R/CA.R
In elagralinska/APLpackage: Association Plots
Defines functions
scree_plot
ca_coords
subset_dims
run_cacomp
var_rows
rm_zeros
comp_std_residuals
Documented in
ca_coords
comp_std_residuals
rm_zeros
run_cacomp
scree_plot
subset_dims
var_rows
#' @include constructor.R
NULL
#' Compute Standardized Residuals
#'
#' @description
#' `comp_std_residuals` computes the standardized residual matrix S,
#' which is the basis for correspondence analysis and serves
#' as input for singular value decomposition (SVD).
#'
#' @details
#' Calculates standardized residual matrix S from the proportion matrix P and
#' the expected values E according to \eqn{S = \frac{(P-E)}{sqrt(E)}}.
#'
#' @param mat A numerical matrix or coercible to one by `as.matrix()`.
#' Should have row and column names.
#' @return
#' A named list with standardized residual matrix "S",
#' grand total of the original matrix "tot"
#' as well as row and column masses "rowm" and "colm" respectively.
#'
comp_std_residuals <- function(mat){
if (!is(mat, "matrix")){
mat <- as.matrix(mat)
}
stopifnot("Input matrix does not have any rownames!" = !is.null(rownames(mat)))
stopifnot("Input matrix does not have any colnames!" = !is.null(colnames(mat)))
tot <- sum(mat)
P <- mat/tot # proportions matrix
rowm <- rowSums(P) # row masses
colm <- colSums(P) # column masses
E <- rowm %o% colm # expected proportions
S <- (P - E) / sqrt(E) # standardized residuals
S[is.nan(S)] <- 0
out <- list("S"=S, "tot"=tot, "rowm"=rowm, "colm"=colm)
return(out)
}
#' removes 0-only rows and columns in a matrix.
#'
#' @param obj A matrix.
#'
rm_zeros <- function(obj){
stopifnot(is(obj, "matrix"))
no_zeros_rows <- rowSums(obj) > 0
no_zeros_cols <- colSums(obj) > 0
if (sum(!no_zeros_rows) != 0){
## Delete genes with only zero values across all columns
warning("Matrix contains rows with only 0s. These rows were removed. If undesired set rm_zeros = FALSE.")
obj <- obj[no_zeros_rows,]
}
if (sum(!no_zeros_cols) != 0){
## Delete cells with only zero values across all genes
warning("Matrix contains columns with only 0s. These columns were removed. If undesired set rm_zeros = FALSE.")
obj <- obj[,no_zeros_cols]
}
return(obj)
}
#' Find most variable rows
#'
#' @description
#' Calculates the variance of the chi-square component matrix and selects the rows with the highest variance, e.g. 5,000.
#'
#' @return
#' Returns a matrix, which consists of the top variable rows of mat.
#'
#' @param mat A numeric matrix. For sequencing a count matrix,
#' gene expression values with genes in rows and samples/cells in columns.
#' Should contain row and column names.
#' @param top Integer. Number of most variable rows to retain. Default 5000.
#' @export
#' @examples
#' 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))
#'
#' # Choose top 5000 most variable genes
#' cnts <- var_rows(mat = cnts, top = 5000)
#'
#'
var_rows <- function(mat, top = 5000){
res <- comp_std_residuals(mat=mat)
if(top>nrow(mat)) warning("Top is larger than the number of rows in matrix. Top was set to nrow(mat).")
top <- min(nrow(mat), top)
chisquare <- res$tot * (res$S^2) # chi-square components matrix
variances <- apply(chisquare,1,var) #row-wise variances
ix_var <- order(-variances)
mat <- mat[ix_var[seq_len(top)],] # choose top rows
return(mat)
}
#' Internal function for `cacomp`
#'
#' @description
#' `run_cacomp` performs correspondence analysis on a matrix and returns the transformed data.
#'
#' @details
#' The calculation is performed according to the work of Michael Greenacre. Singular value decomposition
#' can be performed either with the base R function `svd` or preferably by the faster
#' pytorch implementation (python = TRUE). When working with large matrices, CA coordinates and
#' principal coordinates should only be computed when needed to save computational time.
#'
#' @return
#' 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/genes were retained for the analysis.
#' tot_inertia, row_inertia and col_inertia: Only if inertia = TRUE. Total, row and column inertia respectively.
#' @references
#' Greenacre, M. Correspondence Analysis in Practice, Third Edition, 2017.
#' @param 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.
#' @param coords Logical. Indicates whether CA standard coordinates should be calculated. Default TRUE
#' @param 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.
#' @param princ_coords Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0).
#' Default 1.
#' @param dims Integer. Number of CA dimensions to retain. Default NULL (keeps all dimensions).
#' @param top Integer. Number of most variable rows to retain. Default NULL.
#' @param inertia Logical.. Whether total, row and column inertias should be calculated and returned. Default TRUE.
#' @param rm_zeros Logical. Whether rows & cols containing only 0s should be removed. Keeping zero only rows/cols might lead to unexpected results. Default TRUE.
#' @param ... Arguments forwarded to methods.
run_cacomp <- function(obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = NULL,
top = NULL,
inertia = TRUE,
rm_zeros = TRUE,
...){
stopifnot("Input matrix does not have any rownames!" = !is.null(rownames(obj)))
stopifnot("Input matrix does not have any colnames!" = !is.null(colnames(obj)))
if (rm_zeros == TRUE){
obj <- rm_zeros(obj)
}
# Choose only top # of variable genes
if (is.null(top) || top == nrow(obj)) {
res <- comp_std_residuals(mat=obj)
toptmp <- nrow(obj)
} else if (!is.null(top) && top < nrow(obj)){
# message("Subsetting matrix by the top ", top," most variable rows (chisquare variances) ...")
obj <- var_rows(mat = obj, top = top)
res <- comp_std_residuals(mat=obj)
toptmp <- top
} else if (top > nrow(obj)) {
warning("\nParameter top is >nrow(obj) and therefore ignored.")
res <- comp_std_residuals(mat=obj)
toptmp <- nrow(obj)
} else {
warning("\nUnusual input for top, argument ignored.")
res <- comp_std_residuals(mat=obj)
toptmp <- nrow(obj)
}
S <- res$S
tot <- res$tot
rowm <- res$rowm
colm <- res$colm
rm(res)
# n <- nrow(S)
# p <- ncol(S)
k <- min(dim(S))-1
if (is.null(dims)) dims <- k
if (dims > k) dims <- k
# S <- (diag(1/sqrt(r)))%*%(P-r%*%t(c))%*%(diag(1/sqrt(c)))
# message("Running singular value decomposition ...")
if (python == TRUE){
# require(reticulate)
# source_python('./python_svd.py')
reticulate::source_python(system.file("python/python_svd.py", package = "APL"))
SVD <- svd_torch(S)
# SVD <- svd_linalg_torch(S)
names(SVD) <- c("U", "D", "V")
SVD$D <- as.vector(SVD$D)
} else {
SVD <- svd(S, nu = dims, nv = dims)
names(SVD) <- c("D", "U", "V")
SVD <- SVD[c(2, 1, 3)]
if(length(SVD$D) > dims) SVD$D <- SVD$D[seq_len(dims)]
}
names(SVD$D) <- paste0("Dim", seq_len(length(SVD$D)))
dimnames(SVD$V) <- list(colnames(S), paste0("Dim", seq_len(ncol(SVD$V))))
dimnames(SVD$U) <- list(rownames(S), paste0("Dim", seq_len(ncol(SVD$U))))
if(inertia == TRUE){
#calculate inertia
SVD$tot_inertia <- sum(SVD$D^2)
SVD$row_inertia <- rowSums(S^2)
SVD$col_inertia <- colSums(S^2)
}
SVD$row_masses <- rowm
SVD$col_masses <- colm
SVD$top_rows <- toptmp
SVD <- do.call(new_cacomp, SVD)
SVD <- subset_dims(SVD, dims)
# class(SVD) <- "cacomp"
if (coords == TRUE){
# message("Calculating coordinates...")
SVD <- ca_coords(caobj = SVD,
dims = dims,
princ_coords = princ_coords,
princ_only = FALSE)
} else {
if(!is.null(dims)){
if (dims >= length(SVD@D)){
if (dims > length(SVD@D)){
warning("Chosen number of dimensions is larger than the number of dimensions obtained from the singular value decomposition. Argument ignored.")
