Figure 2
Read data, functions and packages
Most important markers
## Overall
## .CD279 100.0000000
## .CD4 82.4463897
## .CD95 82.4303483
## integratedrna_MKI67 80.9565571
## .CD127 63.1639318
## tfactivity_FOXP3-E 59.1942647
## .CD244 57.8861583
## .CD31 54.2644573
## .CD25 54.2067250
## .CD38 47.3966638
## .CD45RA 44.0812741
## .KLRG1 42.9929987
## .CD69 35.9047131
## integratedrna_IKZF3 34.9307454
## .CD366 33.4871027
## .CD185 33.0957211
## .CD278 27.6571296
## .CD8a 27.5828688
## .CD29 22.0760197
## .CD45RO 20.0210563
## .CD27 16.3993173
## .CD161 15.3763637
## .CD47 14.8225454
## .CD39 14.7842141
## .CD195 14.1737708
## .CD62L 13.0361310
## .CD5 12.8674828
## .TIGIT 12.7531256
## .CD48 11.7467447
## .CD7 10.8524031
## .CD183 9.3610239
## .CD43 8.5656629
## .CD57 7.7217130
## .CD134 6.8171674
## .CD44 6.6495843
## .CD2 6.3130648
## .CD194 4.8190625
## .CD28 4.4820337
## .CD45 3.9233299
## .CD3 3.7850078
## .CD86 3.7547510
## .CD10 2.8469448
## .CD56 2.7054040
## .CD11c 2.5142886
## .CD137 2.5084547
## .CD103 2.4943983
## .CD73 2.2263472
## .CD150 2.1428834
## .CD223 2.0853611
## .CD21 1.9588623
## .CD197 1.9572646
## .CD20 1.9167947
## .CD22 1.9078029
## .CD16 1.6164796
## .mouseIgG1 1.5719767
## .CD32 1.5299717
## .mouseIgG2b 1.4007958
## .CD70 1.3886195
## .CD19 1.3060245
## .CD184 1.2608836
## .CD24 1.1020146
## .Kappa 1.0543088
## .Lambda 1.0394889
## .CD152 0.9766114
## .ratIgG2b 0.9546538
## .CD11b 0.7070605
## .CD274 0.5306019
## .CD273 0.4592048
## .CD79b 0.4213194
## .hamsterIgG 0.3718775
## .CD357 0.2912255
## .CD23 0.2861991
## .mouseIgG2a 0.0000000plot_markers <- varImp(GBresults_surfaceplus, scale = TRUE)[[1]] %>%
data.frame %>%
rownames_to_column("Parameter") %>%
top_n(n = 35, Overall) %>%
mutate(Parameter=gsub(Parameter, pattern = ".", fixed = T, replacement = "")) %>%
mutate(Parameter=gsub(Parameter, pattern = "tfactivity_|-E|integratedadt_|integratedrna_", replacement = "")) %>%
mutate(Parameter=ifelse(Parameter=="FOXP3", "FoxP3", Parameter)) %>%
mutate(Code=ifelse(Parameter %in% c("CD279", "CD4", "MKI67", "CD244", "FoxP3", "CD25",
"CD31", "CD45RA", "CD69", "CD185", "CD366", "IKZF3",
"CD45RO", "CD278", "CD8a"), 1, 0)) %>%
ggplot(aes(x=0, y=reorder(Parameter, -Overall)))+
geom_segment(aes(xend=Overall, yend=reorder(Parameter, Overall)), size=0.35)+
geom_point(aes(x=Overall, color=as.character(Code), y=reorder(Parameter, Overall)), inherit.aes = F, size=1)+
scale_color_manual(values = c("grey65", "#c51b8a")) +
scale_x_continuous(expand = c(0,0.25), limits = c(0,110), name = "Scaled importance")+
ggtitle("Top 35 features")+
coord_flip()+
mytheme_1+
theme(axis.title.x = element_blank(),
plot.title =element_text(hjust=0.5, face = "plain"),
panel.border = element_rect(size=0.25),
axis.ticks.y = element_line(size=0.25),
axis.ticks.x = element_blank(),
axis.text.y = element_text(size=7),
axis.title.y = element_text(size=7),
axis.text.x = element_text(size=7, angle=45, hjust=1))+
labs(tag = "A")
plot_markers
Correlation: Flow cytometry ~ CITE-seq
df_cor <- left_join(df_freq %>% filter(!Population %in% c(df_comb$IdentI)),
df_facs, by=c("PatientID", "Population")) %>%
filter(!is.na(FACS)) %>%
filter(!PatientID %in% c("LN0262", "LN0302"))
cor_plots_facs <- list()
cor_plots_facs[["TFH"]] <- df_cor %>%
filter(Population=="TFH") %>%
ggplot(aes(x=FACS, y=RNA))+
geom_point(color=colors_umap_cl[["6"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["6"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson", size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,75), breaks = c(0, 25, 50, 75))+
