R/5_pca.R
In bhagwataditya/importomics: Unified statistal Modeling of Omics Data
Defines functions
sma
pls
pca
loadings
loadingmat
scores
scoremat
variances
variancenames
methodname
loadingnames
scorenames
.filter_minvar
evenify_upwards
has_odd_length
has_even_length
is_odd
is_even
flip_sign_if_all_exprs_are_negative
Documented in
loadingmat
loadings
pca
pls
scoremat
scores
sma
#=============================================================================
#
# flip_sign_if_all_exprs_are_negative.
# evenify_upwards
#
#=============================================================================
#' Flip sign if all expr values are negative
#' @param x matrix
#' @param verbose TRUE (default) or FALSE
#' @return updated matrix
#' @noRd
flip_sign_if_all_exprs_are_negative <- function(x, verbose=TRUE){
idx <- !is.na(x)
if (all(sign(x[idx])==-1)){
if (verbose) message('\t\tAll values negative: ',
'flip signs to prevent singularities.')
x %<>% multiply_by(-1)
}
x
}
#' Is even/odd?
#' @param x integer
#' @return TRUE or FALSE
#' @examples
#' is_even(13)
#' is_even(12)
#' is_odd(13)
#' is_odd(12)
#' @noRd
is_even <- function(x) (x %% 2) == 0
is_odd <- function(x) !is_even(x)
#' Has even/odd length?
#' @param x vector
#' @return logical
#' @examples
#' has_even_length(1:2)
#' has_odd_length(1:2)
#' has_even_length(1:3)
#' has_odd_length(1:3)
#' @noRd
has_even_length <- function(x) is_even(length(x))
has_odd_length <- function(x) is_odd(length(x))
#' Evenify upwards
#' @param x integer
#' @return integer
#' @examples
#' evenify_upwards(3)
#' @noRd
evenify_upwards <- function(x) if (is_odd(x)) x+1 else x
#============================================================================
#
# pca sma lda pls spls ropls
# .filter_minvar
#
#============================================================================
.filter_minvar <- function(object, method, minvar) {
variances <- metadata(object)[[method]]
discard_components <- variances[variances < minvar] %>% names()
sdata(object)[discard_components] <- NULL
fdata(object)[discard_components] <- NULL
metadata(object)[[method]] <- variances[!names(variances) %in% discard_components]
object
}
scorenames <- function( method = 'pca', by, dims = 1:2, sep = FITSEP ) paste0('effect', sep, by, sep, method, dims)
loadingnames <- function( method = 'pca', by, dims = 1:2, sep = FITSEP ) paste0('effect', sep, by, sep, method, dims)
methodname <- function( method = 'pca', by, sep = FITSEP ) paste0( by, sep, method )
variancenames <- function( dims = 1:2 ) paste0('effect', dims)
variances <- function(
object, method = 'pca', by = biplot_by(object, method), dims = 1:2, sep = FITSEP
){
y <- metadata(object)
y %<>% extract2(methodname(method, by = by, sep = sep))
y %<>% extract(variancenames(dims))
y
}
#' Extract scores/loadings
#' @param object SummarizedExperiment
#' @param method 'pca', 'pls', etc.
#' @param by svar (string)
#' @param dim numeric vector
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file)
#' object %<>% pca()
#' scores(object)[1:2]
#' loadings(object)[1:2]
#' scoremat(object)[1:2, ]
#' loadingmat(object)[1:2, ]
#' @export
scoremat <- function(
object, method = 'pca', by = biplot_by(object, method), dim = 1:2
){
sep <- guess_fitsep(fdt(object))
cols <- 'sample_id'
cols %<>% c(scorenames(method = method, by = by, dims = dim, sep = sep))
mat <- sdt(object)[, cols, with = FALSE]
mat %<>% dt2mat()
mat
}
#' @rdname scoremat
#' @export
scores <- function(
object, method = 'pca', by = biplot_by(object, method), dim = 1
){
sep <- guess_fitsep(fdt(object))
cols <- scorenames(method = method, by = by, dims = dim, sep = sep)
sdt(object)[[cols]]
}
#' @rdname scoremat
#' @export
loadingmat <- function(
object, method = 'pca', by = biplot_by(object, method), dim = 1:2
){
sep <- guess_fitsep(fdt(object))
cols <- 'feature_id'
cols %<>% c(loadingnames(method = method, by = by, dims = dim, sep = sep))
mat <- fdt(object)[, cols, with = FALSE]
mat %<>% dt2mat()
mat
}
#' @rdname scoremat
#' @export
loadings <- function(
object, method = 'pca', by = biplot_by(object, method), dim = 1
){
sep <- guess_fitsep(fdt(object))
cols <- loadingnames(method = method, by = by, dims = dim, sep = sep)
fdt(object)[[cols]]
}
#' PCA, SMA, LDA, PLS, SPLS, OPLS
#'
#' Perform a dimension reduction.
#' Store sample scores, feature loadings, and dimension variances.
#'
#' @param object SummarizedExperiment
#' @param by svar or NULL
#' @param sep string
#' @param assay string
#' @param ndim number
#' @param minvar number
#' @param center_samples TRUE/FALSE: center samples prior to pca ?
#' @param verbose TRUE/FALSE: message ?
#' @param plot TRUE/FALSE: plot ?
#' @param ... passed to biplot
#' @return SummarizedExperiment
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file)
#' pca(object, plot = TRUE) # Principal Component Analysis
#' pls(object, plot = TRUE) # Partial Least Squares
#' lda(object, plot = TRUE) # Linear Discriminant Analysis
#' sma(object, plot = TRUE) # Spectral Map Analysis
#' spls(object, plot = TRUE) # Sparse PLS
#' # opls(object, plot = TRUE) # OPLS # outcommented because it produces a file named FALSE
#' @author Aditya Bhagwat, Laure Cougnaud (LDA)
#' @export
pca <- function(
object,
by = 'sample_id',
assay = assayNames(object)[1],
ndim = 2,
sep = FITSEP,
minvar = 0,
center_samples = TRUE,
verbose = TRUE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('pcaMethods', quietly = TRUE)){
message("\t\t\tBiocManager::install('pcaMethods'). Then re-run.")
return(object) } # tabs: align with other msgs in read_xxx
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_less_than_or_equal_to(ndim, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
assert_is_a_bool(center_samples)
assert_is_a_bool(verbose)
assert_is_a_bool(plot)
if (verbose) cmessage('%sAdd PCA', spaces(14))
. <- NULL
# Prepare
tmpobj <- object
assays(tmpobj)[[assay]] %<>% inf_to_na(verbose=verbose)
assays(tmpobj)[[assay]] %<>% nan_to_na(verbose=verbose)
tmpobj %<>% rm_missing_in_all_samples(verbose = verbose)
# (Double) center and (global) normalize
row_means <- rowMeans( assays(tmpobj)[[assay]], na.rm = TRUE)
col_means <- colWeightedMeans(assays(tmpobj)[[assay]], abs(row_means), na.rm = TRUE)
global_mean <- mean(col_means)
if (center_samples) assays(tmpobj)[[assay]] %<>% apply(1, '-', col_means) %>% t() # Center columns (samples)
assays(tmpobj)[[assay]] %<>% apply(2, '-', row_means) # Center rows (features)
if (center_samples) assays(tmpobj)[[assay]] %<>% add(global_mean) # Add doubly subtracted
assays(tmpobj)[[assay]] %<>% divide_by(sd(., na.rm=TRUE)) # Normalize
# Perform PCA
pca_res <- pcaMethods::pca(t(assays(tmpobj)[[assay]]), nPcs = ndim, scale = 'none', center = FALSE, method = 'nipals')
samples <- pca_res@scores
features <- pca_res@loadings
variances <- round(100*pca_res@R2)
colnames(samples) <- scorenames(method = 'pca', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'pca', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(seq_len(ndim))
# Add
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features, 'feature_id'))
metavar <- methodname(method = 'pca', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('pca', minvar)
# Return
if (plot) print(biplot(object, method = 'pca', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
pls <- function(
object,
by = 'subgroup',
assay = assayNames(object)[1],
ndim = 2,
sep = FITSEP,
minvar = 0,
verbose = FALSE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('mixOmics', quietly = TRUE)){
message("\t\t\tBiocManager::install('mixOmics'). Then re-run.")
