# CITE-seq data

df_comb <- FetchData(Combined_T, vars = c("wnnUMAP_1", "wnnUMAP_2", colnames(Combined_T@meta.data))) %>%  

  mutate(IdentI=factor (IdentI, levels = cluster_order)) %>% 

  left_join(., df_celltypes, by = "IdentI") %>% 

  mutate(CellType=factor(CellType, levels=rev(celltypes), labels = rev(labels_celltypes_pars)))

# 5'scRNA and scTCR data

DFtotal_5prime <- 

  lapply(sobjs_T_5prime, function(x){

    FetchData(x, vars = c("PatientID", "umapRNA_1", "umapRNA_2", "refUMAP_1", "refUMAP_2",

                          "predicted.celltype", "predicted.celltype.score", "CD4", "CD8A")) 

  }) %>% 

  bind_rows() %>% 

  rownames_to_column("Barcode_full") %>% 

  add_entity() %>% 

  rename(IdentI=predicted.celltype) %>% 

  left_join(., DF_TCRrep, by=c("Barcode_full", "PatientID"))

# ADT data from CITE-seq data

df_ADT <- FetchData(Combined_T, slot = "scale.data", 

                    vars = c("Barcode_full", 

                             paste0("integratedadt_", rownames(Combined_T@assays$integratedADT)))) %>% 

  left_join(df_comb %>% select(IdentI, Barcode_full, Entity, PatientID), ., by = "Barcode_full") %>% 

  pivot_longer(cols = 6:ncol(.), names_to = "Epitope", values_to = "Expression") %>% 

  mutate(Epitope=gsub(Epitope, pattern = "integratedadt_.", replacement = "")) %>% 

  na.omit()

df_ADTdenoised <- FetchData(Combined_T, slot = "data", 

                            vars = c("Barcode_full", 

                                     paste0("denoisedprotein_", rownames(Combined_T@assays$denoisedProtein)))) %>% 

  left_join(df_comb %>% select(IdentI, Barcode_full, Entity, PatientID), .,  by = "Barcode_full") %>% 

  pivot_longer(cols = 6:ncol(.), names_to = "Epitope", values_to = "Expression") %>% 

  mutate(Epitope=gsub(Epitope, pattern = "denoisedprotein_", replacement = "")) %>% 

  left_join(., thresh, by="Epitope") %>%  

  na.omit()

percentageADT <- 

  df_ADTdenoised %>% 

  mutate(Pos=Expression>value) %>% 

  add_prop(vars = c("Pos", "Epitope", "IdentI", "PatientID"), group.vars = c(2:4)) %>% 

  filter(Pos==T) %>% 

  select(-Pos) %>% 

  group_by(Epitope, IdentI) %>% 

  summarise(Prop=mean(Prop), `.groups`="drop")

meanADT <- 

  df_ADT %>% group_by(IdentI, Epitope) %>% 

  summarise(Expression=mean(Expression), `.groups`="drop") %>% 

  group_by(Epitope) %>% 

  mutate(Expression=(Expression-min(Expression))/(max(Expression)-min(Expression)))

# Align FACS and CITE-seq data

df_freq <- 

  df_comb %>% 

  add_prop(vars = c("IdentI", "PatientID"), group.vars = 2) %>% 

  pivot_wider(names_from = "IdentI", values_from = "Prop", values_fill = 0) %>% 

  mutate(TREG= `8`+`11`+`13`+`15`,

         TFH=`6`,

         `TREGCM1`=`8`,

         `TREGCM2`=`13`,

         `TREGEM1`=`15`,

         `TREGEM2`=`11`,

         `TREG/CM1`=`8`/TREG,

         `TREG/CM2`=`13`/TREG,

         `TREG/EM1`=`15`/TREG,

         `TREG/EM2`=`11`/TREG,

         THNaive=`1`,

         TTOXNaive=`12`,

         TDN=`19`,

         TTOX=`5`+`3`+`16`,

         TPR=`14`,

         `TTOXEM1`=`3`,

         `TTOXEM2`=`16`,

         `TTOXEM3`=`5`,

         `TTOX/EM1`=(`3`)/TTOX,

         `TTOX/EM2`=(`16`)/TTOX,

         `TTOX/EM3`=(`5`)/TTOX,

         THCM1=`2`,

         THCM2=`9`

         ) %>% 

    pivot_longer(cols = 2:ncol(.), names_to = "Population", values_to = "RNA") %>% 

    mutate(RNA=100*RNA)