Diagnostic plot for seqData — dispersion

For generating statistics for 'seqData' objects

dispersion_est(
  omicsData,
  method,
  interactive = FALSE,
  x_lab = NULL,
  x_lab_size = 11,
  x_lab_angle = NULL,
  y_lab = NULL,
  y_lab_size = 11,
  title_lab = NULL,
  title_lab_size = 14,
  legend_lab = NULL,
  legend_position = "right",
  bw_theme = TRUE,
  palette = NULL,
  point_size = 0.2,
  custom_theme = NULL
)

Arguments

omicsData: seqData object used to terst dispersions
method: either "DESeq2", "edgeR", or "voom" for testing dispersion
interactive: Logical. If TRUE produces an interactive plot.
x_lab: A character string specifying the x-axis label when the metric argument is NULL. The default is NULL in which case the x-axis label will be "count".
x_lab_size: An integer value indicating the font size for the x-axis. The default is 11.
x_lab_angle: An integer value indicating the angle of x-axis labels.
y_lab: A character string specifying the y-axis label. The default is NULL in which case the y-axis label will be the metric selected for the metric argument.
y_lab_size: An integer value indicating the font size for the y-axis. The default is 11.
title_lab: A character string specifying the plot title when the metric argument is NULL.
title_lab_size: An integer value indicating the font size of the plot title. The default is 14.
legend_lab: A character string specifying the legend title.
legend_position: A character string specifying the position of the legend. Can be one of "right", "left", "top", or "bottom". The default is "right".
bw_theme: Logical. If TRUE uses the ggplot2 black and white theme.
palette: A character string indicating the name of the RColorBrewer palette to use. For a list of available options see the details section in RColorBrewer.
point_size: An integer specifying the size of the points. The default is 0.2.
custom_theme: a ggplot `theme` object to be applied to non-interactive plots, or those converted by plotly::ggplotly().

Value

plot result

Details

DESeq2 option requires package "survival" to be available.

References

Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140. doi: 10.1093/bioinformatics/btp616.

Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.

Examples

# \donttest{
library(pmartRdata)
myseqData <- group_designation(omicsData = rnaseq_object, main_effects = "Virus")
dispersion_est(omicsData = myseqData, method = "edgeR")
dispersion_est(omicsData = myseqData, method = "DESeq2")
dispersion_est(omicsData = myseqData, method = "voom")
# }