return(object) } # tabs : align with other msgs in read_xxx
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
assert_is_subset(by, svars(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Transform
obj <- object[, !is.na(object[[by]]) ]
x <- t(assays(obj)[[assay]])
y <- svalues(obj, by)
pls_out <- mixOmics::plsda( x, y, ncomp = ndim)
samples <- pls_out$variates$X
features <- pls_out$loadings$X
variances <- round(100*pls_out$prop_expl_var$X)
colnames(samples) <- scorenames(method = 'pls', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'pls', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(seq_len(ndim))
# Add
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features, 'feature_id'))
metavar <- methodname(method = 'pls', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('pls', minvar)
# Return
if (plot) print(biplot(object, method = 'pls', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
sma <- function(
object,
by = 'sample_id',
assay = assayNames(object)[1],
ndim = 2,
sep = FITSEP,
minvar = 0,
verbose = TRUE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('mpm', quietly = TRUE)){
message("\t\t\tFirst Biocinstaller::install('mpm'). Then re-run.")
return(object) } # tabs: align with other msgs in read_xxx
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Preprocess
tmpobj <- object
assays(tmpobj)[[assay]] %<>% minusinf_to_na(verbose = verbose) # else SVD singular
assays(tmpobj)[[assay]] %<>% flip_sign_if_all_exprs_are_negative(verbose = verbose)
tmpobj %<>% rm_missing_in_some_samples(verbose = verbose)
# Transform
df <- data.frame(feature = rownames(tmpobj), assays(tmpobj)[[assay]])
mpm_tmp <- mpm::mpm(
df, logtrans = FALSE, closure = 'none', center = 'double',
normal = 'global', row.weight = 'mean', col.weight = 'constant')
ncomponents <- length(mpm_tmp$contrib)
mpm_out <- mpm::plot.mpm(mpm_tmp, do.plot = FALSE, dim = seq_len(ncomponents))
# Extract
samples <- mpm_out$Columns
features <- mpm_out$Rows
variances <- round(100*mpm_tmp$contrib[seq_len(ncomponents)])
names(samples) <- scorenames(method = 'sma', by = by, dims = seq_len(ndim), sep = sep)
names(features) <- loadingnames(method = 'sma', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(dims = seq_len(ndim))
# Restrict
if (is.infinite(ndim)) ndim <- ncol(samples)
samples %<>% extract(, seq_len(ndim), drop = FALSE)
features %<>% extract(, seq_len(ndim), drop = FALSE)
variances %<>% extract( seq_len(ndim))
# Add
samples %<>% cbind( sample_id = rownames(.), .)
features %<>% cbind(feature_id = rownames(.), .)
object %<>% merge_sdt(data.table(samples), 'sample_id')
object %<>% merge_fdt(data.table(features), 'feature_id')
metavar <- methodname(method = 'sma', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('sma', minvar)
# Return
if (plot) print(biplot(object, method = 'sma', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
lda <- function(
object,
assay = assayNames(object)[1],
by = 'subgroup',
ndim = 2,
sep = FITSEP,
minvar = 0,
verbose = TRUE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('MASS', quietly = TRUE)){
message("\t\t\tBiocManager::install('MASS'). Then re-run.")
return(object) } # tabs: align with other msgs in read_xxx
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
assert_is_subset(by, svars(object))
nsubgroup <- length(slevels(object, by))
if (is.infinite(ndim)) ndim <- nsubgroup - 1
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, nsubgroup-1)
if (ndim > (nsubgroup-1)) stop(
sprintf('LDA requires ndim (%d) <= nsubgroup-1 (%d)',ndim, nsubgroup-1))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Preprocess
tmpobj <- object
assays(tmpobj)[[assay]] %<>% minusinf_to_na(verbose = verbose) # SVD singular
assays(tmpobj)[[assay]] %<>% flip_sign_if_all_exprs_are_negative(verbose = verbose)
tmpobj %<>% rm_missing_in_some_samples(verbose = verbose)
# Transform
exprs_t <- t(assays(tmpobj)[[assay]])
lda_out <- suppressWarnings(MASS::lda( exprs_t,grouping = object[[by]]))
features <- lda_out$scaling
if (ncol(features)==1) features %<>% cbind(LD2 = 0)
exprs_t %<>% scale(center = colMeans(lda_out$means), scale = FALSE)
samples <- exprs_t %*% features
variances <- round((lda_out$svd^2)/sum(lda_out$svd^2)*100)
features %<>% extract(, seq_len(ndim))
samples %<>% extract(, seq_len(ndim))
variances %<>% extract( seq_len(ndim))
if (length(variances)==1) variances <- c(LD1 = variances, LD2 = 0)
# Rename
colnames(samples) <- scorenames(method = 'lda', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'lda', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(ndim)
# Restrict
samples %<>% extract(, seq_len(ndim), drop = FALSE)
features %<>% extract(, seq_len(ndim), drop = FALSE)
variances %<>% extract( seq_len(ndim))
# Merge - Filter - Return
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features, 'feature_id'))
metavar <- methodname(method = 'lda', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
object %<>% .filter_minvar('lda', minvar)
if (plot) print(biplot(object, method = 'lda', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
spls <- function(
object,
assay = assayNames(object)[1],
by = 'subgroup',
ndim = 2,
sep = FITSEP,
minvar = 0,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('mixOmics', quietly = TRUE)){
message("\t\t\tBiocManager::install('mixOmics'). Then re-run.")
return(object) # tabs: align with other msgs in read_xxx
}
assert_is_valid_sumexp(object); assert_is_scalar(assay)
assert_is_subset(assay, assayNames(object))
assert_is_subset(by, svars(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Transform
x <- t(assays(object)[[assay]])
y <- object[[by]]
pls_out <- mixOmics::splsda( x, y, ncomp = ndim)
samples <- pls_out$variates$X
features <- pls_out$loadings$X
variances <- round(100*pls_out$prop_expl_var$X)
colnames(samples) <- scorenames(method = 'spls', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'spls', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(seq_len(ndim))
# Add
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features,'feature_id'))
metavar <- methodname(method = 'spls', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('spls', minvar)
# Return
if (plot) print(biplot(object, method = 'spls', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
opls <- function(
object,
by = 'subgroup',
assay = assayNames(object)[1],
ndim = 2,
sep = FITSEP,
minvar = 0,
verbose = FALSE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('ropls', quietly = TRUE)){
message("\t\t\tBiocManager::install('ropls'). Then re-run.")
return(object) # tabs: align with other msgs in read_xxx
}
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Transform
x <- t(assays(object)[[assay]])
y <- svalues(object, by)
pls_out <- ropls::opls(x, y, predI = ndim, permI = 0, fig.pdfC = FALSE)
samples <- pls_out@scoreMN
features <- pls_out@loadingMN
variances <- round(pls_out@modelDF$R2X*100)
colnames(samples) <- scorenames(method = 'opls', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'opls', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(dims = seq_len(ndim))
# Add
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features, 'feature_id'))
metavar <- methodname(method = 'opls', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('opls', minvar)
# Return
if (plot) print(biplot(object, method = 'opls', dims = seq(1,ndim)[1:2], ...))