scale_y_continuous(limits = c(0,75), breaks = c(0, 25, 50, 75))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>-</sup><br><span>CXCR5<sup>+</sup> PD1<sup>+</sup></span>",
y="T<sub>FH</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TPR"]] <- df_cor %>%
filter(Population=="TPR") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["14"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["14"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,12), breaks = c(0, 4, 8, 12))+
scale_y_continuous(limits = c(0,12), breaks = c(0, 4, 8, 12))+
labs(
x="Ki67<sup>+</sup>",
y="T<sub>Pr</sub>",
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TDN"]] <- df_cor %>%
filter(Population=="TDN") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["19"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["19"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,8), breaks = c(0, 2, 4, 6, 8))+
scale_y_continuous(limits = c(0,8), breaks = c(0, 2, 4, 6, 8))+
labs(
x="CD45RA<sup>+</sup><br><span>CD4<sup>-</sup> CD8<sup>-</sub></span>",
y="T<sub>DN</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["THCM1"]] <- df_cor %>%
filter(Population=="THCM1") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["2"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["2"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.35), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,17), breaks = c(0, 5, 10, 15))+
scale_y_continuous(limits = c(0,17), breaks = c(0, 5, 10, 15))+
labs(
x="CD45RA<sup>-</sup> FOXP3<sup>-</sup><br><span>CD69<sup>-</sup> PD1<sup>Low</sup></span>",
y="T<sub>H</sub> CM<sub>1</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["THCM2"]] <- df_cor %>%
filter(Population=="THCM2") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["9"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["9"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,15), breaks = c(0, 5, 10, 15))+
scale_y_continuous(limits = c(0,15), breaks = c(0, 5, 10, 15))+
labs(
x="CD45RA<sup>-</sup> FOXP3<sup>-</sup><br><span>CD69<sup>+</sup> PD1<sup>Low</sup></span>",
y="T<sub>H</sub> CM<sub>2</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["THNaive"]] <- df_cor %>%
filter(Population=="THNaive") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["1"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["1"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,25), breaks = c(0, 8, 16, 24))+
scale_y_continuous(limits = c(0,25), breaks = c(0, 8, 16, 24))+
labs(
x="CD4<sup>+</sup> CD45RA<sup>+</sup>",
y="CD4<sup>+</sup> Naive"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG"]] <- df_cor %>%
filter(Population=="TREG") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color="#578bb9", size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color="#578bb9", se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.45), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,45), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,45), breaks = c(0, 15, 30, 45))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>+</sup>",
y="T<sub>REG</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG/CM1"]] <- df_cor %>%
filter(Population=="TREG/CM1") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["8"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["8"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,55), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,55), breaks = c(0, 15, 30, 45))+
labs(
x="CD4<sup>+</sup><span> FOXP3<sup>+</sup> /<br>CD69<sup>-</sub></span>",
y="T<sub>REG</sub> CM<sub>1</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG/EM2"]] <- df_cor %>%
filter(Population=="TREG/EM2") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["11"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["11"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,92), breaks = c(0, 30, 60, 90))+
