Question

我有一些数据，我正在创建一个使用ggplot2的堆积条形图。对于x轴上的每个样本，另外我每个样本也有三个因子。我想在条形图和样本名称之间根据因子绘制每个样本的不同颜色的正方形。由于每个样本有三个因素，我想绘制这些是三行正方形，有点像华夫饼图。下面的数据显示了我对每个样本的三个因素 - “tissue_type”，“biopsy_type”和“gleason_score”。有什么方法可以将所有这些一起绘制出来吗？堆积的条形图和一种华夫饼图？

数据：

.collapse

我目前如何使用ggplot

制作堆积条形图

> total
 aberration_type Freq              sample_name tissue_type biopsy_type
1    homozygous_loss   42 160078-N_S16_L001_R1_001      Normal      Normal
2  heterozygous_loss  200 160078-N_S16_L001_R1_001      Normal      Normal
3    homozygous_loss   56 160078-T_S17_L001_R1_001      Tumour      Repeat
4  heterozygous_loss 1917 160078-T_S17_L001_R1_001      Tumour      Repeat
5               gain  666 160078-T_S17_L001_R1_001      Tumour      Repeat
6    homozygous_loss   42  160079-N_S7_L001_R1_001      Normal      Normal
7  heterozygous_loss   78  160079-N_S7_L001_R1_001      Normal      Normal
8    homozygous_loss  193  160079-T_S8_L001_R1_001      Tumour      Repeat
9  heterozygous_loss 4336  160079-T_S8_L001_R1_001      Tumour      Repeat
10              gain  129  160079-T_S8_L001_R1_001      Tumour      Repeat
11   homozygous_loss   42 160080-N_S20_L001_R1_001      Normal      Normal
12 heterozygous_loss   78 160080-N_S20_L001_R1_001      Normal      Normal
13   homozygous_loss   42 160081-N_S21_L001_R1_001      Normal      Normal
14 heterozygous_loss   76 160081-N_S21_L001_R1_001      Normal      Normal
15   homozygous_loss   42 160081-T_S22_L001_R1_001      Tumour      Repeat
16 heterozygous_loss 1191 160081-T_S22_L001_R1_001      Tumour      Repeat
17              gain   59 160081-T_S22_L001_R1_001      Tumour      Repeat
18   homozygous_loss   42 160082-N_S23_L001_R1_001      Normal      Normal
19 heterozygous_loss    6 160082-N_S23_L001_R1_001      Normal      Normal
20   homozygous_loss   42 160083-N_S24_L001_R1_001      Normal      Normal
21 heterozygous_loss    6 160083-N_S24_L001_R1_001      Normal      Normal
22   homozygous_loss   42 160083-T_S25_L001_R1_001      Tumour      Repeat
23 heterozygous_loss  515 160083-T_S25_L001_R1_001      Tumour      Repeat
24              gain   88 160083-T_S25_L001_R1_001      Tumour      Repeat
25   homozygous_loss   42 160084-N_S26_L001_R1_001      Normal      Normal
26 heterozygous_loss   79 160084-N_S26_L001_R1_001      Normal      Normal
27   homozygous_loss   42 160084-T_S27_L001_R1_001      Tumour     Initial
28 heterozygous_loss  671 160084-T_S27_L001_R1_001      Tumour     Initial
29              gain   56 160084-T_S27_L001_R1_001      Tumour     Initial
30   homozygous_loss   42  160088-N_S5_L001_R1_001      Normal      Normal
31 heterozygous_loss   63  160088-N_S5_L001_R1_001      Normal      Normal
32   homozygous_loss   42  160088-T_S6_L001_R1_001      Tumour     Initial
33 heterozygous_loss    6  160088-T_S6_L001_R1_001      Tumour     Initial
34   homozygous_loss   42 160089-N_S28_L001_R1_001      Normal      Normal
35 heterozygous_loss  114 160089-N_S28_L001_R1_001      Normal      Normal
36   homozygous_loss  113 160089-T_S29_L001_R1_001      Tumour      Repeat
37 heterozygous_loss 4196 160089-T_S29_L001_R1_001      Tumour      Repeat
38              gain    8 160089-T_S29_L001_R1_001      Tumour      Repeat
39   homozygous_loss   42 160090-N_S13_L001_R1_001      Normal      Normal
40 heterozygous_loss   75 160090-N_S13_L001_R1_001      Normal      Normal
41   homozygous_loss   42 160091-N_S14_L001_R1_001      Normal      Normal