}
SVD@dims <- length(SVD@D)
} else {
dims <- min(dims, length(SVD@D))
SVD@dims <- dims
dims <- seq(dims)
# subset to number of dimensions
SVD@U <- SVD@U[,dims]
SVD@V <- SVD@V[,dims]
SVD@D <- SVD@D[dims]
}
} else {
SVD@dims <- length(SVD@D)
}
}
stopifnot(validObject(SVD))
return(SVD)
}
#' Correspondance Analysis
#'
#' @description
#' `cacomp` performs correspondence analysis on a matrix or
#' Seurat/SingleCellExperiment object and returns the transformed data.
#'
#' @details
#' The calculation is performed according to the work of Michael Greenacre. Singular value decomposition
#' can be performed either with the base R function `svd` or preferably by the faster
#' pytorch implementation (python = TRUE). When working with large matrices, CA coordinates and
#' principal coordinates should only be computed when needed to save computational time.
#'
#' @return
#' 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.
#' @references
#' Greenacre, M. Correspondence Analysis in Practice, Third Edition, 2017.
#' @param 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.
#' @param coords Logical. Indicates whether CA standard coordinates should be calculated. Default TRUE
#' @param 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.
#' @param princ_coords Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0).
#' Default 1.
#' @param dims Integer. Number of CA dimensions to retain. Default NULL (keeps all dimensions).
#' @param top Integer. Number of most variable rows to retain. Default NULL.
#' @param inertia Logical.. Whether total, row and column inertias should be calculated and returned. Default TRUE.
#' @param rm_zeros Logical. Whether rows & cols containing only 0s should be removed.
#' Keeping zero only rows/cols might lead to unexpected results. Default TRUE.
#' @param ... Arguments forwarded to methods.
#' @examples
#' # 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)
#' @export
setGeneric("cacomp", function(obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = NULL,
top = NULL,
inertia = TRUE,
rm_zeros = TRUE,
...) {
standardGeneric("cacomp")
})
#' @rdname cacomp
#' @export
setMethod(f = "cacomp",
signature=(obj="matrix"),
function(obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = NULL,
top = NULL,
inertia = TRUE,
rm_zeros = TRUE,
...){
caobj <- run_cacomp(obj = obj,
coords = coords,
princ_coords = princ_coords,
python = python,
dims = dims,
top = top,
inertia = inertia,
rm_zeros = rm_zeros,
...)
return(caobj)
})
#' Correspondance Analysis for Seurat objects
#'
#' @description
#' `cacomp.seurat` performs correspondence analysis on an assay from a Seurat container and stores the standardized coordinates
#' of the columns (= cells) and the principal coordinates of the rows (= genes) as a DimReduc Object in the Seurat container.
#'
#' @return
#' 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()`.
#' @param assay Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
#' @param slot character. The slot of the Seurat assay. Default "counts".
#' @param 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.
#' @param ... Other parameters
#' @rdname cacomp
#' @export
#' @examples
#' 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")
setMethod(f = "cacomp",
signature=(obj="Seurat"),
function(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){
stopifnot("obj doesnt belong to class 'Seurat'" = is(obj, "Seurat"))
stopifnot("Set coords = TRUE when inputting a Seurat object and return_input = TRUE." = coords == TRUE)
seu <- Seurat::GetAssayData(object = obj, assay = assay, slot = slot)
seu <- as.matrix(seu)
caobj <- run_cacomp(obj = seu,
coords = coords,
top = top,
princ_coords = princ_coords,
dims = dims,
python = python,
rm_zeros = rm_zeros,
inertia = inertia,
...)
if (return_input == TRUE){
colnames(caobj@V) <- paste0("DIM_", seq(ncol(caobj@V)))
colnames(caobj@U) <- paste0("DIM_", seq(ncol(caobj@U)))
obj[["CA"]] <- Seurat::CreateDimReducObject(embeddings = caobj@std_coords_cols,
loadings = caobj@prin_coords_rows,
stdev = caobj@D,
key = "Dim_",
assay = assay)
return(obj)
} else {
return(caobj)
}
})
#' Correspondance Analysis for SingleCellExperiment objects
#'
#' @description
#' `cacomp.SingleCellExperiment` performs correspondence analysis on an assay from a SingleCellExperiment and stores the standardized coordinates
#' of the columns (= cells) and the principal coordinates of the rows (= genes) as a matrix in the SingleCellExperiment container.
#'
#' @return
#' If return_input =TRUE for SingleCellExperiment input returns a SingleCellExperiment object with a matrix of standardized 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()`.
#' @param assay Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
#' @param 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.
#' @rdname cacomp
#' @export
#' @examples
#' 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")
setMethod(f = "cacomp",
signature=(obj="SingleCellExperiment"),
function(obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = NULL,
top = NULL,
inertia = TRUE,
rm_zeros = TRUE,
...,
assay = "counts",
return_input = FALSE){
stopifnot("obj doesnt belong to class 'SingleCellExperiment'" = is(obj, "SingleCellExperiment"))
stopifnot("Set coords = TRUE when inputting a SingleCellExperiment object and return_input = TRUE." = coords == TRUE)
mat <- SummarizedExperiment::assay(obj, assay)
mat <- as.matrix(mat)
top <- min(nrow(mat), top)
caobj <- run_cacomp(obj = mat,
coords = coords,
top = top,
princ_coords = princ_coords,
dims = dims,
python = python,
rm_zeros = rm_zeros,
inertia = inertia,
...)
if (return_input == TRUE){
prinInertia <- caobj@D^2
percentInertia <- prinInertia / sum(prinInertia) * 100 #TODO IS THIS CORRECT ??
# Saving the results
ca <- caobj@std_coords_cols
attr(ca, "prin_coords_rows") <- caobj@prin_coords_rows
attr(ca, "singval") <- caobj@D
attr(ca, "percInertia") <- percentInertia
SingleCellExperiment::reducedDim(obj, "CA") <- ca
return(obj)
} else {
return(caobj)
}
})
#' Subset dimensions of a caobj
#'
#' @description Subsets the dimensions according to user input.
#'
#' @return Returns caobj.
#'
#' @param caobj A caobj.
#' @param dims Integer. Number of dimensions.
#' @examples
#' # 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(cnts)
#' ca <- subset_dims(ca, 2)
#' @export
subset_dims <- function(caobj, dims){
stopifnot(is(caobj, "cacomp"))
if(dims > length(caobj@D)){
warning("dims is larger than the number of available dimensions. Argument ignored")
} else if (dims == length(caobj@D)){
caobj@dims <- dims
return(caobj)
}
dims <- min(dims, length(caobj@D))
caobj@dims <- dims
dims <- seq(dims)
caobj@U <- caobj@U[,dims]
caobj@V <- caobj@V[,dims]
caobj@D <- caobj@D[dims]
if (!is.empty(caobj@std_coords_cols)){
caobj@std_coords_cols <- caobj@std_coords_cols[,dims]
}
if (!is.empty(caobj@prin_coords_cols)){
caobj@prin_coords_cols <- caobj@prin_coords_cols[,dims]
}
if (!is.empty(caobj@std_coords_rows)){
caobj@std_coords_rows <- caobj@std_coords_rows[,dims]
}
if (!is.empty(caobj@prin_coords_rows)){
caobj@prin_coords_rows <- caobj@prin_coords_rows[,dims]
}
stopifnot(validObject(caobj))
return(caobj)
}
#' Calculate correspondence analysis row and column coordinates.