object
}
#=============================================================================
#
# biplot
# add_scores
# add_loadings
# headtail
#
#==============================================================================
num2char <- function(x){
if (is.null(x)) return(x)
if (is.numeric(x)) return(as.character(x))
return(x)
}
add_scores <- function(
p,
object,
x = 'pca1',
y = 'pca2',
color = 'subgroup',
shape = if ('replicate' %in% svars(object)) 'replicate' else NULL,
size = NULL,
alpha = NULL,
group = NULL,
linetype = NULL,
fixed = list(shape = 15, size = 3, na.rm = TRUE)
){
# manual colors require non-numerics
if (!is.null(color)){
if (is.numeric(color)){
levs <- as.character(unique(sort(object[[color]])))
object[[color]] %<>% num2char() %>% factor(levs)
}
}
xsym <- sym(x)
ysym <- sym(y)
colorsym <- if (is.null(color)) quo(NULL) else sym(color)
shapesym <- if (is.null(shape)) quo(NULL) else sym(shape)
sizesym <- if (is.null(size)) quo(NULL) else sym(size )
alphasym <- if (is.null(alpha)) quo(NULL) else sym(alpha)
groupsym <- if (is.null(group)) quo(NULL) else sym(group)
linetypesym <- if (is.null(linetype)) quo(NULL) else sym(linetype)
# Points
p <- p + layer( geom = 'point',
mapping = aes(x = !!xsym,
y = !!ysym,
color = !!colorsym,
shape = !!shapesym,
size = !!sizesym,
alpha = !!alphasym),
stat = "identity",
data = sdt(object),
params = fixed,
position = 'identity' )
# Paths
if (!is.null(group)) p <- p + layer( geom = 'path',
mapping = aes(x = !!xsym,
y = !!ysym,
color = !!colorsym,
group = !!groupsym,
linetype = !!linetypesym),
stat = "identity",
data = sdt(object),
params = list(size = 0.1, na.rm = TRUE),
position = 'identity' )
p
}
headtail <- function(x, n){
c(x[seq(1, n)], x[seq(length(x)+1-n, length(x))])
}
pca1 <- pca2 <- NULL
add_loadings <- function(
p,
object,
x = 'pca1',
y = 'pca2',
label = 'feature_name',
nx = 1,
ny = 1
){
# Process args
if (nx==0 & ny==0) return(p)
assert_is_subset(c(x, y), fvars(object))
assert_is_subset(c(x, y), svars(object))
axis <- angle <- NULL
# Loadings
xloadings <- fdt(object)[[x]]
yloadings <- fdt(object)[[y]]
xscores <- sdt(object)[[x]]
yscores <- sdt(object)[[y]]
maxscore <- min(abs(min(c(xscores, yscores, na.rm = TRUE))),
abs(max(c(xscores, yscores, na.rm = TRUE))), na.rm = TRUE)
scorefactor <- maxscore/max(abs(c(xloadings, yloadings)), na.rm = TRUE)
idx1 <- order(abs(xloadings), decreasing = TRUE)[seq_len(nx)]
idx2 <- order(abs(yloadings), decreasing = TRUE)[seq_len(ny)]
#idx1 <- headtail(order(xloadings, na.last = NA), nx)
#idx2 <- headtail(order(yloadings, na.last = NA), ny)
#idx <- unique(c(idx1, idx2))
idx <- c(idx1, idx2)
loadingdt1 <- fdt(object)[idx1, c(label, x, y), with = FALSE]
loadingdt2 <- fdt(object)[idx2, c(label, x, y), with = FALSE]
loadingdt1[, axis := split_extract_fixed(x, '~', 3)]
loadingdt2[, axis := split_extract_fixed(y, '~', 3)]
loadingdt <- rbind(loadingdt1, loadingdt2)
loadingdt[[x]] %<>% multiply_by(scorefactor) # bring them on same scale
loadingdt[[y]] %<>% multiply_by(scorefactor)
loadingdt[[x]] %<>% multiply_by(1.5) # bring them somewhat outside
loadingdt[[y]] %<>% multiply_by(1.5)
loadingdt$angle <- loadingdt[[y]] / loadingdt[[x]]
loadingdt$angle <- atan(loadingdt$angle)
loadingdt$angle %<>% multiply_by(180/pi)
# Plot
p <- p + geom_segment( data = loadingdt,
mapping = aes( x = 0,
xend = !!sym(x),
y = 0,
yend = !!sym(y),
linetype = axis),
color = 'gray85' )
p <- p + geom_text_repel( data = loadingdt,
mapping = aes( x = !!sym(x),
y = !!sym(y),
label = !!sym(label),
angle = angle),
hjust = 'inward',
color = 'gray30' )
p
}
pca1 <- pca2 <- feature_name <- NULL
#' Make alpha palette
#' @param object SummarizedExperiment
#' @param alpha string
#' @return character vector
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file)
#' make_alpha_palette(object, 'Time')
#' @export
make_alpha_palette <- function(object, alpha){
# Assert
assert_is_valid_sumexp(object)
if (is.null(alpha)) return(NULL)
assert_scalar_subset(alpha, svars(object))
# Create
levels <- slevels(object, alpha)
palette <- seq(1, 0.4, length.out = length(levels))
names(palette) <- levels
# Return
palette
}
biplot_methods <- function(object){
sep <- guess_fitsep(fdt(object))
y <- grep('(pca|pls)', svars(object), value = TRUE)
y %<>% split_extract_fixed(sep, 3)
y <- gsub('[0-9]+', '', y)
y %<>% unique()
y
}
biplot_by <- function(object, method = 'pca'){
sep <- guess_fitsep(fdt(object))
y <- grep(method, svars(object), value = TRUE, fixed = TRUE)
y %<>% split_extract_fixed(sep, 2)
y %<>% unique()
y
}
biplot_dims <- function(
object, method = 'pca', by = biplot_by(object, method)
){
sep <- guess_fitsep(fdt(object))
x <- paste0('effect', sep, by, sep, method)
y <- grep(x, svars(object), value = TRUE, fixed = TRUE)
y <- gsub(x, '', y)
y %<>% as.numeric()
y
}
#' Biplot
#' @param object SummarizedExperiment
#' @param method 'pca', 'pls', 'lda', 'spls', 'opls', 'sma'
#' @param by svar
#' @param dims numeric vector: e.g. 1:2
#' @param alpha svar
#' @param color svar
#' @param shape svar
#' @param size svar
#' @param label svar
#' @param group svar
#' @param linetype svar
#' @param feature_label fvar
#' @param fixed fixed plot aesthetics
#' @param nx number of x features to plot
#' @param ny number of y features to plot
#' @param colorpalette character vector
#' @param alphapalette character vector
#' @param title string
#' @param theme ggplot2::theme output
#' @return ggplot object
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file)
#' object %<>% pca(ndim = 4)
#' object %<>% pls(ndim = 4)
#' biplot(object)
#' biplot(object, nx = 1)
#' biplot(object, dims = 3:4, nx = 1)
#' biplot(object, method = 'pls')
#' biplot(object, method = 'pls', dims = 3:4)
#' biplot(object, method = 'pls', dims = 3:4, group = 'Subject')
#' @export
biplot <- function(
object,
method = biplot_methods(object)[1],
by = biplot_by(object, method)[1],
dims = biplot_dims(object, method, by)[1:2],
color = if (method %in% DIMREDSUPER) by else 'subgroup',
shape = NULL,
size = NULL,
alpha = NULL,
group = NULL, # Use 'feature_id' (not 'gene')
linetype = NULL, # To align with `plot_exprs` and `plot_volcano`
label = NULL, # Which use 'feature_id' to guarantee uniqueness
feature_label = 'feature_id', # if ('gene' %in% fvars(object)) 'gene' else 'feature_id',
fixed = list(shape = 15, size = 3),
nx = 0,
ny = 0,
colorpalette = make_svar_palette(object, color),
alphapalette = make_alpha_palette(object, alpha),
title = paste0(method, guess_fitsep(fdt(object)), by),
theme = ggplot2::theme(plot.title = element_text(hjust = 0.5),
panel.grid = element_blank())
){
# Assert / Process
assert_is_all_of(object, 'SummarizedExperiment')
if (!is.null(color)){ assert_is_a_string(color)
assert_is_subset(color, svars(object)) }
if (!is.null(group)){ assert_is_a_string(group)
assert_is_subset(group, svars(object)) }
if (!is.null(shape)){ assert_is_a_string(shape)
assert_is_subset(shape, svars(object))
fixed %<>% extract(names(.) %>% setdiff('shape'))}
if (!is.null(size)){ assert_is_a_string(size)
assert_is_subset(size, svars(object))
fixed %<>% extract(names(.) %>% setdiff('size'))}
ndim <- max(dims)
sep <- guess_fitsep(fdt(object))
x <- scorenames(method, by = by, dims = dims[[1]], sep = sep)
y <- scorenames(method, by = by, dims = dims[[2]], sep = sep)
vars <- round(variances(object, method = method, by = by, dims = dims, sep = sep))
xlab <- sprintf('X%d : %d%%', dims[[1]], vars[[1]])
ylab <- sprintf('X%d : %d%%', dims[[2]], vars[[2]])
# Plot
p <- ggplot() + theme_bw() + theme
p <- p + ggplot2::xlab(xlab) + ggplot2::ylab(ylab)
p <- p + ggtitle(title)
p %<>% add_loadings(object, x = x, y = y, label = feature_label, nx = nx, ny = ny)
p %<>% add_scores(object, x = x, y = y, color = color, shape = shape,
size = size, alpha = alpha, group = group, linetype = linetype, fixed = fixed)
if (!is.null(colorpalette)) p <- p + scale_color_manual(values = colorpalette, na.value = 'gray80')
if (!is.null(alphapalette)) p <- p + scale_alpha_manual(values = alphapalette)
if (!is.null(label )) p <- p + geom_text_repel(
aes(x = !!sym(x), y = !!sym(y), label = !!sym(label)),
data = sdt(object), na.rm = TRUE)
if (!is.null(shape)){
n <- length(slevels(object, shape))
if (n > 6) p <- p + scale_shape_manual(values = seq(15, 15+n-1))
# Warning messages: The shape palette can deal with a maximum
# of 6 discrete values
# https://stackoverflow.com/questions/16813278
}
# Return
p
}
#=============================================================================
#
# biplot_corrections()
# biplot_covariates()
#
#==============================================================================
#' Biplot batch corrections
#'
#' @param object SummarizedExperiment
#' @param method 'pca', 'pls', 'lda', or 'sma'
#' @param by svar
#' @param color variable mapped to color (symbol)
#' @param covariates covariates to be batch-corrected
#' @param varcols number of covariate columns
#' @param plot TRUE/FALSE: plot?
