In mariiabilous/SuperCellBM:
An example how to build multiple super-cell-like objects, including 'exact' (Super-cells obtained with the exact coarse-gaining), 'approx' (Super-cells obtained with the aaproximate coarse-graining), 'metacell(.*)' (Metacell build on the same genes as super-cells -- 'metacell_SC_like'; and Metacell build in a default set of genes -- 'metacell_default') for a set of graining levels and random seeds.
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("SingleCellExperiment")
#
# if (!requireNamespace("remotes")) install.packages("remotes")
# remotes::install_github("GfellerLab/SuperCell")
# remotes::install_github("mariiabilous/SuperCellBM")
library(SingleCellExperiment)
library(SuperCell)
library(SuperCellBM)
Load some default parameters
Such as .gamma.seqfor the set of fraining levels, .seed.seq for the set of random seeds, adata.folder and fig.folder for the folders where to write data and plots.
For the full list of the default parameters, see ./examples/config/Tian_config.R.
source("./config/Tcells_config.R")
Flags
Whether to compute super-cell (ToComputeSC) or whether to compute super-cell gene expression (ToComputeSC_GE) or load saved files. Make sure, these file exists :)
Flag ToTestPackage is used to run stript in 2 modes: package testing (ToTestPackage == TRUE) or generating super-cell structure and super-cell gene expression for the further analyses (ToTestPackage == FALSE).
ToLoadPreprocData <- T
ToComputeSC <- F
ToComputeSC_GE <- F
ToClusterSinglecell <- F
ToTestPackage <- F
filename_suf <- "" # variable to add a suffix to the saved files in case of testing of the package
if(ToTestPackage){
testing_gamma_seq <- c(1, 10, 100)
testing_seed_seq <- .seed.seq[1:3]
warning(paste("The reduced set of graining leveles and seeds will be used, to get real output, turn it ti FALSE"))
warning(paste("Original set of graining levels is:", paste(.gamma.seq, collapse = ", "),
"but used testing set is:", paste(testing_gamma_seq, collapse = ", ")))
warning(paste("Original set of seeds is:", paste(.seed.seq, collapse = ", "),
"but used testing set is:", paste(testing_seed_seq, collapse = ", ")))
.gamma.seq <- testing_gamma_seq
.seed.seq <- testing_seed_seq
filename_suf = "_testing_package"
} else {
.seed.seq = .seed.seq[1:5]
}
Load Tcells data from 10x Genomics database.
if(!ToLoadPreprocData){
source("./config/Tcells_load_data.R")
} else {
sc.GE <- readRDS(file = file.path(data.folder, "sc_ge.Rds"))
sc.counts <- readRDS(file = file.path(data.folder, "sc_counts.Rds"))
cell.meta <- readRDS(file = file.path(data.folder, "cell_meta.Rds"))
}
Get and set the main variables, such as single-cell gene expression (sc.GE), single-cell counts (sc.counts),
number of single cells (N.c) and total number of genes (N.g).
Set matrix column names to cellIDs (cell.ids) and row names to gene names (gene.names).
GT.cell.type <- cell.meta$GT.cell.type
names(GT.cell.type) <- rownames(cell.meta)
GT.cell.type.names <- names(table(cell.meta$GT.cell.type))
GT.cell.type.2.num <- 1:length(unique(GT.cell.type))
names(GT.cell.type.2.num) <- GT.cell.type.names
GT.cell.type.num <- GT.cell.type.2.num[GT.cell.type]
names(GT.cell.type.num) <- names(GT.cell.type)
cell.ids <- colnames(sc.GE)
N.c <- length(cell.ids)
gene.names <- rownames(sc.GE)
N.g <- length(gene.names)
N.clusters <- length(unique(GT.cell.type))
mito.genes <- grep(pattern = "^MT", x = gene.names, value = TRUE)
ribo.genes <- grep(pattern = "^RP[LS]", x = gene.names, value = TRUE)
mito.ribo.genes <- c(mito.genes, ribo.genes)
length(mito.ribo.genes)
.genes.omit <- mito.ribo.genes
## uncomment this when needed
# pal.GT <- color.tsne.
# scales::show_col(pal.GT)
# scales::show_col(color.tsne)
Compute Super-cells structure
for the Exact, Aprox (Super-cells obtained with the exact or approximate coarse-graining), Subsampling or Random (random grouping of cells into super-cells).
