAS", "Sci_NPG", "Sci_Simpsons", "Sci_JAMA", "Sci_GSEA", "Sci_Lancet", "Sci_Futurama", "Sci_JCO", "Sci_NEJM", "Sci_IGV", "Sci_UCSC", "Sci_D3", "Sci_Material".

sci_fill_alpha

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

sci_color_alpha

Numeric: ggsci border color alpha. Default: 1.00, min: 0.00, max: 1.00.

legend_pos

Character: legend position. Default: "right", options: "none", "left", "right", "bottom", "top".

legend_dir

Character: legend direction. Default: "vertical", options: "horizontal", "vertical".

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: box plot support two levels and multiple groups with P value.

Author

benben-miao

Examples

# 1. Library TOmicsVis package
library(TOmicsVis)

# 2. Use example dataset
data(gene_expression3)
head(gene_expression3)
#>   Genes         CT        LT20     LT15      LT12      LT12_6
#> 1 ACAA2  39.903333 123.4366667 272.3533 359.28333  464.940000
#> 2  ACAN  14.660000 142.4800000 226.0333 316.43667 1083.523333
#> 3  ADH1   1.713333  14.8066667  12.2000  17.54333    9.343333
#> 4  AHSG 637.330000   0.0000000   0.0000   0.00000    0.000000
#> 5 ALDH2   2.490000   0.6033333   0.2700   0.13000    0.210000
#> 6 AP1S3  10.170000  27.3433333  29.1600  32.44667   44.276667
#>                 Pathways
#> 1 PPAR signaling pathway
#> 2 PPAR signaling pathway
#> 3 PPAR signaling pathway
#> 4 PPAR signaling pathway
#> 5 PPAR signaling pathway
#> 6 PPAR signaling pathway

# 3. Default parameters
trend_plot(gene_expression3[1:50,])


# 4. Set line_alpha = 0.30
trend_plot(gene_expression3[1:50,], line_alpha = 0.30)


# 5. Set sci_fill_color = "Sci_NPG"
trend_plot(gene_expression3[1:50,], sci_fill_color = "Sci_NPG")