R/seurat_integration.R
In connorhknight/IBRAP: Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP)
Defines functions
perform.seurat.integration
Documented in
perform.seurat.integration
#' @name perform.seurat.cca
#' @aliases perform.seurat.cca
#'
#' @title Performs Seurat Integration
#'
#' @description Performs Seurat integration on the supplied assay names. Results are saved under integration_reductions
#'
#' @param object IBRAP S4 class object
#' @param object.list list of individual sample IBRAP S4 class objects.
#' @param assay Character. String containing indicating which assay to use
#' @param reduction.save.suffix. Character. What should be added as a suffix to reduction name. Default = ''
#' @param nfeatures Numerical. How many features should be found as integration anchors. Default = 3000
#' @param l2.norm Logical. Perform L2 normalization on the CCA cell embeddings after dimensional reduction. Default = TRUE
#' @param k.anchor Numerical. How many neighbors (k) to use when picking anchors. Default = 5
#' @param k.filter Numerical. How many neighbors (k) to use when filtering anchors. Default = 200
#' @param k.score Numerical. How many neighbors (k) to use when scoring anchors. Default = 30
#' @param max.features. Numerical. The maximum number of features to use when specifying the neighborhood search space in the anchor filtering. Default = 200
#' @param nn.method Character. Method for nearest neighbor finding. Options include: rann, annoy. Default = annoy
#' @param n.tree Numerical. More trees gives higher precision when using annoy approximate nearest neighbor search. Default = 50
#' @param anchor.eps Numerical. Error bound on the neighbor finding algorithm (from RANN/Annoy) when finding integration genes.
#' @param features Character. Vector of features to use when computing the PCA to determine the weights. Only set if you want a different set from those used in the anchor finding process. Default = NULL
#' @param integrate.dims Numerical. Number of dimensions to use in the anchor weighting procedure. Default = 1:30
#' @param k.weight Numerical. Number of neighbors to consider when weighting anchors. Default = 100
#' @param sd.weight Numerical. Controls the bandwidth of the Gaussian kernel for weighting. Default = 1
#' @param sample.tree Character. Specify the order of integration. If NULL, will compute automatically. Default = NULL
#' @param integrate.eps Numerical. Error bound on the neighbor finding algorithm (from RANN)
#' @param save.plot Boolean. Should the automatically genewrated plot be saved? Default = TRUE
#'
#' @return Produces a new 'methods' assay containing normalised, scaled and HVGs.
#'
#' @examples perform.seurat.cca <- function(object = object,
#' assay = c('SCT', 'SCRAN', 'SCANPY'),
#' reduction.save.suffix=NULL,
#' nfeatures = 3000,
#' reduction = 'cca',
#' anchors.dims = 1:30,
#' l2.norm = T,
#' k.anchor = 5,
#' k.filter = 200,
#' k.score = 30,
#' max.features = 200,
#' nn.method = 'annoy',
#' n.trees = 50,
#' anchor.eps = 0,
#' features = NULL,
#' integrate.dims = 1:30,
#' k.weight = 100,
#' sd.weight = 1,
#' sample.tree = NULL,
#' integrate.eps = 0)
#'
#' @export
perform.seurat.integration <- function(object,
object.list,
assay,
reduction.save.suffix=NULL,
nfeatures = 2000,
anchors.dims = 1:30,
l2.norm = T,
k.anchor = 5,
k.filter = 200,
k.score = 30,
max.features = 200,
nn.method = 'annoy',
n.trees = 50,
anchor.eps = 0,
features = NULL,
integrate.dims = 1:30,
k.weight = 100,
sd.weight = 1,
sample.tree = NULL,
integrate.eps = 0,
print.variance = TRUE,
verbose=FALSE,
seed = 1234,
...) {
if(!is(object = object, class2 = 'IBRAP')) {
stop('object must be of class IBRAP \n')
}
if(!is.list(object.list)) {
stop('object.list must be a list of objects \n')
} else if (is.list(object.list)) {
for(x in object.list) {
if(!is(object = x, class2 = 'IBRAP')) {
stop('items in object.list must be IBRAP S4 class objects \n')
}
}
}
if(!is.character(assay)) {
