• Load the data sets
  • Pre-processing
  • Perform dimensional reduction
  • Cluster the cells
  • Finding differentially expressed genes using MAST
  • Associate perturbations with clusters
  • Find DE genes for each cluster and assign DE genes to associated perturbations
  • Compare single-gene DE p-value distributions between two-step clustering analysis and GSFA

Last updated: 2022-08-31

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mkdir -p /project2/xinhe/kevinluo/GSFA/data

cp /project2/xinhe/yifan/Factor_analysis/shared_data/LUHMES_cropseq_data_seurat.rds \
  /project2/xinhe/kevinluo/GSFA/data

cp /project2/xinhe/yifan/Factor_analysis/LUHMES/GSE142078_raw/GSM4219575_Run1_genes.tsv.gz \
  /project2/xinhe/kevinluo/GSFA/data/LUHMES_GSM4219575_Run1_genes.tsv.gz

Load the data sets

CROP-seq datasets: /project2/xinhe/yifan/Factor_analysis/shared_data/LUHMES_cropseq_data_seurat.rds

The data are Seurat objects, with raw gene counts stored in obj@assays$RNA@counts, and cell meta data stored in obj@meta.data. Normalized and scaled data used for GSFA are stored in obj@assays$RNA@scale.data, the rownames of which are the 6k genes used for GSFA.

Load packages

suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(ComplexHeatmap))
suppressPackageStartupMessages(library(ggplot2))
require(reshape2)
require(dplyr)
require(ComplexHeatmap)
theme_set(theme_bw() + theme(plot.title = element_text(size = 14, hjust = 0.5),
                             axis.title = element_text(size = 14),
                             axis.text = element_text(size = 13),
                             legend.title = element_text(size = 13),
                             legend.text = element_text(size = 12),
                             panel.grid.minor = element_blank())
)
suppressPackageStartupMessages(library(gridExtra))
source("code/plotting_functions.R")

Set directories

data_dir <- "/project2/xinhe/kevinluo/GSFA/data/"
res_dir <- "/project2/xinhe/kevinluo/GSFA/twostep_clustering/LUHMES"
dir.create(res_dir, recursive = TRUE, showWarnings = FALSE)

Load input data

combined_obj <- readRDS(file.path(data_dir,"LUHMES_cropseq_data_seurat.rds"))

Pre-processing

The steps below encompass the standard pre-processing workflow for scRNA-seq data in Seurat.

These represent the selection and filtration of cells based on QC metrics, data normalization and scaling, and the detection of highly variable features.

# The number of unique genes detected in each cell.
cat("The number of unique genes detected in each cell:\n")
range(combined_obj$nFeature_RNA)

# The total number of molecules detected within a cell
cat("The total number of molecules detected within a cell:\n")
range(combined_obj$nCount_RNA)

# The percentage of reads that map to the mitochondrial genome
cat("The percentage of reads that map to the mitochondrial genome:\n")
range(combined_obj$percent_mt)
The number of unique genes detected in each cell:
[1]  375 4861
The total number of molecules detected within a cell:
[1]  1572 19991
The percentage of reads that map to the mitochondrial genome:
[1] 0.128082 9.804772
# Visualize QC metrics as a violin plot
VlnPlot(combined_obj, features = c("nFeature_RNA", "nCount_RNA", "percent_mt"), ncol = 3)

We filter cells that have more than 500 genes identified, as in the Shifrut et al. paper.

combined_obj <- subset(combined_obj, subset = nFeature_RNA > 500)

Normalizing the data

combined_obj <- NormalizeData(combined_obj, normalization.method = "LogNormalize", scale.factor = 10000)

Identification of highly variable features (feature selection)

Select a subset of features that exhibit high cell-to-cell variation in the dataset, by modeling the mean-variance relationship inherent in single-cell data.