#' @return grid object
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file, pca = TRUE, plot = FALSE)
#' biplot_corrections(object, color = 'subgroup', covariates = c('Sex', 'Diabetes', 'Subject', 'Time'))
#' @seealso biplot_covariates
#' @export
biplot_corrections <- function(
object,
method = 'pca',
by = 'sample_id',
color = 'subgroup',
covariates = character(0),
varcols = ceiling(sqrt(1+length(covariates))),
plot = TRUE
){
x <- scorenames(method, by = by, dims = 1)
y <- scorenames(method, by = by, dims = 2)
p <- biplot(object, method = method, by = by, dims = 1:2, color = color)
p <- p + ggtitle('INPUT')
legend <- gglegend(p + theme(legend.position = 'bottom', legend.title = element_blank()))
p <- p + guides(color = 'none', fill = 'none')
plotlist <- list(p)
for (ibatch in covariates){
tmp_object <- object
tmp_b <- sdata(tmp_object)[[ibatch]]
if (any(is.na(tmp_b))) {
tmp_object %<>% filter_samples(!is.na(!!sym(ibatch)))
}
values(tmp_object) %<>% removeBatchEffect(batch = tmp_b)
tmp_object <- get(method)(tmp_object, ndim=2, verbose=FALSE)
p <- biplot(tmp_object, method = method, by = by, dims = 1:2, color = color)
p <- p + ggtitle(paste0(' - ', ibatch))
p <- p + guides(color = 'none', fill = 'none')
plotlist %<>% c(list(p))
}
pp <- arrangeGrob(grobs = plotlist, ncol = varcols, bottom = legend)
if (plot) grid::grid.draw(pp)
invisible(pp)
}
#' Biplot covariates
#'
#' @param object SummarizedExperiment
#' @param method 'pca', 'pls', 'lda', or 'sma'
#' @param by svar
#' @param block svar
#' @param covariates covariates: mapped to color or batch-corrected
#' @param ndim number of dimensions to plot
#' @param dimcols number of dimension columns
#' @param varcols number of covariate columns
#' @param plot TRUE or FALSE: whether to plot
#' @return ggplot object
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file, pca = TRUE)
#' biplot_covariates(object, covariates = 'subgroup', ndim = 12, dimcols = 3)
#' biplot_covariates(object, covariates = c('Sex', 'Diabetes', 'Subject', 'Time'))
#' biplot_covariates(object, covariates = c('Sex', 'Diabetes', 'Subject', 'Time'), ndim = 2)
#' biplot_covariates(object, covariates = c('subgroup'), dimcols = 3)
#' @seealso biplot_corrections
#' @export
biplot_covariates <- function(
object,
method = 'pca',
by = 'sample_id',
block = NULL,
covariates = 'subgroup',
ndim = 6,
dimcols = 1,
varcols = length(covariates),
plot = TRUE
){
# Assert
assert_is_valid_sumexp(object)
assert_scalar_subset(method, c('pca', 'sma', 'pls', 'spls', 'opls', 'lda'))
assert_is_subset(covariates, svars(object))
blocksym <- if (is.null(block)) quo(NULL) else sym(block)
dims <- NULL
# Plot
x <- y <- NULL
plotdt <- prep_covariates(object, method = method, by = by, ndim = ndim)
plotlist <- list()
for (covar in covariates){
p <- ggplot(plotdt, aes(x = x, y = y, color = !!sym(covar), group = !!blocksym))
p <- p + theme_bw()
p <- p + facet_wrap(vars(dims), ncol = dimcols, scales = 'free')
p <- p + geom_point(shape = 15, size = 3)
if (!is.null(block)) p <- p + geom_line()
p <- p + xlab(NULL) + ylab(NULL) + ggtitle(covar)
p <- p + theme(legend.position = 'bottom', legend.title = element_blank())
plotlist %<>% c(list(p))
}
pp <- gridExtra::arrangeGrob(grobs = plotlist, ncol = varcols)
if (plot) grid::grid.draw(pp)
invisible(pp)
}
prep_covariates <- function(
object,
method = 'pca',
by = 'sample_id',
ndim = 6
){
# Dimred
for (var in svars(object)) if (grepl(paste0('~', method), var)) sdt(object)[[var]] <- NULL
for (var in fvars(object)) if (grepl(paste0('~', method), var)) fdt(object)[[var]] <- NULL
object %<>% get(method)(by = by, ndim = ndim)
# Initialize plotdt
plotdt <- sdt(object)
plotdt %<>% extract(, !stri_detect_fixed(names(.), '~'), with = FALSE)
plotdt %<>% extract(FALSE, )
plotdt %<>% cbind(x = numeric(0), y = numeric(0), dims = character(0))
# Add pairs
sampledt <- sdt(object)
idx <- stri_detect_fixed(names(sampledt), '~')
scoredt <- sampledt[, idx, with = FALSE]
sampledt %<>% extract(, !idx, with = FALSE)
npairs <- ndim %/% 2
for (idim in seq_len(npairs)){
xdim <- idim*2-1
ydim <- idim*2
xvar <- scorenames(method, by = by, dims = xdim)
yvar <- scorenames(method, by = by, dims = ydim)
tmpdt <- cbind(sampledt, x = scoredt[[xvar]], y = scoredt[[yvar]], dims = sprintf('%d:%d', xdim, ydim))
plotdt %<>% rbind(tmpdt)
}
plotdt$dims %<>% factor(unique(.))
plotdt
}
#' @rdname DIMRED
#' @export
DIMREDUN <- c('pca', 'sma')
#' @rdname DIMRED
#' @export
DIMREDSUPER <- c('lda', 'pls', 'opls', 'spls')
#' Dimension Reduction Methods
#' @details \itemize{
#' \item \code{DIMREDUN: c('pca', 'sma')}
#' \item \code{DIMREDSUPER: c('lda', 'pls', 'opls', 'spls')}
#' \item \code{DIMRED: c('pca', 'sma', 'lda', 'pls', 'opls', 'spls')}
#' }
#' @export
DIMRED <- c(DIMREDUN, DIMREDSUPER)
bhagwataditya/importomics documentation built on Oct. 29, 2024, 3:19 p.m.
