corral: corral: Correspondence analysis on a single matrix
In corral: Correspondence Analysis for Single Cell Data

Description Usage Arguments Value Examples

corral can be used for dimensionality reduction to find a set of low-dimensional embeddings for a count matrix.

corral is a wrapper for corral_mat and corral_sce, and can be called on any of the acceptable input types.

corral_mat(
  inp,
  method = c("irl", "svd"),
  ncomp = 30,
  row.w = NULL,
  col.w = NULL,
  ...
)

corral_sce(
  inp,
  method = c("irl", "svd"),
  ncomp = 30,
  whichmat = "counts",
  fullout = FALSE,
  subset_row = NULL,
  ...
)

corral(inp, ...)

## S3 method for class 'corral'
print(x, ...)

`inp`	matrix (any type), `SingleCellExperiment`, or `SummarizedExperiment`. If using `SingleCellExperiment` or `SummarizedExperiment`, then include the `whichmat` argument to specify which slot to use (defaults to `counts`).
`method`	character, the algorithm to be used for svd. Default is irl. Currently supports 'irl' for irlba::irlba or 'svd' for stats::svd
`ncomp`	numeric, number of components; Default is 30
`row.w`	numeric vector; the row weights to use in chi-squared scaling. Defaults to 'NULL', in which case row weights are computed from the input matrix.
`col.w`	numeric vector; the column weights to use in chi-squared scaling. For instance, size factors could be given here. Defaults to 'NULL', in which case column weights are computed from the input matrix.
`...`	(additional arguments for methods)
`whichmat`	character; defaults to `counts`, can also use `logcounts` or `normcounts` if stored in the `sce` object
`fullout`	boolean; whether the function will return the full `corral` output as a list, or a SingleCellExperiment; defaults to SingleCellExperiment (`FALSE`). To get back the `corral_mat`-style output, set this to `TRUE`.
`subset_row`	numeric, character, or boolean vector; the rows to include in corral, as indices (numeric), rownames (character), or with booleans (same length as the number of rows in the matrix). If this parameter is `NULL`, then all rows will be used.
`x`	(print method) corral object; the list output from `corral_mat`

When run on a matrix, a list with the correspondence analysis matrix decomposition result:

d: a vector of the diagonal singular values of the input mat (from SVD output)
u: a matrix of with the left singular vectors of mat in the columns (from SVD output)
v: a matrix of with the right singular vectors of mat in the columns. When cells are in the columns, these are the cell embeddings. (from SVD output)
eigsum: sum of the eigenvalues for calculating percent variance explained
SCu and SCv: standard coordinates, left and right, respectively
PCu and PCv: principal coordinates, left and right, respectively

When run on a SingleCellExperiment, returns a SCE with the embeddings (PCv from the full corral output) in the reducedDim slot corral (default). Also can return the same output as corral_mat when fullout is set to TRUE.

For matrix and SummarizedExperiment input, returns list with the correspondence analysis matrix decomposition result (u,v,d are the raw svd output; SCu and SCv are the standard coordinates; PCu and PCv are the principal coordinates)

For SummarizedExperiment input, returns the same as for a matrix.

mat <- matrix(sample(0:10, 5000, replace=TRUE), ncol=50)
result <- corral_mat(mat)
result <- corral_mat(mat, method = 'svd')
result <- corral_mat(mat, method = 'irl', ncomp = 5)

library(DuoClustering2018)
sce <- sce_full_Zhengmix4eq()[1:100,1:100]
result_1 <- corral_sce(sce)
result_2 <- corral_sce(sce, method = 'svd')
result_3 <- corral_sce(sce, method = 'irl', ncomp = 30, whichmat = 'logcounts')


library(DuoClustering2018)
sce <- sce_full_Zhengmix4eq()[1:100,1:100]
corral_sce <- corral(sce,whichmat = 'counts')

mat <- matrix(sample(0:10, 500, replace=TRUE), ncol=25)
corral_mat <- corral(mat, ncomp=5)

mat <- matrix(sample(1:100, 10000, replace = TRUE), ncol = 100)
corral(mat)