stop('assay must be character string(s) \n')
} else if(is.character(assay)) {
for(x in assay) {
if(!x %in% names(object@methods)) {
stop(paste0(x, ' is not contained within object@methods \n'))
}
for(g in object.list) {
if(!x %in% names(g@methods)) {
stop(paste0(x, ' is not contained within one of your object.list items \n'))
}
}
}
}
if(!is.null(reduction.save.suffix)) {
if(!is.character(reduction.save.suffix)) {
stop('reduction.save.suffix must be a character string \n')
}
}
if(!is.numeric(nfeatures)) {
stop('nfeatures must be cnumerical \n')
}
if(!is.logical(l2.norm)) {
stop('l2.norm must be logical. TRUE/FALSE \n')
}
if(!is.numeric(anchors.dims)) {
stop('anchors.dims must be numerical \n')
}
if(!is.numeric(k.anchor)) {
stop('k.anchor must be numerical \n')
}
if(!is.numeric(k.filter)) {
stop('k.filter must be numerical \n')
}
if(!is.numeric(k.score)) {
stop('k.score must be numerical \n')
}
if(!is.numeric(max.features)) {
stop('max.features must be numerical \n')
}
if(!is.character(nn.method)) {
stop('nn.method must be numerical \n')
}
if(!is.numeric(anchor.eps)) {
stop('anchor.eps must be numerical \n')
}
if(!is.null(features)) {
if(!is.character(features)) {
stop('features must be character string(s)')
}
}
if(!is.numeric(integrate.dims)) {
stop('integrate.dims must be numerical \n')
}
if(!is.numeric(k.weight)) {
stop('k.weight must be numerical \n')
}
if(!is.numeric(sd.weight)) {
stop('sd.weight must be numerical \n')
}
if(!is.null(sample.tree)) {
if(!is.character(sample.tree)) {
stop('must be character stirng \n')
}
}
if(!is.numeric(integrate.eps)) {
stop('integrate.eps must be numerical. TRUE/FALSE \n')
}
if(!is.logical(print.variance)) {
stop('print.variance must be boolean. TRUE/FALSE \n')
}
if(!is.logical(verbose)) {
stop('verbose must be boolean. TRUE/FALSE \n')
}
for(x in names(object@methods)) {
for(g in object.list) {
if(!x %in% names(g@methods)) {
stop(paste0(x, ' is not contained within one of your items in onf of your object.list \n'))
}
}
}
# if(length(batch) > 1) {
#
# temp <- function(x) {
#
# return(paste(x, collapse = '_'))
#
# }
#
# df <- object@sample_metadata[,batch]
# object2 <- object
# object2@sample_metadata$batch <- apply(X = df, MARGIN = 1, FUN = temp)
#
# batch <- 'batch'
#
# } else {
#
# object2 <- object
#
# }
if(!is.numeric(seed)) {
stop('seed must be numerical \n')
}
set.seed(seed = seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
if(!'integration_method' %in% colnames(object@pipelines)) {
tmp <- tibble::add_column(.data = object@pipelines, integration_method=NA, integration_time=NA)
} else {
tmp <- object@pipelines
}
count <- 1
for(a in assay) {
start_time <- Sys.time()
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': initialising seurat integration for assay: ', a, '\n')))
}
new.list <- list()
for(x in 1:length(object.list)) {
if('SCT' %in% strsplit(x = a, split = '_')) {
new.list[[x]] <- suppressWarnings(Seurat::CreateSeuratObject(counts = object.list[[x]]@methods[[a]]@counts))
new.list[[x]]@assays$SCT <- object.list[[x]]@methods[[a]]@misc$SCT_Seurat_object
new.list[[x]]@assays$SCT@counts <- object.list[[x]]@methods[[a]]@counts
new.list[[x]]@assays$SCT@data <- object.list[[x]]@methods[[a]]@normalised
new.list[[x]]@assays$SCT@scale.data <- object.list[[x]]@methods[[a]]@norm.scaled
new.list[[x]]@assays$SCT@var.features <- object.list[[x]]@methods[[a]]@highly.variable.genes
Seurat::DefaultAssay(new.list[[x]]) <- 'SCT'
} else {
new.list[[x]] <- suppressWarnings(Seurat::CreateSeuratObject(counts = object.list[[x]]@methods[[a]]@counts))
new.list[[x]]@assays$RNA@data <- object.list[[x]]@methods[[a]]@normalised
new.list[[x]]@assays$RNA@scale.data <- object.list[[x]]@methods[[a]]@norm.scaled
new.list[[x]]@assays$RNA@var.features <- object.list[[x]]@methods[[a]]@highly.variable.genes
}
}
features.list <- suppressWarnings(Seurat::SelectIntegrationFeatures(object.list = new.list, nfeatures = nfeatures, verbose = verbose, ...))
