Figure 6
Read data, functions and packages
Part 1
Frequencies
df_freq_treg <- df_comb %>%
add_prop(vars = c("PatientID", "Entity", "IdentI"), group.vars = 1) %>%
fill_zeros(values_from = "Prop", names_from = "IdentI") %>%
filter(IdentI %in% c(6,11))
pvalues <- df_freq_treg %>%
group_by(IdentI) %>%
wilcox_test(data=., formula = Prop ~ Entity, comparisons = list(c("FL", "rLN"), c("MZL", "rLN"))) %>%
select(IdentI, Entity=group1, p) %>%
mutate(p_s=format(p, scientific = TRUE, digits=1)) %>%
mutate(p_f=case_when(p > 0.05 ~ "NA",
p==0.05 ~ "0.05",
p < 0.05 & p > 0.001 ~ as.character(round(p, 3)),
p==0.001 ~ "0.001",
p < 0.001 ~ p_s))
p1 <-
df_freq_treg %>%
filter(IdentI %in% c(6)) %>%
ggplot(aes(x=Entity, y=100*Prop))+
geom_boxplot(width=0.5, outlier.alpha = 0, size=0.25)+
ggbeeswarm::geom_beeswarm(size=0.75, shape=21, stroke=0.25, cex = 2.25, aes(fill=Entity))+
geom_text(inherit.aes = F, data = pvalues %>% filter(IdentI==6) %>% mutate(Y=c(38,45)),
aes(x=Entity, y=Y, label=p_f), size=2.5)+
scale_fill_brewer(palette = "Paired", limits=c("DLBCL", "MCL", "FL", "MZL", "rLN"))+
ggtitle(expression('T'[FH]))+
scale_y_continuous(limits = c(0,60), name="% of total T-cells (CITE-seq)")+
scale_x_discrete(limits=c("rLN", "DLBCL", "MCL", "FL", "MZL"))+
mytheme_1+
theme(legend.position = "none",
strip.background = element_rect(color=NA),
axis.title.x = element_blank(),
panel.border = element_rect(size=0.5),
plot.title = element_text(vjust = -1, color=colors_umap_cl[["6"]]),
axis.text.x = element_text(angle=45, hjust = 1),
panel.background = element_rect(fill=NA),
plot.margin = unit(c(0,0.1,0,0.25), "cm"))+
labs(tag = "A")
p2 <-
df_freq_treg %>%
filter(IdentI %in% c(11)) %>%
ggplot(aes(x=Entity, y=100*Prop))+
geom_boxplot(width=0.5, outlier.alpha = 0, size=0.25)+
ggbeeswarm::geom_beeswarm(size=0.75, shape=21, stroke=0.25, cex = 2.25, aes(fill=Entity))+
geom_text(inherit.aes = F, data = pvalues %>% filter(IdentI==11) %>% mutate(Y=c(16,17.5)),
aes(x=Entity, y=Y, label=p_f), size=2.5)+
scale_fill_brewer(palette = "Paired", limits=c("DLBCL", "MCL", "FL", "MZL", "rLN"))+
ggtitle(expression('T'[REG]~'EM'[2]))+
scale_y_continuous(limits = c(0,18.25), name="% of total T-cells (CITE-seq)")+
scale_x_discrete(limits=c("rLN", "DLBCL", "MCL", "FL", "MZL"))+
mytheme_1+
theme(strip.background = element_rect(color=NA),
plot.title = element_text(vjust = -1, color=colors_umap_cl[["11"]]),
axis.text.x = element_text(angle=45, hjust = 1),
panel.border = element_rect(size=0.5),
axis.title = element_blank(),
panel.background = element_rect(fill=NA),
plot.margin = unit(c(0,0.25,0,0.1), "cm"))Surface proteins
proteins_selected <- c("CD69"="CD69",
"CD25"="CD25",
"ICOS"="CD278",
"CD134"="CD134",
"CD161"="CD161",
"CCR4"="CD194",
"CCR5"="CD195",
"CXCR3"="CD183",