#'
#' @description `ca_coords` calculates the standardized and principal coordinates of the rows and columns in CA space.
#'
#' @details
#' Takes a "cacomp" object and calculates standardized and principal coordinates for the visualization of CA results in a biplot or
#' to subsequently calculate coordinates in an Association Plot.
#'
#' @return
#' Returns input object with coordinates added.
#' std_coords_rows/std_coords_cols: Standardized coordinates of rows/columns.
#' prin_coords_rows/prin_coords_cols: Principal coordinates of rows/columns.
#'
#' @param caobj A "cacomp" object as outputted from `cacomp()`.
#' @param dims Integer indicating the number of dimensions to use for the calculation of coordinates.
#' All elements of caobj (where applicable) will be reduced to the given number of dimensions. Default NULL (keeps all dimensions).
#' @param princ_only Logical, whether only principal coordinates should be calculated.
#' Or, in other words, whether the standardized coordinates are already calculated and stored in `caobj`. Default `FALSE`.
#' @param princ_coords Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0).
#' Default 3.
#' @examples
#' # 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 = 1)
#' ca <- ca_coords(ca, princ_coords = 3)
#' @export
ca_coords <- function(caobj, dims=NULL, princ_coords = 3, princ_only = FALSE){
stopifnot(is(caobj, "cacomp"))
stopifnot(dims <= length(caobj@D))
if(!is.null(dims)){
if (dims > length(caobj@D)){
warning("Chosen dimensions are larger than the number of dimensions obtained from the singular value decomposition. Argument ignored.")
}
caobj <- subset_dims(caobj = caobj, dims = dims)
}
if(princ_only == FALSE){
#standard coordinates
if(dims == 1 && !is.null(dims)){
caobj@std_coords_rows <- caobj@U/sqrt(caobj@row_masses)
caobj@std_coords_cols <- caobj@V/sqrt(caobj@col_masses)
} else {
caobj@std_coords_rows <- sweep(x = caobj@U, MARGIN = 1, STATS = sqrt(caobj@row_masses), FUN = "/")
caobj@std_coords_cols <- sweep(x = caobj@V, MARGIN = 1, STATS = sqrt(caobj@col_masses), FUN = "/")
}
# Ensure no NA/Inf after dividing by 0.
caobj@std_coords_rows[is.na(caobj@std_coords_rows)] <- 0
caobj@std_coords_cols[is.na(caobj@std_coords_cols)] <- 0
caobj@std_coords_rows[is.infinite(caobj@std_coords_rows)] <- 0
caobj@std_coords_cols[is.infinite(caobj@std_coords_cols)] <- 0
}
stopifnot("princ_coords must be either 0, 1, 2 or 3" = (princ_coords == 0 || princ_coords == 1 || princ_coords == 2 || princ_coords == 3))
if(princ_coords != 0){
stopifnot(!is.empty(caobj@std_coords_rows))
stopifnot(!is.empty(caobj@std_coords_cols))
if (princ_coords == 1){
#principal coordinates for rows
if (dims == 1 && !is.null(dims)){
caobj@prin_coords_rows <- caobj@std_coords_rows*caobj@D
} else {
caobj@prin_coords_rows <- sweep(caobj@std_coords_rows, 2, caobj@D, "*")
}
} else if (princ_coords == 2) {
#principal coordinates for columns
if (dims == 1 && !is.null(dims)){
caobj@prin_coords_cols <- caobj@std_coords_cols*caobj@D
} else {
caobj@prin_coords_cols <- sweep(caobj@std_coords_cols, 2, caobj@D, "*")
}
} else if (princ_coords == 3) {
if (dims == 1 && !is.null(dims)){
#principal coordinates for rows
caobj@prin_coords_rows <- caobj@std_coords_rows*caobj@D
#principal coordinates for columns
caobj@prin_coords_cols <- caobj@std_coords_cols*caobj@D
} else {
#principal coordinates for rows
caobj@prin_coords_rows <- sweep(caobj@std_coords_rows, 2, caobj@D, "*")
#principal coordinates for columns
caobj@prin_coords_cols <- sweep(caobj@std_coords_cols, 2, caobj@D, "*")
}
}
}
stopifnot(validObject(caobj))
return(caobj)
}
#' Scree Plot
#'
#'@description Plots a scree plot.
#'
#'@return
#'Returns a ggplot object.
#'
#'@param df A data frame with columns "dims" and "inertia".
scree_plot <- function(df){
stopifnot(c("dims", "inertia") %in% colnames(df))
avg_inertia <- 100/nrow(df)
max_num_dims <- nrow(df)
screeplot <- ggplot2::ggplot(df, ggplot2::aes(x=dims, y=inertia)) +
ggplot2::geom_col(fill="#4169E1") +
ggplot2::geom_line(color="#B22222", size=1) +
# ggplot2::geom_abline(slope = 0, intercept = avg_inertia, linetype=3, alpha=0.8, color ="#606060")+
ggplot2::labs(title = "Scree plot of explained inertia per dimensions and the average inertia",
y="Explained inertia [%]",
x="Dimension") +
# ggplot2::annotate(
# "text",
# x = max_num_dims*0.9,
# y = avg_inertia,
# label = "avg. inertia")+
ggplot2::theme_bw()
return(screeplot)
}
#' Compute statistics to help choose the number of dimensions
#'
#' @description
#' Allow the user to choose from 4 different methods ("avg_inertia", "maj_inertia", "scree_plot" and "elbow_rule")
#' to estimate the number of dimensions that best represent the data.
#'
#' @details
#' "avg_inertia" calculates the number of dimensions in which the inertia is above the average inertia.
#' "maj_inertia" calculates the number of dimensions in which cumulatively explain up to 80\% of the total inertia.
#' "scree_plot" plots a scree plot.
#' "elbow_rule" formalization of the commonly used elbow rule. Permutes the rows for each column and reruns `cacomp()` for a total of `reps` times.
#' The number of relevant dimensions is obtained from the point where the line for the explained inertia of the permuted data intersects with the actual data.
#'
#' @return
#' For `avg_inertia`, `maj_inertia` and `elbow_rule` (when `return_plot=FALSE`) returns an integer, indicating the suggested number of dimensions to use.
#' `scree_plot` returns a ggplot object.
#' `elbow_rule` (for `return_plot=TRUE`) returns a list with two elements: "dims" contains the number of dimensions and "plot" a ggplot.
#'
#' @param obj A "cacomp" object as outputted from `cacomp()`,
#' a "Seurat" object with a "CA" DimReduc object stored,
#' or a "SingleCellExperiment" object with a "CA" dim. reduction stored.
#' @param mat A numeric matrix. For sequencing a count matrix, gene expression values with genes in rows and samples/cells in columns.
#' Should contain row and column names.
#' @param method String. Either "scree_plot", "avg_inertia", "maj_inertia" or "elbow_rule" (see Details section). Default "scree_plot".
#' @param reps Integer. Number of permutations to perform when choosing "elbow_rule". Default 3.
#' @param return_plot TRUE/FALSE. Whether a plot should be returned when choosing "elbow_rule". Default FALSE.
#' @param 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.
#' @param ... Arguments forwarded to methods.
#' @examples
#' # 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))
#'
#' # Run correspondence analysis.
#' ca <- cacomp(obj = cnts)
#'
#' # pick dimensions with the elbow rule. Returns list.
#'
#' set.seed(2358)
#' pd <- pick_dims(obj = ca,
#' mat = cnts,
#' method = "elbow_rule",
#' return_plot = TRUE,
#' reps = 10)
#' pd$plot
#' ca_sub <- subset_dims(ca, dims = pd$dims)
#'
#' # pick dimensions which explain cumulatively >80% of total inertia. Returns vector.