if('SCT' %in% strsplit(x = a, split = '_')) {
new.list <- suppressWarnings(Seurat::PrepSCTIntegration(object.list = new.list, anchor.features = features.list, verbose = verbose))
anchors <- suppressWarnings(Seurat::FindIntegrationAnchors(object.list = new.list,
normalization.method = "SCT",
anchor.features = features.list,
reduction = 'cca',
scale = F,
l2.norm = l2.norm,
dims = anchors.dims,
k.anchor = k.anchor,
k.filter = k.filter,
k.score = k.score,
max.features = max.features,
nn.method = nn.method,
n.trees = n.trees,
eps = anchor.eps,
verbose = verbose))
} else {
anchors <- suppressWarnings(Seurat::FindIntegrationAnchors(object.list = new.list,
anchor.features = features.list,
reduction = 'cca',
scale = T,
l2.norm = l2.norm,
dims = anchors.dims,
k.anchor = k.anchor,
k.filter = k.filter,
k.score = k.score,
max.features = max.features,
nn.method = nn.method,
n.trees = n.trees,
eps = anchor.eps,
verbose = verbose))
}
to_integrate <- Reduce(intersect, lapply(anchors@object.list, rownames))
combined <- suppressWarnings(Seurat::IntegrateData(anchorset = anchors,
features = features,
features.to.integrate = to_integrate,
dims = integrate.dims,
k.weight = k.weight,
sd.weight = sd.weight,
sample.tree = sample.tree,
preserve.order = T,
eps = integrate.eps,
verbose = verbose))
Seurat::DefaultAssay(combined) <- 'integrated'
combined <- suppressWarnings(Seurat::ScaleData(object = combined))
tmp.obj <- IBRAP::createIBRAPobject(counts = combined@assays$integrated@data,
original.project = 'tmp',
add.suffix = F)
tmp.obj@methods[['RAW']]@norm.scaled <- as(combined@assays$integrated@scale.data,'matrix')
tmp.obj@methods[['RAW']]@highly.variable.genes <- combined@assays$integrated@var.features
tmp.obj <- IBRAP::perform.pca(object = tmp.obj, assay = 'RAW', slot = 'norm.scaled', print.variance = T)
if(is.null(reduction.save.suffix)) {
object@methods[[a]]@integration_reductions[[paste0('CCA', reduction.save.suffix[[count]])]] <- tmp.obj@methods[[1]]@computational_reductions[[1]]
} else if(!is.null(reduction.save.suffix)) {
if(!is.character(reduction.save.suffix)) {
stop('reduction.save.suffix must be character string \n')
} else {
if('_' %in% unlist(strsplit(x = reduction.save.suffix, split = ''))) {
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': _ cannot be used in reduction.save.suffix, replacing with - \n')))
}
reduction.save.suffix <- sub(pattern = '_', replacement = '-', x = reduction.save.suffix)
}
object@methods[[a]]@integration_reductions[[paste0('CCA', reduction.save.suffix[[count]])]] <- tmp.obj@methods[[1]]@computational_reductions[[1]]
}
}
count <- count + 1
end_time <- Sys.time()
function_time <- end_time - start_time
