Figure 5
Read data, functions and packages
Overview plot
Inset
ori <- c(-8.25,-8.5)
l <- 3
off <- 1
colors_umap_cl_mod <- colors_umap_cl
for(i in names(colors_umap_cl)){
if(!i %in% c("3", "5", "12", "16")){
colors_umap_cl_mod[i] <- "grey"
}
}
p1_inset <-
df_comb %>% sample_frac(0.075) %>%
mutate(Code=ifelse(IdentI %in% c(12,3,16,5), T, F)) %>%
filter(!(IdentI %in% c(12,3,16,5) & wnnUMAP_2>1.25)) %>%
ggplot(aes(x=wnnUMAP_1, y=wnnUMAP_2, fill=as.factor(IdentI), color=as.factor(IdentI), alpha=Code))+
#geom_point(size=0.25, stroke=0, shape=21)+
ggrastr::geom_point_rast(size=0.35, stroke=0, shape=21, raster.dpi = 300)+
scale_color_manual(values = colors_umap_cl_mod, limits=factor(cluster_order),
labels=unlist(labels_cl))+
scale_fill_manual(values = colors_umap_cl_mod, limits=factor(cluster_order),
labels=unlist(labels_cl))+
scale_alpha_manual(values = c(0.1,0.75))+
scale_x_continuous(limits = c(ori[1],10), expand = c(0,0))+
scale_y_continuous(limits = c(ori[2],10), expand = c(0,0))+
mytheme_1+
coord_fixed(clip = "off")+
theme(axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_rect(fill = NA),
axis.title = element_blank(),
axis.text = element_blank())Pseudotime
df_pseudotime <- FetchData(ttox, vars = c("wnnUMAP_1", "wnnUMAP_2", "Pseudotime", "IdentI", "PatientID")) %>%
add_entity() %>%
mutate(CellType="Cytotoxic T cells") %>%
mutate(IdentI=as.character(IdentI)) %>%
drop_na() %>%
filter(wnnUMAP_2<(0.7*wnnUMAP_1-1)) %>%
mutate(name="Trajectory analysis / Pseudotime")
p1 <- ggplot()+
ggrastr::geom_point_rast(data=df_pseudotime %>% filter(IdentI=="16"), aes(x=wnnUMAP_1, y=wnnUMAP_2), fill="grey75",
size=0.35, shape=21, stroke=0, raster.dpi = 600)+
ggrastr::geom_point_rast(data=df_pseudotime %>% filter(IdentI!="16"), aes(x=wnnUMAP_1, y=wnnUMAP_2, fill=Pseudotime),
size=0.35, shape=21, stroke=0, raster.dpi = 600)+
scale_fill_viridis(option = "viridis", direction = -1, breaks=c(0,15,30), name="Ps.time")+
facet_wrap(~name)+
scale_x_continuous(name="umapWNN-1", limits=c(-6, 11), expand = c(0,0))+
scale_y_continuous(name="umapWNN-2", limits=c(-8, 3), expand = c(0,0))+
mytheme_1+
coord_cartesian(clip = "off")+
theme(legend.box.margin = unit(c(0,-0.25,-0.35,-0.25), units = "cm"),
panel.border = element_rect(size=0.25, color="black"),
legend.title = element_text(size=7),
legend.text = element_text(size=7),
legend.position = c(0.88,0.25),
panel.background = element_rect(fill=NA),
strip.background = element_rect(color=NA),
legend.key.height = unit(units="cm", 0.15),
legend.key.width = unit(units="cm", 0.15),
legend.box.background = element_rect(fill=NA, color=NA),
legend.background = element_rect(fill=NA, color=NA)
)+
labs(tag="A")
p1 <- p1+inset_element(p1_inset, left = -0.05, right = 0.4, bottom = 0.5, top = 1.05, on_top = F)Differentially expressed features
df_tmp516 <-
rbind(FindMarkers(ttox, ident.1 = 5, ident.2 = 16, test.use = "roc", assay = "integratedADT") %>%
rownames_to_column("Feature") %>% mutate(Assay="Protein"),
