scGPS
#Load everyting for scGPS Benchmarking
library(scGPS)
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library(scater)
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library(scran)
#Copy the dataset for scGPS analysis
yan_dat <- dataset
#Find the genes with all zero entries and remove
keep_features <- rowSums(assays(yan_dat)[["normcounts"]] > 0) > 0
yan_dat <- yan_dat[keep_features, ]
table(keep_features)
## keep_features
## FALSE TRUE
## 619 19595
#Use scran normalisation
computeSumFactors(yan_dat, assay.type = "normcounts")
## class: SingleCellExperiment
## dim: 19595 90
## metadata(0):
## assays(2): normcounts logcounts
## rownames(19595): C9orf152 RPS11 ... CTSC AQP7
## rowData names(1): feature_symbol
## colnames(90): Oocyte..1.RPKM. Oocyte..2.RPKM. ...
## Late.blastocyst..3..Cell.7.RPKM.
## Late.blastocyst..3..Cell.8.RPKM.
## colData names(2): cell_type1 cell_type2
## reducedDimNames(0):
## spikeNames(1): ERCC
normalize(yan_dat, exprs_values = "normcounts")
## Warning in .local(object, ...): using library sizes as size factors
## Warning in .get_all_sf_sets(object): spike-in set 'ERCC' should have its
## own size factors
## class: SingleCellExperiment
## dim: 19595 90
## metadata(1): log.exprs.offset
## assays(2): normcounts logcounts
## rownames(19595): C9orf152 RPS11 ... CTSC AQP7
## rowData names(1): feature_symbol
## colnames(90): Oocyte..1.RPKM. Oocyte..2.RPKM. ...
## Late.blastocyst..3..Cell.7.RPKM.
## Late.blastocyst..3..Cell.8.RPKM.
## colData names(2): cell_type1 cell_type2
## reducedDimNames(0):
## spikeNames(1): ERCC
#Remove spikes
is.spike <-grepl("^ERCC", rownames(yan_dat))
yan_dat <- yan_dat[!is.spike, ]
#Start the time here
start_time <- Sys.time()
#Extract the needed variables
yan_dat_exprs <- assays(yan_dat)[["logcounts"]]
yan_dat_cellnames <- colnames(yan_dat)
yan_dat_cellnames <- data.frame("cellBarcodes" = yan_dat_cellnames)
yan_dat_GeneMetaData <- rownames(yan_dat)
yan_dat_GeneMetaData <- data.frame("GeneSymbol" = yan_dat_GeneMetaData)
#Store Data in scGPS format
mixedpop <- new_summarized_scGPS_object(ExpressionMatrix = yan_dat_exprs, GeneMetadata = yan_dat_GeneMetaData, CellMetadata = yan_dat_cellnames)
#Cluster and plot data using SCORE
CORE_cluster_bagging <- CORE_bagging(mixedpop, remove_outlier = c(0), PCA=FALSE)
## Performing 1 round of filtering
## Identifying top variable genes
## Calculating distance matrix
## Performing hierarchical clustering
## Finding clustering information
## No more outliers detected in filtering round 1
## Identifying top variable genes
## Calculating distance matrix
## Performing hierarchical clustering
## Finding clustering information
## 90 cells left after filtering
## Running 20 bagging runs, with 0.8 subsampling...
## Done clustering, moving to stability calculation...
## Done finding optimal clustering
plot_CORE(CORE_cluster_bagging$tree, list_clusters = CORE_cluster_bagging$Cluster)
plot_optimal_CORE(original_tree= CORE_cluster_bagging$tree, optimal_cluster = unlist(CORE_cluster_bagging$Cluster[CORE_cluster_bagging$optimal_index]), shift = -100)