filename <- paste0('initial', filename_suf)
SC.list <- compute_supercells(
sc.GE,
ToComputeSC = ToComputeSC,
data.folder = data.folder,
filename = filename,
gamma.seq = .gamma.seq,
n.var.genes = .N.var.genes,
k.knn = .k.knn,
n.pc = .N.comp,
approx.N = .approx.N,
fast.pca = TRUE,
genes.use = .genes.use,
genes.exclude = .genes.omit,
seed.seq = .seed.seq,
verbose = TRUE
)
cat(paste("Super-cell computed for:", paste(names(SC.list), collapse = ", "),
"\nat graining levels:", paste(names(SC.list[['Approx']]), collapse = ", "),
"\nfor seeds:", paste(names(SC.list[['Approx']][[1]]), collapse = ", "), "\n",
"\nand saved to / loaded from", paste0(filename, ".Rds")))
Compute metacells in two settings:
- 'metacell_default' - when metacell is computed with the default parameters (from the tutorial), using gene set filtered by MC
- 'metacell_SC_like' - when metacell is computed with the same default parameters, but at the same set of genes as Super-cells
SC.mc <- compute_supercells_metacells(
sc.counts = sc.counts,
gamma.seq = .gamma.seq,
SC.list = SC.list,
proj.name = proj.name,
ToComputeSC = ToComputeSC,
mc.k.knn = 100,
T_vm_def = 0.08,
MC.folder = "MC",
MC_gene_settings = c('Metacell_default', 'Metacell_SC_like') # do not change
)
Get actual graining levels obtained with Metacell
additional_gamma_seq <- get_actual_gammas_metacell(SC.mc)
cat(paste("Metacells were computed in", length(names(SC.mc)), "settings:", paste(names(SC.mc), collapse = ", "),
"\nfor Gammas:", paste(names(SC.mc[[1]]), collapse = ", "),
"\nbut actual gammas are:", paste(additional_gamma_seq, collapse = ", ")
))
# manually expand MC because later we will have 2 different setting for MC profile: fp - footpring of MC, av - averaged
SC.mc.fp <- SC.mc
names(SC.mc.fp) <- sapply(names(SC.mc), FUN = function(x){paste0(x, '_fp')})
SC.mc.av <- SC.mc
names(SC.mc.av) <- sapply(names(SC.mc), FUN = function(x){paste0(x, '_av')})
SC.mc.expanded <- c(SC.mc.fp, SC.mc.av)
names(SC.mc.expanded)
rm(SC.mc.fp, SC.mc.av, SC.mc)
Compute super-cells (Exact, Approx, Subsampling and Random) at the addidional grainig levels obtained with Metacell.
So that we can dirrectly compare the results of Super-cells and Metacells at the same graining levels.
filename <- paste0('additional_gammas', filename_suf)
SC.list <- compute_supercells_additional_gammas(
SC.list,
additional_gamma_seq = additional_gamma_seq,
ToComputeSC = ToComputeSC,
data.folder = data.folder,
filename = filename,
approx.N = .approx.N,
fast.pca = TRUE
)
cat(paste("Super-cells of methods:", paste(names(SC.list), collapse = ", "),
"\nwere computed at aggitional graining levels:", paste(additional_gamma_seq, collapse = ", "),
"\nand added to SC.list"
))
Concatenate Metacells to the list of Super-cells
SC.list <- c(SC.list, SC.mc.expanded)
rm(SC.mc.expanded)
filename <- paste0("all", filename_suf)
saveRDS(SC.list, file = file.path(data.folder, "SC", paste0(filename, ".Rds")))
cat(paste(
"Metacell data added to SC.list \nand now it contains:",
paste(names(SC.list), collapse = ", "),
"\nSC.list was saved to", file.path(data.folder, "SC", paste0(filename, ".Rds"))
))
SC.list <- readRDS(file = file.path(data.folder, "SC", paste0(filename, ".Rds")))
START FROM HERE!!!