R Package Documentation
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Defines functions sma pls pca loadings loadingmat scores scoremat variances variancenames methodname loadingnames scorenames .filter_minvar evenify_upwards has_odd_length has_even_length is_odd is_even flip_sign_if_all_exprs_are_negative
Documented in loadingmat loadings pca pls scoremat scores sma
#=============================================================================
#
# flip_sign_if_all_exprs_are_negative.
# evenify_upwards
#
#=============================================================================
#' Flip sign if all expr values are negative
#' @param x matrix
#' @param verbose TRUE (default) or FALSE
#' @return updated matrix
#' @noRd
flip_sign_if_all_exprs_are_negative <- function(x, verbose=TRUE){
idx <- !is.na(x)
if (all(sign(x[idx])==-1)){
if (verbose) message('\t\tAll values negative: ',
'flip signs to prevent singularities.')
x %<>% multiply_by(-1)
}
x
}
#' Is even/odd?
#' @param x integer
#' @return TRUE or FALSE
#' @examples
#' is_even(13)
#' is_even(12)
#' is_odd(13)
#' is_odd(12)
#' @noRd
is_even <- function(x) (x %% 2) == 0
is_odd <- function(x) !is_even(x)
#' Has even/odd length?
#' @param x vector
#' @return logical
#' @examples
#' has_even_length(1:2)
#' has_odd_length(1:2)
#' has_even_length(1:3)
#' has_odd_length(1:3)
#' @noRd
has_even_length <- function(x) is_even(length(x))
has_odd_length <- function(x) is_odd(length(x))
#' Evenify upwards
#' @param x integer
#' @return integer
#' @examples
#' evenify_upwards(3)
#' @noRd
evenify_upwards <- function(x) if (is_odd(x)) x+1 else x
#============================================================================
#
# pca sma lda pls spls ropls
# .filter_minvar
#
#============================================================================
.filter_minvar <- function(object, method, minvar) {
variances <- metadata(object)[[method]]
discard_components <- variances[variances < minvar] %>% names()
sdata(object)[discard_components] <- NULL
fdata(object)[discard_components] <- NULL
metadata(object)[[method]] <- variances[!names(variances) %in% discard_components]
object
}
scorenames <- function( method = 'pca', by, dims = 1:2, sep = FITSEP ) paste0('effect', sep, by, sep, method, dims)
loadingnames <- function( method = 'pca', by, dims = 1:2, sep = FITSEP ) paste0('effect', sep, by, sep, method, dims)
methodname <- function( method = 'pca', by, sep = FITSEP ) paste0( by, sep, method )
variancenames <- function( dims = 1:2 ) paste0('effect', dims)
variances <- function(
object, method = 'pca', by = biplot_by(object, method), dims = 1:2, sep = FITSEP
){
y <- metadata(object)
y %<>% extract2(methodname(method, by = by, sep = sep))
y %<>% extract(variancenames(dims))
y
}
#' Extract scores/loadings
#' @param object SummarizedExperiment
#' @param method 'pca', 'pls', etc.
#' @param by svar (string)
#' @param dim numeric vector
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file)
#' object %<>% pca()
#' scores(object)[1:2]
#' loadings(object)[1:2]
#' scoremat(object)[1:2, ]
#' loadingmat(object)[1:2, ]
#' @export
scoremat <- function(
object, method = 'pca', by = biplot_by(object, method), dim = 1:2
){
sep <- guess_fitsep(fdt(object))
cols <- 'sample_id'
cols %<>% c(scorenames(method = method, by = by, dims = dim, sep = sep))
mat <- sdt(object)[, cols, with = FALSE]
mat %<>% dt2mat()
mat
}
#' @rdname scoremat
#' @export
scores <- function(
object, method = 'pca', by = biplot_by(object, method), dim = 1
){
sep <- guess_fitsep(fdt(object))
cols <- scorenames(method = method, by = by, dims = dim, sep = sep)
sdt(object)[[cols]]
}
#' @rdname scoremat
#' @export
loadingmat <- function(
object, method = 'pca', by = biplot_by(object, method), dim = 1:2
){
sep <- guess_fitsep(fdt(object))
cols <- 'feature_id'
cols %<>% c(loadingnames(method = method, by = by, dims = dim, sep = sep))
mat <- fdt(object)[, cols, with = FALSE]
mat %<>% dt2mat()
mat
}
#' @rdname scoremat
#' @export
loadings <- function(
object, method = 'pca', by = biplot_by(object, method), dim = 1
){
sep <- guess_fitsep(fdt(object))
cols <- loadingnames(method = method, by = by, dims = dim, sep = sep)
fdt(object)[[cols]]
}
#' PCA, SMA, LDA, PLS, SPLS, OPLS
#'
#' Perform a dimension reduction.
#' Store sample scores, feature loadings, and dimension variances.
#'
#' @param object SummarizedExperiment
#' @param by svar or NULL
#' @param sep string
#' @param assay string
#' @param ndim number
#' @param minvar number
#' @param center_samples TRUE/FALSE: center samples prior to pca ?
#' @param verbose TRUE/FALSE: message ?
#' @param plot TRUE/FALSE: plot ?
#' @param ... passed to biplot
#' @return SummarizedExperiment
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file)
#' pca(object, plot = TRUE) # Principal Component Analysis
#' pls(object, plot = TRUE) # Partial Least Squares
#' lda(object, plot = TRUE) # Linear Discriminant Analysis
#' sma(object, plot = TRUE) # Spectral Map Analysis
#' spls(object, plot = TRUE) # Sparse PLS
#' # opls(object, plot = TRUE) # OPLS # outcommented because it produces a file named FALSE
#' @author Aditya Bhagwat, Laure Cougnaud (LDA)
#' @export
pca <- function(
object,
by = 'sample_id',
assay = assayNames(object)[1],
ndim = 2,
sep = FITSEP,
minvar = 0,
center_samples = TRUE,
verbose = TRUE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('pcaMethods', quietly = TRUE)){
message("\t\t\tBiocManager::install('pcaMethods'). Then re-run.")
return(object) } # tabs: align with other msgs in read_xxx
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_less_than_or_equal_to(ndim, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
assert_is_a_bool(center_samples)
assert_is_a_bool(verbose)
assert_is_a_bool(plot)
if (verbose) cmessage('%sAdd PCA', spaces(14))
. <- NULL
# Prepare
tmpobj <- object
assays(tmpobj)[[assay]] %<>% inf_to_na(verbose=verbose)
assays(tmpobj)[[assay]] %<>% nan_to_na(verbose=verbose)
tmpobj %<>% rm_missing_in_all_samples(verbose = verbose)
# (Double) center and (global) normalize
row_means <- rowMeans( assays(tmpobj)[[assay]], na.rm = TRUE)
col_means <- colWeightedMeans(assays(tmpobj)[[assay]], abs(row_means), na.rm = TRUE)
global_mean <- mean(col_means)
if (center_samples) assays(tmpobj)[[assay]] %<>% apply(1, '-', col_means) %>% t() # Center columns (samples)
assays(tmpobj)[[assay]] %<>% apply(2, '-', row_means) # Center rows (features)
if (center_samples) assays(tmpobj)[[assay]] %<>% add(global_mean) # Add doubly subtracted
assays(tmpobj)[[assay]] %<>% divide_by(sd(., na.rm=TRUE)) # Normalize
# Perform PCA
pca_res <- pcaMethods::pca(t(assays(tmpobj)[[assay]]), nPcs = ndim, scale = 'none', center = FALSE, method = 'nipals')
samples <- pca_res@scores
features <- pca_res@loadings
variances <- round(100*pca_res@R2)
colnames(samples) <- scorenames(method = 'pca', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'pca', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(seq_len(ndim))
# Add
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features, 'feature_id'))
metavar <- methodname(method = 'pca', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('pca', minvar)
# Return
if (plot) print(biplot(object, method = 'pca', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
pls <- function(
object,
by = 'subgroup',
assay = assayNames(object)[1],
ndim = 2,
sep = FITSEP,
minvar = 0,
verbose = FALSE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('mixOmics', quietly = TRUE)){
message("\t\t\tBiocManager::install('mixOmics'). Then re-run.")