# if(!'integration_method' %in% colnames(object@pipelines)) {
#
# tmp[which(x = tmp$normalisation_method==a),'integration_method'] <- paste0('CCA', reduction.save.suffix)
#
# tmp[which(x = tmp$normalisation_method==a),'integration_time'] <- as.difftime(function_time, units = 'secs')
#
# }
#
# if('integration_method' %in% colnames(object@pipelines)) {
#
# if(paste0('CCA', reduction.save.suffix) %in% tmp$integration_method) {
#
# tmp[which(tmp$normalisation_method==a & tmp$integration_method==paste0('CCA', reduction.save.suffix)),] <- c(tmp[which(tmp$normalisation_method==a & tmp$integration_method==paste0('CCA', reduction.save.suffix)),c('normalisation_method','normalisation_time')], paste0('CCA', reduction.save.suffix), as.difftime(function_time, units = 'secs'))
#
# }
#
# if(!paste0('CCA', reduction.save.suffix) %in% object@pipelines$integration_method) {
#
# df <- tmp[which(tmp$normalisation_method==a),]
#
# df <- df[!duplicated(df$normalisation_method),]
#
# df[,'integration_method'] <- paste0('CCA', reduction.save.suffix)
#
# df[,'integration_time'] <- function_time
#
# tmp <- rbind(tmp, df)
#
# }
#
# }
}
# if(!'integration_method' %in% colnames(object@pipelines)) {
#
# tmp$integration_time <- as.difftime(tim = tmp$integration_time, units = 'secs')
#
# rownames(tmp) <- 1:nrow(tmp)
#
# object@pipelines <- tmp
#
# } else if ('integration_method' %in% colnames(object@pipelines)) {
#
# tmp$integration_time <- as.difftime(tim = tmp$integration_time, units = 'secs')
#
# rownames(tmp) <- 1:nrow(tmp)
#
# object@pipelines <- tmp
#
# }
return(object)
}
connorhknight/IBRAP documentation built on March 9, 2023, 7:01 p.m.
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Defines functions perform.seurat.integration
Documented in perform.seurat.integration
#' @name perform.seurat.cca
#' @aliases perform.seurat.cca
#'
#' @title Performs Seurat Integration
#'
#' @description Performs Seurat integration on the supplied assay names. Results are saved under integration_reductions
#'
#' @param object IBRAP S4 class object
#' @param object.list list of individual sample IBRAP S4 class objects.
#' @param assay Character. String containing indicating which assay to use
#' @param reduction.save.suffix. Character. What should be added as a suffix to reduction name. Default = ''
#' @param nfeatures Numerical. How many features should be found as integration anchors. Default = 3000
#' @param l2.norm Logical. Perform L2 normalization on the CCA cell embeddings after dimensional reduction. Default = TRUE
#' @param k.anchor Numerical. How many neighbors (k) to use when picking anchors. Default = 5
#' @param k.filter Numerical. How many neighbors (k) to use when filtering anchors. Default = 200
#' @param k.score Numerical. How many neighbors (k) to use when scoring anchors. Default = 30
#' @param max.features. Numerical. The maximum number of features to use when specifying the neighborhood search space in the anchor filtering. Default = 200
#' @param nn.method Character. Method for nearest neighbor finding. Options include: rann, annoy. Default = annoy
#' @param n.tree Numerical. More trees gives higher precision when using annoy approximate nearest neighbor search. Default = 50
#' @param anchor.eps Numerical. Error bound on the neighbor finding algorithm (from RANN/Annoy) when finding integration genes.