scale_y_continuous(limits = c(0,92), breaks = c(0, 30, 60, 90))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>+</sup> /<br><span>CD69<sup>+</sup> IKZF3<sup>+</sub></span>",
y="T<sub>REG</sub> EM<sub>2</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG/EM1"]] <- df_cor %>%
filter(Population=="TREG/EM1") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["15"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["15"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,50), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,50), breaks = c(0, 15, 30, 45))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>+</sup> /<br><span>CD69<sup>+</sup> IKZF3<sup>-</sup> ICOS<sup>-</sup></span>",
y="T<sub>REG</sub> EM<sub>1</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG/CM2"]] <- df_cor %>%
filter(Population=="TREG/CM2") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["13"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["13"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,47), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,47), breaks = c(0, 15, 30, 45))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>+</sup> /<br><span>CD69<sup>+</sup> IKZF3<sup>-</sup> ICOS<sup>+</sup></span>",
y="T<sub>REG</sub> CM<sub>2</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOXNaive"]] <- df_cor %>%
filter(Population=="TTOXNaive") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["12"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["12"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,27), breaks = c(0, 8, 16, 24))+
scale_y_continuous(limits = c(0,27), breaks = c(0, 8, 16, 24))+
labs(
x="CD8<sup>+</sup> CD45RA<sup>+</sup>",
y="CD8<sup>+</sup> Naive"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOX"]] <- df_cor %>%
filter(Population=="TTOX") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color="#b50923", size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color="#b50923", se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.45), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,80), breaks = c(0, 25, 50, 75))+
scale_y_continuous(limits = c(0,80), breaks = c(0, 25, 50, 75))+
labs(
x="CD31<sup>+</sup> U CD244<sup>+</sup>",
y="T<sub>TOX</sub> non-Naive"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOX/EM1"]] <- df_cor %>%
filter(Population=="TTOX/EM1") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["3"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["3"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,85), breaks = c(0, 25, 50, 75))+
scale_y_continuous(limits = c(0,85), breaks = c(0, 25, 50, 75))+
labs(
x="CD31<sup>+</sup> U CD244<sup>+</sup> /<br><span>TIM3<sup>-</sup> PD1<sup>Low</sup></span>",
y="T<sub>TOX</sub> EM<sub>1</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOX/EM2"]] <- df_cor %>%
filter(Population=="TTOX/EM2") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["16"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["16"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,57), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,57), breaks = c(0, 15, 30, 45))+
labs(
x="CD31<sup>+</sup> U CD244<sup>+</sup> /<br><span>TIM3<sup>-</sup> PD1<sup>High</sup></span>",
y="T<sub>TOX</sub> EM<sub>2</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOX/EM3"]] <- df_cor %>%
filter(Population=="TTOX/EM3") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["5"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["5"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,80), breaks = c(0, 25, 50, 75))+