return(object) } # tabs : align with other msgs in read_xxx
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
assert_is_subset(by, svars(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Transform
obj <- object[, !is.na(object[[by]]) ]
x <- t(assays(obj)[[assay]])
y <- svalues(obj, by)
pls_out <- mixOmics::plsda( x, y, ncomp = ndim)
samples <- pls_out$variates$X
features <- pls_out$loadings$X
variances <- round(100*pls_out$prop_expl_var$X)
colnames(samples) <- scorenames(method = 'pls', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'pls', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(seq_len(ndim))
# Add
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features, 'feature_id'))
metavar <- methodname(method = 'pls', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('pls', minvar)
# Return
if (plot) print(biplot(object, method = 'pls', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
sma <- function(
object,
by = 'sample_id',
assay = assayNames(object)[1],
ndim = 2,
sep = FITSEP,
minvar = 0,
verbose = TRUE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('mpm', quietly = TRUE)){
message("\t\t\tFirst Biocinstaller::install('mpm'). Then re-run.")
return(object) } # tabs: align with other msgs in read_xxx
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Preprocess
tmpobj <- object
assays(tmpobj)[[assay]] %<>% minusinf_to_na(verbose = verbose) # else SVD singular
assays(tmpobj)[[assay]] %<>% flip_sign_if_all_exprs_are_negative(verbose = verbose)
tmpobj %<>% rm_missing_in_some_samples(verbose = verbose)
# Transform
df <- data.frame(feature = rownames(tmpobj), assays(tmpobj)[[assay]])
mpm_tmp <- mpm::mpm(
df, logtrans = FALSE, closure = 'none', center = 'double',
normal = 'global', row.weight = 'mean', col.weight = 'constant')
ncomponents <- length(mpm_tmp$contrib)
mpm_out <- mpm::plot.mpm(mpm_tmp, do.plot = FALSE, dim = seq_len(ncomponents))
# Extract
samples <- mpm_out$Columns
features <- mpm_out$Rows
variances <- round(100*mpm_tmp$contrib[seq_len(ncomponents)])
names(samples) <- scorenames(method = 'sma', by = by, dims = seq_len(ndim), sep = sep)
names(features) <- loadingnames(method = 'sma', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(dims = seq_len(ndim))
# Restrict
if (is.infinite(ndim)) ndim <- ncol(samples)
samples %<>% extract(, seq_len(ndim), drop = FALSE)
features %<>% extract(, seq_len(ndim), drop = FALSE)
variances %<>% extract( seq_len(ndim))
# Add
samples %<>% cbind( sample_id = rownames(.), .)
features %<>% cbind(feature_id = rownames(.), .)
object %<>% merge_sdt(data.table(samples), 'sample_id')
object %<>% merge_fdt(data.table(features), 'feature_id')
metavar <- methodname(method = 'sma', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('sma', minvar)
# Return
if (plot) print(biplot(object, method = 'sma', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
lda <- function(
object,
assay = assayNames(object)[1],
by = 'subgroup',
ndim = 2,
sep = FITSEP,
minvar = 0,
verbose = TRUE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('MASS', quietly = TRUE)){
message("\t\t\tBiocManager::install('MASS'). Then re-run.")
return(object) } # tabs: align with other msgs in read_xxx
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
assert_is_subset(by, svars(object))
nsubgroup <- length(slevels(object, by))
if (is.infinite(ndim)) ndim <- nsubgroup - 1
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, nsubgroup-1)
if (ndim > (nsubgroup-1)) stop(
sprintf('LDA requires ndim (%d) <= nsubgroup-1 (%d)',ndim, nsubgroup-1))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Preprocess
tmpobj <- object
assays(tmpobj)[[assay]] %<>% minusinf_to_na(verbose = verbose) # SVD singular
assays(tmpobj)[[assay]] %<>% flip_sign_if_all_exprs_are_negative(verbose = verbose)
tmpobj %<>% rm_missing_in_some_samples(verbose = verbose)
# Transform
exprs_t <- t(assays(tmpobj)[[assay]])
lda_out <- suppressWarnings(MASS::lda( exprs_t,grouping = object[[by]]))
features <- lda_out$scaling
if (ncol(features)==1) features %<>% cbind(LD2 = 0)
exprs_t %<>% scale(center = colMeans(lda_out$means), scale = FALSE)
samples <- exprs_t %*% features
variances <- round((lda_out$svd^2)/sum(lda_out$svd^2)*100)
features %<>% extract(, seq_len(ndim))
samples %<>% extract(, seq_len(ndim))
variances %<>% extract( seq_len(ndim))
if (length(variances)==1) variances <- c(LD1 = variances, LD2 = 0)
# Rename
colnames(samples) <- scorenames(method = 'lda', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'lda', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(ndim)
# Restrict
samples %<>% extract(, seq_len(ndim), drop = FALSE)
features %<>% extract(, seq_len(ndim), drop = FALSE)
variances %<>% extract( seq_len(ndim))
# Merge - Filter - Return
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features, 'feature_id'))
metavar <- methodname(method = 'lda', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
object %<>% .filter_minvar('lda', minvar)
if (plot) print(biplot(object, method = 'lda', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
spls <- function(
object,
assay = assayNames(object)[1],
by = 'subgroup',
ndim = 2,
sep = FITSEP,
minvar = 0,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('mixOmics', quietly = TRUE)){
message("\t\t\tBiocManager::install('mixOmics'). Then re-run.")
return(object) # tabs: align with other msgs in read_xxx
}
assert_is_valid_sumexp(object); assert_is_scalar(assay)
assert_is_subset(assay, assayNames(object))
assert_is_subset(by, svars(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Transform
x <- t(assays(object)[[assay]])
y <- object[[by]]
pls_out <- mixOmics::splsda( x, y, ncomp = ndim)
samples <- pls_out$variates$X
features <- pls_out$loadings$X
variances <- round(100*pls_out$prop_expl_var$X)
colnames(samples) <- scorenames(method = 'spls', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'spls', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(seq_len(ndim))
# Add
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features,'feature_id'))
metavar <- methodname(method = 'spls', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('spls', minvar)
# Return
if (plot) print(biplot(object, method = 'spls', dims = seq(1,ndim)[1:2], ...))
object
}
#' @rdname pca
#' @export
opls <- function(
object,
by = 'subgroup',
assay = assayNames(object)[1],
ndim = 2,
sep = FITSEP,
minvar = 0,
verbose = FALSE,
plot = FALSE,
...
){
# Assert
if (!requireNamespace('ropls', quietly = TRUE)){
message("\t\t\tBiocManager::install('ropls'). Then re-run.")
return(object) # tabs: align with other msgs in read_xxx
}
assert_is_valid_sumexp(object)
assert_is_scalar(assay); assert_is_subset(assay, assayNames(object))
if (is.infinite(ndim)) ndim <- ncol(object)
assert_is_a_number(ndim)
assert_all_are_in_range(ndim, 1, ncol(object))
assert_is_a_number(minvar)
assert_all_are_in_range(minvar, 0, 100)
. <- NULL
# Transform
x <- t(assays(object)[[assay]])
y <- svalues(object, by)
pls_out <- ropls::opls(x, y, predI = ndim, permI = 0, fig.pdfC = FALSE)
samples <- pls_out@scoreMN
features <- pls_out@loadingMN
variances <- round(pls_out@modelDF$R2X*100)
colnames(samples) <- scorenames(method = 'opls', by = by, dims = seq_len(ndim), sep = sep)
colnames(features) <- loadingnames(method = 'opls', by = by, dims = seq_len(ndim), sep = sep)
names(variances) <- variancenames(dims = seq_len(ndim))
# Add
object %<>% merge_sdt(mat2dt(samples, 'sample_id'))
object %<>% merge_fdt(mat2dt(features, 'feature_id'))
metavar <- methodname(method = 'opls', by = by, sep = sep)
metadata(object)[[metavar]] <- variances
# Filter for minvar
object %<>% .filter_minvar('opls', minvar)
# Return
if (plot) print(biplot(object, method = 'opls', dims = seq(1,ndim)[1:2], ...))
