单细胞分析中的叠加条形图

Question

我正试图用下面的代码用ggplot2绘制一个堆叠的桶形图

barplot <- ggplot() + geom_bar(aes(y = percentage, x = TBD, fill = TBD), data = charts.data, stat="identity")

我想为我的单细胞分析创建一个条形图，它有两个条件，一个是经过处理的，一个是未经处理的。我想要显示的是，在每个条件下，不同的单元格类型所占的百分比，看看处理后是否会对不同的单元类型产生影响。

如何在每种情况下确定每种单元格类型的百分比，然后再绘制桶形图？

dput(head(comparison))的输出

structure(c(6051L, 1892L, 1133L, 893L, 148L, 868L, 5331L, 3757L, 
1802L, 1061L, 2786L, 704L), .Dim = c(6L, 2L), .Dimnames = structure(list(c("Fibroblast", "T cell", "Macrophage", "Stellate", "Acinar", "Endothelial"), c("treated", "untreated")), .Names = c("", 
"")), class = "table")

dput(head(cell_cycle_data))的输出

structure(list(orig.ident = c("treated", "treated", "treated", 
    "treated", "treated", "treated"), nCount_RNA = c(1892, 307, 1348, 
    3699, 4205, 4468), nFeature_RNA = c(960L, 243L, 765L, 1612L, 
    1341L, 1644L), percent.mt = c(0.211416490486258, 1.62866449511401, 
    4.45103857566766, 4.4065963773993, 0.0713436385255648, 3.87197851387645
    ), RNA_snn_res.0.5 = structure(c(11L, 11L, 5L, 6L, 11L, 13L), .Label = c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", 
    "13", "14", "15", "16", "17", "18", "19"), class = "factor"), seurat_clusters = structure(c(11L, 11L, 5L, 6L, 11L, 13L), .Label = c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19"), class = "factor"), S.Score = c(0.476893835992198, -0.0200784617568548, -0.0335915198305002, -0.0247184276246385, 0.010785196602457, 0.0190008903712199), G2M.Score = c(0.204441469200986, 0.173804859670862, -0.0313235510969097, -0.0376796363661889, -0.0559526905696905, -0.0122031631356698), Phase = structure(c(3L, 2L, 1L, 1L, 3L, 3L), .Label = c("G1", "G2M", "S"), class = "factor"), old.ident = structure(c(7L,7L, 1L, 4L, 7L, 9L), .Label = c("Fibroblast", "T cell", "Macrophage", "Stellate", "Acinar", "Endothelial", "Tumor", "B cell", "Mast cell", "Ductal", "Islets of Langerhans"), class = "factor")), row.names = c("treated_AAACGCTAGCGGGTTA-1", "treated_AAAGGTAAGTACAGAT-1", "treated_AAAGTGAGTTTGATCG-1", "treated_AAATGGACAAAGTGTA-1", 
    "treated_AACAAAGGTCGACTTA-1", "treated_AACAGGGTCCTAGCCT-1"), class = "data.frame")

dput(tail(comparison))的输出

structure(list(orig.ident = c("untreated", "untreated", "untreated", 
"untreated", "untreated", "untreated"), nCount_RNA = c(901, 823, 
1184, 1835, 1147, 1407), nFeature_RNA = c(482L, 479L, 649L, 1043L, 
604L, 709L), percent.mt = c(1.77580466148724, 2.91616038882138, 
4.22297297297297, 3.86920980926431, 2.0052310374891, 4.05117270788913
), RNA_snn_res.0.5 = structure(c(7L, 7L, 7L, 14L, 7L, 7L), .Label = c("0", 
"1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", 
"13", "14", "15", "16", "17", "18", "19"), class = "factor"), 
    seurat_clusters = structure(c(7L, 7L, 7L, 14L, 7L, 7L), .Label = c("0", 
    "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", 
    "12", "13", "14", "15", "16", "17", "18", "19"), class = "factor"), 
    S.Score = c(-0.0320858200243315, 0.0304725660342869, 0.0215996091745327, 
    0.0384166213301423, 0.144956251122548, -0.0242770509986111
    ), G2M.Score = c(0.0904224391544142, 0.050148242050667, -0.0178041670730754, 
    -0.0112596867977946, -0.0519554524339088, -0.0136533184257381
    ), Phase = structure(c(2L, 2L, 3L, 3L, 3L, 1L), .Label = c("G1", 
    "G2M", "S"), class = "factor"), old.ident = structure(c(5L, 
    5L, 5L, 5L, 5L, 5L), .Label = c("Fibroblast", "T cell", "Macrophage", 
    "Stellate", "Acinar", "Endothelial", "Tumor", "B cell", "Mast cell", 
    "Ductal", "Islets of Langerhans"), class = "factor")), row.names = c("untreated_TTTGGTTGTCTAATCG-18", 
"untreated_TTTGGTTTCCCGAGGT-18", "untreated_TTTGTTGAGAACTGAT-18", 
"untreated_TTTGTTGAGCTCGGCT-18", "untreated_TTTGTTGAGTGCCTCG-18", 
"untreated_TTTGTTGCACGGTGCT-18"), class = "data.frame")

