R/perform.seurat.cluster.R
In connorhknight/IBRAP: Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP)
Defines functions
perform.graph.cluster
Documented in
perform.graph.cluster
#' @name perform.graph.cluster
#' @aliases perform.graph.cluster
#'
#' @title Performs graph-based clustering
#'
#' @description Performs graph-based clustering on previously generated neighbouhood graphs.
#'
#' @param object IBRAP S4 class object
#' @param assay Character. String containing indicating which assay to use
#' @param neighbours Character. String indicating which neighbourhood graphs should be used.
#' @param algorithm Numerical. Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python. Default = 1 Default = NULL
#' @param cluster.df.name Character. What to call the df contained in clusters. Default = 'seurat
#' @param res Numerical vector. Which resolution to run the clusterign algorithm at, a smaller and larger value identified less and more clusters, respectively. Default = c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5)
#' @param verbose Logical. Should system information be printed. Default = FALSE
#' @param seed Numeric. What should the seed be set as. Default = 1234
#' @param ... arguments to be passed to Seurat::FindClusters
#'
#' @return Cluster assignments using the list of resolutions provided contained within cluster_assignments under cluster.df.name
#'
#' @examples
#'
#' object <- perform.nn.v1(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'),
#' reduction = c('pca_harmony','scanorama'),
#' dims = list(0,0), generate.diffmap = T)
#'
#' object <- perform.nn.v1(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'),
#' reduction = c('pca_bbknn_bbknn:diffmap','pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'),
#' dims = list(0,0,0))
#'
#' object <- perform.nn.v2(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'),
#' reduction = c('pca_harmony','scanorama','pca_bbknn_bbknn:diffmap',
#' 'pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'),
#' dims = list(0,0,0,0,0))
#'
#' object <- perform.graph.cluster(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'),
#' neighbours = c("pca_bbknn_bbknn",
#' "pca_harmony_nn.v1",
#' "scanorama_nn.v1",
#' "pca_bbknn_bbknn:diffmap_nn.v1",
#' "pca_harmony_nn.v1:diffmap_nn.v1",
#' "scanorama_nn.v1:diffmap_nn.v1",
#' "pca_harmony_nn.v2",
#' "scanorama_nn.v2",
#' "pca_bbknn_bbknn:diffmap_nn.v2",
#' "pca_harmony_nn.v1:diffmap_nn.v2",
#' "scanorama_nn.v1:diffmap_nn.v2" ),
#' algorithm = 1)
#'
#' @export
perform.graph.cluster <- function(object,
assay,
neighbours,
algorithm=1,
cluster.df.name.suffix = '',
res=c(0.1,0.2,0.3,0.4,0.5,
0.6,0.7,0.8,0.9,1,
1.1,1.2,1.3,1.4,1.5),
verbose=FALSE,
seed=1234,
...) {
if(!is(object = object, class2 = 'IBRAP')) {
stop('object must be of class IBRAP\n')
}
if(!is.character(assay)) {
stop('assay must be character string(s)')
} else if (is.character(assay)) {
for(x in assay) {
if(!x %in% names(object@methods)) {
stop(paste0('assay: ', x, ' is not contained within object@methods \n'))
}
}
}
for(p in assay) {
if(!is.character(neighbours)) {
stop('neighbours must be character string(s)')
} else if (is.character(neighbours)) {
for(x in neighbours) {
if(!x %in% names(object@methods[[p]]@neighbours)) {
stop(paste0('neighbours: ', x, ' is not contained within assay: ', p, ' \n'))
}
}
}
}
if(!is.numeric(algorithm)) {
stop('method must either be numeric\n')
}
if(!algorithm %in% c(1,2,3,4)) {
stop('method must either: 1 (original Louvain), 2 (Louvain with multilevel refinement), 3 (SLM) or 4 (Leiden)\n')
}
if(algorithm==1) {
algo.name <- 'LOUVAIN'
}
if(algorithm==2) {
algo.name <- 'LOUVAINMLR'
}
if(algorithm==3) {
algo.name <- 'SLM'
}
if(algorithm==4) {
algo.name <- 'LEIDEN'