"CXCR5"="CD185",
"PD1"="CD279",
"CD38"="CD38",
"CD39"="CD39",
"TIGIT"="TIGIT")
p3 <-
left_join(percentageADT, meanADT) %>%
filter(IdentI %in% c(6, 8, 11, 15, 13), Epitope %in% proteins_selected) %>%
ggplot(aes(x=Epitope, y=IdentI, size=100*Prop, fill=Expression))+
geom_point(shape=21, stroke=0.1, color="grey45")+
scale_size_continuous(range=c(0, 4), name="% pos. cells", limits=c(0, 100))+
scale_fill_gradientn(name="Expression", colours = brewer.pal(5, "BuGn"), limits=c(0,1))+
scale_y_discrete(limits=rev(as.character(c(6, 8, 13, 15, 11))),
labels=parse(text=rev(labels_cl_parsed[5:9]))
)+
scale_x_discrete(limits=unname(proteins_selected), labels=names(proteins_selected))+
ggtitle("Protein level")+
coord_cartesian(clip = "off")+
theme_bw()+
theme(axis.title = element_blank(),
legend.position = "right",
axis.text.x = element_text(angle = 45, hjust = 1),
axis.text.y = element_blank(),
axis.text = element_text(size=7),
axis.ticks.y = element_blank(),
legend.text = element_text(size = 7, color="black"),
legend.title = element_text(size = 7, color="black", vjust = 0.5),
legend.box.margin=margin(-10,-10,-8,-8),
legend.key.height = unit(0.3, "cm"),
legend.key.width = unit(0.3, "cm"),
panel.border = element_rect(size=0.25),
plot.margin = unit(c(0,0.5,0,0.5), "cm"),
plot.tag = element_text(margin = unit(c(0,1,0,0), "cm")),
plot.title = element_text(size = 7, color="black", vjust = -1, hjust = 0.5, face = "bold"),
panel.grid = element_blank())+
labs(tag = "B")
for(i in 5:9) {
p3 <- p3+
annotation_custom(grob = rectGrob(gp = gpar(fill=colors_umap_cl[as.character(cluster_order)[i]], col="white")),
ymin = rev(c(seq(0.5, 4.5, 1)+0.25))[i-4],
ymax = rev(c(seq(1.5, 5.5, 1)-0.25))[i-4],
xmin = 0, xmax = -0.75)+
annotation_custom(grob = textGrob(label = labels_cl[[i]], rot = 0, hjust = 1, gp = gpar(cex=0.6)),
ymin = rev(c(seq(0.5, 4.5, 1)+0.25))[i-4],
ymax = rev(c(seq(1.5, 5.5, 1)-0.25))[i-4],
xmin = -1, xmax = -1)
}
Part 2
Differentially expressed genes
height.label <- 0.94
position.label <- 1.1
Idents(Combined_T) <- "IdentI"
df_markers1115 <- FindMarkers(Combined_T, ident.1 = 11, ident.2 = c(15), test.use = "roc", assay = "integratedRNA") %>%
rownames_to_column("Feature") %>% mutate(Assay="Gene")
labels <- c("KLF2", "IKZF3", "IL21", "ASCL2", "IKZF2", "FOXP3", "CXCL13")
df_tmp <- df_markers1115 %>% mutate(Label=ifelse(Feature %in% labels, Feature, NA)) %>%
mutate(Label=gsub(Label, pattern = ".", fixed = T, replacement = ""))
df_tmp1 <- df_tmp %>% filter(avg_log2FC<0)
df_tmp2 <- df_tmp %>% filter(avg_log2FC>0)
p4 <-
ggplot()+
geom_point(data=df_tmp1, aes(x=avg_log2FC, y=power), alpha=ifelse(!is.na(df_tmp1$Label), 1, 0.25), stroke=0, size=1.25)+
geom_point(data=df_tmp2, aes(x=avg_log2FC, y=power), alpha=ifelse(!is.na(df_tmp2$Label), 1, 0.25), stroke=0, size=1.25)+