FindMarkers(ttox, ident.1 = 5, ident.2 = 16, test.use = "roc", assay = "integratedRNA") %>%
rownames_to_column("Feature") %>% mutate(Assay="Gene"))
labels <- c("CCL4", "NKG7", "CST7", "TCF7", ".CD279", ".CD69", ".CD366")
df_tmp516 <- df_tmp516 %>%
mutate(Label=ifelse(Feature %in% labels, Feature, NA)) %>%
mutate(Label=gsub(Label, pattern = ".", fixed = T, replacement = "")) %>%
mutate(Label=gsub(Label, pattern = "CD279", fixed = T, replacement = "PD1")) %>%
mutate(Label=gsub(Label, pattern = "CD366", fixed = T, replacement = "TIM3"))
height.label <- 1.02
position.label_left <- 1.6
position.label_right <- 2.25
Idents(ttox) <- "IdentI"
df_tmp1 <- df_tmp516 %>% filter(avg_log2FC<0)
df_tmp2 <- df_tmp516 %>% filter(avg_log2FC>0)
p3 <- ggplot()+
geom_point(data=df_tmp1, aes(x=avg_log2FC, y=power, color=Assay), alpha=ifelse(!is.na(df_tmp1$Label), 1, 0.25), stroke=0, size=1)+
geom_point(data=df_tmp2, aes(x=avg_log2FC, y=power, color=Assay), alpha=ifelse(!is.na(df_tmp2$Label), 1, 0.25), stroke=0, size=1)+
ggrepel::geom_text_repel(data=df_tmp1, aes(x=avg_log2FC, y=power, color=Assay, label=Label), show.legend = F, size=2.4, segment.size=0.25, xlim = c(-1.9, -2.5), ylim = c(0.75,0.85), seed = 1)+
ggrepel::geom_text_repel(data=df_tmp2 %>% filter(Feature==".CD366"), aes(x=avg_log2FC, y=power, color=Assay, label=Label), show.legend = F, size=2.4, segment.size=0.25, seed = 2, xlim = c(0.5,0.6),)+
ggrepel::geom_text_repel(data=df_tmp2 %>% filter(Feature!=".CD366"), aes(x=avg_log2FC, y=power, color=Assay, label=Label), show.legend = F, size=2.4, segment.size=0.25, ylim = c(0.62, 0.9), seed = 1)+
geom_vline(xintercept = 0, linetype="dashed", size=0.25)+
scale_color_manual(name=NULL, values = c("black", "#f1a340"))+
scale_x_continuous(limits = c(-3.2, 3.2), expand = c(0,0), name=expression('log'[2]~'fold change'))+
scale_y_continuous(limits = c(0, 0.9), name="2 x abs(AUC-0.5)")+
annotation_custom(grob = textGrob(label = expression('EM'[2]~''), hjust = 0.5, gp = gpar(cex=0.6, fontface="bold", col=colors_umap_cl["5"])),
xmin = -position.label_left, xmax = -position.label_left,
ymin = height.label, ymax = height.label)+
annotation_custom(grob = textGrob(label = expression('EM'[3]~''), hjust = 0.5, gp = gpar(cex=0.6, fontface="bold", col=colors_umap_cl["16"])),
xmin = position.label_right, xmax = position.label_right,
ymin = height.label, ymax = height.label)+
mytheme_1+
coord_cartesian(clip = "off")+
theme(legend.position = c(0.85, 0.14),
legend.key.height = unit(units="cm", 0.3),
legend.box.spacing = unit(units="cm", 0.01),
panel.border = element_rect(size=0.25),
legend.background = element_rect(fill = NA),
legend.box.margin=margin(-20,-20,-20,-20),
legend.key.width = unit(units="cm", 0.1))+
labs(tag = "B")Exhaustion signature
Stimulatory markers
marker <- c("CD45RO", "CD69", "CD38", "CD278", "CD244")
marker_full <- paste0("integratedadt_.", marker)
p4 <- FetchData(ttox, slot = "data", vars = c(marker_full, "integratedadt_.CD4", "integratedadt_.CD8a",