}
if('_' %in% unlist(strsplit(x = cluster.df.name.suffix, split = ''))) {
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': _ cannot be used in cluster.df.name.suffix, replacing with - \n')))
}
cluster.df.name.suffix <- sub(pattern = '_', replacement = '-', x = cluster.df.name.suffix)
}
if(!is.logical(verbose)) {
stop('verbose should be logical, TRUE/FALSE \n')
}
if(!is.numeric(seed)) {
stop('seed should be numerical \n')
}
set.seed(seed = seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
reticulate::py_set_seed(seed, disable_hash_randomization = TRUE)
# if(!'clustering_method' %in% colnames(object@pipelines)) {
#
# tmp <- tibble::add_column(.data = object@pipelines, clustering_method=NA, clustering_time=NA)
#
# } else {
#
# tmp <- object@pipelines
#
# }
for(p in assay) {
for(t in neighbours) {
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': calculating Seurat clusters for assay: ', p, ' using graph: ', t, '\n')))
}
seuobj <- suppressWarnings(Seurat::CreateSeuratObject(counts = object@methods[[p]]@counts))
seuobj@graphs[['neighbourhood_graph']] <- suppressWarnings(Seurat::as.Graph(x = object@methods[[p]]@neighbours[[t]]$connectivities))
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': graph added\n')))
}
for(x in res) {
start_time <- Sys.time()
if(isTRUE(verbose)) {
seuobj <- suppressWarnings(Seurat::FindClusters(object = seuobj, resolution = x, graph.name = 'neighbourhood_graph',
algorithm = algorithm, verbose = TRUE, ...))
} else if(isFALSE(verbose)) {
seuobj <- suppressWarnings(Seurat::FindClusters(object = seuobj, resolution = x, graph.name = 'neighbourhood_graph',
algorithm = algorithm, verbose = FALSE, ...))
}
# end_time <- Sys.time()
#
# function_time <- end_time - start_time
#
# if('integration_method' %in% colnames(tmp) & 'normalisation_method' %in% colnames(tmp)) {
#
# if(nrow(tmp[which(tmp$normalisation_method==p & tmp$integration_method==unique(tmp$integration_method[which(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_')))]) & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),])!=0) {
#
# tmp[which(tmp$normalisation_method==p & tmp$integration_method==unique(tmp$integration_method[which(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_')))]) & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),'clustering_method'] <- paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)
#
# tmp[which(tmp$normalisation_method==p & tmp$integration_method==unique(tmp$integration_method[which(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_')))]) & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# } else {
#
# if(any(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_'))==TRUE)){
#
# int_meth <- unique(tmp$integration_method[which(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_')))])
#
# roww <- which(tmp$normalisation_method==p & tmp$integration_method==int_meth)
#
# tmp_row <- tmp[which(tmp$normalisation_method==p & tmp$integration_method==int_meth),]
#
# if(NA %in% tmp_row[,'clustering_method']) {
#
# tmp <- tmp[-roww,]
#
# }
#
# tmp_row <- tmp_row[1,]
#
# tmp_row[,'clustering_method'] <- paste0(algo.name, '_resolution(', x, ')', cluster.df.name.suffix)
#
# tmp_row[,'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# tmp <- rbind(tmp, tmp_row)
#
# }
#
# if(all(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_'))==FALSE)) {
#
# tmp_row <- tmp[which(x = tmp$normalisation_method==p),]
#
# tmp_row <- tmp_row[1,]
#
# tmp_row[,c('integration_method','integration_time')] <- NA
#
# tmp_row[,'clustering_method'] <- paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)
#
# tmp_row[,'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# tmp <- rbind(tmp, tmp_row)
#
# }
#
# }
#
# } else if ('normalisation_method' %in% colnames(tmp) & !'integration_method' %in% colnames(tmp)) {
#
# tmp_sub <- tmp[tmp$normalisation_method==p,]
#
# if(paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix) %in% tmp_sub$clustering_method) {
#
# tmp[which(tmp$normalisation_method==p & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),'clustering_method'] <- paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)
#
# tmp[which(tmp$normalisation_method==p & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# } else if(!paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix) %in% tmp_sub$clustering_method) {
#
# tmp_sub[1,'clustering_method'] <- paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)
#
# tmp_sub[1,'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# tmp <- rbind(tmp, tmp_sub)
#
# tmp <- tmp[!duplicated(tmp),]
#
# }
#
# }
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': clusters identified\n')))
}
}
new.clusters <- seuobj@meta.data[,colnames(seuobj@meta.data)[grepl(pattern = 'res', x = colnames(seuobj@meta.data))]]
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': seurat clusters added\n')))
}
if(!is.data.frame(new.clusters)) {
new.clusters <- as.data.frame(new.clusters)
rownames(new.clusters) <- rownames(seuobj@meta.data)
colnames(new.clusters) <- paste0('res_', res)
} else {
colnames(new.clusters) <- paste0('res_', res)
}
object@methods[[p]]@cluster_assignments[[paste0(t, ':', algo.name, cluster.df.name.suffix)]] <- new.clusters
}
}
# for(p in assay) {
#
# if(any(is.na(tmp[which(tmp$normalisation_method==p),'clustering_method']))) {
#
# tmp <- tmp[-which(tmp$normalisation_method==p & is.na(tmp$clustering_method)),]
#
# }
#
# }
#
# tmp$clustering_time <- as.difftime(tim = tmp$clustering_time, units = 'secs')
#
# if('integration_method' %in% colnames(tmp) & 'normalisation_method' %in% colnames(tmp)) {
#
# tmp$clustering_time <- tmp$normalisation_time + tmp$integration_time + tmp$clustering_time
#
# } else if ('normalisation_method' %in% colnames(tmp) & !'integration_method' %in% colnames(tmp)) {
#
# tmp$clustering_time <- tmp$normalisation_time + tmp$clustering_time
#
# }
#
# 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.graph.cluster
Documented in perform.graph.cluster
#' @name perform.graph.cluster
#' @aliases perform.graph.cluster
#'
#' @title Performs graph-based clustering
#'
#' @description Performs graph-based clustering on previously generated neighbouhood graphs.
#'
#' @param object IBRAP S4 class object
#' @param assay Character. String containing indicating which assay to use
#' @param neighbours Character. String indicating which neighbourhood graphs should be used.
#' @param algorithm Numerical. Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python. Default = 1 Default = NULL
#' @param cluster.df.name Character. What to call the df contained in clusters. Default = 'seurat
#' @param res Numerical vector. Which resolution to run the clusterign algorithm at, a smaller and larger value identified less and more clusters, respectively. Default = c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5)
#' @param verbose Logical. Should system information be printed. Default = FALSE
#' @param seed Numeric. What should the seed be set as. Default = 1234
#' @param ... arguments to be passed to Seurat::FindClusters
#'
#' @return Cluster assignments using the list of resolutions provided contained within cluster_assignments under cluster.df.name
#'
#' @examples
#'
#' object <- perform.nn.v1(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'),
#' reduction = c('pca_harmony','scanorama'),
#' dims = list(0,0), generate.diffmap = T)
#'
#' object <- perform.nn.v1(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'),
