AD_CO_integrated_analysis
petrsh
2022-01-10
Last updated: 2022-01-12
Checks: 6 1
Knit directory: AD_CO_scRNAseq/
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Dependencies
library(Seurat)
library(SingleR)
library(BiocParallel)
library(simspec)
library(scProportionTest)
library(dplyr)
library(slingshot)
library(tradeSeq)
library(pheatmap)
library(ggplot2)
library(ggthemes)
library(here)
# not loaded but required:
#library(gridExtra)We used ‘cellranger aggr’ to aggregate datasets into a single feature-barcode matrix and subsample reads such that the datasets have the same effective sequencing depth.
D1D2_merged <- Read10X(here("data","AD_CO_aggregated"))
Sat_D1D2_merged <- CreateSeuratObject(D1D2_merged, min.cells = 3)
Sat_D1D2_merged <- PercentageFeatureSet(Sat_D1D2_merged, pattern = "^MT-", col.name = "percent.mt")
Sat_D1D2_merged <- PercentageFeatureSet(Sat_D1D2_merged, pattern = "^RP[SL][[:digit:]]|^RPLP[[:digit:]]|^RPSA", col.name = "percent.ribo")
Sat_D1D2_merged$dataset <- sub(".*-","D", colnames(Sat_D1D2_merged))
AD_filtered_barcodes <- read.csv(here("output", "AD_filtered_barcodes.csv")) %>%
pull(x)
CO_filtered_barcodes <- read.csv(here("output", "CO_filtered_barcodes.csv")) %>%
pull(x)
Sat_D1D2_merged <- subset(Sat_D1D2_merged, cells = c(CO_filtered_barcodes, AD_filtered_barcodes))Subset cells in the datasets such that the datasets contain same number of cells.
Sat_D1D2_merged_D1 <- subset(Sat_D1D2_merged, dataset == "D1")
Sat_D1D2_merged_D2 <- subset(Sat_D1D2_merged, dataset == "D2")
set.seed(42)
Sat_D1D2_merged_D2 <- subset(Sat_D1D2_merged_D2, cells = sample(Cells(Sat_D1D2_merged_D2), length(Cells(Sat_D1D2_merged_D1))))
Sat_D1D2_merged <- subset(Sat_D1D2_merged, cells = c(Cells(Sat_D1D2_merged_D1),Cells(Sat_D1D2_merged_D2)))
# Use regularized negative binomial regression to normalize counts
Sat_D1D2_merged <- SCTransform(Sat_D1D2_merged, verbose = FALSE)
# Assign cell-cycle score
Sat_D1D2_merged <- CellCycleScoring(Sat_D1D2_merged, s.features = cc.genes.updated.2019$s.genes, g2m.features = cc.genes.updated.2019$g2m.genes,
set.ident = TRUE)
# Regress out the difference between the G2M and S phase scores
Sat_D1D2_merged$cc_difference <- Sat_D1D2_merged$S.Score - Sat_D1D2_merged$G2M.Score
Sat_D1D2_merged <- SCTransform(Sat_D1D2_merged, assay = 'RNA', new.assay.name = 'SCT', vars.to.regress = "cc_difference", verbose = FALSE)
Sat_D1D2_merged <- RunPCA(Sat_D1D2_merged, features = VariableFeatures(Sat_D1D2_merged))
Sat_D1D2_merged <- RunUMAP(Sat_D1D2_merged, dims = 1:15)
DimPlot(Sat_D1D2_merged, group.by = "Phase") +
DimPlot(Sat_D1D2_merged, group.by = "dataset")
## Add AD and CO cluster labels
AD_clusters <- read.csv(here("output", "AD_clusters_res08.csv")) %>%
pull(x, X)
names(AD_clusters) <- sub("-1", "-2", names(AD_clusters))
AD_clusters <- AD_clusters[names(AD_clusters) %in% AD_filtered_barcodes]
CO_clusters <- read.csv(here("output", "CO_clusters_res08.csv")) %>%
pull(x, X)
Sat_D1D2_merged$CO_clusters <- CO_clusters
Sat_D1D2_merged$AD_clusters <- AD_clustersUMAP embedding of the data without any integration method revealed segregation of cells based on datasets. We decided to apply the cluster similarity spectrum (CSS) method that achieves integration by representing each cell by its transcriptome’s similarity to every cell cluster in each sample.
