[18]:

suppressMessages(library(Seurat))
suppressMessages(library(dplyr))
suppressMessages(library(tidyverse))
suppressMessages(library(viridis))
suppressMessages(library(ggalluvial))
suppressMessages(library("ggsci"))
suppressMessages(library("ggplot2"))
suppressMessages(library("gridExtra"))
library(cowplot)

APCside gene expression#

[3]:

my24_1colors <- c('#53868B','#00F5FF','#7FFFD4','#C1FFC1','#0000FF','#7B68EE',
                  '#CDCD00','#FFF68F','#CD9B1D','#8B658B','#FF6A6A','#8B3A3A',
                  '#1E90FF','#FF69B4','#8DB6CD','#CAE1FF','#EECFA1','#8B7B8B',
                  '#4F4F4F','#FF4500','#BC8F8F','#FFA500','#228B22','#8B4513')

my23colors <- c('#53868B','#00F5FF','#C1FFC1','#0000FF','#7B68EE',
                  '#CDCD00','#FFF68F','#CD9B1D','#8B658B','#FF6A6A','#8B3A3A',
                  '#1E90FF','#FF69B4','#8DB6CD','#CAE1FF','#EECFA1','#8B7B8B',
                  '#4F4F4F','#FF4500','#BC8F8F','#FFA500','#228B22','#8B4513')

[86]:


modify_vlnplot<- function(obj,
                          features,
                          pt.size = 0,
                          plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
                          cols=my23colors,
                          slot="data",
                          assay="RNA",
                          ...) {
  p <- VlnPlot(obj, features = features, pt.size = pt.size, cols=cols, slot=slot,
              assay=assay,... ) + theme_bw() +
    xlab("") + ylab(features) + ggtitle("") +
    theme(legend.position = "none",
          panel.grid=element_blank(),
          axis.text.x = element_blank(),
          axis.text.y = element_blank(),
          #axis.ticks.x = element_blank(),
          #axis.ticks.x = element_line(color="red"),
          axis.ticks.length = unit(0, "pt"),
          #axis.ticks.y = element_line(color="red"),
          axis.ticks.y = element_blank(),
          plot.title= element_blank(),
          axis.title.x = element_blank(),
          #axis.title.x = element_text(size = rel(1), angle = 45, vjust = 0),
          axis.title.y = element_text(size = rel(1), angle=0, vjust = 0.5, hjust = 0),
          plot.margin = plot.margin, axis.line.x=element_blank())

          #plot.margin = plot.margin) +
     #annotate(
     #   geom = 'segment',
      #  y = -Inf,
     #   yend = Inf,
      #  x = Inf,
       # xend = Inf
      #  )

  return(p)
}


## main function
StackedVlnPlot<- function(obj, features,
                          pt.size = 0,
                          slot="data",
                          assay="RNA",
                          plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
                          ...) {

  plot_list<- purrr::map(features, function(x) modify_vlnplot(obj = obj, features = x, slot=slot, assay=assay,...))

  plot_list[[1]] <- plot_list[[1]] + theme(legend.position = "top", axis.line.x=element_blank())
  #plot_list[[1]] <- plot_list[[1]] + theme(panel.background = element_rect(fill = 'grey75'))
  #plot_list[[1]] <- plot_list[[1]] + scale_x_continuous(position="top")
  #plot_list[[1]] <- plot_list[[1]] + annotate(geom = 'segment', y = Inf, yend = Inf, x = -Inf, xend = Inf, size=0.5 )

  plot_list[[length(plot_list)]]<- plot_list[[length(plot_list)]] +
    theme(axis.text.x=element_text(size = rel(1), angle = 45, vjust = 1, hjust = 1),
          axis.ticks.x = element_line(), axis.ticks.length = unit(5, "pt"))
          #axis.line.x=element_line(linetype=1,color="black",size=0.5))
                         #+
    #theme(axis.title.y=element_text(size = rel(1), angle = 0, vjust = 0.5, hjust = 0), axis.ticks.y = element_blank())
  #plot_list[[1]]
  #print(names(plot_list))
  #p <- do.call(what = '+', args = plot_list)
  #p <- p + theme_cowplot()
  #p <- p + scale_x_continuous(
  #          expand = c(0, 0),
  #          labels = function(x) c(rep(x = '', times = length(x)-2), x[length(x) - 1], '')
  #    )
#
  #p <- patchwork::wrap_plots(plotlist = plot_list, ncol = 1 )
                             #+ theme(plot.margin = plot.margin))
  #p <- p + theme(plot.margin = plot.margin)
  p <- plot_list[[1]]

  for (i in 2:length(plot_list)){
      p <- p + plot_list[[i]]
  }

  return(p)
}

StackedVlnPlot(subT, features=Tside_genes2, split.by="status2", cols= c('#EE8084','#19BBC1'))
    #theme(plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm")) + theme_cowplot() +
    #theme(panel.spacing.x = unit(10, "cm"),
    #      panel.spacing.y = unit(-1, "cm")
    #     )
#ggsave("aaaaaa.pdf", w=8, h=7)
../../_images/notebooks_geneExp_THU-6-20220805_3_0.png 
[ ]:



