DEG_plot: Differential expression plots (1D or 2D)

In m-swirski/RiboCrypt: Interactive visualization in genomics

Differential expression plots (1D or 2D)

Description

Gives you interactive 1D or 2D DE plots

Usage

DEG_plot(
  dt,
  draw_non_regulated = FALSE,
  xlim = ifelse(two_dimensions, "bidir.max", "auto"),
  ylim = "bidir.max",
  xlab = ifelse(two_dimensions, "RNA fold change (log2)", "Mean counts (log2)"),
  ylab = ifelse(two_dimensions, "RFP fold change (log2)", "Fold change (log2)"),
  two_dimensions = ifelse("LFC" %in% colnames(dt), FALSE, TRUE),
  color.values = c(`No change` = "black", Significant = "red", Buffering = "purple",
    `mRNA abundance` = "darkgreen", Expression = "blue", Forwarded = "yellow", Inverse =
    "aquamarine", Translation = "orange4")
)

Arguments

dt	a data.table with results from a differential expression run. Normally from: ORFik::DTEG.analysis(df1, df2)
draw_non_regulated	logical, default FALSE. Should non-regulated rows be included in the plot? Will make the plot faster to render if skipped (FALSE)
xlim	numeric vector or character preset, default: ifelse(two_dimensions, "bidir.max", "auto") (Equal in both + / - direction, using max value + 0.5 of meanCounts(in 1d) / rna(in 2d) column of dt). If you want ggplot to decide limit, set to "auto". For numeric vector, specify min and max x limit: like c(-5, 5)
ylim	numeric vector or character preset, default: "bidir.max" (Equal in both + / - direction, using max value + 0.5 of LFC(in 1d) / rfp(in 2d) column of dt). If you want ggplot to decide limit, set to "auto". For numeric vector, specify min and max x limit: like c(-5, 5)
xlab	character, default: ifelse(two_dimensions, "RNA fold change (log2)", "Mean counts (log2)")
ylab	character, default: ifelse(two_dimensions, "RFP fold change (log2)", "Fold change (log2)")
two_dimensions	logical, default: ifelse("LFC" %in% colnames(dt), FALSE, TRUE) Is this two dimensional, like: Ribo-seq vs RNA-seq. Alternative, FALSE: Then Log fold change vs mean counts
color.values	named character vector, default: c("No change" = "black", "Significant" = "red", "Buffering" = "purple", "mRNA abundance" = "darkgreen", "Expression" = "blue", "Forwarded" = "yellow", "Inverse" = "aquamarine", "Translation" = "orange4")

Value

plotly object

Examples

# Load experiment
df <- ORFik.template.experiment()
# 1 Dimensional analysis
dt <- DEG.analysis(df[df$libtype == "RNA",])
dt$Regulation[1] <- "Significant" # Fake sig level
DEG_plot(dt, draw_non_regulated = TRUE)
# 2 Dimensional analysis
dt_2d <- DTEG.analysis(df[df$libtype == "RFP",], df[df$libtype == "RNA",],
                    output.dir = NULL)
dt_2d$Regulation[4] <- "Translation" # Fake sig level
dt_2d$Regulation[5] <- "Buffering" # Fake sig level
DEG_plot(dt_2d, draw_non_regulated = TRUE)

m-swirski/RiboCrypt documentation built on Oct. 30, 2024, 8:44 p.m.