cacomp: Correspondance Analysis
In elagralinska/APLpackage: Association Plots

Description Usage Arguments Details Value References Examples

'cacomp' performs correspondence analysis on a matrix or Seurat/SingleCellExperiment object and returns the transformed data.

'cacomp.seurat' performs correspondence analysis on a assay from a Seurat container and stores the standard coordinates of the columns (= cells) and the principal coordinates of the rows (= genes) as a DimReduc Object in the Seurat container.

'cacomp.SingleCellExperiment' performs correspondence analysis on an assay from a SingleCellExperiment and stores the standard coordinates of the columns (= cells) and the principal coordinates of the rows (= genes) as a matrix in the SingleCellExperiment container.

cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = NULL,
  inertia = TRUE,
  rm_zeros = TRUE,
  ...
)

## S4 method for signature 'matrix'
cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = NULL,
  inertia = TRUE,
  rm_zeros = TRUE,
  ...
)

## S4 method for signature 'Seurat'
cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = NULL,
  inertia = TRUE,
  rm_zeros = TRUE,
  ...,
  assay = DefaultAssay(obj),
  slot = "counts",
  return_input = FALSE
)

## S4 method for signature 'SingleCellExperiment'
cacomp(
  obj,
  coords = TRUE,
  princ_coords = 3,
  python = FALSE,
  dims = NULL,
  top = NULL,
  inertia = TRUE,
  rm_zeros = TRUE,
  ...,
  assay = "counts",
  return_input = FALSE
)

`obj`	A numeric matrix or Seurat/SingleCellExperiment object. For sequencing a count matrix, gene expression values with genes in rows and samples/cells in columns. Should contain row and column names.
`coords`	Logical. Indicates whether CA standard coordinates should be calculated. Default TRUE
`princ_coords`	Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0). Default 1.
`python`	A logical value indicating whether to use singular-value decomposition from the python package torch. This implementation dramatically speeds up computation compared to 'svd()' in R.
`dims`	Integer. Number of CA dimensions to retain. Default NULL (keeps all dimensions).
`top`	Integer. Number of most variable rows to retain. Default NULL.
`inertia`	Logical.. Whether total, row and column inertias should be calculated and returned. Default TRUE.
`rm_zeros`	Logical. Whether rows & cols containing only 0s should be removed. Keeping zero only rows/cols might lead to unexpected results. Default TRUE.
`...`	Other parameters
`assay`	Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
`slot`	character. The slot of the Seurat assay. Default "counts".
`return_input`	Logical. If TRUE returns the input (SingleCellExperiment/Seurat object) with the CA results saved in the reducedDim/DimReduc slot "CA". Otherwise returns a "cacomp". Default FALSE.

The calculation is performed according to Greenacre. Singular value decomposition can be performed either with the base R function 'svd' or preferably by the much faster pytorch implementation (python = TRUE). When working on large matrices, CA coordinates and principal coordinates should only computed when needed to save computational time.

Returns a named list of class "cacomp" with components U, V and D: The results from the SVD. row_masses and col_masses: Row and columns masses. top_rows: How many of the most variable rows were retained for the analysis. tot_inertia, row_inertia and col_inertia: Only if inertia = TRUE. Total, row and column inertia respectively.

If return_imput = TRUE with Seurat container: Returns input obj of class "Seurat" with a new Dimensional Reduction Object named "CA". Standard coordinates of the cells are saved as embeddings, the principal coordinates of the genes as loadings and the singular values (= square root of principal intertias/eigenvalues) are stored as stdev. To recompute a regular "cacomp" object without rerunning cacomp use 'as.cacomp()'.

If return_input =TRUE for SingleCellExperiment input returns a SingleCellExperiment object with a matrix of standard coordinates of the columns in reducedDim(obj, "CA"). Additionally, the matrix contains the following attributes: "prin_coords_rows": Principal coordinates of the rows. "singval": Singular values. For the explained inertia of each principal axis calculate singval^2. "percInertia": Percent explained inertia of each principal axis. To recompute a regular "cacomp" object from a SingleCellExperiment without rerunning cacomp use 'as.cacomp()'.

Greenacre, M. Correspondence Analysis in Practice, Third Edition, 2017.

# Simulate scRNAseq data.
cnts <- data.frame(cell_1 = rpois(10, 5),
                   cell_2 = rpois(10, 10),
                   cell_3 = rpois(10, 20))
rownames(cnts) <- paste0("gene_", 1:10)
cnts <- as.matrix(cnts)

# Run correspondence analysis.
ca <- cacomp(obj = cnts, princ_coords = 3, top = 5)
library(Seurat)
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)}, x = sample(1:20, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

# Create Seurat object
seu <- CreateSeuratObject(counts = cnts)

# run CA and save in dim. reduction slot.
seu <- cacomp(seu, return_input = TRUE, assay = "RNA", slot = "counts")

# run CA and return cacomp object.
ca <- cacomp(seu, return_input = FALSE, assay = "RNA", slot = "counts")
library(SingleCellExperiment)
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)}, x = sample(1:20, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))
logcnts <- log2(cnts + 1)

# Create SingleCellExperiment object
sce <- SingleCellExperiment(assays=list(counts=cnts, logcounts=logcnts))

# run CA and save in dim. reduction slot.
sce <- cacomp(sce, return_input = TRUE, assay = "counts") # on counts
sce <- cacomp(sce, return_input = TRUE, assay = "logcounts") # on logcounts

# run CA and return cacomp object.
ca <- cacomp(sce, return_input = FALSE, assay = "counts")