scale_y_continuous(limits = c(0,80), breaks = c(0, 25, 50, 75))+
labs(
x="CD31<sup>+</sup> U CD244<sup>+</sup> /<br><span>TIM3<sup>+</sup> PD1<sup>+</sup></span>",
y="T<sub>TOX</sub> EM<sub>3</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_subMedian Pearson’s R
df_cor %>%
group_by(Population) %>%
filter(!Population %in% c("TREG", "TTOX")) %>%
summarise(R=cor.test(RNA, FACS, method="pearson")$estimate) %>% pull(R) %>% median()## [1] 0.8877222Assemble plot
plot_cor_full <- cor_plots_facs$TPR+labs(tag="B")+theme(plot.tag = element_text(margin = unit(c(0,0.15,0,0), units = "cm")))+
cor_plots_facs$THNaive+
cor_plots_facs$THCM1+
cor_plots_facs$THCM2+
cor_plots_facs$TFH+
cor_plots_facs$`TREG/CM1`+
cor_plots_facs$`TREG/CM2`+
cor_plots_facs$`TREG/EM1`+
cor_plots_facs$`TREG/EM2`+
cor_plots_facs$TTOXNaive+
cor_plots_facs$`TTOX/EM1`+
cor_plots_facs$`TTOX/EM2`+
cor_plots_facs$`TTOX/EM3`+
cor_plots_facs$TDN+
plot_layout(nrow = 2)
plot_cor_full
Mini scatter plot
set.seed(1)
FetchData(Combined_T, vars = c("integratedadt_.CD366", "integratedadt_.CD279", "IdentI")) %>%
sample_n(3000) %>%
mutate(IdentI=as.character(IdentI)) %>%
ggplot(aes(x=integratedadt_.CD366, y=integratedadt_.CD279, fill=IdentI, color=IdentI))+
ggrastr::geom_point_rast(size=0.25, stroke=0, shape=21, alpha=0.75, na.rm=T)+
scale_fill_manual(values = colors_umap_cl, limits=factor(cluster_order), labels=unlist(labels_cl))+
geom_rect(aes(xmin=0.95, xmax=2, ymin=0.25, ymax=3), size=0.15, fill=NA, color="black", linetype="solid")+
scale_y_continuous(limits = c(-0, 5), name = "Marker 1")+
scale_x_continuous(limits = c(-0, 2), name = "Marker 2")+
mytheme_1+
theme(axis.text = element_text(color="white"),
panel.border = element_rect(size=0.5),
axis.title.x = element_text(vjust = 7, size=8),
axis.title.y = element_text(vjust = -5, size=8),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank())
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 bit64_4.0.5
## [6] irlba_2.3.5 rpart_4.1-15 doParallel_1.0.17 generics_0.1.3 RANN_2.6.1
## [11] future_1.23.0 bit_4.0.4 tzdb_0.3.0 rlist_0.4.6.2 spatstat.data_2.1-2
## [16] xml2_1.3.2 lubridate_1.8.0 httpuv_1.6.6 assertthat_0.2.1 gower_0.2.2
## [21] xfun_0.33 hms_1.1.2 jquerylib_0.1.4 evaluate_0.16 promises_1.2.0.1
## [26] DEoptimR_1.0-11 fansi_1.0.3 dbplyr_2.1.1 km.ci_0.5-2 DBI_1.1.2
## [31] htmlwidgets_1.5.4 spatstat.geom_2.3-2 stringdist_0.9.8 stats4_4.1.2 ellipsis_0.3.2
## [36] backports_1.4.1 bookdown_0.29 markdown_1.1 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] highr_0.9 yaml_2.3.5 survMisc_0.5.5 sass_0.4.2 fastmatch_1.1-3
## [106] tools_4.1.2 future.apply_1.8.1 parallel_4.1.2 circlize_0.4.15 rstudioapi_0.13
## [111] uuid_1.1-0 foreach_1.5.2 gridExtra_2.3 prodlim_2019.11.13 farver_2.1.1
## [116] Rtsne_0.16 digest_0.6.29 shiny_1.7.2 lava_1.6.10 quadprog_1.5-8
## [121] fpc_2.2-9 Rcpp_1.0.9 gridtext_0.1.4 car_3.1-0 broom_1.0.1
## [126] later_1.3.0 RcppAnnoy_0.0.19 httr_1.4.2 kernlab_0.9-31 colorspace_2.0-3
## [131] rvest_1.0.2 fs_1.5.2 tensor_1.5 reticulate_1.24 splines_4.1.2
## [136] uwot_0.1.11 yulab.utils_0.0.4 spatstat.utils_2.3-0 graphlayouts_0.8.2 xgboost_1.4.1.1
## [141] shinythemes_1.2.0 flexmix_2.3-18 plotly_4.10.0 xtable_1.8-4 jsonlite_1.8.0
## [146] tidygraph_1.2.2 timeDate_3043.102 UpSetR_1.4.0 modeltools_0.2-23 ipred_0.9-12
## [151] R6_2.5.1 pillar_1.8.1 htmltools_0.5.3 mime_0.12 glue_1.6.1
## [156] fastmap_1.1.0 class_7.3-19 codetools_0.2-18 mvtnorm_1.1-3 utf8_1.2.2
## [161] bslib_0.4.0 spatstat.sparse_2.1-0 ggbeeswarm_0.6.0 leiden_0.3.9 rmarkdown_2.17
## [166] munsell_0.5.0 iterators_1.0.14 haven_2.4.3 reshape2_1.4.4 gtable_0.3.1
## [171] spatstat.core_2.4-0