Sat_D1D2_merged_batch_cor <- cluster_sim_spectrum(object = Sat_D1D2_merged, label_tag = "dataset",
cluster_resolution = 0.8, verbose=FALSE)
Sat_D1D2_merged_batch_cor <- RunUMAP(Sat_D1D2_merged_batch_cor, reduction = "css", dims = 1:ncol(Embeddings(Sat_D1D2_merged_batch_cor, "css")))
Sat_D1D2_merged_batch_cor <- FindNeighbors(Sat_D1D2_merged_batch_cor, reduction = "css", dims = 1:ncol(Embeddings(Sat_D1D2_merged_batch_cor, "css")))
Sat_D1D2_merged_batch_cor <- FindClusters(Sat_D1D2_merged_batch_cor, resolution = c(0.1, 0.4, 0.8), verbose = FALSE)
DimPlot(Sat_D1D2_merged_batch_cor, group.by = "SCT_snn_res.0.8", label = T) +
DimPlot(Sat_D1D2_merged_batch_cor, group.by = "SCT_snn_res.0.4", label = T) +
DimPlot(Sat_D1D2_merged_batch_cor, group.by = "SCT_snn_res.0.1", label = T) +
DimPlot(Sat_D1D2_merged_batch_cor, group.by = "dataset", label = T) 
autumn_palette <- c("#751A33", "#B34233", "#D28F33", "#D4B95E", "#4EA2A2", "#506432",
"#1A8693", "#cbdfbd", "#d4e09b", "#f6f4d2", "#f19c79", "#a44a3f")
DimPlot(Sat_D1D2_merged_batch_cor, group.by = "SCT_snn_res.0.4", label = F, cols = autumn_palette) +
theme_tufte() +
theme(axis.ticks = element_blank())
We can better visualize differences between AD and CO by computing the imbalance score for each cell based on the condition label distribution of its neighbors compared to the overall distribution.
imb_scores <- condiments::imbalance_score(Object = Sat_D1D2_merged_batch_cor@reductions$umap@cell.embeddings,
conditions = Sat_D1D2_merged_batch_cor$dataset)
df_imb <- as.data.frame(Sat_D1D2_merged_batch_cor@reductions$umap@cell.embeddings)
df_imb$scaled_imb <- imb_scores$scaled_scores
ggplot(df_imb, aes(x = UMAP_1, y = UMAP_2, col = scaled_imb)) +
geom_point() +
scale_color_viridis_c(option = "C") +
labs(col = "Imbalance score") +
ggthemes::theme_tufte(ticks = F) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank())
We test the difference between the proportion of cells in clusters between AD and CO.
test_scProportionTest <- sc_utils(Sat_D1D2_merged_batch_cor)
test_scProportionTest <- permutation_test(test_scProportionTest,
cluster_identity = "SCT_snn_res.0.4",
sample_1 = "D1",
sample_2 = "D2",
sample_identity = "dataset")
permutation_plot(test_scProportionTest)
Identification of specific genes for AD/CO
cond_spec_markers <- list()
for (i in levels(Sat_D1D2_merged_batch_cor$SCT_snn_res.0.1)) {
tmp <- subset(Sat_D1D2_merged_batch_cor, SCT_snn_res.0.1 == i)
cond_spec_markers[[i]] <- SoupX::quickMarkers(tmp@assays$RNA@counts, tmp$dataset, N = 50)
}
for (i in names(cond_spec_markers)){
file_name <- paste("cond_spec_markers_cl", i, ".csv", sep = "")
write.csv(cond_spec_markers[[i]][,-c(1,5,7,8,9)],here("output", file_name),
row.names = TRUE, quote = FALSE)
}
SCE_D1D2_merged_batch_cor <- as.SingleCellExperiment(Sat_D1D2_merged_batch_cor)
scater::plotExpression(SCE_D1D2_merged_batch_cor, features = cond_spec_markers[["0"]] %>%
arrange(qval) %>%
pull(gene) %>%
head(),
x = "dataset")
scater::plotExpression(SCE_D1D2_merged_batch_cor, features = cond_spec_markers[["1"]] %>%
arrange(qval) %>%
pull(gene) %>%
head(),
x = "dataset")
scater::plotExpression(SCE_D1D2_merged_batch_cor, features = cond_spec_markers[["2"]] %>%
arrange(qval) %>%
pull(gene) %>%
head(),
x = "dataset")
scater::plotExpression(SCE_D1D2_merged_batch_cor, features = cond_spec_markers[["3"]] %>%
arrange(qval) %>%
pull(gene) %>%
head(),
x = "dataset")
scater::plotExpression(SCE_D1D2_merged_batch_cor, features = cond_spec_markers[["4"]] %>%
arrange(qval) %>%
pull(gene) %>%
head(),
x = "dataset")
genes_to_plot <- c("RAX", "CRX", "VSX2", "SIX6")
for (i in genes_to_plot){
g <- Nebulosa::plot_density(Sat_D1D2_merged_batch_cor, features = i, reduction = "umap") +
theme_tufte() +
theme(legend.position="none", axis.ticks = element_blank())
print(g)
}
We compare our datasets with published data (Kanton et al, 2019) using the SingleR package.
