as.cacomp: Create cacomp object from Seurat/SingleCellExperiment...

In ClemensKohl/APL: Association Plots

Create cacomp object from Seurat/SingleCellExperiment container

Description

Converts the values stored in the Seurat/SingleCellExperiment dimensional reduction slot "CA" to a cacomp object. If recompute = TRUE additional parameters are recomputed from the saved values without rerunning SVD (need to specify assay to work).

as.cacomp.cacomp returns input without any calculations.

Recomputes missing values and returns cacomp object from a list. If you have a *complete* cacomp object in list form, use do.call(new_cacomp, obj).

as.cacomp.Seurat: Converts the values stored in the Seurat DimReduc slot "CA" to an cacomp object.

as.cacomp.SingleCellExperiment: Converts the values stored in the SingleCellExperiment reducedDim slot "CA" to a cacomp object.

Usage

as.cacomp(obj, ...)

## S4 method for signature 'cacomp'
as.cacomp(obj, ...)

## S4 method for signature 'list'
as.cacomp(obj, ..., mat = NULL)

## S4 method for signature 'Seurat'
as.cacomp(obj, ..., assay = "RNA", slot = "counts")

## S4 method for signature 'SingleCellExperiment'
as.cacomp(obj, ..., assay = "counts")

Arguments

obj	An object of class "Seurat" or "SingleCellExperiment" with a dim. reduction named "CA" saved. For obj "cacomp" input is returned.
...	Further arguments.
mat	Original input matrix.
assay	Character. The assay from which extract the count matrix, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
slot	character. Slot of the Seurat assay to use. Default "counts".

Details

By default extracts std_coords_cols, D, prin_coords_rows, top_rows and dims from obj and outputs a cacomp object. If recompute = TRUE the following are additionally recalculated (doesn't run SVD): U, V, std_coords_rows, row_masses, col_masses.

Value

A cacomp object.

Examples

#########
# lists #
#########

# 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)
ca_list <- as.list(ca)

# Only keep subset of elements for demonstration
ca_list <- ca_list[c("U", "std_coords_rows", "std_coords_cols")]

# convert (incomplete) list to cacomp object.
ca <- as.cacomp(ca_list, mat = cnts)

##########
# Seurat #
##########
library(Seurat)
set.seed(1234)

# 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))

seu <- CreateSeuratObject(counts = cnts)
seu <- cacomp(seu, return_input = TRUE)

ca <- as.cacomp(seu, assay = "RNA", slot = "counts")

########################
# SingleCellExperiment #
########################
library(SingleCellExperiment)
set.seed(1234)

# 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))

sce <- SingleCellExperiment(assays=list(counts=cnts))
sce <- cacomp(sce, return_input = TRUE)

ca <- as.cacomp(sce, assay = "counts")

ClemensKohl/APL documentation built on May 25, 2024, 3:15 p.m.