ggrepel::geom_text_repel(data=df_tmp1, aes(x=avg_log2FC, y=power, label=Label), show.legend = F, size=2.5, segment.size=0.25, xlim = c(-1.25, -1.75))+
ggrepel::geom_text_repel(data=df_tmp2, aes(x=avg_log2FC, y=power, label=Label), show.legend = F, size=2.5, segment.size=0.25, xlim = c(1.3, 1.5))+
geom_vline(xintercept = 0, linetype="dashed", size=0.25)+
scale_y_continuous(breaks = c(0.2, 0.4, 0.6, 0.8), limits=c(0.1, 0.85), name="2 x abs(AUC-0.5)")+
scale_x_continuous(name=expression('log'[2]~'fold change'), limits = c(-2.2, 2.2), expand = c(0,0))+
annotation_custom(grob = textGrob(label = expression('T'[REG]~'EM'[1]), hjust = 0.5, gp = gpar(cex=0.6, fontface="bold", col=colors_umap_cl["15"])),
xmin = -position.label, xmax = -position.label,
ymin = height.label, ymax = height.label)+
annotation_custom(grob = textGrob(label = expression('T'[REG]~'EM'[2]), hjust = 0.5, gp = gpar(cex=0.6, fontface="bold", col=colors_umap_cl["11"])),
xmin = position.label, xmax = position.label,
ymin = height.label, ymax = height.label)+
mytheme_1+
coord_cartesian(clip = "off")+
theme(legend.position = c(0.17, 0.15),
legend.key.height = unit(units="cm", 0.3),
legend.box.spacing = unit(units="cm", 0.01),
legend.text = element_text(size=7),
panel.border = element_rect(size=0.25),
plot.margin = unit(c(0,0.25,0,0), "cm"),
legend.background = element_rect(fill = NA),
legend.box.margin=margin(-20,-20,-20,-20),
legend.key.width = unit(units="cm", 0.1))+
labs(tag = "C")Association with FL grade
p6 <- df_freq %>%
left_join(., df_meta %>% select(PatientID, FL_Grade, Entity) %>% distinct, by="PatientID") %>%
filter(Population==11, Entity=="FL") %>%
ggplot(aes(x=FL_Grade, y=RNA))+
geom_boxplot(size=0.25, width=0.3, outlier.alpha = 0)+
ggbeeswarm::geom_beeswarm(size=0.8, shape=21, stroke=0.25, cex = 2.75, fill="grey65")+
stat_compare_means(comparisons = list(c("1/2", "3A")), vjust = -0.35, label.y = c(16.75),
size=2.5, tip.length = 0.02, bracket.size = 0.25)+
ggtitle(expression('T'[REG]~'EM'[2]))+
xlab("Grade")+
ylim(0,18.25)+
ylab("% of total T-cells")+
mytheme_1+
theme(legend.position = "none",
strip.background = element_rect(color=NA),
panel.border = element_rect(size=0.5),
plot.title = element_text(vjust = -1, color=colors_umap_cl[["11"]]),
panel.background = element_rect(fill=NA),
plot.margin = unit(c(0,0.25,0,0.25), "cm"))+
labs(tag = "E")
p7 <- df_freq %>%
left_join(., df_meta %>% select(PatientID, FL_Grade, Entity) %>% distinct, by="PatientID") %>%
filter(Population==6, Entity=="FL") %>%
ggplot(aes(x=FL_Grade, y=RNA))+
geom_boxplot(size=0.25, width=0.3, outlier.size = 1, outlier.alpha = 0, outlier.shape = 21, outlier.fill = "grey70")+
ggbeeswarm::geom_beeswarm(size=0.8, shape=21, stroke=0.25, cex = 2.75, fill="grey65")+