[8]:

Tside_genes2 <- c("PDCD1", "CTLA4", "HAVCR2")
subT <- subset(Tcell, cell.type.sub %in% c("CD4-Th17-1", "naïve", "CD8-CT-2"))
data <- as.data.frame(
    x = as.matrix(
        x = t(as.matrix(x = GetAssayData(
            object = subT,
            slot = "data")[Tside_genes2, rownames(subT@meta.data), drop = FALSE]))))


Melt <- function(x) {
  if (!is.data.frame(x = x)) {
    x <- as.data.frame(x = x)
  }
  return(data.frame(
    rows = rep.int(x = rownames(x = x), times = ncol(x = x)),
    cols = unlist(x = lapply(X = colnames(x = x), FUN = rep.int, times = nrow(x = x))),
    vals = unlist(x = x, use.names = FALSE)
  ))
}

data <- Melt(data)

idents <- subT[["cell.type.sub", drop = TRUE]]


data2 <- data.frame(
    feature = data$cols,
    expression = data$vals,
    ident = rep_len(x = idents, length.out = nrow(x = data))
  )

geom <- list(geom_violin(scale = 'width', adjust = 1, trim = TRUE))

plot <- ggplot(
    data = data2,
    mapping = aes_string(x = "ident", y="expression", color = "feature", fill="feature")
  ) +
    #labs(x = xlab, y = ylab, title = feature, fill = NULL) +
    theme_cowplot() +
    theme(plot.title = element_text(hjust = 0.5))

#plot <- do.call(what = '+', args = list(plot, geom))

plot + geom

plot

Error in subset(Tcell, cell.type.sub %in% c("CD4-Th17-1", "naïve", "CD8-CT-2")): 找不到对象'Tcell'
Traceback:

1. subset(Tcell, cell.type.sub %in% c("CD4-Th17-1", "naïve", "CD8-CT-2"))

[ ]:



[9]:

setwd("/annoroad/data1/bioinfo/PROJECT/big_Commercial/Cooperation/B_TET/B_TET-003/std/result/fanxuning/commander_test/THU/")

[10]:

Mye_final <- readRDS("final_Mye_20220805.RDS")

[11]:

options(repr.plot.width=8, repr.plot.height=5)

DimPlot(Mye_final,  group.by="cell.type.sub3", raster=TRUE, cols=my23colors,
        label=FALSE,pt.size=3)
../../_images/notebooks_geneExp_THU-6-20220805_9_0.png 
[12]:

unique(Mye_final@meta.data$cell.type.sub3)
  1. CD141+ DC
  2. mac/mono IL10+HS_ signaling+
  3. neu SLPI+MMP9+
  4. CD1c+ DC
  5. Langerin+ DC
  6. mac/mono EREG+inflammasome+
  7. mac/mono RNASE1+LYVE1+
  8. neu IFN_signaling+
  9. mac/mono CCL4+CLL3+CD14+
  10. mac/mono CXCL10+TIMP1+
  11. mac/mono CDKN1C+TCF7L2+
  12. neu IL1RN+
  13. neu CMTM2+SELL+
  14. mac/mono CCL4+CLL3+CD14-
  15. mac/mono APOE+SPP1+
  16. LAMP3+ DC
Levels:
  1. 'neu IL1RN+'
  2. 'neu CMTM2+SELL+'
  3. 'neu IFN_signaling+'
  4. 'neu SLPI+MMP9+'
  5. 'mac/mono IL10+HS_ signaling+'
  6. 'mac/mono EREG+inflammasome+'
  7. 'mac/mono RNASE1+LYVE1+'
  8. 'mac/mono CCL4+CLL3+CD14+'
  9. 'mac/mono CXCL10+TIMP1+'
  10. 'mac/mono CDKN1C+TCF7L2+'
  11. 'mac/mono CCL4+CLL3+CD14-'
  12. 'mac/mono APOE+SPP1+'
  13. 'CD141+ DC'
  14. 'CD1c+ DC'
  15. 'Langerin+ DC'
  16. 'LAMP3+ DC'
[13]:

mye_cs <- c("CD141+ DC", "CD1c+ DC", "LAMP3+ DC", "Langerin+ DC", "")

[14]:

Tcell <- readRDS("T_clean_5thAnnotation.rds")

