• 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

Last updated: 2022-08-12

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slurm setting

sinteractive --partition=broadwl --account=pi-xinhe --mem=50G --time=10:00:00 --cpus-per-task=8
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

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(MUSIC))
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())
)

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 input data

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

Extract data for stimulated cells

metadata <- combined_obj@meta.data
table(metadata$orig.ident)

combined_obj@meta.data$condition <- "unstimulated"
combined_obj@meta.data$condition[which(endsWith(combined_obj@meta.data$orig.ident, "S"))] <- "stimulated"

# combined_obj.list <- SplitObject(combined_obj, split.by = "condition")
# combined_obj <- combined_obj.list$stimulated
combined_obj <- subset(combined_obj, subset = condition == "stimulated")
combined_obj
table(combined_obj@meta.data$orig.ident)

TCells_D1N TCells_D1S TCells_D2N TCells_D2S 
      5533       6843       5144       7435 
An object of class Seurat 
33694 features across 14278 samples within 1 assay 
Active assay: RNA (33694 features, 1000 variable features)
 2 dimensional reductions calculated: pca, umap

TCells_D1S TCells_D2S 
      6843       7435

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.
range(combined_obj$nFeature_RNA)

# The total number of molecules detected within a cell
range(combined_obj$nCount_RNA)

# The percentage of reads that map to the mitochondrial genome
range(combined_obj$percent_mt)
[1]  973 5889
[1]  2602 39985
[1] 0.01762736 9.96515679
# 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)
dim(combined_obj[["RNA"]]@counts)
dim(combined_obj[["RNA"]]@data)
dim(combined_obj[["RNA"]]@scale.data)

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

Perform dimensional reduction

Perform PCA on the scaled data.

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

Version Author Date
ee884c4 kevinlkx 2022-08-11

Cluster the cells

Embed cells in K-nearest neighbor (KNN) graph using FindNeighbors() using the first 30 PCs, as in the Shifrut et al. paper 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, "TCells_stimulated_seurat_clustered.rds"))

Visualize the clusters using UMAP

combined_obj <- readRDS(file.path(res_dir, "TCells_stimulated_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
ee884c4 kevinlkx 2022-08-11

Finding differentially expressed genes using MAST

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

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

Associate perturbations with clusters

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

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()

Version Author Date
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()

Version Author Date
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)

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
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)

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
ee884c4 kevinlkx 2022-08-11 

sessionInfo()
R version 4.1.0 (2021-05-18)
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      stats4    parallel  stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] lattice_0.20-45        dplyr_1.0.8            reshape2_1.4.4        
 [4] ggplot2_3.3.5          ComplexHeatmap_2.6.2   MUSIC_1.0             
 [7] SAVER_1.1.2            clusterProfiler_3.18.1 hash_2.2.6.2          
[10] topicmodels_0.2-12     Biostrings_2.58.0      XVector_0.30.0        
[13] IRanges_2.24.1         S4Vectors_0.28.1       BiocGenerics_0.36.1   
[16] SeuratObject_4.0.4     Seurat_4.1.0           data.table_1.14.2     
[19] workflowr_1.7.0       