genes.use <- SC.list$Exact$`10`$`12345`$genes.use
if(length(.N.comp) == 1) {N.comp <- 1:.N.comp} else {N.comp <- .N.comp}
X.for.pca <- scale(Matrix::t(sc.GE[genes.use,]))
sc.pca <- irlba::irlba(X.for.pca, nv = max(N.comp, 25))
sc.pca$x <- sc.pca$u %*% diag(sc.pca$d)
if(ToClusterSinglecell){
sc.dist <- dist(sc.pca$x[,N.comp])
sc.clustering.hcl <- hclust(sc.dist, method = "ward.D2")
saveRDS(sc.clustering.hcl, file = file.path(data.folder, "sc_clustering_hcl.Rds"))
} else {
sc.clustering.hcl <- readRDS(file.path(data.folder, "sc_clustering_hcl.Rds"))
}
sc.clustering <- cutree(sc.clustering.hcl, k = N.clusters)
Annotate super-cells to the cell lines based on the cell line annotation of single-cell and compute super-cell purity in terms of this annotation
SC.list <- annotate_supercells_to_cluster(
SC.list = SC.list,
sc.annotation = sc.clustering,
annotation.name = 'sc_clustering'
)
purity.df <- plot_annotation_purity(
SC.list,
annotation.name = 'sc_clustering', w = 2.5, h = 2.5,
)
Compute GE for Super-cell data
GE profile for the super-cell data is computede:
- for super-cells (Exact, Approx) by averaging gene expression within super-cells,
- for Random, also averaging gene expression within super-cells
- for the Subsampling, sc.GE matrix is just subsampled,
- for Metacells, gene expression is computed in 2 ways:
1) the same as super-cells (averaging gene expression within Metacells) ->
metacell_(.*)_av
2) using the default output of Metacell maned footprint -> metacell_(.*)_fp
filename <- paste0("all", filename_suf)
SC.GE.list <- compute_supercells_GE(
sc.GE = sc.GE,
SC.list = SC.list,
ToComputeSC_GE = ToComputeSC_GE,
data.folder = data.folder,
filename = filename,
verbose = FALSE
)
cat(paste("Gene expression profile computed for:", paste(names(SC.GE.list), collapse = ", "),
"\nat graining levels:", paste(sort(as.numeric(names(SC.GE.list[['Approx']]))), collapse = ", "),
"\nfor seeds:", paste(names(SC.GE.list[['Approx']][[1]]), collapse = ", "),
"\nand saved to / loaded from", paste0(filename, ".Rds")
))
dim(SC.GE.list$Metacell_SC_like_fp$`100`$`12345`)
dim(SC.list$Metacell_default_fp$`100`$`12345`$mc_info$mc@mc_fp)
dim(SC.GE.list$Exact$`100`$`12345`)
dim(SC.list$Metacell_default_av$`100`$`12345`$mc_info$mc@mc_fp)
Dimensionality reduction of Super-cell data
filename <- paste0("all_PCA", filename_suf)
SC.list <- compute_supercells_PCA(
SC.list = SC.list,
SC.GE.list = SC.GE.list,
N.comp = .N.comp
)
saveRDS(SC.list, file = file.path(data.folder, 'SC', paste0(filename, '.Rds')))
filename <- paste0("all_PCA", filename_suf)
SC.list <- readRDS(file = file.path(data.folder, 'SC', paste0(filename, '.Rds')))
Clustering
SC.list <- compute_supercells_clustering(
SC.list,
N.comp = 10,
N.clusters.seq = c(2:10),
pca_name = 'SC_PCA'
)
filename <- paste0("all_PCA_clust", filename_suf)
saveRDS(SC.list, file = file.path(data.folder, 'SC', paste0(filename, '.Rds')))
filename <- paste0("all_PCA_clust", filename_suf)