orgFull_huma <- readRDS(here("data", "Kanton_2019", "timecourse_human_pseudocells_consensusGenome.rds"))
DimPlot(orgFull_huma, reduction = "spring", group.by = "stage_group") 
The cells in our datasets are from 2-month old organoids, therefore, we expect the most cells to be classified as “Organoid-2M” or younger
# Extract log-normalized counts and subset the matrix to genes detected in our dataset
orgFull_huma_logCounts <- orgFull_huma@assays$RNA@data[rownames(orgFull_huma@assays$RNA@data) %in% rownames(Sat_D1D2_merged_batch_cor@assays$RNA@counts),]
# Build a reference
orgFull_human_stage_ref <- trainSingleR(orgFull_huma_logCounts,
labels = orgFull_huma$stage_group,
BPPARAM = MulticoreParam(),
de.n = 300,
aggr.ref = TRUE)
# Classify cells in our dataset
D1D2_orgFull_human_stage_pred <- classifySingleR(Sat_D1D2_merged_batch_cor@assays$RNA@counts, orgFull_human_stage_ref, BPPARAM = MulticoreParam())
#table(D1D2_orgFull_human_stage_pred$pruned.labels)
Sat_D1D2_merged_batch_cor$orgFull_human_stage_pred <- as.factor(D1D2_orgFull_human_stage_pred$pruned.labels)
DimPlot(Sat_D1D2_merged_batch_cor, group.by = "orgFull_human_stage_pred", label=F)
Sat_D1D2_merged_batch_cor_D1 <- subset(Sat_D1D2_merged_batch_cor, dataset == "D1")
Sat_D1D2_merged_batch_cor_D2 <- subset(Sat_D1D2_merged_batch_cor, dataset == "D2")
DimPlot(Sat_D1D2_merged_batch_cor_D1, group.by = "orgFull_human_stage_pred", split.by = "orgFull_human_stage_pred", label=F) &
theme_tufte() &
theme(axis.ticks = element_blank())
DimPlot(Sat_D1D2_merged_batch_cor_D2, group.by = "orgFull_human_stage_pred", split.by = "orgFull_human_stage_pred", label=F) &
theme_tufte() &
theme(axis.ticks = element_blank())
Trajectory inference across conditions
DimPlot(Sat_D1D2_merged_batch_cor, label = F, group.by = "SCT_snn_res.0.1")
clust <- subset(Sat_D1D2_merged_batch_cor, SCT_snn_res.0.1 %in% c(0,1))
# remove AD cluster 6 and CO cluster 8
clust <- subset(clust, AD_clusters %in% 6, invert=T)
clust <- subset(clust, CO_clusters %in% 8, invert=T)
#
clust$TTR_on <- clust@assays$RNA@counts["TTR",] > 2
clust <- subset(clust, TTR_on == FALSE)
rd <- clust@reductions$umap@cell.embeddings
clust <- as.character(clust$SCT_snn_res.0.1)
sds <- slingshot(rd, clust, start.clus="1",end.clus ="0", stretch=0, extend="n")
#SlingshotDataSet(sds)
#plot(rd[,1], rd[,2])
#lines(SlingshotDataSet(sds))
psts <- slingPseudotime(sds)
df_psts <- as.data.frame(rd)
df_psts$psts <- psts
ggplot(df_psts, aes(x = UMAP_1, y = UMAP_2, col = psts)) +
geom_point() +
ggthemes::theme_tufte(ticks = F) +
scale_color_viridis_c() +
labs(col="pseudotime")
Sat_D1D2_merged_batch_cor_psts <- subset(Sat_D1D2_merged_batch_cor, cells = rownames(rd))
df_psts$condition <- Sat_D1D2_merged_batch_cor_psts$dataset
df_psts$condition[df_psts$condition == "D1"] <- "CO"
df_psts$condition[df_psts$condition == "D2"] <- "AD"
ggplot(df_psts, aes(x = psts, fill = condition)) +
geom_density(alpha = .5) +
scale_fill_brewer(type = "qual") +
theme(legend.position = "bottom") +
ggthemes::theme_tufte(ticks = F) +
labs(x="pseudotime")
Trajectory inference across conditions: differential expression between conditions along pseudotime
For both datasets we will estimate a smooth average gene expression profile along pseudotime using a negative binomial generalized additive model (NB-GAM). Then we will identify genes differentially expressed between AD and CO within a trajectory.