Tcell@meta.data$status2 <- "Malignant"
Tcell@meta.data[Tcell@meta.data$status == "P", "status2"] <- "Non-Malignant"
Tcell@meta.data[Tcell@meta.data$status == "T", "status2"] <- "Malignant"

Tcell@meta.data$status2 <- factor(Tcell@meta.data$status2,levels = c("Malignant", "Non-Malignant"))
#
Tcell@meta.data$treat <- "NoTreat"
Tcell@meta.data[Tcell@meta.data$patient == "MIBC5", "treat"] <- "Treat"
Tcell@meta.data[Tcell@meta.data$patient == "MIBC6", "treat"] <- "Treat"
Tcell@meta.data[Tcell@meta.data$patient == "MIBC8", "treat"] <- "Treat"
Tcell@meta.data[Tcell@meta.data$patient == "MIBC13", "treat"] <- "Treat"

Tcell@meta.data$status_treat <- paste(Tcell@meta.data$status2, Tcell@meta.data$treat, sep="_")

[15]:

Tcell@meta.data$treat <- factor(Tcell@meta.data$treat, levels=c("Treat", "NoTreat"))

[16]:

Idents(Tcell) <- "cell.type.sub"

[87]:

options(repr.plot.width=8, repr.plot.height=7)

Tside_genes2 <- c("PDCD1", "CTLA4", "TIGIT", "HAVCR2", "LAG3", "BTLA", "TNFSF14", "TNFRSF9", "TNFRSF4")
#Tside_genes2 <- c("PDCD1", "CTLA4")
StackedVlnPlot(Tcell, features=Tside_genes2, split.by="treat", cols= c('#EE8084','#19BBC1'), pt.size = 0) + theme(legend.position = "right")
ggsave("20220810_Tside_genes.pdf", w=8, h=7)
../../_images/notebooks_geneExp_THU-6-20220805_15_0.png 
[ ]:



[26]:

Tside_genes2 <- c("PDCD1", "CTLA4", "HAVCR2")
subT <- subset(Tcell, cell.type.sub %in% c("CD4-Th17-1", "naïve", "CD8-CT-2"))

[48]:

StackedVlnPlot(subT, features=Tside_genes2, split.by="status2", cols= c('#EE8084','#19BBC1')) +
    #theme(plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm")) + theme_cowplot() +
    theme(panel.spacing.x = unit(10, "cm"),
          panel.spacing.y = unit(-1, "cm")
         )
../../_images/notebooks_geneExp_THU-6-20220805_18_0.png 
[ ]:



[ ]:



[ ]:

DC macrophage#

[20]:

APC <- readRDS("/annoroad/data1/bioinfo/PROJECT/big_Commercial/Cooperation/B_TET/B_TET-072/supplement/yaojiaying/AN202202140002/Analysis/Data/Fig5_APC.rds")

[21]:

APC$cluster<-factor(APC$cluster,levels=c('CD141+ DC','CD1c+ DC','LAMP3+ DC','Langerin+ DC','pDC','Neutrophil','Macrophage','Monocyte','MonoDC','MCt','MCtc'))

tmp1<-subset(APC,cluster %in% c('Macrophage','Monocyte','MonoDC')) #合并后Mac 3个群
tmp2<-subset(APC,cluster %in% c('MCt','MCtc')) #合并后Mast 2个群

tmp1$cluster<-as.vector(tmp1$cluster)
tmp2$cluster<-as.vector(tmp2$cluster)

[22]:

subAPC <- subset(APC, cluster != "pDC")

[23]:

subAPC@meta.data$treat <- "NoTreat"
subAPC@meta.data[subAPC@meta.data$patient == "MIBC5", "treat"] <- "Treat"
subAPC@meta.data[subAPC@meta.data$patient == "MIBC6", "treat"] <- "Treat"
subAPC@meta.data[subAPC@meta.data$patient == "MIBC8", "treat"] <- "Treat"
subAPC@meta.data[subAPC@meta.data$patient == "MIBC13", "treat"] <- "Treat"

[24]:

subAPC@meta.data$treat <- factor(subAPC@meta.data$treat, levels=c("Treat", "NoTreat"))

[88]:

APC_side_genes <- c("CD274","PDCD1LG2","CD40","CD80","CD86","PVR","LGALS9","TNFRSF14","TNFSF4","TNFSF9")
options(repr.plot.width=6, repr.plot.height=7)
StackedVlnPlot(subAPC, features=APC_side_genes, split.by="treat", cols= c('#EE8084','#19BBC1'), pt.size = 0)
ggsave("20220810_APCside_genes.pdf", w=6, h=7)
../../_images/notebooks_geneExp_THU-6-20220805_28_0.png 
[ ]:



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