colnames(ttox@meta.data))) %>%
filter(!IdentI %in% c(16)) %>%
mutate(Pseudotime_round=round(Pseudotime, 1)) %>%
pivot_longer(cols = all_of(marker_full)) %>%
select(-Entity) %>%
add_entity() %>%
group_by(Pseudotime_round, name) %>%
dplyr::summarise(Mean=mean(value), `.groups`="drop") %>%
group_by(name) %>%
mutate(name=gsub(name, pattern = "integratedadt_.", replacement = "")) %>%
mutate(name=factor(name, levels = marker, labels = c("CD45RO", "CD69", "CD38", "ICOS", "CD244"))) %>%
group_by(name) %>%
dplyr::mutate(Mean=(Mean-min(Mean))/(max(Mean)-min(Mean))) %>%
filter(Mean < 0.98) %>%
dplyr::mutate(Mean=(Mean-min(Mean))/(max(Mean)-min(Mean))) %>%
ggplot(aes(x=Pseudotime_round, y=Mean, color=Pseudotime_round, fill=Pseudotime_round))+
scale_fill_viridis(direction = -1, option = "viridis")+
scale_color_viridis(direction = -1, option = "viridis")+
geom_point(size=0.5, shape=21, stroke=0)+
facet_wrap(~name, strip.position="top", ncol = 1)+
scale_y_continuous(name="Protein / RNA / TF level", breaks = c(0,0.5,1.0))+
scale_x_continuous(name="Pseudotime", breaks = c(0, 10, 20, 30))+
mytheme_1+
theme(strip.text = element_text(angle = 0, size=7),
panel.border = element_rect(size=0.5),
plot.tag = element_text(margin = unit(c(0,0,-0.2,0), "cm")),
plot.margin = unit(c(-0.2,0,-0.25,0.1), "cm"))Inhibitory markers
marker <- c("CD279", "CD366", "CD223", "TIGIT", "CD39")
marker_full <- paste0("integratedadt_.", marker)
p5 <- FetchData(ttox, slot = "data", vars = c(marker_full, "integratedadt_.CD4", "integratedadt_.CD8a",
colnames(ttox@meta.data))) %>%
filter(!IdentI %in% c(16)) %>%
select(-integratedadt_.CD4, -integratedadt_.CD8a) %>%
mutate(Pseudotime_round=round(Pseudotime, 1)) %>%
pivot_longer(cols = all_of(marker_full)) %>%
group_by(Pseudotime_round, name) %>%
dplyr::summarise(Mean=mean(value), `.groups`="drop") %>%
group_by(name) %>%
mutate(name=gsub(name, pattern = "integratedadt_.", replacement = "")) %>%
mutate(name=factor(name, levels = marker, labels = c("PD1", "TIM3", "LAG3", "TIGIT", "CD39"))) %>%
group_by(name) %>%
dplyr::mutate(Mean=(Mean-min(Mean))/(max(Mean)-min(Mean))) %>%
filter(Mean < 0.98 & Mean>0.02) %>%
dplyr::mutate(Mean=(Mean-min(Mean))/(max(Mean)-min(Mean))) %>%
ggplot(aes(x=Pseudotime_round, y=Mean, color=Pseudotime_round, fill=Pseudotime_round))+
scale_fill_viridis(direction = -1, option = "viridis")+
scale_color_viridis(direction = -1, option = "viridis")+
geom_point(size=0.5, shape=21, stroke=0)+
facet_wrap(~name, strip.position="top", ncol = 1)+
scale_y_continuous(name="Protein / RNA expression level", breaks = c(0,0.5,1.0))+
scale_x_continuous(name="Pseudotime", breaks = c(0, 10, 20, 30))+
mytheme_1+
theme(strip.text = element_text(angle = 0, size=7),
axis.title.y = element_blank(),
panel.border = element_rect(size=0.5),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
plot.tag = element_text(margin = unit(c(0,0,-0.2,0), "cm")),
plot.margin = unit(c(-0.2,0,-0.25,0.1), "cm"))Effector molecules