Select the 1,000 most variable genes across cells, as in the Shifrut et al. paper.

combined_obj <- FindVariableFeatures(combined_obj, selection.method = "vst", nfeatures = 1000)

Regress out total UMI counts per cell and percent of mitochondrial genes detected per cell and scaled to obtain gene level z-scores, as in the Shifrut et al. paper.

combined_obj <- ScaleData(combined_obj, vars.to.regress = c("nCount_RNA", "percent_mt"))
# combined_obj <- ScaleData(combined_obj, vars.to.regress = c("nCount_RNA", "percent_mt"), features = selected_gene_id)

saveRDS(combined_obj, file = file.path(res_dir, "LUHMES_seurat_processed_data.rds"))

Perform dimensional reduction

Perform PCA on the scaled data.

combined_obj <- readRDS(file.path(res_dir, "LUHMES_seurat_processed_data.rds"))
combined_obj <- RunPCA(combined_obj, features = VariableFeatures(object = combined_obj))
ElbowPlot(combined_obj, ndims = 50)

Version Author Date
da6979c kevinlkx 2022-08-11

Cluster the cells

Embed cells in K-nearest neighbor (KNN) graph using FindNeighbors() using the first 30 PCs. Then apply the Louvain algorithm to find clusters using FindClusters() function with default resolution (0.8).

combined_obj <- FindNeighbors(combined_obj, dims = 1:30)
combined_obj <- FindClusters(combined_obj)

saveRDS(combined_obj, file = file.path(res_dir, "LUHMES_seurat_clustered.rds"))

Visualize the clusters using UMAP

combined_obj <- readRDS(file.path(res_dir, "LUHMES_seurat_clustered.rds"))

cluster_labels <- Idents(combined_obj)
cluster_labels <- as.factor(as.numeric(as.character(cluster_labels))+1)
new_cluster_labels <- paste0("k", levels(cluster_labels))
names(new_cluster_labels) <- levels(combined_obj)
combined_obj <- RenameIdents(combined_obj, new_cluster_labels)

combined_obj <- RunUMAP(combined_obj, dims = 1:30)
DimPlot(combined_obj, reduction = "umap", label = TRUE)

Version Author Date
da6979c kevinlkx 2022-08-11

Finding differentially expressed genes using MAST

combined_obj <- readRDS(file.path(res_dir, "LUHMES_seurat_clustered.rds"))
cat(length(levels(combined_obj)), "clusters.\n")
registerDoParallel(cores=n_cores)
de.markers <- foreach(i=levels(combined_obj), .packages="Seurat") %dopar% {
  FindMarkers(combined_obj, ident.1 = i, test.use = "MAST")
}
stopImplicitCluster()
saveRDS(de.markers, file = file.path(res_dir, "LUHMES_seurat_MAST_DEGs.rds"))

Associate perturbations with clusters

combined_obj <- readRDS(file.path(res_dir, "LUHMES_seurat_clustered.rds"))
perturb_matrix <- combined_obj@meta.data[, 4:18]

cluster_labels <- Idents(combined_obj)
cluster_labels <- as.factor(as.numeric(as.character(cluster_labels))+1)
new_cluster_labels <- paste0("k", levels(cluster_labels))
names(new_cluster_labels) <- levels(combined_obj)
combined_obj <- RenameIdents(combined_obj, new_cluster_labels)

cluster_matrix <- matrix(0, nrow = nrow(perturb_matrix), ncol = length(levels(cluster_labels)))
cluster_matrix[cbind(1:nrow(perturb_matrix), cluster_labels)] <- 1
rownames(cluster_matrix) <- rownames(perturb_matrix)
colnames(cluster_matrix) <- new_cluster_labels