counts_D1D2_merged <- as.matrix(Sat_D1D2_merged_batch_cor@assays$RNA@counts[VariableFeatures(Sat_D1D2_merged_batch_cor),rownames(df_psts)])
df_psts$condition <- as.factor(df_psts$condition)
# comp demanding, don't run again when generating the report
BPPARAM <- BiocParallel::bpparam()
BPPARAM$workers <- 30 # use n cores
#set.seed(42)
#aicK <- evaluateK(counts = counts_D1D2_merged, sds = sds, conditions = df_psts$condition,
# parallel = TRUE, BPPARAM = BPPARAM)
set.seed(42)
sceGAM <- fitGAM(counts = counts_D1D2_merged, sds = sds, conditions = df_psts$condition,
nknots = 7, parallel=TRUE, BPPARAM = BPPARAM, verbose = FALSE)
# Identify DE genes between AD and CO
cond_res <- conditionTest(sceGAM, l2fc = log2(2))
cond_res$padj <- p.adjust(cond_res$pvalue, "fdr")
sum(cond_res$padj <= 0.01, na.rm = TRUE)[1] 141# export
cond_res %>%
filter(padj <= 0.1) %>%
arrange(padj) %>%
write.csv(here("output","COvsAD_DE_dyn_genes.csv"))
condition_genes <- cond_res %>%
filter(padj <= 0.01) %>%
arrange(padj) %>%
rownames()
### based on mean smoother
yhat_smooth <- predictSmooth(sceGAM, gene = condition_genes, nPoints = 50, tidy = FALSE)
yhat_smooth_scaled <- t(scale(t(yhat_smooth)))
heat_smooth_CO <- pheatmap(yhat_smooth_scaled[,51:100],
cluster_cols = FALSE,
show_rownames = TRUE, show_colnames = FALSE, main = "CO", legend = FALSE,
silent = TRUE, fontsize = 6, treeheight_row=0, border_color = NA
)
matching_heatmap_AD <- pheatmap(yhat_smooth_scaled[heat_smooth_CO$tree_row$order, 1:50],
cluster_cols = FALSE, cluster_rows = FALSE,
show_rownames = TRUE, show_colnames = FALSE, main = "AD",
legend = FALSE, silent = TRUE, fontsize = 6, border_color = NA
)
#heat_smooth_CO$tree_row$labels[heat_smooth_CO$tree_row$order]Heatmaps of DE genes between AD and CO within a trajectory
gridExtra::grid.arrange(heat_smooth_CO[[4]], matching_heatmap_AD[[4]], ncol = 2)
dyn_genes <- read.delim(here("data","selection_dyn_genes.txt"))
heat_smooth_CO <- pheatmap(yhat_smooth_scaled[dyn_genes$WT,51:100],
cluster_cols = FALSE, cluster_rows = FALSE,
show_rownames = TRUE, show_colnames = FALSE, main = "CO", legend = FALSE,
silent = TRUE, fontsize = 6, treeheight_row=0, border_color = NA
)
matching_heatmap_AD <- pheatmap(yhat_smooth_scaled[dyn_genes$WT, 1:50],
cluster_cols = FALSE, cluster_rows = FALSE,
show_rownames = TRUE, show_colnames = FALSE, main = "AD",
legend = FALSE, silent = TRUE, fontsize = 6, border_color = NA
)
gridExtra::grid.arrange(heat_smooth_CO[[4]], matching_heatmap_AD[[4]], ncol = 2)
heat_smooth_AD <- pheatmap(yhat_smooth_scaled[dyn_genes$AD, 1:50],
cluster_cols = FALSE, cluster_rows = FALSE,
show_rownames = TRUE, show_colnames = FALSE, main = "AD",
legend = FALSE, silent = TRUE, fontsize = 6, border_color = NA
)
matching_heatmap_CO <- pheatmap(yhat_smooth_scaled[dyn_genes$AD,51:100],
cluster_cols = FALSE, cluster_rows = FALSE,
