Two step clustering analysis on CD8+ T Cell (stimulated) CROP-seq data

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Last updated: 2022-09-20

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Knit directory: GSFA_analysis/

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html	9a47c79	kevinlkx	2022-09-08	Build site.
Rmd	8bcdaac	kevinlkx	2022-09-08	updated the Seurat results
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Rmd	2d2298a	kevinlkx	2022-08-31	added qqplots for combined results
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Rmd	ce094cd	kevinlkx	2022-08-11	update intro formating and barplot for DEGs

---

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

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

cp /project2/xinhe/yifan/Factor_analysis/Stimulated_T_Cells/GSE119450_RAW/D1N/genes.tsv \
  /project2/xinhe/kevinluo/GSFA/data/Stimulated_T_Cells_GSE119450_RAW_D1N_genes.tsv.gz

Scripts for running the analysis:

• R script.

mkdir -p /project2/xinhe/kevinluo/GSFA/twostep_clustering/log
cd /project2/xinhe/kevinluo/GSFA/twostep_clustering/log

sbatch --mem=40G ~/projects/GSFA_analysis/code/run_twostep_clustering_Tcells_data.sbatch

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

dyn.load('/software/geos-3.7.0-el7-x86_64/lib64/libgeos_c.so') # attach the geos lib for Seurat
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())
)

library(gridExtra)
source("code/plotting_functions.R")

Set directories

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

Load two-step clustering analysis result

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

Associate perturbations with clusters

perturb_matrix <- combined_obj@meta.data[, 4:24]

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, "TCells_stimulated_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

Plot perturbation ~ cluster associations (show FDR)

KO_names <- rownames(fdr_mat)
dotplot_effectsize(t(stdres_mat), t(fdr_mat),
                   reorder_markers = c(KO_names[KO_names!="NonTarget"], "NonTarget"),
                   color_lgd_title = "Chi-squared test\nstandardized residuals",
                   size_lgd_title = "FDR",
                   max_score = 4,
                   min_score = -4,
                   by_score = 2) + coord_flip()

Past versions of stdres-fdr-dotplot-1.png

Version	Author	Date
9a47c79	kevinlkx	2022-09-08
ee884c4	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!="NonTarget"], "NonTarget"),
                   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()

Past versions of stdres-bonferroni-dotplot-1.png

Version	Author	Date
9a47c79	kevinlkx	2022-09-08
ee884c4	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, "Stimulated_T_Cells_GSE119450_RAW_D1N_genes.tsv.gz"),
                                  header = FALSE), stringsAsFactors = FALSE)

de.markers <- readRDS(file.path(res_dir, "TCells_stimulated_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 <- rownames(fdr_mat)
perturb_names <- c("NonTarget", perturb_names[perturb_names!="NonTarget"])

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))

Past versions of count-de-genes-fdr-1.png

Version	Author	Date
9a47c79	kevinlkx	2022-09-08
ee884c4	kevinlkx	2022-08-11

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

perturb_names <- rownames(bonferroni_mat)
perturb_names <- c("NonTarget", perturb_names[perturb_names!="NonTarget"])

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))

Past versions of count-de-genes-bonferroni-1.png

Version	Author	Date
9a47c79	kevinlkx	2022-09-08
ee884c4	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/Stimulated_T_Cells/"
fit <- readRDS(paste0(data_folder,
                      "gsfa_output_detect_01/all_uncorrected_by_group.use_negctrl/All.gibbs_obj_k20.svd_negctrl.restart.light.rds"))
gibbs_PM <- fit$posterior_means
lfsr_mat1 <- fit$lfsr1[, -ncol(fit$lfsr1)]
lfsr_mat0 <- fit$lfsr0[, -ncol(fit$lfsr0)]
total_effect1 <- fit$total_effect1[, -ncol(fit$total_effect1)]
total_effect0 <- fit$total_effect0[, -ncol(fit$total_effect0)]
KO_names <- colnames(lfsr_mat1)

DEGs detected by GSFA

   ARID1A      BTLA  C10orf54      CBLB      CD3D       CD5    CDKN1B      DGKA 
      393       107        66       631         0       645       468        32 
     DGKZ    HAVCR2      LAG3      LCP2     MEF2D NonTarget     PDCD1     RASA2 
      113        35         1       589        15         0         0       277 
    SOCS1     STAT6     TCEB2   TMEM222   TNFRSF9 
      356         1       300         4        14 

Load MAST single-gene DE result

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

mast_list <- list()
for (m in guides){
  fname <- paste0(data_folder, "processed_data/MAST/all_by_stim_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){
    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,
                        'all genes'=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 = 3, ncol = 2)

Past versions of qqplots-gsfa-tsc-all-1.png

Version	Author	Date
9a47c79	kevinlkx	2022-09-08
748aea1	kevinlkx	2022-08-31
104e391	kevinlkx	2022-08-25

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,
                    'all genes'=combined_mast_res$PValue)

qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
      ggtitle("") + theme(plot.title = element_text(hjust = 0.5)) +
      scale_colour_discrete(name="Method")

Past versions of qqplots-gsfa-tsc-all-combined-1.png

Version	Author	Date
9a47c79	kevinlkx	2022-09-08
748aea1	kevinlkx	2022-08-31

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){
    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,
                        'all genes'=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 = 3, ncol = 2)