SC.list <- readRDS(file = file.path(data.folder, 'SC', paste0(filename, '.Rds')))
devtools::document()
devtools::install()
detach('package:SuperCellBM', unload = TRUE)
library(SuperCellBM)
alt.clusterings <- compute_alternative_clustering(
sc.pca = sc.pca$x,
N.comp = .N.comp,
N.clusters.seq = .N.clusters.seq,
hclust_methods = c("average", "mcquitty", "median"),
seed.seq = .seed.seq,
verbose = T
)
saveRDS(sc.clustering, file = file.path(data.folder, "sc_alternative_clustering.Rds"))
Consistency between super-cell clustering and cell lines
clust.consistency <- compute_consistency_of_supercell_clustering(
SC.list = SC.list,
sc.annotation = sc.clustering,
sc.clustering = sc.clustering,
sc.alternative.clustering = alt.clusterings
)
clust.consistency_summary <- plot_clustering_consistency(
clust.consistency,
min.value.alt.clustering = 0.,
error_bars = "extr"
)
clust.consistency_summary
cur.SC <- SC.list$Exact$`100`$`12345`
set.seed(12345)
lay <- igraph::layout.fruchterman.reingold(cur.SC$graph.supercells)
supercell_plot(cur.SC$graph.supercells, group = cur.SC$hclust$`4`, lay = lay)
supercell_plot(cur.SC$graph.supercells, group = cur.SC$sc_clustering, lay = lay)
set.seed(123)
supercell_plot(SC.list.pca$Exact$`20`$`12345`$graph.supercells)
Final
print("Done! Congrats!")
if(ToTestPackage){
warning(paste("(!) Script was run in a test mode, to get real cell_line super-cell data, run this script with ToTestPackage <- FALSE"))
}
mariiabilous/SuperCellBM documentation built on Jan. 28, 2022, 7:45 p.m.
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An example how to build multiple super-cell-like objects, including 'exact' (Super-cells obtained with the exact coarse-gaining), 'approx' (Super-cells obtained with the aaproximate coarse-graining), 'metacell(.*)' (Metacell build on the same genes as super-cells -- 'metacell_SC_like'; and Metacell build in a default set of genes -- 'metacell_default') for a set of graining levels and random seeds.
# if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("SingleCellExperiment") # # if (!requireNamespace("remotes")) install.packages("remotes") # remotes::install_github("GfellerLab/SuperCell") # remotes::install_github("mariiabilous/SuperCellBM") library(SingleCellExperiment) library(SuperCell) library(SuperCellBM)
Load some default parameters
Such as .gamma.seqfor the set of fraining levels, .seed.seq for the set of random seeds, adata.folder and fig.folder for the folders where to write data and plots.
For the full list of the default parameters, see ./examples/config/Tian_config.R.
source("./config/Tcells_config.R")
Flags
Whether to compute super-cell (ToComputeSC) or whether to compute super-cell gene expression (ToComputeSC_GE) or load saved files. Make sure, these file exists :)
Flag ToTestPackage is used to run stript in 2 modes: package testing (ToTestPackage == TRUE) or generating super-cell structure and super-cell gene expression for the further analyses (ToTestPackage == FALSE).