#' pd <- pick_dims(obj = ca,
#' method = "maj_inertia")
#' ca_sub <- subset_dims(ca, dims = pd)
#' @export
setGeneric("pick_dims", function(obj,
mat = NULL,
method="scree_plot",
reps=3,
python = TRUE,
return_plot = FALSE,
...) {
standardGeneric("pick_dims")
})
#' @rdname pick_dims
#' @export
setMethod(f = "pick_dims",
signature=(obj="cacomp"),
function(obj,
mat = NULL,
method="scree_plot",
reps=3,
python = TRUE,
return_plot = FALSE,
...){
if (!is(obj,"cacomp")){
stop("Not a CA object. Please run cacomp() first!")
}
ev <- obj@D^2
expl_inertia <- (ev/sum(ev)) *100
max_num_dims <- length(obj@D)
if (method == "avg_inertia"){
# Method 1: Dim's > average inertia
avg_inertia <- 100/max_num_dims # percentage of inertia explained by 1 dimension (on average)
dim_num <- sum(expl_inertia > avg_inertia) # result: number of dimensions, all of which explain more than avg_inertia
return(dim_num)
} else if (method == "maj_inertia"){
# Method 2: Sum of dim's > 80% of the total inertia
dim_num <- min(which(cumsum(expl_inertia)>80)) # the first dimension for which the cumulative sum of inertia (from dim1 up to given dimension) is higher than 80%
return(dim_num)
} else if (method == "scree_plot"){
# Method 3: Graphical representation of explained inertia (scree plot)
# the user can set the threshold based on the scree plot
df <- data.frame(dims = seq_len(max_num_dims),
inertia = expl_inertia)
screeplot <- scree_plot(df)
return(screeplot)
} else if (method == "elbow_rule") {
if(is.null(mat)){
cat("When running method=\"elbow_rule\", please provide the original data matrix (paramater mat) which was earlier submitted to cacomp()!")
stop()
}
# Method 4: Formalization of the elbow rule
# 1.Generate an artificial data matrix by randomly permuting the responses (rows)
# of each sample (column) of the original expression matrix
# 2.Perform the CA calculations (I) for the generated data
# 3.Repeat the steps 1. to 3. 5 times and save the calculated
# explained inertia vector (expl_inertia_perm) for each artificial matrix
# as a row in an overall inertia matrix (matrix_expl_inertia_perm)
matrix_expl_inertia_perm <- matrix(0, nrow = max_num_dims , ncol = reps)
pb <- txtProgressBar(min = 0, max = reps, style = 3)
for (k in seq(reps)) {
mat <- as.matrix(mat)
mat_perm <- apply(mat, 2, FUN=sample)
colnames(mat_perm) <- colnames(mat)
rownames(mat_perm) <- seq_len(nrow(mat_perm))
obj_perm <- cacomp(obj=mat_perm, top = obj@top_rows, dims = obj@dims, coords = FALSE, python = python)
ev_perm <- obj_perm@D^2
expl_inertia_perm <- (ev_perm/sum(ev_perm))*100
matrix_expl_inertia_perm[,k] <- expl_inertia_perm
colnames(matrix_expl_inertia_perm) <- paste0("perm",seq_len(reps))
setTxtProgressBar(pb, k)
}
close(pb)
if (return_plot == TRUE){
df <- data.frame(dims = seq_len(max_num_dims),
inertia = expl_inertia)
df <- cbind(df, matrix_expl_inertia_perm)
screeplot <- scree_plot(df)
for (k in seq_len(reps)) {
colnm <- paste0("perm",k)
screeplot <- screeplot +
ggplot2::geom_line(data = df, ggplot2::aes(x=dims, y=.data[[colnm]]), color="black", alpha=0.8, linetype=2)
}
}
# 5.Identify the largest number of dimension based on intersection of the real data's scree plot
# with the average of simulated scree plots
avg_inertia_perm <- rowMeans(matrix_expl_inertia_perm) # average artificial inertia vector
tmp <- as.integer(expl_inertia>avg_inertia_perm) # check for each position (dimension) if expl_inertia is higher than the avg_artificial_inertia. If yes =1, if no =0
if (sum(tmp)==0 || sum(tmp)==max_num_dims){
dim_number <- max_num_dims # result: if the lines do not intersect, choose max_num_dims
} else if (tmp[1] == 0){
stop("Average inertia of the permutated data is above the explained inertia of the data in the first dimension. Please either try more permutations or a different method.")
}else{
dim_number <- length(tmp[cumsum(tmp == 0)<1 & tmp!=0]) # result: if the lines intersect at 1 or more than 1 positions, choose the intersection with the lowest (BEFORE: it was with highest) x-coordinate (:= the first intersection!)
}
#TODO if permutations have higher average inertia in the beginning this results in 0.
if (return_plot == FALSE){
return(dim_number)
} else {
return(list("dims" = dim_number, "plot" = screeplot))
}
} else {
cat("Please pick a valid method!")
stop()
}
})
#' @param assay Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
#' @param slot Character. Data slot of the Seurat assay. E.g. "data" or "counts". Default "counts".
#'
#' @rdname pick_dims
#' @export
#' @examples
#'
#' ################################
#' # pick_dims for Seurat objects #
#' ################################
#' 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")
#'
#' # pick dimensions
#' pd <- pick_dims(obj = seu,
#' method = "maj_inertia",
#' assay = "RNA",
#' slot = "counts")
setMethod(f = "pick_dims",
signature=(obj="Seurat"),
function(obj,
mat = NULL,
method="scree_plot",
reps=3,
python = TRUE,
return_plot = FALSE,
...,
assay = Seurat::DefaultAssay(obj),
slot = "counts"){
stopifnot("obj doesn't belong to class 'Seurat'" = is(obj, "Seurat"))
if (method == "elbow_rule") {
seu <- Seurat::GetAssayData(object = obj, assay = assay, slot = slot)
seu <- as.matrix(seu)
} else {
seu <- NULL
}
if ("CA" %in% Seurat::Reductions(obj)){
caobj <- as.cacomp(obj, assay = assay)
} else {
stop("No 'CA' dimension reduction object found. Please run cacomp(seurat_obj, top, coords = FALSE, return_input=TRUE) first.")
}
stopifnot(is(caobj, "cacomp"))
pick_dims(obj = caobj,
mat = seu,
method = method,
reps = reps,
return_plot = return_plot,
python = python)
})
#' @param assay Character. The assay from which to extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
#'
#' @rdname pick_dims
#' @export
#' @examples
#'
#' ##############################################
#' # pick_dims for SingleCellExperiment objects #
#' ##############################################
#' 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))
#'
#' # Create SingleCellExperiment object
#' sce <- SingleCellExperiment(assays=list(counts=cnts))
#'
#' # run CA and save in dim. reduction slot.
#' sce <- cacomp(sce, return_input = TRUE, assay = "counts")
#'
#' # pick dimensions
#' pd <- pick_dims(obj = sce,
#' method = "maj_inertia",
#' assay = "counts")
setMethod(f = "pick_dims",
signature=(obj="SingleCellExperiment"),
function(obj,
mat = NULL,
method="scree_plot",
reps=3,
python = TRUE,
return_plot = FALSE,
...,
assay = "counts"){
stopifnot("obj doesn't belong to class 'SingleCellExperiment'" = is(obj, "SingleCellExperiment"))
if (method == "elbow_rule") {
mat <- SummarizedExperiment::assay(obj, assay)
} else {
mat <- NULL
}
if ("CA" %in% SingleCellExperiment::reducedDimNames(obj)){
caobj <- as.cacomp(obj, assay = assay)
} else {
stop("No 'CA' dim. reduction object found. Please run cacomp(sce, top, coords = FALSE, return_input=TRUE) first.")
}
stopifnot(is(caobj, "cacomp"))
pick_dims(obj = caobj,
mat = mat,
method = method,
reps = reps,
return_plot = return_plot,
python = python)
})
elagralinska/APLpackage documentation built on Dec. 20, 2021, 4:15 a.m.