Use Chi-squared tests for the association of perturbations and clusters (2 x 2 tables)

summary_df <- expand.grid(colnames(perturb_matrix), colnames(cluster_matrix))
colnames(summary_df) <- c("perturb", "cluster")

summary_df <- cbind(summary_df, statistic = NA, stdres = NA, pval = NA)

for(i in 1:nrow(summary_df)){
  dt <- table(data.frame(perturb = perturb_matrix[,summary_df$perturb[i]], 
                         cluster = cluster_matrix[,summary_df$cluster[i]]))
  chisq <- chisq.test(dt)
  summary_df[i, ]$statistic <- chisq$statistic
  summary_df[i, ]$stdres <- chisq$stdres[2,2]
  summary_df[i, ]$pval <- chisq$p.value
}

summary_df$fdr <- p.adjust(summary_df$pval, method = "BH")
summary_df$bonferroni_adj <- p.adjust(summary_df$pval, method = "bonferroni")

saveRDS(summary_df, file = file.path(res_dir, "LUHMES_seurat_guide_cluster_chisq_summary_df.rds"))

stdres_mat <- reshape2::dcast(summary_df %>% dplyr::select(perturb, cluster, stdres), perturb ~ cluster, value.var = "stdres")
rownames(stdres_mat) <- stdres_mat$perturb
stdres_mat$perturb <- NULL

fdr_mat <- reshape2::dcast(summary_df %>% dplyr::select(perturb, cluster, fdr), perturb ~ cluster, value.var = "fdr")
rownames(fdr_mat) <- fdr_mat$perturb
fdr_mat$perturb <- NULL

bonferroni_mat <- reshape2::dcast(summary_df %>% dplyr::select(perturb, cluster, bonferroni_adj), 
                                  perturb ~ cluster, value.var = "bonferroni_adj")
rownames(bonferroni_mat) <- bonferroni_mat$perturb
bonferroni_mat$perturb <- NULL
KO_names <- rownames(fdr_mat)
dotplot_effectsize(t(stdres_mat), t(fdr_mat),
                   reorder_markers = c(KO_names[KO_names!="Nontargeting"], "Nontargeting"),
                   color_lgd_title = "Chi-squared test\nstandardized residuals",
                   size_lgd_title = "FDR",
                   max_score = 4,
                   min_score = -4,
                   by_score = 2) + coord_flip()

Version Author Date
da6979c kevinlkx 2022-08-11

Plot perturbation ~ cluster associations (show Bonferroni adjusted p-values)

KO_names <- rownames(bonferroni_mat)
dotplot_effectsize(t(stdres_mat), t(bonferroni_mat),
                   reorder_markers = c(KO_names[KO_names!="Nontargeting"], "Nontargeting"),
                   color_lgd_title = "Chi-squared test\nstandardized residuals",
                   size_lgd_title = "Bonferroni\nadjusted p-value",
                   max_score = 4,
                   min_score = -4,
                   by_score = 2) + coord_flip()

Version Author Date
da6979c kevinlkx 2022-08-11

Find DE genes for each cluster and assign DE genes to associated perturbations

First, find DE genes for each cluster using MAST (Bonferroni adjusted p-values < 0.05), Then, for each perturbation, find the associated clusters, and pull the DE genes for those clusters.

feature.names <- data.frame(fread(file.path(data_dir, "LUHMES_GSM4219575_Run1_genes.tsv.gz"),
                                  header = FALSE), stringsAsFactors = FALSE)

de.markers <- readRDS(file.path(res_dir, "LUHMES_seurat_MAST_DEGs.rds"))
names(de.markers) <- paste0("k", levels(cluster_labels))

de.genes.clusters <- vector("list", length = length(de.markers))
names(de.genes.clusters) <- names(de.markers)
for( i in 1:length(de.genes.clusters)){
  de_sumstats <- de.markers[[i]]
  de_genes <- unique(rownames(de_sumstats[de_sumstats$p_val_adj < 0.05,]))
  # de_genes <- feature.names$V2[match(de_genes, feature.names$V1)]
  de.genes.clusters[[i]] <- de_genes
}