loaded via a namespace (and not attached):
  [1] utf8_1.2.2            R.utils_2.11.0        reticulate_1.24      
  [4] tidyselect_1.1.2      RSQLite_2.2.11        AnnotationDbi_1.52.0 
  [7] htmlwidgets_1.5.4     BiocParallel_1.24.1   Rtsne_0.15           
 [10] scatterpie_0.1.7      munsell_0.5.0         codetools_0.2-18     
 [13] ica_1.0-2             future_1.24.0         miniUI_0.1.1.1       
 [16] withr_2.5.0           spatstat.random_2.1-0 colorspace_2.0-3     
 [19] GOSemSim_2.16.1       Biobase_2.50.0        highr_0.9            
 [22] NLP_0.2-1             knitr_1.38            rstudioapi_0.13      
 [25] ROCR_1.0-11           tensor_1.5            DOSE_3.16.0          
 [28] listenv_0.8.0         labeling_0.4.2        git2r_0.30.1         
 [31] slam_0.1-50           polyclip_1.10-0       bit64_4.0.5          
 [34] farver_2.1.0          rprojroot_2.0.2       downloader_0.4       
 [37] parallelly_1.31.0     vctrs_0.4.1           generics_0.1.2       
 [40] xfun_0.30             R6_2.5.1              clue_0.3-60          
 [43] graphlayouts_0.8.0    spatstat.utils_2.3-0  cachem_1.0.6         
 [46] fgsea_1.21.0          assertthat_0.2.1      promises_1.2.0.1     
 [49] scales_1.2.0          ggraph_2.0.5          enrichplot_1.10.2    
 [52] gtable_0.3.0          Cairo_1.5-15          globals_0.14.0       
 [55] processx_3.5.3        goftest_1.2-3         tidygraph_1.2.0      
 [58] rlang_1.0.4           GlobalOptions_0.1.2   splines_4.1.0        
 [61] lazyeval_0.2.2        spatstat.geom_2.3-2   BiocManager_1.30.16  
 [64] yaml_2.3.5            abind_1.4-5           httpuv_1.6.5         
 [67] qvalue_2.22.0         tools_4.1.0           ellipsis_0.3.2       
 [70] spatstat.core_2.4-0   jquerylib_0.1.4       RColorBrewer_1.1-3   
 [73] ggridges_0.5.3        Rcpp_1.0.9            plyr_1.8.6           
 [76] zlibbioc_1.36.0       purrr_0.3.4           ps_1.6.0             
 [79] rpart_4.1-15          deldir_1.0-6          GetoptLong_1.0.5     
 [82] pbapply_1.5-0         viridis_0.6.2         cowplot_1.1.1        
 [85] zoo_1.8-9             ggrepel_0.9.1         cluster_2.1.2        
 [88] fs_1.5.2              magrittr_2.0.3        RSpectra_0.16-0      
 [91] scattermore_0.7       DO.db_2.9             circlize_0.4.14      
 [94] lmtest_0.9-40         RANN_2.6.1            whisker_0.4          
 [97] fitdistrplus_1.1-8    matrixStats_0.61.0    patchwork_1.1.1      
[100] mime_0.12             evaluate_0.15         xtable_1.8-4         
[103] shape_1.4.6           gridExtra_2.3         compiler_4.1.0       
[106] tibble_3.1.6          KernSmooth_2.23-20    crayon_1.5.1         
[109] shadowtext_0.1.1      R.oo_1.24.0           htmltools_0.5.2      
[112] ggfun_0.0.5           mgcv_1.8-39           later_1.3.0          
[115] tidyr_1.2.0           DBI_1.1.2             tweenr_1.0.2         
[118] MASS_7.3-56           Matrix_1.4-1          cli_3.3.0            
[121] R.methodsS3_1.8.1     igraph_1.2.11         pkgconfig_2.0.3      
[124] getPass_0.2-2         rvcheck_0.2.1         plotly_4.10.0        
[127] spatstat.sparse_2.1-0 foreach_1.5.2         xml2_1.3.3           
[130] bslib_0.3.1           yulab.utils_0.0.4     stringr_1.4.0        
[133] callr_3.7.0           digest_0.6.29         sctransform_0.3.3    
[136] RcppAnnoy_0.0.19      spatstat.data_2.1-2   tm_0.7-8             
[139] rmarkdown_2.13        leiden_0.3.9          fastmatch_1.1-3      
[142] uwot_0.1.11           shiny_1.7.1           modeltools_0.2-23    
[145] rjson_0.2.21          lifecycle_1.0.1       nlme_3.1-155         
[148] jsonlite_1.8.0        viridisLite_0.4.0     fansi_1.0.3          
[151] pillar_1.7.0          fastmap_1.1.0         httr_1.4.2           
[154] survival_3.3-1        GO.db_3.12.1          glue_1.6.2           
[157] iterators_1.0.14      png_0.1-7             bit_4.0.4            
[160] ggforce_0.3.3         stringi_1.7.6         sass_0.4.1           
[163] blob_1.2.3            memoise_2.0.1         irlba_2.3.5          
[166] future.apply_1.8.1