This is the function to generate a box plot to show CPM levels of DE genes among selected treatment samples and control samples.
Usage
plot_counts(
data = NULL,
genes = NULL,
group_by = NULL,
treatment_samples = NULL,
control_samples = NULL,
color_by = NULL,
normalisation = NULL,
batch = 1
)Arguments
- data
A tidyseurat object merged with metadata. Must contain columns "Well_ID", "Row", "Column".
- genes
Genes to be plotted
- group_by
A column that specifies the treatment group in the input data
- treatment_samples
Value in the column "combined_id" representing replicates of treatment samples in the data
- control_samples
Value in the column "combined_id" representing replicates of control samples in the data
- color_by
A column that specifies the group coloring
- normalisation
One of "raw", "logNorm", "cpm", "clr", "SCT", "DESeq2", "edgeR", "RUVg", "RUVs", "RUVr", "limma_voom"
- batch
To indicate patch factor
Examples
data(mini_mac)
genes <- mini_mac@tools$diff_exprs$Staurosporine_10$gene[1:6]
p <- plot_counts(mini_mac, genes = genes, group_by = "combined_id",
treatment_samples = "Staurosporine_10",
control_samples = "DMSO_0",
normalisation = "clr")
#> Normalizing layer: counts
#> Normalizing across cells
#>
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