show_rownames = TRUE, show_colnames = FALSE, main = "CO", legend = FALSE,
silent = TRUE, fontsize = 6, treeheight_row=0, border_color = NA
)
gridExtra::grid.arrange(heat_smooth_AD[[4]], matching_heatmap_CO[[4]], ncol = 2)
genes_to_plot <- c("HES5", "HES4", "HES1", "NOTCH1")
Sat_D1D2_merged_batch_cor$dataset_SCT_snn_res.0.4 <- paste(Sat_D1D2_merged_batch_cor$SCT_snn_res.0.4, Sat_D1D2_merged_batch_cor$dataset, sep = "_")
DotPlot(Sat_D1D2_merged_batch_cor, features = genes_to_plot, group.by = "dataset_SCT_snn_res.0.4") +
scale_color_viridis_c() +
theme_tufte(ticks = F)
DotPlot(Sat_D1D2_merged_batch_cor, features = genes_to_plot, group.by = "dataset_SCT_snn_res.0.4") +
scale_color_viridis_c() +
theme_tufte(ticks = F) +
coord_flip() +
theme( axis.text.x = element_text(angle = 90),
legend.position="none")
DotPlot(Sat_D1D2_merged_batch_cor, features = genes_to_plot, group.by = "dataset_SCT_snn_res.0.4") +
scale_color_viridis_c() +
theme_tufte(ticks = F) +
coord_flip() +
theme( axis.text.x = element_text(angle = 90),
legend.key.size = unit(0.2, 'cm'), #change legend key size
legend.key.height = unit(0.2, 'cm'), #change legend key height
legend.key.width = unit(0.2, 'cm'), #change legend key width
legend.title = element_text(size=8), #change legend title font size
legend.text = element_text(size=8))
DotPlot(Sat_D1D2_merged_batch_cor, features = genes_to_plot, group.by = "dataset") +
scale_color_viridis_c() +
theme_tufte(ticks = F) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90),
legend.position="none")
DotPlot(Sat_D1D2_merged_batch_cor, features = genes_to_plot, group.by = "dataset") +
scale_color_viridis_c() +
theme_tufte(ticks = F) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90),
legend.key.size = unit(0.2, 'cm'), #change legend key size
legend.key.height = unit(0.2, 'cm'), #change legend key height
legend.key.width = unit(0.2, 'cm'), #change legend key width
legend.title = element_text(size=8), #change legend title font size
legend.text = element_text(size=8))
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] presto_1.0.0 data.table_1.14.0
[3] Rcpp_1.0.6 here_1.0.1
[5] ggthemes_4.2.4 ggplot2_3.3.5
[7] pheatmap_1.0.12 tradeSeq_1.6.0
[9] slingshot_2.0.0 TrajectoryUtils_1.0.0
[11] SingleCellExperiment_1.14.1 princurve_2.1.6
[13] dplyr_1.0.7 scProportionTest_0.0.0.9000
[15] simspec_0.0.0.9000 BiocParallel_1.26.1
[17] SingleR_1.6.1 SummarizedExperiment_1.22.0
[19] Biobase_2.52.0 GenomicRanges_1.44.0
[21] GenomeInfoDb_1.28.1 IRanges_2.26.0
[23] S4Vectors_0.30.0 BiocGenerics_0.38.0
[25] MatrixGenerics_1.4.0 matrixStats_0.59.0
[27] SeuratObject_4.0.2 Seurat_4.0.3.9011
[29] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] scattermore_0.7 ModelMetrics_1.2.2.2
[3] Ecume_0.9.1 tidyr_1.1.3
[5] knitr_1.33 irlba_2.3.3
[7] DelayedArray_0.18.0 rpart_4.1-15
[9] RCurl_1.98-1.3 generics_0.1.0
