gene_plot {GeneTonic} | R Documentation |
Plot expression values (e.g. normalized counts) for a gene of interest, grouped by experimental group(s) of interest
gene_plot( dds, gene, intgroup = "condition", assay = "counts", annotation_obj = NULL, normalized = TRUE, transform = TRUE, labels_repel = TRUE, plot_type = "auto", return_data = FALSE )
dds |
A |
gene |
Character, specifies the identifier of the feature (gene) to be plotted |
intgroup |
A character vector of names in |
assay |
Character, specifies with assay of the |
annotation_obj |
A |
normalized |
Logical value, whether the expression values should be
normalized by their size factor. Defaults to TRUE, applies when |
transform |
Logical value, corresponding whether to have log scale y-axis or not. Defaults to TRUE. |
labels_repel |
Logical value. Whether to use |
plot_type |
Character, one of "auto", "jitteronly", "boxplot", "violin",
or "sina". Defines the type of |
return_data |
Logical, whether the function should just return the data.frame of expression values and covariates for custom plotting. Defaults to FALSE. |
The result of this function can be fed directly to plotly::ggplotly()
for interactive visualization, instead of the static ggplot
viz.
A ggplot
object
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)