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Plot expression values for a gene

Source: R/gene_plot.R
gene_plot.Rd

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

Usage

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

Arguments

de_container

An object containing the data for a Differential Expression workflow (e.g. DESeq2, edgeR or limma). Currently, this can be 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(de_container) to use for grouping. Note: the vector components should be categorical variables. Defaults to NULL, which which would then select the first column of the colData slot.

assay

Character, specifies with assay of the de_container 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_display

Logical value. Whether to display the labels of samples, 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.

Value

A ggplot object

Details

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

Examples

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

# dds object
data(gse, package = "macrophage")
dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
#> using counts and average transcript lengths from tximeta
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
keep <- rowSums(counts(dds_macrophage) >= 10) >= 6
dds_macrophage <- dds_macrophage[keep, ]
# dds_macrophage <- DESeq(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)
)
#> 'select()' returned 1:many mapping between keys and columns

gene_plot(
  de_container = dds_macrophage,
  gene = "ENSG00000125347",
  intgroup = "condition",
  annotation_obj = anno_df
)
#> using 'avgTxLength' from assays(dds), correcting for library size