Last updated: 2021-12-27
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Knit directory: cacoaAnalysis/
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| File | Version | Author | Date | Message |
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| Rmd | eafb34e | viktor_petukhov | 2021-12-27 | Updated cluster-free expression figures |
| Rmd | 48929dd | viktor_petukhov | 2021-12-22 | Cluster-free expression figure |
library(tidyverse)
library(magrittr)
library(conos)
library(cowplot)
library(cacoa)
library(sccore)
library(dataorganizer)
library(ggrastr)
options(ggrastr.default.dpi=100)
rast <- function(...) rasterise(..., dev="ragg_png")
devtools::load_all()
theme_set(theme_bw())
N_CORES <- 50
score_palette <- brewerPalette("YlOrRd", rev=FALSE)Cluster-free shifts, PF
cao_pf <- read_rds(DataPath("PF/cao.rds")) %>% Cacoa$new()cao_endo <- readOrCreate(CachePath("cao_pf_endo.rds"), function() {
cms_endo <- lapply(cao_pf$data.object$samples, function(p2) {
p2$misc$rawCounts %>% .[cao_pf$cell.groups[rownames(.)] == "Endothelial Cells",] %>% t()
}) %>% .[sapply(., ncol) > 80]
p2s_endo <- plapply(cms_endo, createPagoda, n.pcs=50, n.cores=N_CORES, progress=TRUE,
mc.preschedule=TRUE)
if ("value" %in% names(p2s_endo)) p2s_endo <- p2s_endo$value
con_endo <- conos::Conos$new(p2s_endo, n.cores=N_CORES)
con_endo$buildGraph(k=30, k.self.weight=0.5)
con_endo$embedGraph(min.prob.lower=1e-4, method="UMAP", verbose=FALSE)
con_endo$findCommunities(method=leiden.community, resolution=1, n.iterations=10, name='leiden')
endo_cluster_ann <- c(
"general capillary", # (AFF3, BTNL9)
"arterial", # (DKK2)
"systemic-venous", # (COL15A1)
"pulmonary-venous", # (CPE)
"aerocyte" # (EDNRB)
)
clusts <- con_endo$clusters$leiden$groups
ann_per_clust <- con_endo$samples[[1]]$counts[,c("AFF3", "DKK2", "COL15A1", "CPE", "EDNRB")] %>%
collapseCellsByType(clusts) %>%
{t(.) / colSums(.)} %>% t() %>%
apply(1, which.max) %>% endo_cluster_ann[.]
ann_endo <- setNames(ann_per_clust[clusts], names(clusts))
cao_endo <- Cacoa$new(
con_endo, cell.groups=ann_endo,
sample.groups=cao_pf$sample.groups[names(con_endo$samples)],
ref.level=cao_pf$ref.level, target.level=cao_pf$target.level, n.cores=N_CORES
)
cao_endo$plot.theme %<>% `+`(theme(legend.background=element_blank()))
cao_endo$estimateDEPerCellType(independent.filtering=TRUE, test="DESeq2.Wald")
return(cao_endo)
})
cao_endo$plotEmbedding(color.by='cell.groups')
leg_theme <- theme(legend.key.width=unit(12, "pt"), legend.key.height=unit(14, "pt"))
ggs_pf <- cao_pf$plotClusterFreeExpressionShifts(
legend.position=c(0, 1), size=0.1, alpha=0.1, cell.groups=NULL, build.panel=FALSE,
adj.list=leg_theme
)
p_lims <- getCellTypeEmbeddingLimits(