ToLoadPreprocData <- T ToComputeSC <- F ToComputeSC_GE <- F ToClusterSinglecell <- F ToTestPackage <- F filename_suf <- "" # variable to add a suffix to the saved files in case of testing of the package if(ToTestPackage){ testing_gamma_seq <- c(1, 10, 100) testing_seed_seq <- .seed.seq[1:3] warning(paste("The reduced set of graining leveles and seeds will be used, to get real output, turn it ti FALSE")) warning(paste("Original set of graining levels is:", paste(.gamma.seq, collapse = ", "), "but used testing set is:", paste(testing_gamma_seq, collapse = ", "))) warning(paste("Original set of seeds is:", paste(.seed.seq, collapse = ", "), "but used testing set is:", paste(testing_seed_seq, collapse = ", "))) .gamma.seq <- testing_gamma_seq .seed.seq <- testing_seed_seq filename_suf = "_testing_package" } else { .seed.seq = .seed.seq[1:5] }
Load Tcells data from 10x Genomics database.
if(!ToLoadPreprocData){ source("./config/Tcells_load_data.R") } else { sc.GE <- readRDS(file = file.path(data.folder, "sc_ge.Rds")) sc.counts <- readRDS(file = file.path(data.folder, "sc_counts.Rds")) cell.meta <- readRDS(file = file.path(data.folder, "cell_meta.Rds")) }
Get and set the main variables, such as single-cell gene expression (sc.GE), single-cell counts (sc.counts),
number of single cells (N.c) and total number of genes (N.g).
Set matrix column names to cellIDs (cell.ids) and row names to gene names (gene.names).
GT.cell.type <- cell.meta$GT.cell.type names(GT.cell.type) <- rownames(cell.meta) GT.cell.type.names <- names(table(cell.meta$GT.cell.type)) GT.cell.type.2.num <- 1:length(unique(GT.cell.type)) names(GT.cell.type.2.num) <- GT.cell.type.names GT.cell.type.num <- GT.cell.type.2.num[GT.cell.type] names(GT.cell.type.num) <- names(GT.cell.type) cell.ids <- colnames(sc.GE) N.c <- length(cell.ids) gene.names <- rownames(sc.GE) N.g <- length(gene.names) N.clusters <- length(unique(GT.cell.type)) mito.genes <- grep(pattern = "^MT", x = gene.names, value = TRUE) ribo.genes <- grep(pattern = "^RP[LS]", x = gene.names, value = TRUE) mito.ribo.genes <- c(mito.genes, ribo.genes) length(mito.ribo.genes) .genes.omit <- mito.ribo.genes ## uncomment this when needed # pal.GT <- color.tsne. # scales::show_col(pal.GT) # scales::show_col(color.tsne)
Compute Super-cells structure
for the Exact, Aprox (Super-cells obtained with the exact or approximate coarse-graining), Subsampling or Random (random grouping of cells into super-cells).
filename <- paste0('initial', filename_suf) SC.list <- compute_supercells( sc.GE, ToComputeSC = ToComputeSC, data.folder = data.folder, filename = filename, gamma.seq = .gamma.seq, n.var.genes = .N.var.genes, k.knn = .k.knn, n.pc = .N.comp, approx.N = .approx.N, fast.pca = TRUE, genes.use = .genes.use, genes.exclude = .genes.omit, seed.seq = .seed.seq, verbose = TRUE ) cat(paste("Super-cell computed for:", paste(names(SC.list), collapse = ", "), "\nat graining levels:", paste(names(SC.list[['Approx']]), collapse = ", "), "\nfor seeds:", paste(names(SC.list[['Approx']][[1]]), collapse = ", "), "\n", "\nand saved to / loaded from", paste0(filename, ".Rds")))
Compute metacells in two settings:
- 'metacell_default' - when metacell is computed with the default parameters (from the tutorial), using gene set filtered by MC
- 'metacell_SC_like' - when metacell is computed with the same default parameters, but at the same set of genes as Super-cells