object
}
#=============================================================================
#
# biplot
# add_scores
# add_loadings
# headtail
#
#==============================================================================
num2char <- function(x){
if (is.null(x)) return(x)
if (is.numeric(x)) return(as.character(x))
return(x)
}
add_scores <- function(
p,
object,
x = 'pca1',
y = 'pca2',
color = 'subgroup',
shape = if ('replicate' %in% svars(object)) 'replicate' else NULL,
size = NULL,
alpha = NULL,
group = NULL,
linetype = NULL,
fixed = list(shape = 15, size = 3, na.rm = TRUE)
){
# manual colors require non-numerics
if (!is.null(color)){
if (is.numeric(color)){
levs <- as.character(unique(sort(object[[color]])))
object[[color]] %<>% num2char() %>% factor(levs)
}
}
xsym <- sym(x)
ysym <- sym(y)
colorsym <- if (is.null(color)) quo(NULL) else sym(color)
shapesym <- if (is.null(shape)) quo(NULL) else sym(shape)
sizesym <- if (is.null(size)) quo(NULL) else sym(size )
alphasym <- if (is.null(alpha)) quo(NULL) else sym(alpha)
groupsym <- if (is.null(group)) quo(NULL) else sym(group)
linetypesym <- if (is.null(linetype)) quo(NULL) else sym(linetype)
# Points
p <- p + layer( geom = 'point',
mapping = aes(x = !!xsym,
y = !!ysym,
color = !!colorsym,
shape = !!shapesym,
size = !!sizesym,
alpha = !!alphasym),
stat = "identity",
data = sdt(object),
params = fixed,
position = 'identity' )
# Paths
if (!is.null(group)) p <- p + layer( geom = 'path',
mapping = aes(x = !!xsym,
y = !!ysym,
color = !!colorsym,
group = !!groupsym,
linetype = !!linetypesym),
stat = "identity",
data = sdt(object),
params = list(size = 0.1, na.rm = TRUE),
position = 'identity' )
p
}
headtail <- function(x, n){
c(x[seq(1, n)], x[seq(length(x)+1-n, length(x))])
}
pca1 <- pca2 <- NULL
add_loadings <- function(
p,
object,
x = 'pca1',
y = 'pca2',
label = 'feature_name',
nx = 1,
ny = 1
){
# Process args
if (nx==0 & ny==0) return(p)
assert_is_subset(c(x, y), fvars(object))
assert_is_subset(c(x, y), svars(object))
axis <- angle <- NULL
# Loadings
xloadings <- fdt(object)[[x]]
yloadings <- fdt(object)[[y]]
xscores <- sdt(object)[[x]]
yscores <- sdt(object)[[y]]
maxscore <- min(abs(min(c(xscores, yscores, na.rm = TRUE))),
abs(max(c(xscores, yscores, na.rm = TRUE))), na.rm = TRUE)
scorefactor <- maxscore/max(abs(c(xloadings, yloadings)), na.rm = TRUE)
idx1 <- order(abs(xloadings), decreasing = TRUE)[seq_len(nx)]
idx2 <- order(abs(yloadings), decreasing = TRUE)[seq_len(ny)]
#idx1 <- headtail(order(xloadings, na.last = NA), nx)
#idx2 <- headtail(order(yloadings, na.last = NA), ny)
#idx <- unique(c(idx1, idx2))
idx <- c(idx1, idx2)
loadingdt1 <- fdt(object)[idx1, c(label, x, y), with = FALSE]
loadingdt2 <- fdt(object)[idx2, c(label, x, y), with = FALSE]
loadingdt1[, axis := split_extract_fixed(x, '~', 3)]
loadingdt2[, axis := split_extract_fixed(y, '~', 3)]
loadingdt <- rbind(loadingdt1, loadingdt2)
loadingdt[[x]] %<>% multiply_by(scorefactor) # bring them on same scale
loadingdt[[y]] %<>% multiply_by(scorefactor)
loadingdt[[x]] %<>% multiply_by(1.5) # bring them somewhat outside
loadingdt[[y]] %<>% multiply_by(1.5)
loadingdt$angle <- loadingdt[[y]] / loadingdt[[x]]
loadingdt$angle <- atan(loadingdt$angle)
loadingdt$angle %<>% multiply_by(180/pi)
# Plot
p <- p + geom_segment( data = loadingdt,
mapping = aes( x = 0,
xend = !!sym(x),
y = 0,
yend = !!sym(y),
linetype = axis),
color = 'gray85' )
p <- p + geom_text_repel( data = loadingdt,
mapping = aes( x = !!sym(x),
y = !!sym(y),
label = !!sym(label),
angle = angle),
hjust = 'inward',
color = 'gray30' )
p
}
pca1 <- pca2 <- feature_name <- NULL
#' Make alpha palette
#' @param object SummarizedExperiment
#' @param alpha string
#' @return character vector
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file)
#' make_alpha_palette(object, 'Time')
#' @export
make_alpha_palette <- function(object, alpha){
# Assert
assert_is_valid_sumexp(object)
if (is.null(alpha)) return(NULL)
assert_scalar_subset(alpha, svars(object))
# Create
levels <- slevels(object, alpha)
palette <- seq(1, 0.4, length.out = length(levels))
names(palette) <- levels
# Return
palette
}
biplot_methods <- function(object){
sep <- guess_fitsep(fdt(object))
y <- grep('(pca|pls)', svars(object), value = TRUE)
y %<>% split_extract_fixed(sep, 3)
y <- gsub('[0-9]+', '', y)
y %<>% unique()
y
}
biplot_by <- function(object, method = 'pca'){
sep <- guess_fitsep(fdt(object))
y <- grep(method, svars(object), value = TRUE, fixed = TRUE)
y %<>% split_extract_fixed(sep, 2)
y %<>% unique()
y
}
biplot_dims <- function(
object, method = 'pca', by = biplot_by(object, method)
){
sep <- guess_fitsep(fdt(object))
x <- paste0('effect', sep, by, sep, method)
y <- grep(x, svars(object), value = TRUE, fixed = TRUE)
y <- gsub(x, '', y)
y %<>% as.numeric()
y
}
#' Biplot
#' @param object SummarizedExperiment
#' @param method 'pca', 'pls', 'lda', 'spls', 'opls', 'sma'
#' @param by svar
#' @param dims numeric vector: e.g. 1:2
#' @param alpha svar
#' @param color svar
#' @param shape svar
#' @param size svar
#' @param label svar
#' @param group svar
#' @param linetype svar
#' @param feature_label fvar
#' @param fixed fixed plot aesthetics
#' @param nx number of x features to plot
#' @param ny number of y features to plot
#' @param colorpalette character vector
#' @param alphapalette character vector
#' @param title string
#' @param theme ggplot2::theme output
#' @return ggplot object
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file)
#' object %<>% pca(ndim = 4)
#' object %<>% pls(ndim = 4)
#' biplot(object)
#' biplot(object, nx = 1)
#' biplot(object, dims = 3:4, nx = 1)
#' biplot(object, method = 'pls')
#' biplot(object, method = 'pls', dims = 3:4)
#' biplot(object, method = 'pls', dims = 3:4, group = 'Subject')
#' @export
biplot <- function(
object,
method = biplot_methods(object)[1],
by = biplot_by(object, method)[1],
dims = biplot_dims(object, method, by)[1:2],
color = if (method %in% DIMREDSUPER) by else 'subgroup',
shape = NULL,
size = NULL,
alpha = NULL,
group = NULL, # Use 'feature_id' (not 'gene')
linetype = NULL, # To align with `plot_exprs` and `plot_volcano`
label = NULL, # Which use 'feature_id' to guarantee uniqueness
feature_label = 'feature_id', # if ('gene' %in% fvars(object)) 'gene' else 'feature_id',
fixed = list(shape = 15, size = 3),
nx = 0,
ny = 0,
colorpalette = make_svar_palette(object, color),
alphapalette = make_alpha_palette(object, alpha),
title = paste0(method, guess_fitsep(fdt(object)), by),
theme = ggplot2::theme(plot.title = element_text(hjust = 0.5),
panel.grid = element_blank())
){
# Assert / Process
assert_is_all_of(object, 'SummarizedExperiment')
if (!is.null(color)){ assert_is_a_string(color)
assert_is_subset(color, svars(object)) }
if (!is.null(group)){ assert_is_a_string(group)
assert_is_subset(group, svars(object)) }
if (!is.null(shape)){ assert_is_a_string(shape)
assert_is_subset(shape, svars(object))
fixed %<>% extract(names(.) %>% setdiff('shape'))}
if (!is.null(size)){ assert_is_a_string(size)
assert_is_subset(size, svars(object))
fixed %<>% extract(names(.) %>% setdiff('size'))}
ndim <- max(dims)
sep <- guess_fitsep(fdt(object))
x <- scorenames(method, by = by, dims = dims[[1]], sep = sep)
y <- scorenames(method, by = by, dims = dims[[2]], sep = sep)
vars <- round(variances(object, method = method, by = by, dims = dims, sep = sep))
xlab <- sprintf('X%d : %d%%', dims[[1]], vars[[1]])
ylab <- sprintf('X%d : %d%%', dims[[2]], vars[[2]])
# Plot
p <- ggplot() + theme_bw() + theme
p <- p + ggplot2::xlab(xlab) + ggplot2::ylab(ylab)
p <- p + ggtitle(title)
p %<>% add_loadings(object, x = x, y = y, label = feature_label, nx = nx, ny = ny)
p %<>% add_scores(object, x = x, y = y, color = color, shape = shape,
size = size, alpha = alpha, group = group, linetype = linetype, fixed = fixed)
if (!is.null(colorpalette)) p <- p + scale_color_manual(values = colorpalette, na.value = 'gray80')
if (!is.null(alphapalette)) p <- p + scale_alpha_manual(values = alphapalette)
if (!is.null(label )) p <- p + geom_text_repel(
aes(x = !!sym(x), y = !!sym(y), label = !!sym(label)),
data = sdt(object), na.rm = TRUE)
if (!is.null(shape)){
n <- length(slevels(object, shape))
if (n > 6) p <- p + scale_shape_manual(values = seq(15, 15+n-1))
# Warning messages: The shape palette can deal with a maximum
# of 6 discrete values
# https://stackoverflow.com/questions/16813278
}
# Return
p
}
#=============================================================================
#
# biplot_corrections()
# biplot_covariates()
#
#==============================================================================
#' Biplot batch corrections
#'
#' @param object SummarizedExperiment
#' @param method 'pca', 'pls', 'lda', or 'sma'
#' @param by svar
#' @param color variable mapped to color (symbol)
#' @param covariates covariates to be batch-corrected
#' @param varcols number of covariate columns
#' @param plot TRUE/FALSE: plot?