cao_pf$embedding, cell.groups=cao_pf$cell.groups, groups.to.plot="Endothelial Cells", quant=0.005
)
gg_endo <- cao_pf$plotClusterFreeExpressionShifts(
legend.position=c(1, 1), size=0.3, alpha=1, cell.groups=NULL, build.panel=FALSE,
)[[1]] + lims(x=p_lims$x, y=p_lims$y) + leg_theme
# ggs_pf[[1]] %<>% transferLabelLayer(cao_pf$plotEmbedding(color.by='cell.groups'), font.size=2)
gg_endo %<>% cacoa:::transferLabelLayer(cao_pf$plotEmbedding(groups=cao_endo$cell.groups), font.size=3)
plot_grid(plotlist=c(ggs_pf, list(gg_endo)), ncol=3)
end_mt_genes <- c("FN1", "S100A4") # endothelial-to-mesenchymal transition
t_sub_cm <- cao_endo$cache$joint.count.matrix[,end_mt_genes]
expr_df <- cao_endo$sample.per.cell %>% {split(names(.), .)} %>%
lapply(function(samp.cbs) {
lapply(split(samp.cbs, cao_endo$cell.groups[samp.cbs]), function(cg.cbs) {
tibble(expr=colMeans(t_sub_cm[cg.cbs,,drop=FALSE]), gene=colnames(t_sub_cm))
}) %>% bind_rows(.id="Type")
}) %>% bind_rows(.id="Sample") %>%
mutate(Condition=cao_endo$sample.groups[Sample])
p_df <- lapply(cao_endo$test.results$de, function(de) {
de$res[colnames(t_sub_cm),] %>%
select(Gene, pvalue) %>% as_tibble()
}) %>%
bind_rows(.id='Type') %>%
mutate(padj=p.adjust(pvalue, method='BH'), Type=stringr::str_wrap(Type, width=6))
expr_df$Type %<>% stringr::str_wrap(width=6) %>% as.factor()
ggs_box_endo <- split(expr_df, expr_df$gene) %>% lapply(function(df) {
pvals <- filter(p_df, Gene == df$gene[1]) %$% setNames(padj, Type)
pval.df <- cacoa:::pvalueToCode(pvals, ns.symbol="") %>%
tibble(Type=factor(names(.), levels=levels(df$Type)), pvalue=.) %>% na.omit()
pvalue.y <- 1.01 * max(df$expr, na.rm=TRUE)
ggplot(df) +
geom_boxplot(aes(x=Type, y=expr, fill=Condition), outlier.alpha=0) +
ggbeeswarm::geom_quasirandom(aes(x=Type, y=expr, group=Condition), width=0.1, dodge.width=0.75, size=0.5) +
geom_text(data=pval.df, mapping=aes(x=Type, label=pvalue), y=pvalue.y, color="black") +
scale_y_continuous(limits=c(0, 1.1 * pvalue.y), expand=c(0, 0), name=paste(df$gene[1], "expression")) +
scale_fill_manual(values=cao_endo$sample.groups.palette) +
cao_endo$plot.theme + theme_legend_position(c(1, 0.9)) +
theme(
legend.background=element_blank(),
axis.title.x=element_blank(), axis.line.y.left=element_line(size=0.25), axis.line.x.bottom=element_line(size=0.25),
panel.border=element_blank(), panel.grid.major.x=element_blank(), panel.grid.minor.y=element_blank()
)
})
ggs_box_endo[[1]] %<>% {. + theme(legend.position="none")}
plot_grid(plotlist=ggs_box_endo, ncol=2)
leg_theme <- theme(legend.key.width=unit(10, "pt"), legend.key.height=unit(12, "pt"), legend.text=element_text(size=8))
p_embs <- c(ggs_pf, list(gg_endo)) %>% lapply(`+`, leg_theme) %>% rast()
scale <- 0.97
