scGPS
#Load everyting for scGPS Benchmarking
library(scGPS)
## Loading required package: SummarizedExperiment
## Loading required package: GenomicRanges
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## Welcome to Bioconductor
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## Loading required package: SingleCellExperiment
## Registered S3 methods overwritten by 'ggplot2':
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library(scater)
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## rename
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## filter
library(scran)
#Retrieve the Dataset
klein_dat <- dataset
#Remove features with no gene expression
keep_features <- rowSums(counts(klein_dat) > 0) > 0
table(keep_features)
## keep_features
## FALSE TRUE
## 128 24047
klein_dat <- klein_dat[keep_features, ]
dim(klein_dat)
## [1] 24047 2717
#Use scran normalisation
klein_dat <- computeSumFactors(klein_dat)
klein_dat <- normalize(klein_dat)
## Warning in .get_all_sf_sets(object): spike-in set 'ERCC' should have its
## own size factors
#Create a count per million assay
cpm(klein_dat) <- calculateCPM(klein_dat)
## Warning in .get_all_sf_sets(object): spike-in set 'ERCC' should have its
## own size factors
#Remove spikes
is.spike <-grepl("^ERCC", rownames(klein_dat))
table(is.spike)
## is.spike
## FALSE
## 24047
klein_dat <- klein_dat[!is.spike, ]
#Start the time here
start_time <- Sys.time()
#Extract the needed variables
klein_dat_exprs <- assays(klein_dat)[["logcounts"]]
klein_dat_cellnames <- colnames(klein_dat)
klein_dat_cellnames <- data.frame("cellBarcodes" = klein_dat_cellnames)
klein_dat_GeneMetaData <- rownames(klein_dat)
klein_dat_GeneMetaData <- data.frame("GeneSymbol" = klein_dat_GeneMetaData)
#Store Data in scGPS format
mixedpop <- new_summarized_scGPS_object(ExpressionMatrix = klein_dat_exprs, GeneMetadata = klein_dat_GeneMetaData, CellMetadata = klein_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
## 2717 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$optimal_index]), shift = -100)
## Ordering and assigning labels...
## 2
## 142413NANANANA
## 3
## 142413764NANANA
## 4
## 1424137641466NANA
## 5
## 14241376414662246NA
## 6
## 142413764146622462632
## Plotting the colored dendrogram now....
## Plotting the bar underneath now....
#Stop the time here
end_time <- Sys.time()
time_difference_SCORE <- end_time - start_time
#Find data needed for comparisons and store in data frame
cell_types1 <- colData(klein_dat)$cell_type1
label_list <- unlist(klein_dat_cellnames$cellBarcodes)
cluster_list <- unlist(CORE_cluster_bagging$Cluster[CORE_cluster_bagging$optimal_index])
compare_frame <- data.frame("Gene_label" = label_list, "type1" = cell_types1,"cluster" = cluster_list)
#Find the adjusted rand index
AdjustedRandIndex_SCORE <- mclust::adjustedRandIndex(compare_frame$type1, compare_frame$cluster)
estimated_k_SCORE <- CORE_cluster_bagging$optimalMax
HighResRand <- mclust::adjustedRandIndex(compare_frame$type1, unlist(CORE_cluster_bagging$Cluster[1]))
#Remove clutter from the environment
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] 0.8004233
## --- dataset ---
## class: SingleCellExperiment
## dim: 24175 2717
## metadata(0):
## assays(2): counts logcounts
## rownames(24175): 0610005C13Rik 0610007P14Rik ... n-R5s146 n-R5s149
## rowData names(10): feature_symbol is_feature_control ...
## total_counts log10_total_counts
## colnames(2717): cell1 cell2 ... cell2716 cell2717
## colData names(29): cell_type1 total_features ... pct_counts_ERCC
## is_cell_control
## reducedDimNames(0):
## spikeNames(1): ERCC
## --- estimated_k_SCORE ---
## [1] 6
## --- HighResRand ---
## [1] 0.8004233
## --- time_difference_SCORE ---
## Time difference of 3.342226 mins
SC3
#Load everything for SC3
library(SC3)
library(scater)
#Retrieve the Dataset
sce <- dataset
#Find the genes with all zero entries and remove
keep_features <- rowSums(counts(sce) > 0) > 0
sce <- sce[keep_features, ]
#Remove the spikes
is.spike <-grepl("^ERCC", rownames(sce))
table(is.spike)
## is.spike
## FALSE
## 24047
sce <- sce[!is.spike, ]
#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...
#Here we change to 50 as there are over 2000 cells as suggested in sc3 methods
#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)$cell_type1
label_list <- rownames(colData(sce))
cluster_list <- as.numeric(colData(sce)[, paste0("sc3_", SC3_k_estimate, "_clusters")])
compare_frame <- data.frame("Gene_label" = label_list, "type1" = cell_types1, "cluster" = cluster_list)
#Find the Adjusted Rand Index
AdjustedRandIndex_SC3 <- mclust::adjustedRandIndex(compare_frame$type1, 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] 0.6362361
## --- dataset ---
## class: SingleCellExperiment
## dim: 24175 2717
## metadata(0):
## assays(2): counts logcounts
## rownames(24175): 0610005C13Rik 0610007P14Rik ... n-R5s146 n-R5s149
## rowData names(10): feature_symbol is_feature_control ...
## total_counts log10_total_counts
## colnames(2717): cell1 cell2 ... cell2716 cell2717
## colData names(29): cell_type1 total_features ... pct_counts_ERCC
## is_cell_control
## reducedDimNames(0):
## spikeNames(1): ERCC
## --- SC3_k_estimate ---
## [1] 16
## --- time_difference_SC3 ---
## Time difference of 15.89139 mins