Number of DE genes for each perturbation (Chi-squared test FDR < 0.05)

perturb_names <- colnames(perturb_matrix)
perturb_names <- c("Nontargeting", perturb_names[perturb_names!="Nontargeting"])

de.genes.perturbs <- vector("list", length = length(perturb_names))
names(de.genes.perturbs) <- perturb_names

for(i in 1:length(de.genes.perturbs)){
  perturb_name <- names(de.genes.perturbs)[i]
  associated_cluster_labels <- colnames(fdr_mat)[which(fdr_mat[perturb_name, ] < 0.05)]
  if(length(associated_cluster_labels) > 0){
    de.genes.perturbs[[i]] <- unique(unlist(de.genes.clusters[associated_cluster_labels]))
  }
}

num.de.genes.perturbs <- sapply(de.genes.perturbs, length)

twostep_clustering_fdr0.05_genes <- de.genes.perturbs

dge_plot_df <- data.frame(Perturbation = names(num.de.genes.perturbs), Num_DEGs = num.de.genes.perturbs)
dge_plot_df$Perturbation <- factor(dge_plot_df$Perturbation, levels = names(num.de.genes.perturbs))

ggplot(data=dge_plot_df, aes(x = Perturbation, y = Num_DEGs+1)) +
  geom_bar(position = "dodge", stat = "identity") +
  geom_text(aes(label = Num_DEGs), position=position_dodge(width=0.9), vjust=-0.25) +
  scale_y_log10() +
  scale_fill_brewer(palette = "Set2") +
  labs(x = "Target gene",
       y = "Number of DEGs",
       title = "Number of DEGs detected by Two-step clustering with MAST DE analysis") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
        legend.position = "bottom",
        legend.text = element_text(size = 13))

Version Author Date
da6979c kevinlkx 2022-08-11

Number of DE genes for each perturbation (Chi-squared test Bonferroni adjusted p-value < 0.05)

perturb_names <- colnames(perturb_matrix)
perturb_names <- c("Nontargeting", perturb_names[perturb_names!="Nontargeting"])

de.genes.perturbs <- vector("list", length = length(perturb_names))
names(de.genes.perturbs) <- perturb_names

for(i in 1:length(de.genes.perturbs)){
  perturb_name <- names(de.genes.perturbs)[i]
  associated_cluster_labels <- colnames(bonferroni_mat)[which(bonferroni_mat[perturb_name, ] < 0.05)]
  if(length(associated_cluster_labels) > 0){
    de.genes.perturbs[[i]] <- unique(unlist(de.genes.clusters[associated_cluster_labels]))
  }
}

num.de.genes.perturbs <- sapply(de.genes.perturbs, length)

twostep_clustering_bonferroni0.05_genes <- de.genes.perturbs

dge_plot_df <- data.frame(Perturbation = names(num.de.genes.perturbs), Num_DEGs = num.de.genes.perturbs)
dge_plot_df$Perturbation <- factor(dge_plot_df$Perturbation, levels = names(num.de.genes.perturbs))

ggplot(data=dge_plot_df, aes(x = Perturbation, y = Num_DEGs+1)) +
  geom_bar(position = "dodge", stat = "identity") +
  geom_text(aes(label = Num_DEGs), position=position_dodge(width=0.9), vjust=-0.25) +
  scale_y_log10() +
  scale_fill_brewer(palette = "Set2") +
  labs(x = "Target gene",
       y = "Number of DEGs",
       title = "Number of DEGs detected by Two-step clustering with MAST DE analysis") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
        legend.position = "bottom",
        legend.text = element_text(size = 13))

Version Author Date
da6979c kevinlkx 2022-08-11

Compare single-gene DE p-value distributions between two-step clustering analysis and GSFA

fdr_cutoff <- 0.05
lfsr_cutoff <- 0.05

Load the output of GSFA fit_gsfa_multivar() run.

data_folder <- "/project2/xinhe/yifan/Factor_analysis/LUHMES/"
fit <- readRDS(paste0(data_folder,
                      "gsfa_output_detect_01/use_negctrl/All.gibbs_obj_k20.svd_negctrl.seed_14314.light.rds"))
gibbs_PM <- fit$posterior_means
lfsr_mat <- fit$lfsr[, -ncol(fit$lfsr)]
total_effect <- fit$total_effect[, -ncol(fit$total_effect)]
KO_names <- colnames(lfsr_mat)