plot_grid(
plot_grid(plotlist=p_embs, ncol=3, scale=scale),
plot_grid(plotlist=ggs_box_endo, ncol=2, scale=scale),
ncol=1, rel_heights=c(1, 0.6), scale=scale
)
Cluster-free DE, Epilepsy
cao_ep <- read_rds(DataPath("EP/cao.rds")) %>% Cacoa$new()
cao_ep$plot.params <- list(size=0.1, alpha=0.1, font.size=c(2, 3), legend.position=c(0, 1))
org.db <- org.Hs.eg.db::org.Hs.eg.db
go.environment <- cao_ep$getGOEnvironment(org.db, verbose=TRUE, ignore.cache=FALSE)
cao_ep$plotEmbedding(color.by='cell.groups')
Global programs
cao_ep$estimateGenePrograms(method="leiden", z.adj=TRUE, smooth=TRUE, n.top.genes=1000,
resolution=1, abs.scores=TRUE, min.z=0.5)cao_ep$plotGeneProgramScores(plot.na=FALSE)
Gene program genes:
prog_id <- 2
# cao_ep$plotGeneProgramGenes(prog_id, ordering="similarity", max.genes=9, plot.na=FALSE) %>%
# plot_grid(plotlist=., ncol=3)
cao_ep$test.results$gene.programs$sim.scores[[prog_id]] %>% {tibble(Gene=names(.), Score=.)}# A tibble: 177 x 2
Gene Score
<chr> <dbl>
1 NEGR1 0.951
2 MAGI2 0.949
3 EFNA5 0.948
4 FBXL17 0.946
5 SHISA9 0.944
6 HIVEP2 0.944
7 GLS 0.940
8 CADPS2 0.940
9 CELF5 0.939
10 AKT3 0.937
# ... with 167 more rowsggs_shisa9 <- cao_ep$plotGeneExpressionComparison(
genes="SHISA9", gene.palette=brewerPalette('YlOrRd', rev=FALSE), plot.na=FALSE,
build.panel=FALSE, plots=c("expression", "z", "z.adj")
) %>% .[c(3, 4, 1, 2)]
plot_grid(plotlist=ggs_shisa9, nrow=1)
ex_cells <- cao_ep$cell.groups %>% .[grep("^L.*", .)] %>% names()
gene_universe_global <- which(colMeans(cao_ep$cache$joint.count.matrix[ex_cells,] > 0) > 0.1) %>%
names() %>% cacoa:::mapGeneIds(org.db)
length(gene_universe_global)[1] 6477go_global <- cao_ep$test.results$gene.programs$sim.scores[[prog_id]] %>%
head(100) %>% names() %>% cacoa:::mapGeneIds(org.db) %>% list() %>%
cacoa:::estimateEnrichedGO(org.db=org.db, go.environment=go.environment,
universe=gene_universe_global, readable=TRUE) %>%
lapply(`[[`, 1)
gg_godot_global <- clusteredOntologyDotplot(go_global$BP, min.genes=5) +
scale_size_continuous(range=c(1, 5), limits=c(5, 25), breaks=c(5, 10, 20, 25)) +
xlab("Gene ratio") +
theme_legend_position(c(1, 0)) +
theme(legend.background=element_blank(), legend.box="horizontal", axis.text.y=element_text(size=11),
legend.key.height=unit(8, "pt"), legend.key.width=unit(8, "pt"),
plot.title=element_blank())
gg_godot_global
Local programs
p_type <- "L2_Cux2_Lamp5"
cell_subset <- cao_ep$cell.groups %>% .[. == p_type] %>% names()
genes <- cao_ep$getMostChangedGenes(200, method="z.adj", cell.subset=cell_subset)
cao_ep$estimateGenePrograms(
name="gp.local", genes=names(genes), method="pam", z.adj=TRUE, smooth=FALSE,
cell.subset=cell_subset, n.programs=4, abs.scores=TRUE, min.z=1