SC.mc <- compute_supercells_metacells( sc.counts = sc.counts, gamma.seq = .gamma.seq, SC.list = SC.list, proj.name = proj.name, ToComputeSC = ToComputeSC, mc.k.knn = 100, T_vm_def = 0.08, MC.folder = "MC", MC_gene_settings = c('Metacell_default', 'Metacell_SC_like') # do not change )
Get actual graining levels obtained with Metacell
additional_gamma_seq <- get_actual_gammas_metacell(SC.mc) cat(paste("Metacells were computed in", length(names(SC.mc)), "settings:", paste(names(SC.mc), collapse = ", "), "\nfor Gammas:", paste(names(SC.mc[[1]]), collapse = ", "), "\nbut actual gammas are:", paste(additional_gamma_seq, collapse = ", ") )) # manually expand MC because later we will have 2 different setting for MC profile: fp - footpring of MC, av - averaged SC.mc.fp <- SC.mc names(SC.mc.fp) <- sapply(names(SC.mc), FUN = function(x){paste0(x, '_fp')}) SC.mc.av <- SC.mc names(SC.mc.av) <- sapply(names(SC.mc), FUN = function(x){paste0(x, '_av')}) SC.mc.expanded <- c(SC.mc.fp, SC.mc.av) names(SC.mc.expanded) rm(SC.mc.fp, SC.mc.av, SC.mc)
Compute super-cells (Exact, Approx, Subsampling and Random) at the addidional grainig levels obtained with Metacell.
So that we can dirrectly compare the results of Super-cells and Metacells at the same graining levels.
filename <- paste0('additional_gammas', filename_suf) SC.list <- compute_supercells_additional_gammas( SC.list, additional_gamma_seq = additional_gamma_seq, ToComputeSC = ToComputeSC, data.folder = data.folder, filename = filename, approx.N = .approx.N, fast.pca = TRUE ) cat(paste("Super-cells of methods:", paste(names(SC.list), collapse = ", "), "\nwere computed at aggitional graining levels:", paste(additional_gamma_seq, collapse = ", "), "\nand added to SC.list" ))
Concatenate Metacells to the list of Super-cells
SC.list <- c(SC.list, SC.mc.expanded) rm(SC.mc.expanded) filename <- paste0("all", filename_suf) saveRDS(SC.list, file = file.path(data.folder, "SC", paste0(filename, ".Rds"))) cat(paste( "Metacell data added to SC.list \nand now it contains:", paste(names(SC.list), collapse = ", "), "\nSC.list was saved to", file.path(data.folder, "SC", paste0(filename, ".Rds")) )) SC.list <- readRDS(file = file.path(data.folder, "SC", paste0(filename, ".Rds")))
START FROM HERE!!!
genes.use <- SC.list$Exact$`10`$`12345`$genes.use if(length(.N.comp) == 1) {N.comp <- 1:.N.comp} else {N.comp <- .N.comp} X.for.pca <- scale(Matrix::t(sc.GE[genes.use,])) sc.pca <- irlba::irlba(X.for.pca, nv = max(N.comp, 25)) sc.pca$x <- sc.pca$u %*% diag(sc.pca$d) if(ToClusterSinglecell){ sc.dist <- dist(sc.pca$x[,N.comp]) sc.clustering.hcl <- hclust(sc.dist, method = "ward.D2") saveRDS(sc.clustering.hcl, file = file.path(data.folder, "sc_clustering_hcl.Rds")) } else { sc.clustering.hcl <- readRDS(file.path(data.folder, "sc_clustering_hcl.Rds")) } sc.clustering <- cutree(sc.clustering.hcl, k = N.clusters)
Annotate super-cells to the cell lines based on the cell line annotation of single-cell and compute super-cell purity in terms of this annotation
SC.list <- annotate_supercells_to_cluster( SC.list = SC.list, sc.annotation = sc.clustering, annotation.name = 'sc_clustering' )
purity.df <- plot_annotation_purity( SC.list, annotation.name = 'sc_clustering', w = 2.5, h = 2.5, )
Compute GE for Super-cell data
GE profile for the super-cell data is computede:
