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Plot of the first 3D CA projection of the data.

ca_3Dplot.Rd

Plots the first 3 dimensions of the rows and columns in the same plot.

Usage

ca_3Dplot(
  obj,
  xdim = 1,
  ydim = 2,
  zdim = 3,
  princ_coords = 1,
  row_labels = NULL,
  col_labels = NULL,
  ...
)

# S4 method for cacomp
ca_3Dplot(
  obj,
  xdim = 1,
  ydim = 2,
  zdim = 3,
  princ_coords = 1,
  row_labels = NULL,
  col_labels = NULL,
  ...
)

# S4 method for Seurat
ca_3Dplot(
  obj,
  xdim = 1,
  ydim = 2,
  zdim = 3,
  princ_coords = 1,
  row_labels = NULL,
  col_labels = NULL,
  ...,
  assay = Seurat::DefaultAssay(obj),
  slot = "counts"
)

# S4 method for SingleCellExperiment
ca_3Dplot(
  obj,
  xdim = 1,
  ydim = 2,
  zdim = 3,
  princ_coords = 1,
  row_labels = NULL,
  col_labels = NULL,
  ...,
  assay = "counts"
)

Arguments

obj

An object of class "cacomp", or alternatively an object of class "Seurat" or "SingleCellExperiment" with a dim. reduction named "CA" saved.

xdim

Integer. The dimension for the x-axis. Default 1.

ydim

Integer. The dimension for the y-axis. Default 2.

zdim

Integer. The dimension for the z-axis. Default 3.

princ_coords

Integer. If 1 then principal coordinates are used for the rows, if 2 for the columns. Default 1 (rows).

row_labels

Numeric vector. Indices for the rows for which a label should be added (label should be stored in rownames). Default NULL.

col_labels

Numeric vector. Indices for the columns for which a label should be added (label should be stored in colnames). Default NULL (no columns).

...

Further arguments.

assay

SingleCellExperiment assay to obtain counts from.

slot

Seurat slot from assay to get count matrix from.

Value

Plot of class "plotly".

Details

Depending on whether `princ_coords` is set to 1 or 2 either the principal coordinates of either the rows (1) or the columns (2) are chosen. For the other the standardized coordinates are plotted (assymetric biplot). Labels for rows and columns should be stored in the row- and column names respectively.

Examples

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
               x = sample(1:100, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

# Run correspondence analysis
ca <- cacomp(obj = cnts, princ_coords = 3)
#> Warning: 
#> Parameter top is >nrow(obj) and therefore ignored.

ca_3Dplot(ca)