)local_prog_genes <- c('CADM1', 'CAMK2D', 'CADPS2', 'NTRK2')
sapply(cao_ep$test.results$gp.local$sim.scores, function(sc)
setNames(local_prog_genes %in% names(sc), local_prog_genes)) [,1] [,2] [,3] [,4]
CADM1 FALSE TRUE FALSE FALSE
CAMK2D FALSE TRUE FALSE FALSE
CADPS2 TRUE FALSE FALSE FALSE
NTRK2 TRUE FALSE FALSE FALSEcont <- cao_ep$.__enclos_env__$private$getDensityContours(p_type, color="black", conf="10%")[[1]]
loc_lims <- getCellTypeEmbeddingLimits(cao_ep$embedding, cao_ep$cell.groups, groups.to.plot=p_type,
quant=0.01)
adj_list <- list(cont, lims(x=loc_lims$x, y=loc_lims$y))
cao_ep$plotGeneProgramScores(
name="gp.local", plot.na=FALSE, adj.list=adj_list, build.panel=FALSE,
palette=score_palette, alpha=0.25
) %>% plot_grid(plotlist=., nrow=1)
# cao_ep$test.results$gp.local$sim.scores[[1]] %>% head(16) %>%
# cao_ep$plotGeneExpressionComparison(scores=., plots="z.adj", plot.na=FALSE, alpha=0.5) %>%
# lapply(`+`, adj_list) %>%
# plot_grid(plotlist=., ncol=4)
cao_ep$test.results$gp.local$sim.scores[[1]] %>% {tibble(Gene=names(.), Score=.)}# A tibble: 28 x 2
Gene Score
<chr> <dbl>
1 PLCB1 0.914
2 GRIA2 0.910
3 PPP3CA 0.909
4 KCND2 0.906
5 ATP2B1 0.906
6 ERC2 0.888
7 ITPR1 0.869
8 RP11-701H24.9 0.862
9 NTRK2 0.859
10 EFNA5 0.838
# ... with 18 more rowscao_ep$test.results$gp.local$sim.scores[[2]] %>% {tibble(Gene=names(.), Score=.)}# A tibble: 54 x 2
Gene Score
<chr> <dbl>
1 CALN1 0.847
2 LINC00486 0.845
3 MAPT 0.844
4 FAM153B 0.834
5 PDE4DIP 0.832
6 SSH2 0.830
7 LARGE 0.820
8 ADGRL2 0.818
9 CELF4 0.817
10 CADM1 0.817
# ... with 44 more rowsgene_universe_local <- colMeans(cao_ep$cache$joint.count.matrix[cell_subset,] > 0) %>%
{which(. > 0.1)} %>% names() %>% cacoa:::mapGeneIds(org.db)
length(gene_universe_local)[1] 6159go_local <- cao_ep$test.results$gp.local$sim.scores %>%
lapply(function(x) head(names(x)[x > 0.5], 20)) %>%
lapply(cacoa:::mapGeneIds, org.db) %>%
cacoa:::estimateEnrichedGO(org.db=org.db, go.environment=go.environment, readable=TRUE,
universe=gene_universe_local)
sapply(go_local$BP, function(r) sum(r@result$p.adjust < 0.05)) %>% setNames(1:length(.)) 1 2 3 4
69 0 128 0 gg_godot_local <- clusteredOntologyDotplot(go_local$BP[[1]], min.genes=4) +
scale_size_continuous(range=c(1, 5), limits=c(4, 7), breaks=c(4, 6, 7)) +
xlab("Gene ratio") +
guides(color=guide_none()) +
theme_legend_position(c(1, 0)) +
theme(legend.background=element_blank(), legend.box="horizontal", axis.text.y=element_text(size=11),
legend.key.height=unit(8, "pt"), legend.key.width=unit(8, "pt"), plot.title=element_blank())
gg_godot_local