Past versions of qqplots-gsfa-tsc-only-1.png

Version	Author	Date
9a47c79	kevinlkx	2022-09-08
748aea1	kevinlkx	2022-08-31
104e391	kevinlkx	2022-08-25

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,
                    'all genes'=combined_mast_res$PValue)

qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
      ggtitle("") + theme(plot.title = element_text(hjust = 0.5)) +
      scale_colour_discrete(name="Method")

Past versions of qqplots-gsfa-tsc-only-combined-1.png

Version	Author	Date
9a47c79	kevinlkx	2022-09-08
748aea1	kevinlkx	2022-08-31

Session information

sessionInfo()

R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

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         LC_TIME=C           
 [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
 [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
[10] LC_TELEPHONE=C       LC_MEASUREMENT=C     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_1.0.9          
 [4] reshape2_1.4.4        ggplot2_3.3.6         ComplexHeatmap_2.12.0
 [7] sp_1.4-7              SeuratObject_4.1.0    Seurat_4.1.1         
[10] data.table_1.14.2     workflowr_1.7.0      

loaded via a namespace (and not attached):
  [1] circlize_0.4.15       plyr_1.8.7            igraph_1.3.4         
  [4] lazyeval_0.2.2        splines_4.2.0         listenv_0.8.0        
  [7] scattermore_0.8       digest_0.6.29         foreach_1.5.2        
 [10] htmltools_0.5.2       fansi_1.0.3           magrittr_2.0.3       
 [13] tensor_1.5            cluster_2.1.3         doParallel_1.0.17    
 [16] ROCR_1.0-11           globals_0.15.0        matrixStats_0.62.0   
 [19] R.utils_2.11.0        spatstat.sparse_2.1-1 colorspace_2.0-3     
 [22] ggrepel_0.9.1         xfun_0.30             callr_3.7.0          
 [25] crayon_1.5.1          jsonlite_1.8.0        progressr_0.10.0     
 [28] spatstat.data_2.2-0   survival_3.3-1        zoo_1.8-10           
 [31] iterators_1.0.14      glue_1.6.2            polyclip_1.10-0      
 [34] gtable_0.3.0          leiden_0.4.2          GetoptLong_1.0.5     
 [37] shape_1.4.6           future.apply_1.9.0    BiocGenerics_0.42.0  
 [40] abind_1.4-5           scales_1.2.0          DBI_1.1.3            
 [43] spatstat.random_2.2-0 miniUI_0.1.1.1        Rcpp_1.0.8.3         
 [46] viridisLite_0.4.0     xtable_1.8-4          clue_0.3-61          
 [49] reticulate_1.24       spatstat.core_2.4-2   stats4_4.2.0         
 [52] htmlwidgets_1.5.4     httr_1.4.3            RColorBrewer_1.1-3   
 [55] ellipsis_0.3.2        ica_1.0-2             R.methodsS3_1.8.1    
 [58] farver_2.1.0          pkgconfig_2.0.3       sass_0.4.1           
 [61] uwot_0.1.11           deldir_1.0-6          utf8_1.2.2           
 [64] labeling_0.4.2        tidyselect_1.1.2      rlang_1.0.2          
 [67] later_1.3.0           munsell_0.5.0         tools_4.2.0          
 [70] cli_3.3.0             generics_0.1.2        ggridges_0.5.3       
 [73] evaluate_0.15         stringr_1.4.0         fastmap_1.1.0        
 [76] yaml_2.3.5            goftest_1.2-3         processx_3.5.3       
 [79] knitr_1.39            fs_1.5.2              fitdistrplus_1.1-8   
 [82] purrr_0.3.4           RANN_2.6.1            pbapply_1.5-0        
 [85] future_1.25.0         nlme_3.1-157          whisker_0.4          
 [88] mime_0.12             R.oo_1.24.0           compiler_4.2.0       
 [91] rstudioapi_0.13       plotly_4.10.0         png_0.1-7            
 [94] spatstat.utils_2.3-1  tibble_3.1.7          bslib_0.3.1          
 [97] stringi_1.7.6         highr_0.9             ps_1.7.0             
[100] rgeos_0.5-9           Matrix_1.4-1          vctrs_0.4.1          
[103] pillar_1.7.0          lifecycle_1.0.1       spatstat.geom_2.4-0  
[106] lmtest_0.9-40         jquerylib_0.1.4       GlobalOptions_0.1.2  
[109] RcppAnnoy_0.0.19      cowplot_1.1.1         irlba_2.3.5          
[112] httpuv_1.6.5          patchwork_1.1.1       R6_2.5.1             
[115] promises_1.2.0.1      KernSmooth_2.23-20    IRanges_2.30.0       
[118] parallelly_1.31.1     codetools_0.2-18      MASS_7.3-56          
[121] assertthat_0.2.1      rprojroot_2.0.3       rjson_0.2.21         
[124] withr_2.5.0           sctransform_0.3.3     S4Vectors_0.34.0     
[127] mgcv_1.8-40           parallel_4.2.0        rpart_4.1.16         
[130] tidyr_1.2.0           rmarkdown_2.14        Rtsne_0.16           
[133] git2r_0.30.1          getPass_0.2-2         shiny_1.7.1