R Package Documentation
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Defines functions scree_plot ca_coords subset_dims run_cacomp var_rows rm_zeros comp_std_residuals
Documented in ca_coords comp_std_residuals rm_zeros run_cacomp scree_plot subset_dims var_rows
#' @include constructor.R
NULL
#' Compute Standardized Residuals
#'
#' @description
#' `comp_std_residuals` computes the standardized residual matrix S,
#' which is the basis for correspondence analysis and serves
#' as input for singular value decomposition (SVD).
#'
#' @details
#' Calculates standardized residual matrix S from the proportion matrix P and
#' the expected values E according to \eqn{S = \frac{(P-E)}{sqrt(E)}}.
#'
#' @param mat A numerical matrix or coercible to one by `as.matrix()`.
#' Should have row and column names.
#' @return
#' A named list with standardized residual matrix "S",
#' grand total of the original matrix "tot"
#' as well as row and column masses "rowm" and "colm" respectively.
#'
comp_std_residuals <- function(mat){
if (!is(mat, "matrix")){
mat <- as.matrix(mat)
}
stopifnot("Input matrix does not have any rownames!" = !is.null(rownames(mat)))
stopifnot("Input matrix does not have any colnames!" = !is.null(colnames(mat)))
tot <- sum(mat)
P <- mat/tot # proportions matrix
rowm <- rowSums(P) # row masses
colm <- colSums(P) # column masses
E <- rowm %o% colm # expected proportions
S <- (P - E) / sqrt(E) # standardized residuals
S[is.nan(S)] <- 0
out <- list("S"=S, "tot"=tot, "rowm"=rowm, "colm"=colm)
return(out)
}
#' removes 0-only rows and columns in a matrix.
#'
#' @param obj A matrix.
#'
rm_zeros <- function(obj){
stopifnot(is(obj, "matrix"))
no_zeros_rows <- rowSums(obj) > 0
no_zeros_cols <- colSums(obj) > 0
if (sum(!no_zeros_rows) != 0){
## Delete genes with only zero values across all columns
warning("Matrix contains rows with only 0s. These rows were removed. If undesired set rm_zeros = FALSE.")
obj <- obj[no_zeros_rows,]
}
if (sum(!no_zeros_cols) != 0){
## Delete cells with only zero values across all genes
warning("Matrix contains columns with only 0s. These columns were removed. If undesired set rm_zeros = FALSE.")
obj <- obj[,no_zeros_cols]
}
return(obj)
}
#' Find most variable rows
#'
#' @description
#' Calculates the variance of the chi-square component matrix and selects the rows with the highest variance, e.g. 5,000.
#'
#' @return
#' Returns a matrix, which consists of the top variable rows of mat.
#'
#' @param mat A numeric matrix. For sequencing a count matrix,
#' gene expression values with genes in rows and samples/cells in columns.
#' Should contain row and column names.
#' @param top Integer. Number of most variable rows to retain. Default 5000.
#' @export
#' @examples
#' 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))
#'
#' # Choose top 5000 most variable genes
#' cnts <- var_rows(mat = cnts, top = 5000)
#'
#'
var_rows <- function(mat, top = 5000){
res <- comp_std_residuals(mat=mat)
if(top>nrow(mat)) warning("Top is larger than the number of rows in matrix. Top was set to nrow(mat).")
top <- min(nrow(mat), top)
chisquare <- res$tot * (res$S^2) # chi-square components matrix
variances <- apply(chisquare,1,var) #row-wise variances
ix_var <- order(-variances)
mat <- mat[ix_var[seq_len(top)],] # choose top rows
return(mat)
}
#' Internal function for `cacomp`
#'
#' @description
#' `run_cacomp` performs correspondence analysis on a matrix and returns the transformed data.
#'
#' @details
#' The calculation is performed according to the work of Michael Greenacre. Singular value decomposition
#' can be performed either with the base R function `svd` or preferably by the faster
#' pytorch implementation (python = TRUE). When working with large matrices, CA coordinates and
#' principal coordinates should only be computed when needed to save computational time.
#'
#' @return
#' 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/genes were retained for the analysis.
#' tot_inertia, row_inertia and col_inertia: Only if inertia = TRUE. Total, row and column inertia respectively.
#' @references
#' Greenacre, M. Correspondence Analysis in Practice, Third Edition, 2017.
#' @param 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.
#' @param coords Logical. Indicates whether CA standard coordinates should be calculated. Default TRUE
#' @param 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.
#' @param princ_coords Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0).
#' Default 1.
#' @param dims Integer. Number of CA dimensions to retain. Default NULL (keeps all dimensions).
#' @param top Integer. Number of most variable rows to retain. Default NULL.
#' @param inertia Logical.. Whether total, row and column inertias should be calculated and returned. Default TRUE.
#' @param rm_zeros Logical. Whether rows & cols containing only 0s should be removed. Keeping zero only rows/cols might lead to unexpected results. Default TRUE.
#' @param ... Arguments forwarded to methods.
run_cacomp <- function(obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = NULL,
top = NULL,
inertia = TRUE,
rm_zeros = TRUE,
...){
stopifnot("Input matrix does not have any rownames!" = !is.null(rownames(obj)))
stopifnot("Input matrix does not have any colnames!" = !is.null(colnames(obj)))
if (rm_zeros == TRUE){
obj <- rm_zeros(obj)
}
# Choose only top # of variable genes
if (is.null(top) || top == nrow(obj)) {
res <- comp_std_residuals(mat=obj)
toptmp <- nrow(obj)
} else if (!is.null(top) && top < nrow(obj)){
# message("Subsetting matrix by the top ", top," most variable rows (chisquare variances) ...")
obj <- var_rows(mat = obj, top = top)
res <- comp_std_residuals(mat=obj)
toptmp <- top
} else if (top > nrow(obj)) {
warning("\nParameter top is >nrow(obj) and therefore ignored.")
res <- comp_std_residuals(mat=obj)
toptmp <- nrow(obj)
} else {
warning("\nUnusual input for top, argument ignored.")
res <- comp_std_residuals(mat=obj)
toptmp <- nrow(obj)
}
S <- res$S
tot <- res$tot
rowm <- res$rowm
colm <- res$colm
rm(res)
# n <- nrow(S)
# p <- ncol(S)
k <- min(dim(S))-1
if (is.null(dims)) dims <- k
if (dims > k) dims <- k
# S <- (diag(1/sqrt(r)))%*%(P-r%*%t(c))%*%(diag(1/sqrt(c)))
# message("Running singular value decomposition ...")
if (python == TRUE){
# require(reticulate)
# source_python('./python_svd.py')
reticulate::source_python(system.file("python/python_svd.py", package = "APL"))
SVD <- svd_torch(S)
# SVD <- svd_linalg_torch(S)
names(SVD) <- c("U", "D", "V")
SVD$D <- as.vector(SVD$D)
} else {
SVD <- svd(S, nu = dims, nv = dims)
names(SVD) <- c("D", "U", "V")
SVD <- SVD[c(2, 1, 3)]
if(length(SVD$D) > dims) SVD$D <- SVD$D[seq_len(dims)]
}
names(SVD$D) <- paste0("Dim", seq_len(length(SVD$D)))
dimnames(SVD$V) <- list(colnames(S), paste0("Dim", seq_len(ncol(SVD$V))))
dimnames(SVD$U) <- list(rownames(S), paste0("Dim", seq_len(ncol(SVD$U))))
if(inertia == TRUE){
#calculate inertia
SVD$tot_inertia <- sum(SVD$D^2)
SVD$row_inertia <- rowSums(S^2)
SVD$col_inertia <- colSums(S^2)
}
SVD$row_masses <- rowm
SVD$col_masses <- colm
SVD$top_rows <- toptmp
SVD <- do.call(new_cacomp, SVD)
SVD <- subset_dims(SVD, dims)
# class(SVD) <- "cacomp"
if (coords == TRUE){
# message("Calculating coordinates...")
SVD <- ca_coords(caobj = SVD,
dims = dims,
princ_coords = princ_coords,
princ_only = FALSE)
} else {
if(!is.null(dims)){
if (dims >= length(SVD@D)){
if (dims > length(SVD@D)){
warning("Chosen number of dimensions is larger than the number of dimensions obtained from the singular value decomposition. Argument ignored.")