# cao_ep$plotGeneExpressionComparison(
# genes=c('CADM1', 'CAMK2D', 'CADPS2', 'NTRK2'),
# plot.na=FALSE, gene.palette=score_palette,
# size=0.2, alpha=0.2#, adj.list=lims(x=c(7, 27), y=c(15, 37))
# )Figure
cont <- cao_ep$.__enclos_env__$private$getDensityContours("L2_Cux2_Lamp5", color="black", conf="10%")[[1]]Global
# cao_ep$plotEmbedding(color.by='cell.groups', show.ticks=TRUE) +
# scale_x_continuous(limits=c(-12, 70), expand=c(0, 0))prog_id <- 2
prog_genes <- cao_ep$test.results$gene.programs$loading.scores[[prog_id]]
prog_cells <- cao_ep$test.results$gene.programs$program.scores[prog_id,] %>% {names(.)[. > 0.1]}
prog_genes <- cao_ep$test.results$cluster.free.de$z.adj[prog_cells, names(prog_genes)] %>%
colMeans(na.rm=TRUE) %>% sign() %>% {split(prog_genes, .)} %>% setNames(c("neg", "pos"))
# p_genes <- c(prog_genes$pos[1:2], prog_genes$neg[1:2]) %>% names()
glob_genes <- names(prog_genes$pos[3]) %>% c('NCAM2', 'GABRB2', 'EFNA5')
ggs <- cao_ep$plotGeneExpressionComparison(genes=glob_genes, plots="z.adj",legend.position="none", plot.na=FALSE)
en_x_lims <- c(-12, 70)
ggs[[1]] <- ggs[[1]] + theme_legend_position(c(0, 1)) +
theme(legend.key.height=unit(12, "pt"), legend.key.width=unit(12, "pt"))
theme_gene <- theme(plot.title=element_blank(), panel.grid.minor.x=element_blank())
# gg_global_genes <- ggs %>% lapply(`+`, list(xlim(en_x_lims), theme_gene)) %>%
# plot_grid(plotlist=., ncol=4, labels=glob_genes, label_fontface="plain",
# hjust=0, label_x=0.04, label_size=12, label_y=0.1)
ggs_global_genes <- ggs %>% lapply(`+`, list(xlim(en_x_lims), theme_gene))
gg_prog_score <- cao_ep$plotGeneProgramScores(prog.ids=prog_id, legend.position=c(0, 1),
plot.na=FALSE, palette=score_palette) +
cont + theme(plot.title=element_blank(), legend.key.height=unit(12, "pt"), legend.key.width=unit(12, "pt"))
plot_grid(plotlist=c(list(gg_prog_score), ggs_global_genes), nrow=1, rel_widths=c(2, 1, 1, 1, 1))
Local
adj_list <- list(
scale_x_continuous(limits=loc_lims$x, expand=c(0, 0)),
scale_y_continuous(limits=loc_lims$y, expand=c(0, 0)),
theme(plot.title=element_blank(), panel.grid.minor.x=element_blank()),
cont
)
ggs_ls <- cao_ep$plotGeneProgramScores(
prog.ids=c(1, 2), name="gp.local", plot.na=FALSE, legend.position="none",
color.range=c(0, 2), build.panel=FALSE, adj.list=adj_list, palette=score_palette
)
ggs_local_genes <- cao_ep$plotGeneExpressionComparison(
genes=local_prog_genes, plots="z.adj",legend.position="none", plot.na=FALSE, adj.list=adj_list,
alpha=0.25
) %>% setNames(local_prog_genes)plot_grid(
plotlist=c(ggs_ls, ggs_local_genes),
nrow=1,
labels=c("", "", names(ggs_local_genes)),
label_fontface="plain", hjust=0, label_x=0.04, label_size=12, label_y=0.2
)
Compile main figure
leg_theme <- theme(
plot.title=element_blank(), legend.key.width=unit(6, "pt"), legend.key.height=unit(8, "pt"),
legend.text=element_text(size=8)
