gene_plot: Plot expression values for a gene

In GeneTonic: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis

Description

Plot expression values (e.g. normalized counts) for a gene of interest, grouped by experimental group(s) of interest

Usage

gene_plot(
  dds,
  gene,
  intgroup = "condition",
  assay = "counts",
  annotation_obj = NULL,
  normalized = TRUE,
  transform = TRUE,
  labels_repel = TRUE,
  plot_type = "auto",
  return_data = FALSE
)

Arguments

dds	A DESeqDataSet object, normally obtained after running your data through the DESeq2 framework.
gene	Character, specifies the identifier of the feature (gene) to be plotted
intgroup	A character vector of names in colData(dds) to use for grouping. Note: the vector components should be categorical variables.
assay	Character, specifies with assay of the dds object to use for reading out the expression values. Defaults to "counts".
annotation_obj	A data.frame object with the feature annotation information, with at least two columns, gene_id and gene_name.
normalized	Logical value, whether the expression values should be normalized by their size factor. Defaults to TRUE, applies when assay is "counts"
transform	Logical value, corresponding whether to have log scale y-axis or not. Defaults to TRUE.
labels_repel	Logical value. Whether to use ggrepel's functions to place labels; defaults to TRUE
plot_type	Character, one of "auto", "jitteronly", "boxplot", "violin", or "sina". Defines the type of geom_ to be used for plotting. Defaults to auto, which in turn chooses one of the layers according to the number of samples in the smallest group defined via intgroup
return_data	Logical, whether the function should just return the data.frame of expression values and covariates for custom plotting. Defaults to FALSE.

Details

The result of this function can be fed directly to plotly::ggplotly() for interactive visualization, instead of the static ggplot viz.

Value

A ggplot object

Examples

library("macrophage")
library("DESeq2")
library("org.Hs.eg.db")

# dds object
data("gse", package = "macrophage")
dds_macrophage <- DESeqDataSet(gse, design = ~line + condition)
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
dds_macrophage <- estimateSizeFactors(dds_macrophage)

# annotation object
anno_df <- data.frame(
  gene_id = rownames(dds_macrophage),
  gene_name = mapIds(org.Hs.eg.db,
                     keys = rownames(dds_macrophage),
                     column = "SYMBOL",
                     keytype = "ENSEMBL"),
  stringsAsFactors = FALSE,
  row.names = rownames(dds_macrophage)
)

gene_plot(dds_macrophage,
          gene = "ENSG00000125347",
          intgroup = "condition",
          annotation_obj = anno_df)

GeneTonic documentation built on Nov. 8, 2020, 5:27 p.m.