#' reduction = c('pca_bbknn_bbknn:diffmap','pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'),
#' dims = list(0,0,0))
#'
#' object <- perform.nn.v2(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'),
#' reduction = c('pca_harmony','scanorama','pca_bbknn_bbknn:diffmap',
#' 'pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'),
#' dims = list(0,0,0,0,0))
#'
#' object <- perform.graph.cluster(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'),
#' neighbours = c("pca_bbknn_bbknn",
#' "pca_harmony_nn.v1",
#' "scanorama_nn.v1",
#' "pca_bbknn_bbknn:diffmap_nn.v1",
#' "pca_harmony_nn.v1:diffmap_nn.v1",
#' "scanorama_nn.v1:diffmap_nn.v1",
#' "pca_harmony_nn.v2",
#' "scanorama_nn.v2",
#' "pca_bbknn_bbknn:diffmap_nn.v2",
#' "pca_harmony_nn.v1:diffmap_nn.v2",
#' "scanorama_nn.v1:diffmap_nn.v2" ),
#' algorithm = 1)
#'
#' @export
perform.graph.cluster <- function(object,
assay,
neighbours,
algorithm=1,
cluster.df.name.suffix = '',
res=c(0.1,0.2,0.3,0.4,0.5,
0.6,0.7,0.8,0.9,1,
1.1,1.2,1.3,1.4,1.5),
verbose=FALSE,
seed=1234,
...) {
if(!is(object = object, class2 = 'IBRAP')) {
stop('object must be of class IBRAP\n')
}
if(!is.character(assay)) {
stop('assay must be character string(s)')
} else if (is.character(assay)) {
for(x in assay) {
if(!x %in% names(object@methods)) {
stop(paste0('assay: ', x, ' is not contained within object@methods \n'))
}
}
}
for(p in assay) {
if(!is.character(neighbours)) {
stop('neighbours must be character string(s)')
} else if (is.character(neighbours)) {
for(x in neighbours) {
if(!x %in% names(object@methods[[p]]@neighbours)) {
stop(paste0('neighbours: ', x, ' is not contained within assay: ', p, ' \n'))
}
}
}
}
if(!is.numeric(algorithm)) {
stop('method must either be numeric\n')
}
if(!algorithm %in% c(1,2,3,4)) {
stop('method must either: 1 (original Louvain), 2 (Louvain with multilevel refinement), 3 (SLM) or 4 (Leiden)\n')
}
if(algorithm==1) {
algo.name <- 'LOUVAIN'
}
if(algorithm==2) {
algo.name <- 'LOUVAINMLR'
}
if(algorithm==3) {
algo.name <- 'SLM'
}
if(algorithm==4) {
algo.name <- 'LEIDEN'
}
if('_' %in% unlist(strsplit(x = cluster.df.name.suffix, split = ''))) {
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': _ cannot be used in cluster.df.name.suffix, replacing with - \n')))
}
cluster.df.name.suffix <- sub(pattern = '_', replacement = '-', x = cluster.df.name.suffix)
}
if(!is.logical(verbose)) {
stop('verbose should be logical, TRUE/FALSE \n')
}
if(!is.numeric(seed)) {
stop('seed should be numerical \n')
}
set.seed(seed = seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
reticulate::py_set_seed(seed, disable_hash_randomization = TRUE)
# if(!'clustering_method' %in% colnames(object@pipelines)) {
#
# tmp <- tibble::add_column(.data = object@pipelines, clustering_method=NA, clustering_time=NA)
#
# } else {
#
# tmp <- object@pipelines
#
# }
for(p in assay) {
for(t in neighbours) {
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': calculating Seurat clusters for assay: ', p, ' using graph: ', t, '\n')))
}
seuobj <- suppressWarnings(Seurat::CreateSeuratObject(counts = object@methods[[p]]@counts))
seuobj@graphs[['neighbourhood_graph']] <- suppressWarnings(Seurat::as.Graph(x = object@methods[[p]]@neighbours[[t]]$connectivities))
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': graph added\n')))
}
for(x in res) {
start_time <- Sys.time()
if(isTRUE(verbose)) {
seuobj <- suppressWarnings(Seurat::FindClusters(object = seuobj, resolution = x, graph.name = 'neighbourhood_graph',
algorithm = algorithm, verbose = TRUE, ...))
} else if(isFALSE(verbose)) {
seuobj <- suppressWarnings(Seurat::FindClusters(object = seuobj, resolution = x, graph.name = 'neighbourhood_graph',
algorithm = algorithm, verbose = FALSE, ...))