) + theme_legend_position(c(0, 0))
p_embs <- c(ggs_pf, list(gg_endo)) %>%
lapply(`+`, leg_theme + theme_legend_position(c(0, 1)))
scale <- 0.95
sg_ggs <- lapply(ggs_shisa9, `+`, list(xlim(en_x_lims), leg_theme))
sg_ggs[[2]] %<>% {. + theme(legend.position="none")}
ggs_global_genes[[1]] %<>% {. + leg_theme}
plot_grid(
plot_grid(
plot_grid(plotlist=rast(p_embs), ncol=3, scale=scale),
plot_grid(plotlist=ggs_box_endo, ncol=2, scale=scale),
ncol=1, rel_heights=c(1, 0.6), scale=scale
),
plot_grid(plotlist=rast(c(
list(cao_ep$plotEmbedding(color.by='cell.groups', font.size=2)), sg_ggs,
list(gg_prog_score + leg_theme), ggs_global_genes,
list(plot_grid(plotlist=rast(ggs_ls), ncol=2)), ggs_local_genes
)),
labels=c(
"", "SHISA9, Control", "SHISA9, Epilepsy", "", "",
"", glob_genes,
"", names(ggs_local_genes)),
label_fontface="italic", hjust=0, label_x=0.07, label_y=0.98, label_size=11,
nrow=3, ncol=5, rel_widths=c(2, 1, 1, 1, 1), rel_heights=c(2, 2, 1), scale=scale * 0.98
),
ncol=1, rel_heights=c(4.2, 11 - 4.2)
)
ggsave(figurePath("6_de_cf.pdf"))Compile supp. figure
scale <- 0.95
leg_theme <- theme(legend.key.width=unit(12, "pt"), legend.key.height=unit(14, "pt"))
theme_go <- theme(
axis.text.y=element_text(size=10), plot.title=element_blank(), plot.margin=margin()
)
score_theme <- theme(
plot.title=element_blank(),
legend.key.width=unit(10, "pt"), legend.key.height=unit(10, "pt")
)
ggs_glob_scores <- cao_ep$plotGeneProgramScores(
plot.na=FALSE, prog.ids=2:9, adj.list=score_theme,
build.panel=FALSE
)
plot_grid(
plot_grid(
plotlist=rast(ggs_glob_scores), ncol=4, scale=0.98
),
plot_grid(
plot_grid(
gg_godot_global + theme_go + theme(plot.margin=margin(t=-2)),
gg_godot_local + theme_go,
ncol=1, rel_heights=c(2.5, 1), align="v", scale=scale
),
plot_grid(
rast(cao_ep$plotDiffCellDensity(name='cell.density.graph', type='subtract', color.range=c("1%", "99%"))) +
leg_theme,
rast(cao_ep$plotDiffCellDensity(name='cell.density.graph', type='wilcox')) + leg_theme,
ncol=1, scale=scale
),
nrow=1, rel_widths=c(2, 1)
),
ncol=1, rel_heights=c(4.4, 5.6), scale=0.97
)
ggsave(figurePath("6s_de_cf.pdf"))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.3/lib/R/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.3/lib/R/lib/libRlapack.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cacoaAnalysis_0.1.0 ggrastr_1.0.1 dataorganizer_0.1.0
[4] sccore_1.0.1 cacoa_0.2.0 cowplot_1.1.1
[7] conos_1.4.4 igraph_1.2.6 Matrix_1.2-18
[10] magrittr_2.0.1 forcats_0.5.1 stringr_1.4.0
[13] dplyr_1.0.7 purrr_0.3.4 readr_1.4.0
[16] tidyr_1.1.4 tibble_3.1.5 ggplot2_3.3.5
[19] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 ks_1.13.1 reticulate_1.22
[4] R.utils_2.10.1 tidyselect_1.1.1 RSQLite_2.2.8