#' @param features Character. Vector of features to use when computing the PCA to determine the weights. Only set if you want a different set from those used in the anchor finding process. Default = NULL
#' @param integrate.dims Numerical. Number of dimensions to use in the anchor weighting procedure. Default = 1:30
#' @param k.weight Numerical. Number of neighbors to consider when weighting anchors. Default = 100
#' @param sd.weight Numerical. Controls the bandwidth of the Gaussian kernel for weighting. Default = 1
#' @param sample.tree Character. Specify the order of integration. If NULL, will compute automatically. Default = NULL
#' @param integrate.eps Numerical. Error bound on the neighbor finding algorithm (from RANN)
#' @param save.plot Boolean. Should the automatically genewrated plot be saved? Default = TRUE
#'
#' @return Produces a new 'methods' assay containing normalised, scaled and HVGs.
#'
#' @examples perform.seurat.cca <- function(object = object,
#' assay = c('SCT', 'SCRAN', 'SCANPY'),
#' reduction.save.suffix=NULL,
#' nfeatures = 3000,
#' reduction = 'cca',
#' anchors.dims = 1:30,
#' l2.norm = T,
#' k.anchor = 5,
#' k.filter = 200,
#' k.score = 30,
#' max.features = 200,
#' nn.method = 'annoy',
#' n.trees = 50,
#' anchor.eps = 0,
#' features = NULL,
#' integrate.dims = 1:30,
#' k.weight = 100,
#' sd.weight = 1,
#' sample.tree = NULL,
#' integrate.eps = 0)
#'
#' @export
perform.seurat.integration <- function(object,
object.list,
assay,
reduction.save.suffix=NULL,
nfeatures = 2000,
anchors.dims = 1:30,
l2.norm = T,
k.anchor = 5,
k.filter = 200,
k.score = 30,
max.features = 200,
nn.method = 'annoy',
n.trees = 50,
anchor.eps = 0,
features = NULL,
integrate.dims = 1:30,
k.weight = 100,
sd.weight = 1,
sample.tree = NULL,
integrate.eps = 0,
print.variance = TRUE,
verbose=FALSE,
seed = 1234,
...) {
if(!is(object = object, class2 = 'IBRAP')) {
stop('object must be of class IBRAP \n')
}
if(!is.list(object.list)) {
stop('object.list must be a list of objects \n')
} else if (is.list(object.list)) {
for(x in object.list) {
if(!is(object = x, class2 = 'IBRAP')) {
stop('items in object.list must be IBRAP S4 class objects \n')
}
}
}
if(!is.character(assay)) {
stop('assay must be character string(s) \n')
} else if(is.character(assay)) {
for(x in assay) {
if(!x %in% names(object@methods)) {
stop(paste0(x, ' is not contained within object@methods \n'))
}
for(g in object.list) {
if(!x %in% names(g@methods)) {
stop(paste0(x, ' is not contained within one of your object.list items \n'))
}
}
}
}
if(!is.null(reduction.save.suffix)) {
if(!is.character(reduction.save.suffix)) {
stop('reduction.save.suffix must be a character string \n')
}
}
if(!is.numeric(nfeatures)) {
stop('nfeatures must be cnumerical \n')
}
if(!is.logical(l2.norm)) {
stop('l2.norm must be logical. TRUE/FALSE \n')
}
if(!is.numeric(anchors.dims)) {
stop('anchors.dims must be numerical \n')
}
if(!is.numeric(k.anchor)) {
stop('k.anchor must be numerical \n')
}
if(!is.numeric(k.filter)) {
stop('k.filter must be numerical \n')
}
if(!is.numeric(k.score)) {