marker <- c("GZMA", "GZMB", "GZMH", "GZMK", "PRF1")
marker_full <- paste0("integratedrna_", marker)
p6 <- FetchData(ttox, slot = "data", vars = c(marker_full,
colnames(ttox@meta.data))) %>%
filter(!IdentI %in% c(16)) %>%
mutate(Pseudotime_round=round(Pseudotime, 1)) %>%
pivot_longer(cols = all_of(marker_full)) %>%
group_by(Pseudotime_round, name) %>%
dplyr::summarise(Mean=mean(value), `.groups`="drop") %>%
group_by(name) %>%
mutate(name=gsub(name, pattern = "integratedrna_", replacement = "")) %>%
mutate(name=factor(name, levels = marker)) %>%
group_by(name) %>%
dplyr::mutate(Mean=(Mean-min(Mean))/(max(Mean)-min(Mean))) %>%
filter(Mean < 0.98) %>%
dplyr::mutate(Mean=(Mean-min(Mean))/(max(Mean)-min(Mean))) %>%
ggplot(aes(x=Pseudotime_round, y=Mean, color=Pseudotime_round, fill=Pseudotime_round))+
scale_fill_viridis(direction = -1, option = "viridis")+
scale_color_viridis(direction = -1, option = "viridis")+
geom_point(size=0.5, shape=21, stroke=0)+
geom_vline(xintercept=24, size=0.25, linetype="dashed")+
facet_wrap(~name, strip.position="top", ncol = 1)+
scale_y_continuous(name="RNA level", breaks = c(0,0.5,1.0))+
scale_x_continuous(name="Pseudotime", breaks = c(0, 10, 20, 20, 30))+
mytheme_1+
theme(strip.text = element_text(angle = 0, size=7, face="italic"),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(size=0.5),
plot.tag = element_text(margin = unit(c(0,0,-0.2,0), "cm")),
plot.margin = unit(c(-0.2,0,-0.25,0.1), "cm"))Transcription factors
marker <- c("PRDM1", "BATF", "IRF4", "EOMES", "TCF7")
marker_full <- paste0("tfactivity_", marker, "-E")
p7 <- FetchData(ttox, slot = "data", vars = c(marker_full, "integratedadt_.CD4", "integratedadt_.CD8a",
colnames(ttox@meta.data))) %>%
filter(!IdentI %in% c(16)) %>%
select(-integratedadt_.CD4, -integratedadt_.CD8a) %>%
pivot_longer(cols = all_of(marker_full)) %>%
mutate(value=ifelse(is.na(value), 0, value)) %>%
mutate(Pseudotime_round=round(Pseudotime, 1)) %>%
group_by(Pseudotime_round, name) %>%
dplyr::summarise(Mean=mean(value), `.groups`="drop") %>%
group_by(name) %>%
mutate(name=gsub(name, pattern = "tfactivity_|-E", replacement = "")) %>%
mutate(name=factor(name, levels = marker)) %>%
group_by(name) %>%
dplyr::mutate(Mean=(Mean-min(Mean))/(max(Mean)-min(Mean))) %>%
filter(Mean < 0.98 & Mean>0.02) %>%
dplyr::mutate(Mean=(Mean-min(Mean))/(max(Mean)-min(Mean))) %>%
ggplot(aes(x=Pseudotime_round, y=Mean, color=Pseudotime_round, fill=Pseudotime_round))+
geom_point(size=0.5, shape=21, stroke=0)+
scale_fill_viridis(direction = -1, option = "viridis")+
scale_color_viridis(direction = -1, option = "viridis")+
facet_wrap(~name, strip.position="top", ncol = 1)+
scale_x_continuous(name="Pseudotime", breaks = c(0, 10, 20, 20, 30))+
mytheme_1+
theme(strip.text = element_text(angle = 0, size=7),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
panel.border = element_rect(size=0.5),
axis.text.y = element_blank(),
plot.tag = element_text(margin = unit(c(0,0,-0.2,0), "cm")),