[7] AnnotationDbi_1.52.0 BiocParallel_1.24.1 grid_4.0.3
[10] Rtsne_0.15 scatterpie_0.1.5 devtools_2.3.2
[13] munsell_0.5.0 codetools_0.2-16 ragg_0.4.1
[16] withr_2.4.2 GOSemSim_2.16.1 colorspace_2.0-2
[19] Biobase_2.50.0 highr_0.9 knitr_1.36
[22] rstudioapi_0.13 stats4_4.0.3 ggsignif_0.6.1
[25] DOSE_3.16.0 labeling_0.4.2 git2r_0.27.1
[28] urltools_1.7.3 polyclip_1.10-0 bit64_4.0.5
[31] farver_2.1.0 downloader_0.4 rprojroot_2.0.2
[34] Matrix.utils_0.9.8 vctrs_0.3.8 generics_0.1.0
[37] xfun_0.26 R6_2.5.1 doParallel_1.0.16
[40] graphlayouts_0.7.1 ggbeeswarm_0.6.0 clue_0.3-59
[43] fgsea_1.16.0 cachem_1.0.6 assertthat_0.2.1
[46] promises_1.1.1 scales_1.1.1 ggraph_2.0.4
[49] enrichplot_1.10.1 beeswarm_0.4.0 gtable_0.3.0
[52] processx_3.4.5 tidygraph_1.2.0 drat_0.1.8
[55] rlang_0.4.11 systemfonts_1.0.0 GlobalOptions_0.1.2
[58] splines_4.0.3 rstatix_0.7.0 broom_0.7.9
[61] brew_1.0-6 BiocManager_1.30.10 yaml_2.2.1
[64] reshape2_1.4.4 abind_1.4-5 modelr_0.1.8
[67] backports_1.2.1 httpuv_1.5.4 qvalue_2.22.0
[70] clusterProfiler_3.18.0 tools_4.0.3 usethis_1.6.3
[73] ellipsis_0.3.2 jquerylib_0.1.4 RColorBrewer_1.1-2
[76] BiocGenerics_0.36.1 sessioninfo_1.1.1 Rcpp_1.0.7
[79] plyr_1.8.6 ps_1.4.0 prettyunits_1.1.1
[82] ggpubr_0.4.0 dendsort_0.3.3 viridis_0.6.1
[85] GetoptLong_1.0.5 S4Vectors_0.28.1 grr_0.9.5
[88] haven_2.4.1 ggrepel_0.9.1 cluster_2.1.0
[91] fs_1.5.0 data.table_1.14.2 DO.db_2.9
[94] openxlsx_4.2.3 circlize_0.4.13 triebeard_0.3.0
[97] reprex_0.3.0 mvtnorm_1.1-2 whisker_0.4
[100] matrixStats_0.61.0 pkgload_1.2.1 hms_1.1.1
[103] evaluate_0.14 rio_0.5.26 mclust_5.4.7
[106] RMTstat_0.3 readxl_1.3.1 N2R_0.1.1
[109] IRanges_2.24.1 gridExtra_2.3 shape_1.4.6
[112] testthat_3.0.0 compiler_4.0.3 KernSmooth_2.23-17
[115] shadowtext_0.0.7 crayon_1.4.1 R.oo_1.24.0
[118] htmltools_0.5.2 mgcv_1.8-33 later_1.1.0.1
[121] lubridate_1.7.9.2 DBI_1.1.1 tweenr_1.0.1
[124] dbplyr_2.0.0 pagoda2_1.0.7 ComplexHeatmap_2.9.4
[127] MASS_7.3-53 car_3.0-10 cli_3.0.1
[130] R.methodsS3_1.8.1 parallel_4.0.3 pkgconfig_2.0.3
[133] rvcheck_0.1.8 foreign_0.8-80 xml2_1.3.2
[136] foreach_1.5.1 vipor_0.4.5 leidenAlg_0.1.0
[139] rvest_0.3.6 callr_3.5.1 digest_0.6.28
[142] pracma_2.3.3 fastmatch_1.1-0 rmarkdown_2.11
[145] cellranger_1.1.0 Rook_1.1-1 curl_4.3.2
[148] rjson_0.2.20 lifecycle_1.0.1 nlme_3.1-149
[151] jsonlite_1.7.2 carData_3.0-4 viridisLite_0.4.0
[154] desc_1.3.0 fansi_0.5.0 pillar_1.6.3
[157] lattice_0.20-41 GO.db_3.12.1 fastmap_1.1.0
[160] httr_1.4.2 pkgbuild_1.1.0 glue_1.4.2
[163] remotes_2.2.0 zip_2.2.0 png_0.1-7
[166] iterators_1.0.13 bit_4.0.4 ggforce_0.3.2
[169] stringi_1.7.5 blob_1.2.2 textshaping_0.2.1
[172] org.Hs.eg.db_3.12.0 memoise_2.0.0 irlba_2.3.3
[175] ape_5.5