#' @return grid object
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file, pca = TRUE, plot = FALSE)
#' biplot_corrections(object, color = 'subgroup', covariates = c('Sex', 'Diabetes', 'Subject', 'Time'))
#' @seealso biplot_covariates
#' @export
biplot_corrections <- function(
object,
method = 'pca',
by = 'sample_id',
color = 'subgroup',
covariates = character(0),
varcols = ceiling(sqrt(1+length(covariates))),
plot = TRUE
){
x <- scorenames(method, by = by, dims = 1)
y <- scorenames(method, by = by, dims = 2)
p <- biplot(object, method = method, by = by, dims = 1:2, color = color)
p <- p + ggtitle('INPUT')
legend <- gglegend(p + theme(legend.position = 'bottom', legend.title = element_blank()))
p <- p + guides(color = 'none', fill = 'none')
plotlist <- list(p)
for (ibatch in covariates){
tmp_object <- object
tmp_b <- sdata(tmp_object)[[ibatch]]
if (any(is.na(tmp_b))) {
tmp_object %<>% filter_samples(!is.na(!!sym(ibatch)))
}
values(tmp_object) %<>% removeBatchEffect(batch = tmp_b)
tmp_object <- get(method)(tmp_object, ndim=2, verbose=FALSE)
p <- biplot(tmp_object, method = method, by = by, dims = 1:2, color = color)
p <- p + ggtitle(paste0(' - ', ibatch))
p <- p + guides(color = 'none', fill = 'none')
plotlist %<>% c(list(p))
}
pp <- arrangeGrob(grobs = plotlist, ncol = varcols, bottom = legend)
if (plot) grid::grid.draw(pp)
invisible(pp)
}
#' Biplot covariates
#'
#' @param object SummarizedExperiment
#' @param method 'pca', 'pls', 'lda', or 'sma'
#' @param by svar
#' @param block svar
#' @param covariates covariates: mapped to color or batch-corrected
#' @param ndim number of dimensions to plot
#' @param dimcols number of dimension columns
#' @param varcols number of covariate columns
#' @param plot TRUE or FALSE: whether to plot
#' @return ggplot object
#' @examples
#' file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
#' object <- read_metabolon(file, pca = TRUE)
#' biplot_covariates(object, covariates = 'subgroup', ndim = 12, dimcols = 3)
#' biplot_covariates(object, covariates = c('Sex', 'Diabetes', 'Subject', 'Time'))
#' biplot_covariates(object, covariates = c('Sex', 'Diabetes', 'Subject', 'Time'), ndim = 2)
#' biplot_covariates(object, covariates = c('subgroup'), dimcols = 3)
#' @seealso biplot_corrections
#' @export
biplot_covariates <- function(
object,
method = 'pca',
by = 'sample_id',
block = NULL,
covariates = 'subgroup',
ndim = 6,
dimcols = 1,
varcols = length(covariates),
plot = TRUE
){
# Assert
assert_is_valid_sumexp(object)
assert_scalar_subset(method, c('pca', 'sma', 'pls', 'spls', 'opls', 'lda'))
assert_is_subset(covariates, svars(object))
blocksym <- if (is.null(block)) quo(NULL) else sym(block)
dims <- NULL
# Plot
x <- y <- NULL
plotdt <- prep_covariates(object, method = method, by = by, ndim = ndim)
plotlist <- list()
for (covar in covariates){
p <- ggplot(plotdt, aes(x = x, y = y, color = !!sym(covar), group = !!blocksym))
p <- p + theme_bw()
p <- p + facet_wrap(vars(dims), ncol = dimcols, scales = 'free')
p <- p + geom_point(shape = 15, size = 3)
if (!is.null(block)) p <- p + geom_line()
p <- p + xlab(NULL) + ylab(NULL) + ggtitle(covar)
p <- p + theme(legend.position = 'bottom', legend.title = element_blank())
plotlist %<>% c(list(p))
}
pp <- gridExtra::arrangeGrob(grobs = plotlist, ncol = varcols)
if (plot) grid::grid.draw(pp)
invisible(pp)
}
prep_covariates <- function(
object,
method = 'pca',
by = 'sample_id',
ndim = 6
){
# Dimred
for (var in svars(object)) if (grepl(paste0('~', method), var)) sdt(object)[[var]] <- NULL
for (var in fvars(object)) if (grepl(paste0('~', method), var)) fdt(object)[[var]] <- NULL
object %<>% get(method)(by = by, ndim = ndim)
# Initialize plotdt
plotdt <- sdt(object)
plotdt %<>% extract(, !stri_detect_fixed(names(.), '~'), with = FALSE)
plotdt %<>% extract(FALSE, )
plotdt %<>% cbind(x = numeric(0), y = numeric(0), dims = character(0))
# Add pairs
sampledt <- sdt(object)
idx <- stri_detect_fixed(names(sampledt), '~')
scoredt <- sampledt[, idx, with = FALSE]
sampledt %<>% extract(, !idx, with = FALSE)
npairs <- ndim %/% 2
for (idim in seq_len(npairs)){
xdim <- idim*2-1
ydim <- idim*2
xvar <- scorenames(method, by = by, dims = xdim)
yvar <- scorenames(method, by = by, dims = ydim)
tmpdt <- cbind(sampledt, x = scoredt[[xvar]], y = scoredt[[yvar]], dims = sprintf('%d:%d', xdim, ydim))
plotdt %<>% rbind(tmpdt)
}
plotdt$dims %<>% factor(unique(.))
plotdt
}
#' @rdname DIMRED
#' @export
DIMREDUN <- c('pca', 'sma')
#' @rdname DIMRED
#' @export
DIMREDSUPER <- c('lda', 'pls', 'opls', 'spls')
#' Dimension Reduction Methods
#' @details \itemize{
#' \item \code{DIMREDUN: c('pca', 'sma')}
#' \item \code{DIMREDSUPER: c('lda', 'pls', 'opls', 'spls')}
#' \item \code{DIMRED: c('pca', 'sma', 'lda', 'pls', 'opls', 'spls')}
#' }
#' @export
DIMRED <- c(DIMREDUN, DIMREDSUPER)
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