}
SVD@dims <- length(SVD@D)
} else {
dims <- min(dims, length(SVD@D))
SVD@dims <- dims
dims <- seq(dims)
# subset to number of dimensions
SVD@U <- SVD@U[,dims]
SVD@V <- SVD@V[,dims]
SVD@D <- SVD@D[dims]
}
} else {
SVD@dims <- length(SVD@D)
}
}
stopifnot(validObject(SVD))
return(SVD)
}
#' Correspondance Analysis
#'
#' @description
#' `cacomp` performs correspondence analysis on a matrix or
#' Seurat/SingleCellExperiment object and returns the transformed data.
#'
#' @details
#' The calculation is performed according to the work of Michael Greenacre. Singular value decomposition
#' can be performed either with the base R function `svd` or preferably by the faster
#' pytorch implementation (python = TRUE). When working with large matrices, CA coordinates and
#' principal coordinates should only be computed when needed to save computational time.
#'
#' @return
#' 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.
#' @references
#' Greenacre, M. Correspondence Analysis in Practice, Third Edition, 2017.
#' @param 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.
#' @param coords Logical. Indicates whether CA standard coordinates should be calculated. Default TRUE
#' @param 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.
#' @param princ_coords Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0).
#' Default 1.
#' @param dims Integer. Number of CA dimensions to retain. Default NULL (keeps all dimensions).
#' @param top Integer. Number of most variable rows to retain. Default NULL.
#' @param inertia Logical.. Whether total, row and column inertias should be calculated and returned. Default TRUE.
#' @param rm_zeros Logical. Whether rows & cols containing only 0s should be removed.
#' Keeping zero only rows/cols might lead to unexpected results. Default TRUE.
#' @param ... Arguments forwarded to methods.
#' @examples
#' # 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)
#' @export
setGeneric("cacomp", function(obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = NULL,
top = NULL,
inertia = TRUE,
rm_zeros = TRUE,
...) {
standardGeneric("cacomp")
})
#' @rdname cacomp
#' @export
setMethod(f = "cacomp",
signature=(obj="matrix"),
function(obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = NULL,
top = NULL,
inertia = TRUE,
rm_zeros = TRUE,
...){
caobj <- run_cacomp(obj = obj,
coords = coords,
princ_coords = princ_coords,
python = python,
dims = dims,
top = top,
inertia = inertia,
rm_zeros = rm_zeros,
...)
return(caobj)
})
#' Correspondance Analysis for Seurat objects
#'
#' @description
#' `cacomp.seurat` performs correspondence analysis on an assay from a Seurat container and stores the standardized coordinates
#' of the columns (= cells) and the principal coordinates of the rows (= genes) as a DimReduc Object in the Seurat container.
#'
#' @return
#' 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()`.
#' @param assay Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
#' @param slot character. The slot of the Seurat assay. Default "counts".
#' @param 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.
#' @param ... Other parameters
#' @rdname cacomp
#' @export
#' @examples
#' 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")
setMethod(f = "cacomp",
signature=(obj="Seurat"),
function(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){
stopifnot("obj doesnt belong to class 'Seurat'" = is(obj, "Seurat"))
stopifnot("Set coords = TRUE when inputting a Seurat object and return_input = TRUE." = coords == TRUE)
seu <- Seurat::GetAssayData(object = obj, assay = assay, slot = slot)
seu <- as.matrix(seu)
caobj <- run_cacomp(obj = seu,
coords = coords,
top = top,
princ_coords = princ_coords,
dims = dims,
python = python,
rm_zeros = rm_zeros,
inertia = inertia,
...)
if (return_input == TRUE){
colnames(caobj@V) <- paste0("DIM_", seq(ncol(caobj@V)))
colnames(caobj@U) <- paste0("DIM_", seq(ncol(caobj@U)))
obj[["CA"]] <- Seurat::CreateDimReducObject(embeddings = caobj@std_coords_cols,
loadings = caobj@prin_coords_rows,
stdev = caobj@D,
key = "Dim_",
assay = assay)
return(obj)
} else {
return(caobj)
}
})
#' Correspondance Analysis for SingleCellExperiment objects
#'
#' @description
#' `cacomp.SingleCellExperiment` performs correspondence analysis on an assay from a SingleCellExperiment and stores the standardized coordinates
#' of the columns (= cells) and the principal coordinates of the rows (= genes) as a matrix in the SingleCellExperiment container.
#'
#' @return
#' If return_input =TRUE for SingleCellExperiment input returns a SingleCellExperiment object with a matrix of standardized 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()`.
#' @param assay Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
#' @param 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.
#' @rdname cacomp
#' @export
#' @examples
#' 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")
setMethod(f = "cacomp",
signature=(obj="SingleCellExperiment"),
function(obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = NULL,
top = NULL,
inertia = TRUE,
rm_zeros = TRUE,
...,
assay = "counts",
return_input = FALSE){
stopifnot("obj doesnt belong to class 'SingleCellExperiment'" = is(obj, "SingleCellExperiment"))
stopifnot("Set coords = TRUE when inputting a SingleCellExperiment object and return_input = TRUE." = coords == TRUE)
mat <- SummarizedExperiment::assay(obj, assay)
mat <- as.matrix(mat)
top <- min(nrow(mat), top)
caobj <- run_cacomp(obj = mat,
coords = coords,
top = top,
princ_coords = princ_coords,
dims = dims,
python = python,
rm_zeros = rm_zeros,
inertia = inertia,
...)
if (return_input == TRUE){
prinInertia <- caobj@D^2
percentInertia <- prinInertia / sum(prinInertia) * 100 #TODO IS THIS CORRECT ??
# Saving the results
ca <- caobj@std_coords_cols
attr(ca, "prin_coords_rows") <- caobj@prin_coords_rows
attr(ca, "singval") <- caobj@D
attr(ca, "percInertia") <- percentInertia
SingleCellExperiment::reducedDim(obj, "CA") <- ca
return(obj)
} else {
return(caobj)
}
})
#' Subset dimensions of a caobj
#'
#' @description Subsets the dimensions according to user input.
#'
#' @return Returns caobj.
#'
#' @param caobj A caobj.
#' @param dims Integer. Number of dimensions.
#' @examples
#' # 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(cnts)
#' ca <- subset_dims(ca, 2)
#' @export
subset_dims <- function(caobj, dims){
stopifnot(is(caobj, "cacomp"))
if(dims > length(caobj@D)){
warning("dims is larger than the number of available dimensions. Argument ignored")
} else if (dims == length(caobj@D)){
caobj@dims <- dims
return(caobj)
}
dims <- min(dims, length(caobj@D))
caobj@dims <- dims
dims <- seq(dims)
caobj@U <- caobj@U[,dims]
caobj@V <- caobj@V[,dims]
caobj@D <- caobj@D[dims]
if (!is.empty(caobj@std_coords_cols)){
caobj@std_coords_cols <- caobj@std_coords_cols[,dims]
}
if (!is.empty(caobj@prin_coords_cols)){
caobj@prin_coords_cols <- caobj@prin_coords_cols[,dims]
}
if (!is.empty(caobj@std_coords_rows)){
caobj@std_coords_rows <- caobj@std_coords_rows[,dims]
}
if (!is.empty(caobj@prin_coords_rows)){
caobj@prin_coords_rows <- caobj@prin_coords_rows[,dims]
}
stopifnot(validObject(caobj))
return(caobj)
}
#' Calculate correspondence analysis row and column coordinates.