DEGs detected by GSFA

        ADNP       ARID1B        ASH1L         CHD2         CHD8       CTNND2 
         795          310          322          756            0            0 
      DYRK1A        HDAC5        MECP2        MYT1L Nontargeting         POGZ 
          23            0            0            0            0            0 
        PTEN         RELN        SETD5 
         895            0          466

Load MAST single-gene DE result

guides <- KO_names[KO_names!="Nontargeting"]

mast_list <- list()
for (m in guides){
  fname <- paste0(data_folder, "processed_data/MAST/dev_top6k_negctrl/gRNA_", m, ".dev_res_top6k.vs_negctrl.rds")
  tmp_df <- readRDS(fname)
  tmp_df$geneID <- rownames(tmp_df)
  tmp_df <- tmp_df %>% dplyr::rename(FDR = fdr, PValue = pval)
  mast_list[[m]] <- tmp_df
}
mast_signif_counts <- sapply(mast_list, function(x){filter(x, FDR < fdr_cutoff) %>% nrow()})

QQ-plots of MAST DE p-values for the GSFA significant genes vs two-step clustering DE genes.

qqplots <- list()
for(i in 1:length(guides)){
  guide <- guides[i]
  mast_res <- mast_list[[guide]]
  tsc_de_genes <- twostep_clustering_fdr0.05_genes[[guide]]
  gsfa_de_genes <- gsfa_sig_genes[[guide]]
  tsc_de_genes <- intersect(tsc_de_genes, rownames(mast_res))
  gsfa_de_genes <- intersect(gsfa_de_genes, rownames(mast_res))
  
  if(length(tsc_de_genes)>0 && length(gsfa_de_genes) >0){
    cat("plot", guide, "\n")
    mast_res$tsc_gene <- 0
    mast_res[tsc_de_genes, ]$tsc_gene <- 1
    mast_res$gsfa_gene <- 0
    mast_res[gsfa_de_genes, ]$gsfa_gene <- 1
    pvalue_list <- list('Two-step clustering'=dplyr::filter(mast_res,tsc_gene==1)$PValue,
                        'GSFA'=dplyr::filter(mast_res,gsfa_gene==1)$PValue,
                        'MAST'=mast_res$PValue)
    qqplots[[guide]] <- qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
      ggtitle(guide) + theme(plot.title = element_text(hjust = 0.5)) +
      scale_colour_discrete(name="Method")
  }
}

grid.arrange(grobs = qqplots, nrow = 2, ncol = 2)

Version Author Date
040c06a kevinlkx 2022-08-25 
plot ADNP 
plot CHD2 
plot DYRK1A 
plot PTEN

Pooling p-values from all perturbations

combined_mast_res <- data.frame()
for(i in 1:length(guides)){
  guide <- guides[i]
  mast_res <- mast_list[[guide]]
  tsc_de_genes <- twostep_clustering_fdr0.05_genes[[guide]]
  gsfa_de_genes <- gsfa_sig_genes[[guide]]
  tsc_de_genes <- intersect(tsc_de_genes, rownames(mast_res))
  gsfa_de_genes <- intersect(gsfa_de_genes, rownames(mast_res))
  mast_res$tsc_gene <- 0
  if(length(tsc_de_genes) >0){
    mast_res[tsc_de_genes, ]$tsc_gene <- 1
  }
  mast_res$gsfa_gene <- 0
  if(length(gsfa_de_genes) >0){
    mast_res[gsfa_de_genes, ]$gsfa_gene <- 1
  }
  combined_mast_res <- rbind(combined_mast_res, mast_res)
}

pvalue_list <- list('Two-step clustering'=dplyr::filter(combined_mast_res,tsc_gene==1)$PValue,
                    'GSFA'=dplyr::filter(combined_mast_res,gsfa_gene==1)$PValue,
                    'MAST'=combined_mast_res$PValue)