plot.margin = unit(c(-0.2,0.1,0,0.1), "cm"))Density per pseudotime
df <- FetchData(ttox, vars = c("Entity", "Pseudotime",
"PatientID", "IdentI")) %>%
left_join(., df_meta %>% select(PatientID, Entity) %>% distinct) %>%
filter(IdentI!="16") %>%
mutate(Entity=gsub(Entity, pattern = ", GCB|, non-GCB", replacement = "")) %>%
mutate(Entity=factor(Entity, levels = c("rLN", "DLBCL", "MCL", "FL", "MZL")))
term_exh <- df %>%
drop_na() %>%
mutate(Exhausted=Pseudotime>24) %>%
add_prop(vars = c("PatientID", "Entity", "Exhausted"), group.vars = 1) %>%
fill_zeros(names_from = "Exhausted", values_from = "Prop") %>%
filter(Exhausted==T) %>%
group_by(Entity) %>%
summarise(MeanProp=100*round(median(Prop), 2))
p8 <- ggplot()+
geom_vline(xintercept = 24, linetype="dashed", size=0.25)+
geom_density(data = df, aes(x=Pseudotime, group=PatientID, color=Entity), alpha=0.25, show.legend = F, adjust=1.6, size=0.25)+
scale_color_brewer(palette = "Paired", limits=c( "DLBCL", "MCL", "FL", "MZL", "rLN"))+
geom_text(data = data.frame(Entity=c("rLN", "DLBCL", "MCL", "FL", "MZL")), aes(label=Entity, x=0, y=0.2), size=2.5, hjust=0, check_overlap = T)+
geom_text(data=term_exh, aes(x=28.3, y=0.2, label=paste0(MeanProp, "%")), hjust=0.5, size=2.5)+
facet_wrap(~Entity, ncol = 1)+
scale_y_continuous(limits=c(-0.015,0.22), breaks = c(0, 0.1, 0.2), name="Density")+
annotation_custom(rasterGrob(t(viridis(n=100, direction = -1, option = "viridis")), width=unit(1,"npc"), height=unit(1,"npc")),
ymin = -0.05, ymax = -0.01, xmin = -2.5, xmax = 32.5)+
xlab("Pseudotime")+
mytheme_1+
theme(legend.position = "none",
legend.text = element_text(size=7),
strip.background = element_blank(),
strip.text = element_blank(),
legend.title = element_blank(),
axis.title.y = element_text(size = 7, vjust = 3),
plot.margin = unit(c(0,0.1,0,0.2), "cm"),
panel.border = element_rect(size=0.5),
plot.tag = element_text(margin = unit(c(0,-0.25,0,0), "cm")),
legend.box.spacing = unit(units = "cm", 0.01),
legend.key.height = unit(units = "cm", 0.15),
legend.key.width = unit(units = "cm", 0.25))+
labs(tag = "D")Survival analysis
PFS analysis (Chapuy)
df_surv <-
left_join(df_surv_chapuy, df_ttoxcompl_chapuy) %>%
mutate(Ratio=Exhausted/Absolute) %>%
select(PatientID, status_pfs, time_pfs, Ratio)
mxs.obj <- maxstat.test(Surv(time_pfs, status_pfs) ~ Ratio, data=df_surv,
smethod="LogRank", pmethod="exactGauss",
minprop = 0.27, maxprop=0.75, abseps=0.01)
df_surv <- mutate(df_surv, Factor=ifelse(Ratio > mxs.obj$estimate, "High", "Low"))
fit <- survfit(Surv(time_pfs, status_pfs) ~ Factor, data = df_surv)
p10 <- ggsurvplot(fit, data = df_surv,
pval = TRUE,
pval.size = 2.5,
censor.size=2,
size=0.25,
pval.coord = c(5.5, 0.95),
xlab = "Progression-free survival (Years)",
ylab = "Survival probability",
palette = c(colors_umap_cl[["18"]], colors_umap_cl[["12"]]),
legend.labs = c("Exhaustion High", "Exhaustion Low"),
legend.title = "",
legend = c(0.3,0.2),
fontsize = 2.5,
title="Chapuy et al. 2018 (DLBCL)",