stop('k.score must be numerical \n')
}
if(!is.numeric(max.features)) {
stop('max.features must be numerical \n')
}
if(!is.character(nn.method)) {
stop('nn.method must be numerical \n')
}
if(!is.numeric(anchor.eps)) {
stop('anchor.eps must be numerical \n')
}
if(!is.null(features)) {
if(!is.character(features)) {
stop('features must be character string(s)')
}
}
if(!is.numeric(integrate.dims)) {
stop('integrate.dims must be numerical \n')
}
if(!is.numeric(k.weight)) {
stop('k.weight must be numerical \n')
}
if(!is.numeric(sd.weight)) {
stop('sd.weight must be numerical \n')
}
if(!is.null(sample.tree)) {
if(!is.character(sample.tree)) {
stop('must be character stirng \n')
}
}
if(!is.numeric(integrate.eps)) {
stop('integrate.eps must be numerical. TRUE/FALSE \n')
}
if(!is.logical(print.variance)) {
stop('print.variance must be boolean. TRUE/FALSE \n')
}
if(!is.logical(verbose)) {
stop('verbose must be boolean. TRUE/FALSE \n')
}
for(x in names(object@methods)) {
for(g in object.list) {
if(!x %in% names(g@methods)) {
stop(paste0(x, ' is not contained within one of your items in onf of your object.list \n'))
}
}
}
# if(length(batch) > 1) {
#
# temp <- function(x) {
#
# return(paste(x, collapse = '_'))
#
# }
#
# df <- object@sample_metadata[,batch]
# object2 <- object
# object2@sample_metadata$batch <- apply(X = df, MARGIN = 1, FUN = temp)
#
# batch <- 'batch'
#
# } else {
#
# object2 <- object
#
# }
if(!is.numeric(seed)) {
stop('seed must be numerical \n')
}
set.seed(seed = seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
if(!'integration_method' %in% colnames(object@pipelines)) {
tmp <- tibble::add_column(.data = object@pipelines, integration_method=NA, integration_time=NA)
} else {
tmp <- object@pipelines
}
count <- 1
for(a in assay) {
start_time <- Sys.time()
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': initialising seurat integration for assay: ', a, '\n')))
}
new.list <- list()
for(x in 1:length(object.list)) {
if('SCT' %in% strsplit(x = a, split = '_')) {
new.list[[x]] <- suppressWarnings(Seurat::CreateSeuratObject(counts = object.list[[x]]@methods[[a]]@counts))
new.list[[x]]@assays$SCT <- object.list[[x]]@methods[[a]]@misc$SCT_Seurat_object
new.list[[x]]@assays$SCT@counts <- object.list[[x]]@methods[[a]]@counts
new.list[[x]]@assays$SCT@data <- object.list[[x]]@methods[[a]]@normalised
new.list[[x]]@assays$SCT@scale.data <- object.list[[x]]@methods[[a]]@norm.scaled
new.list[[x]]@assays$SCT@var.features <- object.list[[x]]@methods[[a]]@highly.variable.genes
Seurat::DefaultAssay(new.list[[x]]) <- 'SCT'
} else {
new.list[[x]] <- suppressWarnings(Seurat::CreateSeuratObject(counts = object.list[[x]]@methods[[a]]@counts))
new.list[[x]]@assays$RNA@data <- object.list[[x]]@methods[[a]]@normalised
new.list[[x]]@assays$RNA@scale.data <- object.list[[x]]@methods[[a]]@norm.scaled
new.list[[x]]@assays$RNA@var.features <- object.list[[x]]@methods[[a]]@highly.variable.genes
}
}
features.list <- suppressWarnings(Seurat::SelectIntegrationFeatures(object.list = new.list, nfeatures = nfeatures, verbose = verbose, ...))