#'
#' @description `ca_coords` calculates the standardized and principal coordinates of the rows and columns in CA space.
#'
#' @details
#' Takes a "cacomp" object and calculates standardized and principal coordinates for the visualization of CA results in a biplot or
#' to subsequently calculate coordinates in an Association Plot.
#'
#' @return
#' Returns input object with coordinates added.
#' std_coords_rows/std_coords_cols: Standardized coordinates of rows/columns.
#' prin_coords_rows/prin_coords_cols: Principal coordinates of rows/columns.
#'
#' @param caobj A "cacomp" object as outputted from `cacomp()`.
#' @param dims Integer indicating the number of dimensions to use for the calculation of coordinates.
#' All elements of caobj (where applicable) will be reduced to the given number of dimensions. Default NULL (keeps all dimensions).
#' @param princ_only Logical, whether only principal coordinates should be calculated.
#' Or, in other words, whether the standardized coordinates are already calculated and stored in `caobj`. Default `FALSE`.
#' @param princ_coords Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0).
#' Default 3.
#' @examples
#' # 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 = 1)
#' ca <- ca_coords(ca, princ_coords = 3)
#' @export
ca_coords <- function(caobj, dims=NULL, princ_coords = 3, princ_only = FALSE){
stopifnot(is(caobj, "cacomp"))
stopifnot(dims <= length(caobj@D))
if(!is.null(dims)){
if (dims > length(caobj@D)){
warning("Chosen dimensions are larger than the number of dimensions obtained from the singular value decomposition. Argument ignored.")
}
caobj <- subset_dims(caobj = caobj, dims = dims)
}
if(princ_only == FALSE){
#standard coordinates
if(dims == 1 && !is.null(dims)){
caobj@std_coords_rows <- caobj@U/sqrt(caobj@row_masses)
caobj@std_coords_cols <- caobj@V/sqrt(caobj@col_masses)
} else {
caobj@std_coords_rows <- sweep(x = caobj@U, MARGIN = 1, STATS = sqrt(caobj@row_masses), FUN = "/")
caobj@std_coords_cols <- sweep(x = caobj@V, MARGIN = 1, STATS = sqrt(caobj@col_masses), FUN = "/")
}
# Ensure no NA/Inf after dividing by 0.
caobj@std_coords_rows[is.na(caobj@std_coords_rows)] <- 0
caobj@std_coords_cols[is.na(caobj@std_coords_cols)] <- 0
caobj@std_coords_rows[is.infinite(caobj@std_coords_rows)] <- 0
caobj@std_coords_cols[is.infinite(caobj@std_coords_cols)] <- 0
}
stopifnot("princ_coords must be either 0, 1, 2 or 3" = (princ_coords == 0 || princ_coords == 1 || princ_coords == 2 || princ_coords == 3))
if(princ_coords != 0){
stopifnot(!is.empty(caobj@std_coords_rows))
stopifnot(!is.empty(caobj@std_coords_cols))
if (princ_coords == 1){
#principal coordinates for rows
if (dims == 1 && !is.null(dims)){
caobj@prin_coords_rows <- caobj@std_coords_rows*caobj@D
} else {
caobj@prin_coords_rows <- sweep(caobj@std_coords_rows, 2, caobj@D, "*")
}
} else if (princ_coords == 2) {
#principal coordinates for columns
if (dims == 1 && !is.null(dims)){
caobj@prin_coords_cols <- caobj@std_coords_cols*caobj@D
} else {
caobj@prin_coords_cols <- sweep(caobj@std_coords_cols, 2, caobj@D, "*")
}
} else if (princ_coords == 3) {
if (dims == 1 && !is.null(dims)){
#principal coordinates for rows
caobj@prin_coords_rows <- caobj@std_coords_rows*caobj@D
#principal coordinates for columns
caobj@prin_coords_cols <- caobj@std_coords_cols*caobj@D
} else {
#principal coordinates for rows
caobj@prin_coords_rows <- sweep(caobj@std_coords_rows, 2, caobj@D, "*")
#principal coordinates for columns
caobj@prin_coords_cols <- sweep(caobj@std_coords_cols, 2, caobj@D, "*")
}
}
}
stopifnot(validObject(caobj))
return(caobj)
}
#' Scree Plot
#'
#'@description Plots a scree plot.
#'
#'@return
#'Returns a ggplot object.
#'
#'@param df A data frame with columns "dims" and "inertia".
scree_plot <- function(df){
stopifnot(c("dims", "inertia") %in% colnames(df))
avg_inertia <- 100/nrow(df)
max_num_dims <- nrow(df)
screeplot <- ggplot2::ggplot(df, ggplot2::aes(x=dims, y=inertia)) +
ggplot2::geom_col(fill="#4169E1") +
ggplot2::geom_line(color="#B22222", size=1) +
# ggplot2::geom_abline(slope = 0, intercept = avg_inertia, linetype=3, alpha=0.8, color ="#606060")+
ggplot2::labs(title = "Scree plot of explained inertia per dimensions and the average inertia",
y="Explained inertia [%]",
x="Dimension") +
# ggplot2::annotate(
# "text",
# x = max_num_dims*0.9,
# y = avg_inertia,
# label = "avg. inertia")+
ggplot2::theme_bw()
return(screeplot)
}
#' Compute statistics to help choose the number of dimensions
#'
#' @description
#' Allow the user to choose from 4 different methods ("avg_inertia", "maj_inertia", "scree_plot" and "elbow_rule")
#' to estimate the number of dimensions that best represent the data.
#'
#' @details
#' "avg_inertia" calculates the number of dimensions in which the inertia is above the average inertia.
#' "maj_inertia" calculates the number of dimensions in which cumulatively explain up to 80\% of the total inertia.
#' "scree_plot" plots a scree plot.
#' "elbow_rule" formalization of the commonly used elbow rule. Permutes the rows for each column and reruns `cacomp()` for a total of `reps` times.
#' The number of relevant dimensions is obtained from the point where the line for the explained inertia of the permuted data intersects with the actual data.
#'
#' @return
#' For `avg_inertia`, `maj_inertia` and `elbow_rule` (when `return_plot=FALSE`) returns an integer, indicating the suggested number of dimensions to use.
#' `scree_plot` returns a ggplot object.
#' `elbow_rule` (for `return_plot=TRUE`) returns a list with two elements: "dims" contains the number of dimensions and "plot" a ggplot.
#'
#' @param obj A "cacomp" object as outputted from `cacomp()`,
#' a "Seurat" object with a "CA" DimReduc object stored,
#' or a "SingleCellExperiment" object with a "CA" dim. reduction stored.
#' @param mat A numeric matrix. For sequencing a count matrix, gene expression values with genes in rows and samples/cells in columns.
#' Should contain row and column names.
#' @param method String. Either "scree_plot", "avg_inertia", "maj_inertia" or "elbow_rule" (see Details section). Default "scree_plot".
#' @param reps Integer. Number of permutations to perform when choosing "elbow_rule". Default 3.
#' @param return_plot TRUE/FALSE. Whether a plot should be returned when choosing "elbow_rule". Default FALSE.
#' @param 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.
#' @param ... Arguments forwarded to methods.
#' @examples
#' # 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))
#'
#' # Run correspondence analysis.
#' ca <- cacomp(obj = cnts)
#'
#' # pick dimensions with the elbow rule. Returns list.
#'
#' set.seed(2358)
#' pd <- pick_dims(obj = ca,
#' mat = cnts,
#' method = "elbow_rule",
#' return_plot = TRUE,
#' reps = 10)
#' pd$plot
#' ca_sub <- subset_dims(ca, dims = pd$dims)
#'
#' # pick dimensions which explain cumulatively >80% of total inertia. Returns vector.
