Dimensionality reduction through PCA

Dimensionality reduction through PCA

dimred_umap(
  MAE,
  tax_level,
  color,
  shape = NULL,
  cx = 1,
  cy = 2,
  cz = NULL,
  n_neighbors = 15,
  metric = c("euclidean", "manhattan"),
  n_epochs = 200,
  init = c("spectral", "random"),
  min_dist = 0.1,
  datatype = c("logcpm", "relabu", "counts")
)

Arguments

MAE	A multi-assay experiment object
tax_level	The taxon level used for organisms
color	A condition to color data points by e.g. "AGE"
shape	A condition to shape data points by e.g. "SEX"
cx	Component on the x-axis e.g. 1
cy	Component on the y-axis e.g. 2
cz	Component on the z-axis e.g. 3
n_neighbors	Number of nearest neighbors
metric	Distance function e.g. c("euclidean", "manhattan")
n_epochs	Number of iterations
init	Initial embedding using eigenvector e.g c("spectral", "random")
min_dist	Determines how close points appear in the final layout
datatype	Datatype to use e.g. c("logcpm", "relabu", "counts")

Value

A list with a plotly object and summary table

Examples

data_dir = system.file("extdata/MAE.rds", package = "animalcules")
toy_data <- readRDS(data_dir)
result <- dimred_umap(toy_data,
                      tax_level="genus",
                      color="AGE",
                      shape="DISEASE",
                      cx=1,
                      cy=2,
                      datatype="logcpm")
result$plot