[11] ScaledMatrix_1.0.0 cowplot_1.1.1
[13] RANN_2.6.1 VGAM_1.1-5
[15] combinat_0.0-8 proxy_0.4-26
[17] future_1.21.0 spatstat.data_2.1-0
[19] lubridate_1.7.10 httpuv_1.6.1
[21] assertthat_0.2.1 viridis_0.6.1
[23] gower_0.2.2 xfun_0.24
[25] jquerylib_0.1.4 evaluate_0.14
[27] promises_1.2.0.1 fansi_0.5.0
[29] igraph_1.2.6 DBI_1.1.1
[31] htmlwidgets_1.5.3 sparsesvd_0.2
[33] spatstat.geom_2.2-2 purrr_0.3.4
[35] ellipsis_0.3.2 ks_1.13.2
[37] RSpectra_0.16-0 DDRTree_0.1.5
[39] deldir_0.2-10 sparseMatrixStats_1.4.0
[41] vctrs_0.3.8 ROCR_1.0-11
[43] abind_1.4-5 caret_6.0-88
[45] withr_2.4.2 sctransform_0.3.2
[47] mclust_5.4.7 goftest_1.2-2
[49] cluster_2.1.2 lazyeval_0.2.2
[51] crayon_1.4.1 edgeR_3.34.0
[53] recipes_0.1.16 pkgconfig_2.0.3
[55] slam_0.1-48 labeling_0.4.2
[57] vipor_0.4.5 nlme_3.1-153
[59] transport_0.12-2 nnet_7.3-16
[61] rlang_0.4.11 globals_0.14.0
[63] lifecycle_1.0.0 miniUI_0.1.1.1
[65] rsvd_1.0.5 rprojroot_2.0.2
[67] polyclip_1.10-0 lmtest_0.9-38
[69] Nebulosa_1.2.0 Matrix_1.4-0
[71] zoo_1.8-9 beeswarm_0.4.0
[73] ggridges_0.5.3 png_0.1-7
[75] viridisLite_0.4.0 bitops_1.0-7
[77] KernSmooth_2.23-20 pROC_1.17.0.1
[79] DelayedMatrixStats_1.14.0 stringr_1.4.0
[81] parallelly_1.26.1 beachmat_2.8.0
[83] scales_1.1.1 magrittr_2.0.1
[85] plyr_1.8.6 ica_1.0-2
[87] zlibbioc_1.38.0 compiler_4.1.1
[89] HSMMSingleCell_1.12.0 RColorBrewer_1.1-2
[91] fitdistrplus_1.1-5 XVector_0.32.0
[93] listenv_0.8.0 patchwork_1.1.1
[95] pbapply_1.4-3 MASS_7.3-54
[97] mgcv_1.8-38 tidyselect_1.1.1
[99] stringi_1.6.2 forcats_0.5.1
[101] highr_0.9 densityClust_0.3
[103] yaml_2.2.1 BiocSingular_1.8.1
[105] locfit_1.5-9.4 ggrepel_0.9.1
[107] grid_4.1.1 sass_0.4.0
[109] spatstat.linnet_2.3-0 tools_4.1.1
[111] future.apply_1.7.0 monocle_2.20.0
[113] foreach_1.5.1 git2r_0.28.0
[115] condiments_1.0.0 gridExtra_2.3
[117] prodlim_2019.11.13 farver_2.1.0
[119] Rtsne_0.15 digest_0.6.27
[121] pracma_2.3.3 FNN_1.1.3
[123] shiny_1.6.0 lava_1.6.9
[125] qlcMatrix_0.9.7 scuttle_1.2.0
[127] later_1.2.0 RcppAnnoy_0.0.18
[129] httr_1.4.2 kernlab_0.9-29
[131] SoupX_1.5.2 colorspace_2.0-2
[133] fs_1.5.0 tensor_1.5
[135] reticulate_1.20 splines_4.1.1
[137] uwot_0.1.10 spatstat.utils_2.2-0
[139] scater_1.20.1 plotly_4.9.4.1
[141] xtable_1.8-4 jsonlite_1.7.2
[143] spatstat_2.2-0 timeDate_3043.102
[145] ipred_0.9-11 R6_2.5.0
[147] pillar_1.6.1 htmltools_0.5.1.1
[149] mime_0.11 glue_1.4.2
[151] fastmap_1.1.0 BiocNeighbors_1.10.0
[153] class_7.3-19 codetools_0.2-18
[155] mvtnorm_1.1-2 utf8_1.2.1
[157] lattice_0.20-45 bslib_0.2.5.1
[159] spatstat.sparse_2.0-0 tibble_3.1.2
[161] ggbeeswarm_0.6.0 leiden_0.3.8
[163] survival_3.2-13 limma_3.48.1
[165] rmarkdown_2.9 docopt_0.7.1
[167] fastICA_1.2-2 munsell_0.5.0
[169] e1071_1.7-7 GenomeInfoDbData_1.2.6
[171] iterators_1.0.13 reshape2_1.4.4
[173] gtable_0.3.0 spatstat.core_2.3-0