# pdf(file.path(res_dir, "qqplot_all_combined.pdf"))
qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
      ggtitle("All perturbations") + theme(plot.title = element_text(hjust = 0.5)) +
      scale_colour_discrete(name="Method")

# dev.off()

QQ-plots of MAST DE p-values for the GSFA only genes vs two-step only genes.

qqplots <- list()
for(i in 1:length(guides)){
  guide <- guides[i]
  mast_res <- mast_list[[guide]]
  tsc_de_genes <- twostep_clustering_fdr0.05_genes[[guide]]
  gsfa_de_genes <- gsfa_sig_genes[[guide]]
  tsc_de_genes <- intersect(tsc_de_genes, rownames(mast_res))
  gsfa_de_genes <- intersect(gsfa_de_genes, rownames(mast_res))
  
  if(length(tsc_de_genes)>0 && length(gsfa_de_genes) >0){
    cat("plot", guide, "\n")
    mast_res$tsc_only_gene <- 0
    mast_res[setdiff(tsc_de_genes, gsfa_de_genes), ]$tsc_only_gene <- 1
    mast_res$gsfa_only_gene <- 0
    mast_res[setdiff(gsfa_de_genes, tsc_de_genes), ]$gsfa_only_gene <- 1
    
    pvalue_list <- list('Two-step clustering only'=dplyr::filter(mast_res,tsc_only_gene==1)$PValue,
                        'GSFA only'=dplyr::filter(mast_res,gsfa_only_gene==1)$PValue,
                        'MAST'=mast_res$PValue)
    qqplots[[guide]] <- qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
      ggtitle(guide) + theme(plot.title = element_text(hjust = 0.5)) +
      scale_colour_discrete(name="Method")

  }
}

grid.arrange(grobs = qqplots, nrow = 2, ncol = 2)

Version Author Date
040c06a kevinlkx 2022-08-25 
plot ADNP 
plot CHD2 
plot DYRK1A 
plot PTEN

Pooling p-values from all perturbations

combined_mast_res <- data.frame()
for(i in 1:length(guides)){
  guide <- guides[i]
  mast_res <- mast_list[[guide]]
  tsc_de_genes <- twostep_clustering_fdr0.05_genes[[guide]]
  gsfa_de_genes <- gsfa_sig_genes[[guide]]
  tsc_de_genes <- intersect(tsc_de_genes, rownames(mast_res))
  gsfa_de_genes <- intersect(gsfa_de_genes, rownames(mast_res))
  mast_res$tsc_only_gene <- 0
  if(length(setdiff(tsc_de_genes, gsfa_de_genes)) >0){
    mast_res[setdiff(tsc_de_genes, gsfa_de_genes), ]$tsc_only_gene <- 1
  }
  mast_res$gsfa_only_gene <- 0
  if(length(setdiff(gsfa_de_genes, tsc_de_genes)) >0){
    mast_res[setdiff(gsfa_de_genes, tsc_de_genes), ]$gsfa_only_gene <- 1
  }
  combined_mast_res <- rbind(combined_mast_res, mast_res)
}

pvalue_list <- list('Two-step clustering only'=dplyr::filter(combined_mast_res,tsc_only_gene==1)$PValue,
                    'GSFA only'=dplyr::filter(combined_mast_res,gsfa_only_gene==1)$PValue,
                    'MAST'=combined_mast_res$PValue)

# pdf(file.path(res_dir, "qqplot_only_combined.pdf"))
qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
      ggtitle("All perturbations") + theme(plot.title = element_text(hjust = 0.5)) +
      scale_colour_discrete(name="Method")