Answer 1

如果不知道数据的结构，就很难猜出示例的好代码是什么。

但是，如果我们假设每个条件都有，那么您就有一个单个单元格的列表，每个单元格都有对应于它们的单元格类型的特定标签，如下面的示例：

set.seed(123)
Untreated <- data.frame(Cell_Type = sample(LETTERS[1:4],10, replace = TRUE))
Treated <- data.frame(Cell_Type =sample(LETTERS[1:4],25, replace = TRUE))

  Cell_Type
1         C
2         C
3         C
4         B
5         C
6         B
...       ...

您可以使用dplyr进行第一次bind_rows

library(dplyr)
Untreated <- Untreated %>% mutate(Condition = "Untreated")
Treated <- Treated %>% mutate(Condition = "Treated")
DF <- bind_rows(Untreated, Treated)

  Cell_Type Condition
1         C Untreated
2         C Untreated
3         C Untreated
4         B Untreated
5         C Untreated
6         B Untreated

然后，您可以将每个单元格类型的数量计数到每个条件中，并将其表示为百分比：

DF <- DF %>% group_by(Condition, Cell_Type) %>% 
  summarise(Nb = n()) %>%
  mutate(C = sum(Nb)) %>%
  mutate(percent = Nb/C*100)

# A tibble: 7 x 5
# Groups:   Condition [2]
  Condition Cell_Type    Nb     C percent
  <chr>     <chr>     <int> <int>   <dbl>
1 Treated   A             7    25     28.
2 Treated   B             7    25     28.
3 Treated   C             6    25     24 
4 Treated   D             5    25     20 
5 Untreated A             1    10     10 
6 Untreated B             4    10     40 
7 Untreated C             5    10     50 

然后，您可以将结果绘制为每个条件的叠加条形图，并根据Cell_Type填充每种颜色：

library(ggplot2)
ggplot(DF, aes(x = Condition, y = percent, fill = Cell_Type))+
  geom_bar(stat = "identity")+
  geom_text(aes(label = paste(percent,"%")), position = position_stack(vjust = 0.5))

​

​

编辑:使用OP提供的数据绘图

使用您在问题中提供的数据，您可以：

df <- structure(c(6051L, 1892L, 1133L, 893L, 148L, 868L, 5331L, 3757L, 
            1802L, 1061L, 2786L, 704L), .Dim = c(6L, 2L), .Dimnames = structure(list(c("Fibroblast", "T cell", "Macrophage", "Stellate", "Acinar", "Endothelial"), c("treated", "untreated")), .Names = c("", 
                                                                                                                                                                                                          "")), class = "table")
df <- data.frame(df)

它提供了以下数据：

          Var1      Var2 Freq
1   Fibroblast   treated 6051
2       T cell   treated 1892
3   Macrophage   treated 1133
4     Stellate   treated  893
5       Acinar   treated  148
6  Endothelial   treated  868
7   Fibroblast untreated 5331
8       T cell untreated 3757
9   Macrophage untreated 1802
10    Stellate untreated 1061
11      Acinar untreated 2786
12 Endothelial untreated  704

然后，您可以重命名列，为每个条件计算每种单元格类型的百分比：

library(dplyr)
DF <- df %>% rename(Cell_Type = Var1, Condition = Var2) %>%
  group_by(Condition) %>% 
  mutate(Percent = Freq / sum(Freq)*100)

# A tibble: 12 x 4
# Groups:   Condition [2]
   Cell_Type   Condition  Freq Percent
   <fct>       <fct>     <int>   <dbl>
 1 Fibroblast  treated    6051   55.1 
 2 T cell      treated    1892   17.2 
 3 Macrophage  treated    1133   10.3 
 4 Stellate    treated     893    8.13
 5 Acinar      treated     148    1.35
 6 Endothelial treated     868    7.90
 7 Fibroblast  untreated  5331   34.5 
 8 T cell      untreated  3757   24.3 
 9 Macrophage  untreated  1802   11.7 
10 Stellate    untreated  1061    6.87
11 Acinar      untreated  2786   18.0 
12 Endothelial untreated   704    4.56

然后，对于作图部分：

library(ggplot2)
ggplot(DF, aes(x = Condition, y = Percent, fill = Cell_Type))+
  geom_bar(stat = "identity")+
  geom_text(aes(label = paste(round(Percent,2),"%")), position = position_stack(vjust =  0.5))

​

​

它能回答你的问题吗？