#' pd <- pick_dims(obj = ca,
#' method = "maj_inertia")
#' ca_sub <- subset_dims(ca, dims = pd)
#' @export
setGeneric("pick_dims", function(obj,
mat = NULL,
method="scree_plot",
reps=3,
python = TRUE,
return_plot = FALSE,
...) {
standardGeneric("pick_dims")
})
#' @rdname pick_dims
#' @export
setMethod(f = "pick_dims",
signature=(obj="cacomp"),
function(obj,
mat = NULL,
method="scree_plot",
reps=3,
python = TRUE,
return_plot = FALSE,
...){
if (!is(obj,"cacomp")){
stop("Not a CA object. Please run cacomp() first!")
}
ev <- obj@D^2
expl_inertia <- (ev/sum(ev)) *100
max_num_dims <- length(obj@D)
if (method == "avg_inertia"){
# Method 1: Dim's > average inertia
avg_inertia <- 100/max_num_dims # percentage of inertia explained by 1 dimension (on average)
dim_num <- sum(expl_inertia > avg_inertia) # result: number of dimensions, all of which explain more than avg_inertia
return(dim_num)
} else if (method == "maj_inertia"){
# Method 2: Sum of dim's > 80% of the total inertia
dim_num <- min(which(cumsum(expl_inertia)>80)) # the first dimension for which the cumulative sum of inertia (from dim1 up to given dimension) is higher than 80%
return(dim_num)
} else if (method == "scree_plot"){
# Method 3: Graphical representation of explained inertia (scree plot)
# the user can set the threshold based on the scree plot
df <- data.frame(dims = seq_len(max_num_dims),
inertia = expl_inertia)
screeplot <- scree_plot(df)
return(screeplot)
} else if (method == "elbow_rule") {
if(is.null(mat)){
cat("When running method=\"elbow_rule\", please provide the original data matrix (paramater mat) which was earlier submitted to cacomp()!")
stop()
}
# Method 4: Formalization of the elbow rule
# 1.Generate an artificial data matrix by randomly permuting the responses (rows)
# of each sample (column) of the original expression matrix
# 2.Perform the CA calculations (I) for the generated data
# 3.Repeat the steps 1. to 3. 5 times and save the calculated
# explained inertia vector (expl_inertia_perm) for each artificial matrix
# as a row in an overall inertia matrix (matrix_expl_inertia_perm)
matrix_expl_inertia_perm <- matrix(0, nrow = max_num_dims , ncol = reps)
pb <- txtProgressBar(min = 0, max = reps, style = 3)
for (k in seq(reps)) {
mat <- as.matrix(mat)
mat_perm <- apply(mat, 2, FUN=sample)
colnames(mat_perm) <- colnames(mat)
rownames(mat_perm) <- seq_len(nrow(mat_perm))
obj_perm <- cacomp(obj=mat_perm, top = obj@top_rows, dims = obj@dims, coords = FALSE, python = python)
ev_perm <- obj_perm@D^2
expl_inertia_perm <- (ev_perm/sum(ev_perm))*100
matrix_expl_inertia_perm[,k] <- expl_inertia_perm
colnames(matrix_expl_inertia_perm) <- paste0("perm",seq_len(reps))
setTxtProgressBar(pb, k)
}
close(pb)
if (return_plot == TRUE){
df <- data.frame(dims = seq_len(max_num_dims),
inertia = expl_inertia)
df <- cbind(df, matrix_expl_inertia_perm)
screeplot <- scree_plot(df)
for (k in seq_len(reps)) {
colnm <- paste0("perm",k)
screeplot <- screeplot +
ggplot2::geom_line(data = df, ggplot2::aes(x=dims, y=.data[[colnm]]), color="black", alpha=0.8, linetype=2)
}
}
# 5.Identify the largest number of dimension based on intersection of the real data's scree plot
# with the average of simulated scree plots
avg_inertia_perm <- rowMeans(matrix_expl_inertia_perm) # average artificial inertia vector
tmp <- as.integer(expl_inertia>avg_inertia_perm) # check for each position (dimension) if expl_inertia is higher than the avg_artificial_inertia. If yes =1, if no =0
if (sum(tmp)==0 || sum(tmp)==max_num_dims){
dim_number <- max_num_dims # result: if the lines do not intersect, choose max_num_dims
} else if (tmp[1] == 0){
stop("Average inertia of the permutated data is above the explained inertia of the data in the first dimension. Please either try more permutations or a different method.")
}else{
dim_number <- length(tmp[cumsum(tmp == 0)<1 & tmp!=0]) # result: if the lines intersect at 1 or more than 1 positions, choose the intersection with the lowest (BEFORE: it was with highest) x-coordinate (:= the first intersection!)
}
#TODO if permutations have higher average inertia in the beginning this results in 0.
if (return_plot == FALSE){
return(dim_number)
} else {
return(list("dims" = dim_number, "plot" = screeplot))
}
} else {
cat("Please pick a valid method!")
stop()
}
})
#' @param assay Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
#' @param slot Character. Data slot of the Seurat assay. E.g. "data" or "counts". Default "counts".
#'
#' @rdname pick_dims
#' @export
#' @examples
#'
#' ################################
#' # pick_dims for Seurat objects #
#' ################################
#' 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")
#'
#' # pick dimensions
#' pd <- pick_dims(obj = seu,
#' method = "maj_inertia",
#' assay = "RNA",
#' slot = "counts")
setMethod(f = "pick_dims",
signature=(obj="Seurat"),
function(obj,
mat = NULL,
method="scree_plot",
reps=3,
python = TRUE,
return_plot = FALSE,
...,
assay = Seurat::DefaultAssay(obj),
slot = "counts"){
stopifnot("obj doesn't belong to class 'Seurat'" = is(obj, "Seurat"))
if (method == "elbow_rule") {
seu <- Seurat::GetAssayData(object = obj, assay = assay, slot = slot)
seu <- as.matrix(seu)
} else {
seu <- NULL
}
if ("CA" %in% Seurat::Reductions(obj)){
caobj <- as.cacomp(obj, assay = assay)
} else {
stop("No 'CA' dimension reduction object found. Please run cacomp(seurat_obj, top, coords = FALSE, return_input=TRUE) first.")
}
stopifnot(is(caobj, "cacomp"))
pick_dims(obj = caobj,
mat = seu,
method = method,
reps = reps,
return_plot = return_plot,
python = python)
})
#' @param assay Character. The assay from which to extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
#'
#' @rdname pick_dims
#' @export
#' @examples
#'
#' ##############################################
#' # pick_dims for SingleCellExperiment objects #
#' ##############################################
#' 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))
#'
#' # Create SingleCellExperiment object
#' sce <- SingleCellExperiment(assays=list(counts=cnts))
#'
#' # run CA and save in dim. reduction slot.
#' sce <- cacomp(sce, return_input = TRUE, assay = "counts")
#'
#' # pick dimensions
#' pd <- pick_dims(obj = sce,
#' method = "maj_inertia",
#' assay = "counts")
setMethod(f = "pick_dims",
signature=(obj="SingleCellExperiment"),
function(obj,
mat = NULL,
method="scree_plot",
reps=3,
python = TRUE,
return_plot = FALSE,
...,
assay = "counts"){
stopifnot("obj doesn't belong to class 'SingleCellExperiment'" = is(obj, "SingleCellExperiment"))
if (method == "elbow_rule") {
mat <- SummarizedExperiment::assay(obj, assay)
} else {
mat <- NULL
}
if ("CA" %in% SingleCellExperiment::reducedDimNames(obj)){
caobj <- as.cacomp(obj, assay = assay)
} else {
stop("No 'CA' dim. reduction object found. Please run cacomp(sce, top, coords = FALSE, return_input=TRUE) first.")
}
stopifnot(is(caobj, "cacomp"))
pick_dims(obj = caobj,
mat = mat,
method = method,
reps = reps,
return_plot = return_plot,
python = python)
})
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