stat_compare_means(comparisons = list(c("1/2", "3A")), vjust = -0.35, label.y = c(59.5),
size=2.5, tip.length = 0.02, bracket.size = 0.25)+
ggtitle(expression('T'[FH]))+
ylim(0,65)+
xlab("Grade")+
ylab("% of total T-cells")+
mytheme_1+
theme(legend.position = "none",
strip.background = element_rect(color=NA),
panel.border = element_rect(size=0.5),
plot.title = element_text(vjust = -1, color=colors_umap_cl[["6"]]),
panel.background = element_rect(fill=NA),
plot.margin = unit(c(0,0.25,0,0.25), "cm"))+
labs(tag = "F")
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 markdown_1.1 deldir_1.0-6
## [41] vctrs_0.4.2 Cairo_1.5-12.2 ROCR_1.0-11 abind_1.4-5 cachem_1.0.6
## [46] withr_2.5.0 ggforce_0.4.0 robustbase_0.95-0 vroom_1.5.7 sctransform_0.3.3
## [51] mclust_5.4.10 goftest_1.2-3 ape_5.6-2 lazyeval_0.2.2 crayon_1.5.2
## [56] labeling_0.4.2 recipes_0.1.17 pkgconfig_2.0.3 tweenr_2.0.2 nlme_3.1-153
## [61] vipor_0.4.5 nnet_7.3-16 rlang_1.0.6 globals_0.14.0 diptest_0.76-0
## [66] lifecycle_1.0.2 miniUI_0.1.1.1 modelr_0.1.8 cellranger_1.1.0 polyclip_1.10-0
## [71] lmtest_0.9-39 phangorn_2.10.0 ggseqlogo_0.1 KMsurv_0.1-5 carData_3.0-5
## [76] zoo_1.8-9 reprex_2.0.1 beeswarm_0.4.0 GlobalOptions_0.1.2 pheatmap_1.0.12
## [81] png_0.1-7 KernSmooth_2.23-20 pROC_1.18.0 shape_1.4.6 parallelly_1.30.0
## [86] spatstat.random_2.1-0 gridGraphics_0.5-1 ggsignif_0.6.3 magrittr_2.0.3 plyr_1.8.7
## [91] ica_1.0-2 compiler_4.1.2 factoextra_1.0.7 fitdistrplus_1.1-6 cli_3.4.1
## [96] listenv_0.8.0 pbapply_1.5-0 MASS_7.3-54 mgcv_1.8-38 tidyselect_1.1.2
## [101] stringi_1.7.8 textshaping_0.3.6 highr_0.9 yaml_2.3.5 survMisc_0.5.5
## [106] sass_0.4.2 fastmatch_1.1-3 tools_4.1.2 future.apply_1.8.1 parallel_4.1.2
## [111] circlize_0.4.15 rstudioapi_0.13 uuid_1.1-0 foreach_1.5.2 gridExtra_2.3
## [116] prodlim_2019.11.13 farver_2.1.1 Rtsne_0.16 digest_0.6.29 shiny_1.7.2
## [121] lava_1.6.10 quadprog_1.5-8 fpc_2.2-9 Rcpp_1.0.9 gridtext_0.1.4
## [126] car_3.1-0 broom_1.0.1 later_1.3.0 RcppAnnoy_0.0.19 httr_1.4.2
## [131] kernlab_0.9-31 colorspace_2.0-3 rvest_1.0.2 fs_1.5.2 tensor_1.5
## [136] reticulate_1.24 splines_4.1.2 uwot_0.1.11 yulab.utils_0.0.4 spatstat.utils_2.3-0
## [141] graphlayouts_0.8.2 xgboost_1.4.1.1 shinythemes_1.2.0 flexmix_2.3-18 systemfonts_1.0.4
## [146] plotly_4.10.0 xtable_1.8-4 jsonlite_1.8.0 tidygraph_1.2.2 timeDate_3043.102
## [151] UpSetR_1.4.0 modeltools_0.2-23 ipred_0.9-12 R6_2.5.1 pillar_1.8.1
## [156] htmltools_0.5.3 mime_0.12 glue_1.6.1 fastmap_1.1.0 class_7.3-19
## [161] codetools_0.2-18 mvtnorm_1.1-3 utf8_1.2.2 bslib_0.4.0 spatstat.sparse_2.1-0
## [166] ggbeeswarm_0.6.0 leiden_0.3.9 rmarkdown_2.17 munsell_0.5.0 iterators_1.0.14
## [171] haven_2.4.3 reshape2_1.4.4 gtable_0.3.1 spatstat.core_2.4-0