-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathLu_DIU.qmd
174 lines (155 loc) · 5.66 KB
/
Lu_DIU.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
---
title: "TM_1_DIU"
format: html
editor: visual
---
## Quarto
Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see <https://quarto.org>.
```{r}
library(rats)
library(tidyverse)
library(data.table)
library(ggplot2)
```
```{r}
Ghir_iso_tpm <- read.table("Ghir_isoform.tpm", header = T, check.names = F) %>%
rownames_to_column(var = "Isoform id")
```
```{r}
Ghid <- read.table("lu_iso_gene_id", col.names = c("target id", "parent id"))
```
```{r}
GhGTF <- gtf2ids("Ghincfib_gene.gtf")
```
```{r}
ovule_3dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_ovule_-3dpa-1_L2", "TM-1_ovule_-3dpa-2_L2", "TM-1_ovule_-3dpa-3_L2")])
ovule_1dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_ovule_-1dpa-1_L2", "TM-1_ovule_-1dpa-2_L2", "TM-1_ovule_-1dpa-3_L2")])
ovule0dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_ovule_0dpa-1_L2", "TM-1_ovule_0dpa-2_L2", "TM-1_ovule_0dpa-3_L1")])
ovule1dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_ovule_1dpa-1_L1", "TM-1_ovule_1dpa-2_L1", "TM-1_ovule_1dpa-3_L2")])
ovule3dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_ovule_3dpa-1_L1", "TM-1_ovule_3dpa-2_L1", "TM-1_ovule_3dpa-3_L2")])
ovule5dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_ovule_5dpa-1_L2", "TM-1_ovule_5dpa-2_L1","TM-1_ovule_5dpa-3_L2")])
fiber10dpa <- data.table(Ghir_iso_tpm[, c(1, 20:22)])
fiber15dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_fiber_15dpa-1_L2", "TM-1_fiber_15dpa-2_L2", "TM-1_fiber_15dpa-3_L3")])
fiber20dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_fiber_20dpa-1_L2", "TM-1_fiber_20dpa-2_L2", "TM-1_fiber_20dpa-3_L2")])
fiber25dpa <- data.table(Ghir_iso_tpm[, c("Isoform id", "TM-1_fiber_25dpa-1_L2", "TM-1_fiber_25dpa-2_L2", "TM-1_fiber_25dpa-3_L2")])
```
```{r}
diu_1dpavs_3dpa <- call_DTU(annot = GhGTF,
count_data_A = ovule_3dpa,
count_data_B = ovule_1dpa,
name_A = "ovule_3dpa",
name_B = "ovule_1dpa",
scaling = 1,
verbose = FALSE,
description = "Ovule_1VS_3DPA",
dprop_thresh = 0.2)
```
```{r}
diu0dpavs_1dpa <- call_DTU(annot = GhGTF,
count_data_A = ovule_1dpa,
count_data_B = ovule0dpa,
name_A = "ovule_1dpa",
name_B = "ovule0dpa",
scaling = 1,
verbose = FALSE,
description = "Ovule0VS_1DPA",
dprop_thresh = 0.2)
```
```{r}
diu1dpavs0dpa <- call_DTU(annot = GhGTF,
count_data_A = ovule0dpa,
count_data_B = ovule3dpa,
name_A = "ovule0dpa",
name_B = "ovule1dpa",
scaling = 1,
verbose = FALSE,
description = "Ovule1VS0DPA",
dprop_thresh = 0.2)
```
```{r}
diu3dpavs1dpa <- call_DTU(annot = GhGTF,
count_data_A = ovule1dpa,
count_data_B = ovule3dpa,
name_A = "ovule1dpa",
name_B = "ovule3dpa",
scaling = 1,
verbose = FALSE,
description = "Ovule3VS1DPA",
dprop_thresh = 0.2)
```
```{r}
diu5dpavs3dpa <- call_DTU(annot = GhGTF,
count_data_A = ovule3dpa,
count_data_B = ovule5dpa,
name_A = "ovule3dpa",
name_B = "ovule5dpa",
scaling = 1,
verbose = FALSE,
description = "Ovule5VS3DPA",
dprop_thresh = 0.2)
```
```{r}
diu10dpavs5dpa <- call_DTU(annot = GhGTF,
count_data_A = ovule5dpa,
count_data_B = fiber10dpa,
name_A = "ovule5dpa",
name_B = "fiber10dpa",
scaling = 1,
verbose = FALSE,
description = "Fiber10VS5DPA",
dprop_thresh = 0.2)
```
```{r}
diu15dpavs10dpa <- call_DTU(annot = GhGTF,
count_data_A = fiber10dpa,
count_data_B = fiber15dpa,
name_A = "Fiber10DPA",
name_B = "fiber15dpa",
scaling = 1,
verbose = FALSE,
description = "Fiber10VS15DPA",
dprop_thresh = 0.2)
```
```{r}
diu20dpavs15dpa <- call_DTU(annot = GhGTF,
count_data_A = fiber15dpa,
count_data_B = fiber20dpa,
name_A = "fiber15dpa",
name_B = "fiber20dpa",
scaling = 1,
verbose = FALSE,
description = "Fiber20VS15DPA",
dprop_thresh = 0.2)
```
```{r}
diu25dpavs20dpa <- call_DTU(annot = GhGTF,
count_data_A = fiber20dpa,
count_data_B = fiber25dpa,
name_A = "fiber20dpa",
name_B = "fiber25dpa",
scaling = 1,
verbose = FALSE,
description = "Fiber25VS20DPA",
dprop_thresh = 0.2)
```
```{r}
library(readxl)
sum_DIU <- read_excel("TM_1_sumDIU.xlsx")
```
```{r}
#展示
sum_DIU$ID <- as.factor(seq_along(sum_DIU$Stage))
sum_DIU$Stage <- factor(sum_DIU$Stage, levels = sum_DIU$Stage[order(as.numeric(sum_DIU$ID))])
```
```{r}
pdf("TM_1_sumDIU.pdf", width = 12, height = 10)
# 绘制圆形图
ggplot(sum_DIU, aes(x = ID, y = DIU, fill = Stage)) +
geom_col() + # 使用 geom_col 绘制柱状图
coord_polar() + # 转换坐标系为极坐标，形成圆形图
theme_void() + # 移除多余的背景和标签
scale_fill_brewer(palette = "YlOrBr") +
labs(title = "Differential Isoform Usage across Stages", fill = "Stage") +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
dev.off()
```