# dev.off()

sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] lattice_0.20-45      gridExtra_2.3        dplyr_1.0.8         
 [4] reshape2_1.4.4       ggplot2_3.3.5        ComplexHeatmap_2.6.2
 [7] SeuratObject_4.0.4   Seurat_4.1.0         data.table_1.14.2   
[10] workflowr_1.7.0     

loaded via a namespace (and not attached):
  [1] circlize_0.4.15       plyr_1.8.6            igraph_1.3.4         
  [4] lazyeval_0.2.2        splines_4.0.4         listenv_0.8.0        
  [7] scattermore_0.7       digest_0.6.29         htmltools_0.5.3      
 [10] fansi_1.0.3           magrittr_2.0.3        tensor_1.5           
 [13] cluster_2.1.3         ROCR_1.0-11           globals_0.16.0       
 [16] matrixStats_0.62.0    R.utils_2.12.0        spatstat.sparse_2.1-0
 [19] colorspace_2.0-3      ggrepel_0.9.1         xfun_0.30            
 [22] callr_3.7.0           crayon_1.5.1          jsonlite_1.8.0       
 [25] spatstat.data_2.1-2   survival_3.3-1        zoo_1.8-9            
 [28] glue_1.6.2            polyclip_1.10-0       gtable_0.3.0         
 [31] leiden_0.3.9          GetoptLong_1.0.5      future.apply_1.8.1   
 [34] shape_1.4.6           BiocGenerics_0.36.1   abind_1.4-5          
 [37] scales_1.2.0          DBI_1.1.3             spatstat.random_2.1-0
 [40] miniUI_0.1.1.1        Rcpp_1.0.9            viridisLite_0.4.0    
 [43] xtable_1.8-4          clue_0.3-60           reticulate_1.25      
 [46] spatstat.core_2.4-0   stats4_4.0.4          htmlwidgets_1.5.4    
 [49] httr_1.4.2            RColorBrewer_1.1-3    ellipsis_0.3.2       
 [52] ica_1.0-2             R.methodsS3_1.8.1     pkgconfig_2.0.3      
 [55] farver_2.1.1          sass_0.4.1            uwot_0.1.11          
 [58] deldir_1.0-6          utf8_1.2.2            tidyselect_1.1.2     
 [61] labeling_0.4.2        rlang_1.0.4           later_1.3.0          
 [64] munsell_0.5.0         tools_4.0.4           cli_3.3.0            
 [67] generics_0.1.3        ggridges_0.5.3        evaluate_0.16        
 [70] stringr_1.4.0         fastmap_1.1.0         yaml_2.3.5           
 [73] goftest_1.2-3         processx_3.5.3        knitr_1.38           
 [76] fs_1.5.2              fitdistrplus_1.1-8    purrr_0.3.4          
 [79] RANN_2.6.1            pbapply_1.5-0         future_1.24.0        
 [82] nlme_3.1-159          whisker_0.4           mime_0.12            
 [85] R.oo_1.24.0           compiler_4.0.4        rstudioapi_0.13      
 [88] plotly_4.10.0         png_0.1-7             spatstat.utils_2.3-0 
 [91] tibble_3.1.6          bslib_0.3.1           stringi_1.7.6        
 [94] highr_0.9             ps_1.7.1              RSpectra_0.16-0      
 [97] Matrix_1.4-1          vctrs_0.4.1           pillar_1.8.0         
[100] lifecycle_1.0.1       spatstat.geom_2.3-2   lmtest_0.9-40        
[103] jquerylib_0.1.4       GlobalOptions_0.1.2   RcppAnnoy_0.0.19     
[106] cowplot_1.1.1         irlba_2.3.5           httpuv_1.6.5         
[109] patchwork_1.1.1       R6_2.5.1              promises_1.2.0.1     
[112] KernSmooth_2.23-20    IRanges_2.24.1        parallelly_1.32.1    
[115] codetools_0.2-18      MASS_7.3-58.1         assertthat_0.2.1     
[118] rprojroot_2.0.2       rjson_0.2.21          withr_2.5.0          
[121] sctransform_0.3.3     S4Vectors_0.28.1      mgcv_1.8-39          
[124] parallel_4.0.4        rpart_4.1-15          tidyr_1.2.0          
[127] rmarkdown_2.13        Cairo_1.6-0           Rtsne_0.15           
[130] git2r_0.30.1          getPass_0.2-2         shiny_1.7.1