- for super-cells (Exact, Approx) by averaging gene expression within super-cells,
- for Random, also averaging gene expression within super-cells
- for the Subsampling, sc.GE matrix is just subsampled,
- for Metacells, gene expression is computed in 2 ways:
1) the same as super-cells (averaging gene expression within Metacells) ->
metacell_(.*)_av2) using the default output of Metacell maned footprint ->metacell_(.*)_fp
filename <- paste0("all", filename_suf) SC.GE.list <- compute_supercells_GE( sc.GE = sc.GE, SC.list = SC.list, ToComputeSC_GE = ToComputeSC_GE, data.folder = data.folder, filename = filename, verbose = FALSE ) cat(paste("Gene expression profile computed for:", paste(names(SC.GE.list), collapse = ", "), "\nat graining levels:", paste(sort(as.numeric(names(SC.GE.list[['Approx']]))), collapse = ", "), "\nfor seeds:", paste(names(SC.GE.list[['Approx']][[1]]), collapse = ", "), "\nand saved to / loaded from", paste0(filename, ".Rds") ))
dim(SC.GE.list$Metacell_SC_like_fp$`100`$`12345`) dim(SC.list$Metacell_default_fp$`100`$`12345`$mc_info$mc@mc_fp) dim(SC.GE.list$Exact$`100`$`12345`) dim(SC.list$Metacell_default_av$`100`$`12345`$mc_info$mc@mc_fp)
Dimensionality reduction of Super-cell data
filename <- paste0("all_PCA", filename_suf) SC.list <- compute_supercells_PCA( SC.list = SC.list, SC.GE.list = SC.GE.list, N.comp = .N.comp ) saveRDS(SC.list, file = file.path(data.folder, 'SC', paste0(filename, '.Rds')))
filename <- paste0("all_PCA", filename_suf) SC.list <- readRDS(file = file.path(data.folder, 'SC', paste0(filename, '.Rds')))
Clustering
SC.list <- compute_supercells_clustering( SC.list, N.comp = 10, N.clusters.seq = c(2:10), pca_name = 'SC_PCA' )
filename <- paste0("all_PCA_clust", filename_suf) saveRDS(SC.list, file = file.path(data.folder, 'SC', paste0(filename, '.Rds')))
filename <- paste0("all_PCA_clust", filename_suf) SC.list <- readRDS(file = file.path(data.folder, 'SC', paste0(filename, '.Rds')))
devtools::document() devtools::install() detach('package:SuperCellBM', unload = TRUE) library(SuperCellBM)
alt.clusterings <- compute_alternative_clustering( sc.pca = sc.pca$x, N.comp = .N.comp, N.clusters.seq = .N.clusters.seq, hclust_methods = c("average", "mcquitty", "median"), seed.seq = .seed.seq, verbose = T ) saveRDS(sc.clustering, file = file.path(data.folder, "sc_alternative_clustering.Rds"))
Consistency between super-cell clustering and cell lines
clust.consistency <- compute_consistency_of_supercell_clustering( SC.list = SC.list, sc.annotation = sc.clustering, sc.clustering = sc.clustering, sc.alternative.clustering = alt.clusterings )
clust.consistency_summary <- plot_clustering_consistency( clust.consistency, min.value.alt.clustering = 0., error_bars = "extr" ) clust.consistency_summary
cur.SC <- SC.list$Exact$`100`$`12345` set.seed(12345) lay <- igraph::layout.fruchterman.reingold(cur.SC$graph.supercells) supercell_plot(cur.SC$graph.supercells, group = cur.SC$hclust$`4`, lay = lay) supercell_plot(cur.SC$graph.supercells, group = cur.SC$sc_clustering, lay = lay)
set.seed(123) supercell_plot(SC.list.pca$Exact$`20`$`12345`$graph.supercells)
Final
print("Done! Congrats!") if(ToTestPackage){ warning(paste("(!) Script was run in a test mode, to get real cell_line super-cell data, run this script with ToTestPackage <- FALSE")) }
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