ggtheme = mytheme_1+theme(plot.title = element_text(hjust = 0.5, size=7, face = "plain"),
legend.text = element_text(size=7),
panel.border = element_rect(size=0.5),
legend.key.height = unit(units = "cm", 0.3),
legend.margin = margin(c(0,0,0,0), unit = "cm"),
legend.spacing.x = unit(units = "cm", 0.2),
legend.title = element_blank()))PFS analysis (Schmitz)
df_surv <-
left_join(df_surv_schmitz, df_ttoxcompl_schmitz) %>%
mutate(Ratio=Exhausted/Absolute) %>%
select(PatientID, Subtype, Gender, Age, time_pfs, status_pfs, Ratio) %>%
filter(time_pfs!="NA") %>%
mutate(time_pfs=as.numeric(time_pfs))
mxs.obj <- maxstat.test(Surv(time_pfs, status_pfs) ~ Ratio, data=df_surv,
smethod="LogRank", pmethod="exactGauss",
minprop = 0.27, maxprop=0.75, abseps=0.01)
df_surv <- mutate(df_surv, Factor=ifelse(Ratio > mxs.obj$estimate, "High", "Low"))
fit <- survfit(Surv(time_pfs, status_pfs) ~ Factor, data = df_surv)
p11 <- ggsurvplot(fit, data = df_surv,
pval = TRUE,
pval.size = 2.5,
censor.size=2,
size=0.25,
pval.coord = c(8.5, 0.95),
xlab = "Progression-free survival (Years)",
ylab = "Survival probability",
palette = c(colors_umap_cl[["18"]], colors_umap_cl[["12"]]),
legend.labs = c("Exhaustion High", "Exhaustion Low"),
legend.title = "",
legend = c(0.3,0.2),
fontsize = 2.5,
title="Schmitz et al. 2018 (DLBCL)",
ggtheme = mytheme_1+theme(plot.title = element_text(hjust = 0.5, size=7, face = "plain"),
legend.text = element_text(size=7),
panel.border = element_rect(size=0.5),
legend.key.height = unit(units = "cm", 0.3),
legend.margin = margin(c(0,0,0,0), unit = "cm"),
legend.spacing.x = unit(units = "cm", 0.2),
legend.title = element_blank()))Assemble plot
p_full_p1 <-
wrap_plots(wrap_plots(p1/plot_spacer()/p3+plot_layout(heights = c(1,0.1,1)))+
wrap_plots(p4+labs(tag = "C")+
p5+
p6+
p7+
plot_layout(ncol = 4))+
p8+plot_layout(widths = c(1.15,2.1,0.6)))
p_full_p2 <- wrap_plots(p10$plot+labs(tag = "E")+p11$plot+labs(tag = "F")+p12$plot+labs(tag = "G"))
p_full <- p_full_p1/p_full_p2+plot_layout(heights = c(13.5,5))
p_full
Session info
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Rocky Linux 8.8 (Green Obsidian)
##
## Matrix products: default
## BLAS/LAPACK: /g/easybuild/x86_64/Rocky/8/haswell/software/FlexiBLAS/3.0.4-GCC-11.2.0/lib64/libflexiblas.so.3.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C 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 LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] pamr_1.56.1 cluster_2.1.2 glmnet_4.1-2 Matrix_1.5-1 immunarch_0.7.0 data.table_1.14.2
## [7] dtplyr_1.2.2 rmdformats_1.0.4 ggplotify_0.1.0 ggraph_2.0.6 igraph_1.3.5 ggrastr_1.0.1
## [13] ggtext_0.1.1 ggalluvial_0.12.3 maxstat_0.7-25 survival_3.2-13 survminer_0.4.9 ggridges_0.5.3
## [19] cowplot_1.1.1 R.utils_2.11.0 R.oo_1.24.0 R.methodsS3_1.8.1 readxl_1.4.1 caret_6.0-90
## [25] lattice_0.20-45 patchwork_1.1.2 rstatix_0.7.0 ggpubr_0.4.0 ggrepel_0.9.1 matrixStats_0.61.0
## [31] scales_1.2.1 RColorBrewer_1.1-3 viridis_0.6.2 viridisLite_0.4.1 forcats_0.5.1 