if('SCT' %in% strsplit(x = a, split = '_')) {
new.list <- suppressWarnings(Seurat::PrepSCTIntegration(object.list = new.list, anchor.features = features.list, verbose = verbose))
anchors <- suppressWarnings(Seurat::FindIntegrationAnchors(object.list = new.list,
normalization.method = "SCT",
anchor.features = features.list,
reduction = 'cca',
scale = F,
l2.norm = l2.norm,
dims = anchors.dims,
k.anchor = k.anchor,
k.filter = k.filter,
k.score = k.score,
max.features = max.features,
nn.method = nn.method,
n.trees = n.trees,
eps = anchor.eps,
verbose = verbose))
} else {
anchors <- suppressWarnings(Seurat::FindIntegrationAnchors(object.list = new.list,
anchor.features = features.list,
reduction = 'cca',
scale = T,
l2.norm = l2.norm,
dims = anchors.dims,
k.anchor = k.anchor,
k.filter = k.filter,
k.score = k.score,
max.features = max.features,
nn.method = nn.method,
n.trees = n.trees,
eps = anchor.eps,
verbose = verbose))
}
to_integrate <- Reduce(intersect, lapply(anchors@object.list, rownames))
combined <- suppressWarnings(Seurat::IntegrateData(anchorset = anchors,
features = features,
features.to.integrate = to_integrate,
dims = integrate.dims,
k.weight = k.weight,
sd.weight = sd.weight,
sample.tree = sample.tree,
preserve.order = T,
eps = integrate.eps,
verbose = verbose))
Seurat::DefaultAssay(combined) <- 'integrated'
combined <- suppressWarnings(Seurat::ScaleData(object = combined))
tmp.obj <- IBRAP::createIBRAPobject(counts = combined@assays$integrated@data,
original.project = 'tmp',
add.suffix = F)
tmp.obj@methods[['RAW']]@norm.scaled <- as(combined@assays$integrated@scale.data,'matrix')
tmp.obj@methods[['RAW']]@highly.variable.genes <- combined@assays$integrated@var.features
tmp.obj <- IBRAP::perform.pca(object = tmp.obj, assay = 'RAW', slot = 'norm.scaled', print.variance = T)
if(is.null(reduction.save.suffix)) {
object@methods[[a]]@integration_reductions[[paste0('CCA', reduction.save.suffix[[count]])]] <- tmp.obj@methods[[1]]@computational_reductions[[1]]
} else if(!is.null(reduction.save.suffix)) {
if(!is.character(reduction.save.suffix)) {
stop('reduction.save.suffix must be character string \n')
} else {
if('_' %in% unlist(strsplit(x = reduction.save.suffix, split = ''))) {
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': _ cannot be used in reduction.save.suffix, replacing with - \n')))
}
reduction.save.suffix <- sub(pattern = '_', replacement = '-', x = reduction.save.suffix)
}
object@methods[[a]]@integration_reductions[[paste0('CCA', reduction.save.suffix[[count]])]] <- tmp.obj@methods[[1]]@computational_reductions[[1]]
}
}
count <- count + 1
end_time <- Sys.time()
function_time <- end_time - start_time
# if(!'integration_method' %in% colnames(object@pipelines)) {
#
# tmp[which(x = tmp$normalisation_method==a),'integration_method'] <- paste0('CCA', reduction.save.suffix)
#
# tmp[which(x = tmp$normalisation_method==a),'integration_time'] <- as.difftime(function_time, units = 'secs')
#
# }
#
# if('integration_method' %in% colnames(object@pipelines)) {
#
# if(paste0('CCA', reduction.save.suffix) %in% tmp$integration_method) {
#
# tmp[which(tmp$normalisation_method==a & tmp$integration_method==paste0('CCA', reduction.save.suffix)),] <- c(tmp[which(tmp$normalisation_method==a & tmp$integration_method==paste0('CCA', reduction.save.suffix)),c('normalisation_method','normalisation_time')], paste0('CCA', reduction.save.suffix), as.difftime(function_time, units = 'secs'))
#
# }
#
# if(!paste0('CCA', reduction.save.suffix) %in% object@pipelines$integration_method) {
#
# df <- tmp[which(tmp$normalisation_method==a),]
#
# df <- df[!duplicated(df$normalisation_method),]
#
# df[,'integration_method'] <- paste0('CCA', reduction.save.suffix)
#
# df[,'integration_time'] <- function_time
#
# tmp <- rbind(tmp, df)
#
# }
#
# }
}
# if(!'integration_method' %in% colnames(object@pipelines)) {
#
# tmp$integration_time <- as.difftime(tim = tmp$integration_time, units = 'secs')
#
# rownames(tmp) <- 1:nrow(tmp)
#
# object@pipelines <- tmp
#
# } else if ('integration_method' %in% colnames(object@pipelines)) {
#
# tmp$integration_time <- as.difftime(tim = tmp$integration_time, units = 'secs')
#
# rownames(tmp) <- 1:nrow(tmp)
#
# object@pipelines <- tmp
#
# }
return(object)
}
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