## Ordering and assigning labels...
## 2
## 1545NA
## 3
## 154575
## Plotting the colored dendrogram now....
## Plotting the bar underneath now....
#Stop the time here
end_time <- Sys.time()
time_difference_SCORE <- end_time - start_time
#Make a dataframe with the results we want to examine
cell_types1 <- colData(yan_dat)$cell_type1
cell_types2 <- colData(yan_dat)$cell_type2
label_list <- unlist(yan_dat_cellnames$cellBarcodes)
cluster_list <- unlist(CORE_cluster_bagging$Cluster[CORE_cluster_bagging$optimal_index])
compare_frame <- data.frame("Gene_label" = label_list, "type1" = cell_types1, "type2" = cell_types2, "cluster" = cluster_list)
#Find the Adjusted Rand Index
AdjustedRandIndex_SCORE <- mclust::adjustedRandIndex(compare_frame$type2, compare_frame$cluster)
HighResRand <- mclust::adjustedRandIndex(compare_frame$type2, unlist(CORE_cluster_bagging$Cluster[1]))
#Store the estimated k from the bagging runs
estimated_k_SCORE <- CORE_cluster_bagging$optimalMax
#Remove unwanted data and Retrieve output
rm(list = setdiff(ls(), c("AdjustedRandIndex_SCORE", "time_difference_SCORE", "estimated_k_SCORE", "HighResRand", "dataset")))
for ( obj in ls() ) { cat('---',obj,'---\n'); print(get(obj)) }
## --- AdjustedRandIndex_SCORE ---
## [1] 0.5880355
## --- dataset ---
## class: SingleCellExperiment
## dim: 20214 90
## metadata(0):
## assays(2): normcounts logcounts
## rownames(20214): C9orf152 RPS11 ... CTSC AQP7
## rowData names(1): feature_symbol
## colnames(90): Oocyte..1.RPKM. Oocyte..2.RPKM. ...
## Late.blastocyst..3..Cell.7.RPKM.
## Late.blastocyst..3..Cell.8.RPKM.
## colData names(2): cell_type1 cell_type2
## reducedDimNames(0):
## spikeNames(1): ERCC
## --- estimated_k_SCORE ---
## [1] 3
## --- HighResRand ---
## [1] 0.5880355
## --- time_difference_SCORE ---
## Time difference of 6.452729 secs
SC3
#Load everything required for SC3
library(SC3)
library(scater)
#Copy the dataset for SC3 analysis
sce <- dataset
#Find the genes with all zero entries and remove
keep_features <- rowSums(assays(sce)[["normcounts"]] > 0) > 0
sce <- sce[keep_features, ]
#Remove the spikes
is.spike <-grepl("^ERCC", rownames(sce))
table(is.spike)
## is.spike
## FALSE TRUE
## 19587 8
sce <- sce[!is.spike, ]
#place the rpkm in the counts column for SC3
counts(sce) <- normcounts(sce)
#Start the time here
start_time <- Sys.time()
#Run sc3 with an estimation for k
sce <- sc3_prepare(sce, n_cores = 1, gene_filter = TRUE, kmeans_nstart = 50)
## Setting SC3 parameters...
sce <- sc3_estimate_k(sce)
## Estimating k...
SC3_k_estimate <- as.integer(unlist(metadata(sce)$sc3$k_estimation))
sce <- sc3_calc_dists(sce)
## Calculating distances between the cells...
sce <- sc3_calc_transfs(sce)
## Performing transformations and calculating eigenvectors...
sce <- sc3_kmeans(sce, ks = SC3_k_estimate)
## Performing k-means clustering...
sce <- sc3_calc_consens(sce)
## Calculating consensus matrix...
#Stop the time here
end_time <- Sys.time()
time_difference_SC3 <- end_time - start_time
#Make a dataframe with the results we want to examine
cell_types1 <- colData(sce)$cell_type1
cell_types2 <- colData(sce)$cell_type2
label_list <- rownames(colData(sce))
cluster_list <- as.numeric(colData(sce)[, paste0("sc3_", SC3_k_estimate, "_clusters")])
compare_frame <- data.frame("Gene_label" = label_list, "type1" = cell_types1, "type2" = cell_types2, "cluster" = cluster_list)
#Find the Adjusted Rand Index
AdjustedRandIndex_SC3 <- mclust::adjustedRandIndex(compare_frame$type2, compare_frame$cluster)
#Remove unwanted data
rm(list = setdiff(ls(), c("AdjustedRandIndex_SC3", "time_difference_SC3", "SC3_k_estimate", "dataset")))
for ( obj in ls() ) { cat('---',obj,'---\n'); print(get(obj)) }
## --- AdjustedRandIndex_SC3 ---
## [1] 0.6499654
## --- dataset ---
## class: SingleCellExperiment
## dim: 20214 90
## metadata(0):
## assays(2): normcounts logcounts
## rownames(20214): C9orf152 RPS11 ... CTSC AQP7
## rowData names(1): feature_symbol
## colnames(90): Oocyte..1.RPKM. Oocyte..2.RPKM. ...
## Late.blastocyst..3..Cell.7.RPKM.
## Late.blastocyst..3..Cell.8.RPKM.
## colData names(2): cell_type1 cell_type2
## reducedDimNames(0):
## spikeNames(1): ERCC
## --- SC3_k_estimate ---
## [1] 6
## --- time_difference_SC3 ---
## Time difference of 14.79941 secs