stringr_1.4.1
## [37] dplyr_1.0.10 purrr_0.3.4 readr_2.1.2 tidyr_1.2.1 tibble_3.1.8 ggplot2_3.3.6
## [43] tidyverse_1.3.1 SeuratObject_4.0.4 Seurat_4.1.0 knitr_1.40
##
## loaded via a namespace (and not attached):
## [1] scattermore_0.8 prabclus_2.3-2 ModelMetrics_1.2.2.2 exactRankTests_0.8-34 ragg_1.2.1
## [6] bit64_4.0.5 irlba_2.3.5 rpart_4.1-15 doParallel_1.0.17 generics_0.1.3
## [11] RANN_2.6.1 future_1.23.0 bit_4.0.4 tzdb_0.3.0 rlist_0.4.6.2
## [16] spatstat.data_2.1-2 xml2_1.3.2 lubridate_1.8.0 httpuv_1.6.6 assertthat_0.2.1
## [21] gower_0.2.2 xfun_0.33 hms_1.1.2 jquerylib_0.1.4 evaluate_0.16
## [26] promises_1.2.0.1 DEoptimR_1.0-11 fansi_1.0.3 dbplyr_2.1.1 km.ci_0.5-2
## [31] DBI_1.1.2 htmlwidgets_1.5.4 spatstat.geom_2.3-2 stringdist_0.9.8 stats4_4.1.2
## [36] ellipsis_0.3.2 backports_1.4.1 bookdown_0.29 deldir_1.0-6 vctrs_0.4.2
## [41] Cairo_1.5-12.2 ROCR_1.0-11 abind_1.4-5 cachem_1.0.6 withr_2.5.0
## [46] ggforce_0.4.0 robustbase_0.95-0 vroom_1.5.7 sctransform_0.3.3 mclust_5.4.10
## [51] goftest_1.2-3 ape_5.6-2 lazyeval_0.2.2 crayon_1.5.2 labeling_0.4.2
## [56] recipes_0.1.17 pkgconfig_2.0.3 tweenr_2.0.2 nlme_3.1-153 vipor_0.4.5
## [61] nnet_7.3-16 rlang_1.0.6 globals_0.14.0 diptest_0.76-0 lifecycle_1.0.2
## [66] miniUI_0.1.1.1 modelr_0.1.8 cellranger_1.1.0 polyclip_1.10-0 lmtest_0.9-39
## [71] phangorn_2.10.0 ggseqlogo_0.1 KMsurv_0.1-5 carData_3.0-5 zoo_1.8-9
## [76] reprex_2.0.1 beeswarm_0.4.0 GlobalOptions_0.1.2 pheatmap_1.0.12 png_0.1-7
## [81] KernSmooth_2.23-20 pROC_1.18.0 shape_1.4.6 parallelly_1.30.0 spatstat.random_2.1-0
## [86] gridGraphics_0.5-1 ggsignif_0.6.3 magrittr_2.0.3 plyr_1.8.7 ica_1.0-2
## [91] compiler_4.1.2 factoextra_1.0.7 fitdistrplus_1.1-6 cli_3.4.1 listenv_0.8.0
## [96] pbapply_1.5-0 MASS_7.3-54 mgcv_1.8-38 tidyselect_1.1.2 stringi_1.7.8
## [101] textshaping_0.3.6 highr_0.9 yaml_2.3.5 survMisc_0.5.5 sass_0.4.2
## [106] fastmatch_1.1-3 tools_4.1.2 future.apply_1.8.1 parallel_4.1.2 circlize_0.4.15
## [111] rstudioapi_0.13 uuid_1.1-0 foreach_1.5.2 gridExtra_2.3 prodlim_2019.11.13
## [116] farver_2.1.1 Rtsne_0.16 digest_0.6.29 shiny_1.7.2 lava_1.6.10
## [121] quadprog_1.5-8 fpc_2.2-9 Rcpp_1.0.9 gridtext_0.1.4 car_3.1-0
## [126] broom_1.0.1 later_1.3.0 RcppAnnoy_0.0.19 httr_1.4.2 kernlab_0.9-31
## [131] colorspace_2.0-3 rvest_1.0.2 fs_1.5.2 tensor_1.5 reticulate_1.24
## [136] splines_4.1.2 uwot_0.1.11 yulab.utils_0.0.4 spatstat.utils_2.3-0 graphlayouts_0.8.2
## [141] xgboost_1.4.1.1 shinythemes_1.2.0 flexmix_2.3-18 systemfonts_1.0.4 plotly_4.10.0
## [146] xtable_1.8-4 jsonlite_1.8.0 tidygraph_1.2.2 timeDate_3043.102 UpSetR_1.4.0
## [151] modeltools_0.2-23 ipred_0.9-12 R6_2.5.1 pillar_1.8.1 htmltools_0.5.3
## [156] mime_0.12 glue_1.6.1 fastmap_1.1.0 class_7.3-19 codetools_0.2-18
## [161] mvtnorm_1.1-3 utf8_1.2.2 bslib_0.4.0 spatstat.sparse_2.1-0 ggbeeswarm_0.6.0
## [166] leiden_0.3.9 rmarkdown_2.17 munsell_0.5.0 iterators_1.0.14 haven_2.4.3
## [171] reshape2_1.4.4 gtable_0.3.1 spatstat.core_2.4-0