}
# end_time <- Sys.time()
#
# function_time <- end_time - start_time
#
# if('integration_method' %in% colnames(tmp) & 'normalisation_method' %in% colnames(tmp)) {
#
# if(nrow(tmp[which(tmp$normalisation_method==p & tmp$integration_method==unique(tmp$integration_method[which(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_')))]) & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),])!=0) {
#
# tmp[which(tmp$normalisation_method==p & tmp$integration_method==unique(tmp$integration_method[which(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_')))]) & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),'clustering_method'] <- paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)
#
# tmp[which(tmp$normalisation_method==p & tmp$integration_method==unique(tmp$integration_method[which(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_')))]) & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# } else {
#
# if(any(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_'))==TRUE)){
#
# int_meth <- unique(tmp$integration_method[which(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_')))])
#
# roww <- which(tmp$normalisation_method==p & tmp$integration_method==int_meth)
#
# tmp_row <- tmp[which(tmp$normalisation_method==p & tmp$integration_method==int_meth),]
#
# if(NA %in% tmp_row[,'clustering_method']) {
#
# tmp <- tmp[-roww,]
#
# }
#
# tmp_row <- tmp_row[1,]
#
# tmp_row[,'clustering_method'] <- paste0(algo.name, '_resolution(', x, ')', cluster.df.name.suffix)
#
# tmp_row[,'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# tmp <- rbind(tmp, tmp_row)
#
# }
#
# if(all(tmp$integration_method %in% unlist(strsplit(x = toupper(t), split = '_'))==FALSE)) {
#
# tmp_row <- tmp[which(x = tmp$normalisation_method==p),]
#
# tmp_row <- tmp_row[1,]
#
# tmp_row[,c('integration_method','integration_time')] <- NA
#
# tmp_row[,'clustering_method'] <- paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)
#
# tmp_row[,'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# tmp <- rbind(tmp, tmp_row)
#
# }
#
# }
#
# } else if ('normalisation_method' %in% colnames(tmp) & !'integration_method' %in% colnames(tmp)) {
#
# tmp_sub <- tmp[tmp$normalisation_method==p,]
#
# if(paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix) %in% tmp_sub$clustering_method) {
#
# tmp[which(tmp$normalisation_method==p & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),'clustering_method'] <- paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)
#
# tmp[which(tmp$normalisation_method==p & tmp$clustering_method==paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)),'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# } else if(!paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix) %in% tmp_sub$clustering_method) {
#
# tmp_sub[1,'clustering_method'] <- paste0(algo.name, '_(resolution=', x, ')', cluster.df.name.suffix)
#
# tmp_sub[1,'clustering_time'] <- as.difftime(function_time, units = 'secs')
#
# tmp <- rbind(tmp, tmp_sub)
#
# tmp <- tmp[!duplicated(tmp),]
#
# }
#
# }
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': clusters identified\n')))
}
}
new.clusters <- seuobj@meta.data[,colnames(seuobj@meta.data)[grepl(pattern = 'res', x = colnames(seuobj@meta.data))]]
if(isTRUE(verbose)) {
cat(crayon::cyan(paste0(Sys.time(), ': seurat clusters added\n')))
}
if(!is.data.frame(new.clusters)) {
new.clusters <- as.data.frame(new.clusters)
rownames(new.clusters) <- rownames(seuobj@meta.data)
colnames(new.clusters) <- paste0('res_', res)
} else {
colnames(new.clusters) <- paste0('res_', res)
}
object@methods[[p]]@cluster_assignments[[paste0(t, ':', algo.name, cluster.df.name.suffix)]] <- new.clusters
}
}
# for(p in assay) {
#
# if(any(is.na(tmp[which(tmp$normalisation_method==p),'clustering_method']))) {
#
# tmp <- tmp[-which(tmp$normalisation_method==p & is.na(tmp$clustering_method)),]
#
# }
#
# }
#
# tmp$clustering_time <- as.difftime(tim = tmp$clustering_time, units = 'secs')
#
# if('integration_method' %in% colnames(tmp) & 'normalisation_method' %in% colnames(tmp)) {
#
# tmp$clustering_time <- tmp$normalisation_time + tmp$integration_time + tmp$clustering_time
#
# } else if ('normalisation_method' %in% colnames(tmp) & !'integration_method' %in% colnames(tmp)) {
#
# tmp$clustering_time <- tmp$normalisation_time + tmp$clustering_time
#
# }
#
# rownames(tmp) <- 1:nrow(tmp)
#
# object@pipelines <- tmp
#
return(object)
}
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