abundance volcano plot

create and save an abundance volcano bar plot in the folder abundance

Usage

abVolPlot(
  object,
  cluster_idents,
  sample,
  cluster_order,
  group_by,
  group1,
  group2,
  color,
  width = 5,
  height = 5,
  min_cells = 10,
  paired = FALSE,
  dir_output = "."
)

Arguments

object

Seurat object

cluster_idents

variable in meta data with cluster names

sample

variable in meta data for each sample

cluster_order

vector determining the order of the clusters

group_by

variable in meta data that categorize samples in groups

group1

first group (nominator)

group2

second group (denominator)

color

color palette

width

width of output plot (default: 5)

height

height of output plot (default: 5)

min_cells

remove all clusters that have less than minimal amount of cells (default = 10)

paired

logical indicating whether you want a paired test (default FALSE)

dir_output

directory to save the output plot (default: ".")

Value

save volcano abundance plot

Examples

library(Seurat)
library(rstatix)
#> 
#> Attaching package: ‘rstatix’
#> The following object is masked from ‘package:stats’:
#> 
#>     filter
set.seed(123)
pbmc_small$predicted.id <- sample(c("Cluster1", "Cluster2"), ncol(pbmc_small), replace = TRUE)
pbmc_small$sample <- sample(c("Sample1", "Sample2"), ncol(pbmc_small), replace = TRUE)
pbmc_small$AIE_type <- sample(c("LGI1", "control"), ncol(pbmc_small), replace = TRUE)
abVolPlot(
  object = pbmc_small,
  cluster_idents = "predicted.id",
  sample = "sample",
  cluster_order = c("Cluster1", "Cluster2"),
  group_by = "AIE_type",
  group1 = "LGI1",
  group2 = "control",
  color = c("Cluster1" = "blue", "Cluster2" = "red"),
  width = 5,
  height = 5,
  dir_output = "."
)
#> Joining with `by = join_by(sample)`

unlink("volcano_plot_predicted.id_pbmc_small_LGI1_control.pdf")