42 heterozygous_loss   74 160091-N_S14_L001_R1_001      Normal      Normal
43   homozygous_loss   42 160091-T_S15_L001_R1_001      Tumour      Repeat
44 heterozygous_loss  194 160091-T_S15_L001_R1_001      Tumour      Repeat
45   homozygous_loss   41  160093-N_S9_L001_R1_001      Normal      Normal
46 heterozygous_loss    6  160093-N_S9_L001_R1_001      Normal      Normal
47   homozygous_loss   42 160093-T_S10_L001_R1_001      Tumour     Initial
48 heterozygous_loss 1034 160093-T_S10_L001_R1_001      Tumour     Initial
49   homozygous_loss   42 160094-N_S11_L001_R1_001      Normal      Normal
50 heterozygous_loss   77 160094-N_S11_L001_R1_001      Normal      Normal
51   homozygous_loss   42 160094-T_S12_L001_R1_001      Tumour      Repeat
52 heterozygous_loss 2192 160094-T_S12_L001_R1_001      Tumour      Repeat
53              gain   10 160094-T_S12_L001_R1_001      Tumour      Repeat
54   homozygous_loss   42  160095-N_S1_L001_R1_001      Normal      Normal
55 heterozygous_loss   76  160095-N_S1_L001_R1_001      Normal      Normal
56   homozygous_loss   41  160095-T_S2_L001_R1_001      Tumour     Initial
57 heterozygous_loss  442  160095-T_S2_L001_R1_001      Tumour     Initial
58   homozygous_loss   42  160096-N_S4_L001_R1_001      Normal      Normal
59 heterozygous_loss    6  160096-N_S4_L001_R1_001      Normal      Normal
60   homozygous_loss   42  160096-T_S4_L001_R1_001      Tumour      Repeat
61 heterozygous_loss  484  160096-T_S4_L001_R1_001      Tumour      Repeat
62   homozygous_loss   42  160098-N_S4_L001_R1_001      Normal      Normal
63 heterozygous_loss   68  160098-N_S4_L001_R1_001      Normal      Normal
64   homozygous_loss   42  160098-T_S4_L001_R1_001      Tumour     Initial
65 heterozygous_loss  598  160098-T_S4_L001_R1_001      Tumour     Initial
   gleason_score
1         Normal
2         Normal
3            3_4
4            3_4
5            3_4
6         Normal
7         Normal
8            3_3
9            3_3
10           3_3
11        Normal
12        Normal
13        Normal
14        Normal
15           3_3
16           3_3
17           3_3
18        Normal
19        Normal
20        Normal
21        Normal
22           3_3
23           3_3
24           3_3
25        Normal
26        Normal
27           3_3
28           3_3
29           3_3
30        Normal
31        Normal
32           3_3
33           3_3
34        Normal
35        Normal
36           3_4
37           3_4
38           3_4
39        Normal
40        Normal
41        Normal
42        Normal
43           3_3
44           3_3
45        Normal
46        Normal
47           3_3
48           3_3
49        Normal
50        Normal
51           3_4
52           3_4
53           3_4
54        Normal
55        Normal
56           3_3
57           3_3
58        Normal
59        Normal
60           3_4
61           3_4
62        Normal
63        Normal
64           3_3
65           3_3

Answer 1

我确信这是可能的 - 但总的来说， faceting 是一种通过不同因素可视化数据的好方法。这是一个初步尝试。它使样品标签有点拥挤，条形不太清晰（至少在这个小版本中），但确实说明了主要发现：即肿瘤活检中杂合性损失较高。

ggplot(total, aes(x = reorder(sample_name, -Freq),
                  y = Freq,
                  fill = aberration_type)) +
geom_col() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size=5)) +
labs(title = "Frequency aberrant bins",
     x = "Sample Name",
     y = "Frequency") + 
facet_grid(biopsy_type ~ tissue_type + gleason_score)

结果：

对于更清晰的图表，您可以使用较少的因素例如。只是活组织检查类型：

+ facet_grid(biopsy_type ~ .)

如何在堆积的条形图下面附上华夫饼图

1 个答案: