forked from JauntyJJS/BioPAN_Tutorial
-
Notifications
You must be signed in to change notification settings - Fork 0
/
2_BioPAN_species_case.Rmd
294 lines (198 loc) · 8.82 KB
/
2_BioPAN_species_case.Rmd
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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
---
output: html_document
editor_options:
chunk_output_type: console
---
## Active Case
Let us consider the pathway of `PS(42:8)` to `PE(42:8)` to `PC(42:8)`.
![](figures/giz061_Active42_8_pathway.PNG)
The dataset are as follows:
```{r label=Active_Species_Data, fig.height=figheight, fig.width=figwidth, message=FALSE, warning=FALSE, dpi=150}
samples <- c("24h CON", "24h CON", "24h CON",
"24h AA", "24h AA", "24h AA")
group <- c("CON", "CON", "CON",
"AA", "AA", "AA")
PS42_8 <- c(0.039096671, 0.048629063, 0.033723608,
0.047177176, 0.041427748, 0.043042335)
PE42_8 <- c(0.005358486, 0.00509418, 0.00489321,
0.160215808, 0.151076058, 0.166159881)
PC42_8 <- c(0.01543998, 0.015312104, 0.015252706,
1.286319709, 1.083053662, 1.261169034)
active_data <- tibble::tibble(
Sample = samples,
Group = group,
`PS(42:8)` = PS42_8,
`PE(42:8)` = PE42_8,
`PC(42:8)` = PC42_8
)
active_data
```
### `PS(42:8)` to `PE(42:8)`
**Compute the weight for product `PE(42:8)` over reactant `PS(42:8)` for each sample.**
```{r label=weight_vector_PE42_8_over_PS42_8, message=FALSE, warning=FALSE}
weights <- (active_data$`PE(42:8)`/active_data$`PS(42:8)`)
active_data <- active_data %>%
dplyr::mutate(`PE(42:8) over PS(42:8)` = weights)
active_data %>%
dplyr::select(-c(.data$`PC(42:8)`))
```
**Compute a one-sided Welch $t$-test between the samples of interest (Group AA) and the control samples (Group CON).**
```{r label=t.test_PE42_8_over_PS42_8, message=FALSE, warning=FALSE, results='asis'}
aa_samples <- active_data %>%
dplyr::filter(.data$Group == "AA") %>%
dplyr::pull(.data$`PE(42:8) over PS(42:8)`)
control_samples <- active_data %>%
dplyr::filter(.data$Group == "CON") %>%
dplyr::pull(.data$`PE(42:8) over PS(42:8)`)
t1 <- t.test(aa_samples, control_samples, alternative = "greater")
report::report(t1)
cat(paste("$p$ value is", format(t1$p.value, scientific = TRUE, nsmall = 3)))
```
**Convert the $p$ value into a $Z$ Score.**
This is also the pathway score for `PS(42:8)` to `PE(42:8)`.
```{r label=Zscore_PE42_8_over_PS42_8, message=FALSE, warning=FALSE, results='asis'}
z_score1 <- qnorm(1 - t1$p.value)
cat(paste("$Z$ score for `PS(42:8)` to `PE(42:8)` is", format(z_score1, nsmall = 3)))
```
### `PE(42:8)` to `PC(42:8)`
**Compute the weight for product `PC(42:8)` over reactant `PE(42:8)` for each sample.**
```{r label=weight_vector_PC42_8_over_PE42_8, message=FALSE, warning=FALSE}
weights <- (active_data$`PC(42:8)`/active_data$`PE(42:8)`)
active_data <- active_data %>%
dplyr::mutate(`PC(42:8) over PE(42:8)` = weights)
active_data %>%
dplyr::select(-c(.data$`PS(42:8)`,.data$`PE(42:8) over PS(42:8)`))
```
**Compute a one-sided Welch $t$-test between the samples of interest (Group AA) and the control samples (Group CON).**
```{r label=t.test_PC42_8_over_PE42_8, message=FALSE, warning=FALSE, results='asis'}
aa_samples <- active_data %>%
dplyr::filter(.data$Group == "AA") %>%
dplyr::pull(.data$`PC(42:8) over PE(42:8)`)
control_samples <- active_data %>%
dplyr::filter(.data$Group == "CON") %>%
dplyr::pull(.data$`PC(42:8) over PE(42:8)`)
t2 <- t.test(aa_samples, control_samples, alternative = "greater")
report::report(t2)
cat(paste("$p$ value is", format(t2$p.value, scientific = TRUE, nsmall = 3)))
```
**Convert the $p$ value into a $Z$ score.**
This is also the pathway score for `PE(42:8)` to `PC(42:8)`.
```{r label=Zscore_PC42_8_over_PE42_8, message=FALSE, warning=FALSE, results='asis'}
z_score2 <- qnorm(1 - t2$p.value)
cat(paste("$Z$ score for `PE(42:8)` to `PC(42:8)` is", format(z_score2, nsmall = 3)))
```
**Compute $Z_{A}$ for pathway `PS(42:8)` to `PE(42:8)` to `PC(42:8)`.**
Recall the formula is defined as:
![](figures/giz061_BioPAN_ZA_score.PNG)
where $k$ is 2 and $Z_{i}$ are the pathway scores `PS(42:8)` to `PE(42:8)` and `PE(42:8)` to `PC(42:8)` computed earlier.
```{r label=Zscore_active_pathway, message=FALSE, warning=FALSE, results='asis'}
z_a <- (1/sqrt(2)) * (z_score1 + z_score2)
cat(paste("$Z_{A}$ is", format(z_a, nsmall = 3)))
```
With this settings,
![](figures/giz061_Pvalue_0_05.PNG)
Since $Z_{A} > 1.645$, the pathway is classified as active.
## Suppressed Case
Let us consider the pathway of `PS(32:0)` to `PE(32:0)` to `PC(32:0)`.
![](figures/giz061_Suppressed32_0_pathway.PNG)
The dataset are as follows:
```{r label=Suppressed_Species_Data_1, fig.height=figheight, fig.width=figwidth, message=FALSE, warning=FALSE, dpi=150}
samples <- c("24h CON", "24h CON", "24h CON",
"24h AA", "24h AA", "24h AA")
group <- c("CON", "CON", "CON",
"AA", "AA", "AA")
PS32_0 <- c(0.11392858, 0.080762026, 0.128541348,
0.656895224, 0.800790573, 0.592724899)
PE32_0 <- c(0.046063214, 0.043759251, 0.047335343,
0.175927791, 0.183855506, 0.194325215)
PC32_0_1 <- c(1.074150848, 0.726798053, 0.412228743,
1.94494173, 1.520645133, 1.337827826)
PC32_0_2 <- c(0.928888882, 0.964353269, 0.789633482,
1.666428947, 1.375398801, 1.616097007)
suppressed_data <- tibble::tibble(
Sample = samples,
Group = group,
`PS(32:0)` = PS32_0,
`PE(32:0)` = PE32_0,
`PC(32:0) 1` = PC32_0_1,
`PC(32:0) 2` = PC32_0_2
)
suppressed_data
```
BioPAN will give a warning on the duplicated transition `PC(32:0)` and sum them up.
![](figures/giz061_32_0_warning.PNG)
```{r label=Suppressed_Species_Data_2, fig.height=figheight, fig.width=figwidth, message=FALSE, warning=FALSE, dpi=150}
suppressed_data <- tibble::tibble(
Sample = samples,
Group = group,
`PS(32:0)` = PS32_0,
`PE(32:0)` = PE32_0,
`PC(32:0)` = PC32_0_1 + PC32_0_2
)
suppressed_data
```
### `PS(32:0)` to `PE(32:0)`
**Compute the weight for product `PE(32:0)` over reactant `PS(32:0)` for each sample.**
```{r label=weight_vector_PE32_0_over_PS32_0, message=FALSE, warning=FALSE}
weights <- (suppressed_data$`PE(32:0)`/suppressed_data$`PS(32:0)`)
suppressed_data <- suppressed_data %>%
dplyr::mutate(`PE(32:0) over PS(32:0)` = weights)
suppressed_data %>%
dplyr::select(-c(.data$`PC(32:0)`))
```
**Compute a one-sided Welch $t$-test between the samples of interest (Group AA) and the control samples (Group CON).**
```{r label=t.test_PE32_0_over_PS32_0, message=FALSE, warning=FALSE, results='asis'}
aa_samples <- suppressed_data %>%
dplyr::filter(.data$Group == "AA") %>%
dplyr::pull(.data$`PE(32:0) over PS(32:0)`)
control_samples <- suppressed_data %>%
dplyr::filter(.data$Group == "CON") %>%
dplyr::pull(.data$`PE(32:0) over PS(32:0)`)
t1 <- t.test(aa_samples, control_samples, alternative = "less")
report::report(t1)
cat(paste("$p$ value is", format(t1$p.value, scientific = TRUE, nsmall = 3)))
```
**Convert the $p$ value into a $Z$ score.**
This is also the pathway score for `PS(32:0)` to `PE(32:0)`.
```{r label=Zscore_PE32_0_over_PS32_0, message=FALSE, warning=FALSE, results='asis'}
z_score1 <- qnorm(1 - t1$p.value)
cat(paste("$Z$ score for`PS(32:0)` to `PE(32:0)` is", format(z_score1, nsmall = 3)))
```
### `PE(32:0)` to `PC(32:0)`
**Compute the weight for product `PC(32:0)` over reactant `PE(32:0)` for each sample.**
```{r label=weight_vector_PC32_0_over_PE32_0, message=FALSE, warning=FALSE}
weights <- (suppressed_data$`PC(32:0)`/suppressed_data$`PE(32:0)`)
suppressed_data <- suppressed_data %>%
dplyr::mutate(`PC(32:0) over PE(32:0)` = weights)
suppressed_data %>%
dplyr::select(-c(.data$`PS(32:0)`,.data$`PE(32:0) over PS(32:0)`))
```
**Compute a one-sided Welch $t$-test between the samples of interest (Group AA) and the control samples (Group CON).**
```{r label=t.test_PC32_0_over_PE32_0, message=FALSE, warning=FALSE, results='asis'}
aa_samples <- suppressed_data %>%
dplyr::filter(.data$Group == "AA") %>%
dplyr::pull(.data$`PC(32:0) over PE(32:0)`)
control_samples <- suppressed_data %>%
dplyr::filter(.data$Group == "CON") %>%
dplyr::pull(.data$`PC(32:0) over PE(32:0)`)
t2 <- t.test(aa_samples, control_samples, alternative = "less")
report::report(t2)
cat(paste("$p$ value is", format(t2$p.value, scientific = TRUE, nsmall = 3)))
```
**Convert the $p$ value into a $Z$ score.**
This is also the pathway score for `PE(32:0)` to `PC(32:0)`.
```{r label=Zscore_PC32_0_over_PE32_0, message=FALSE, warning=FALSE, results='asis'}
z_score2 <- qnorm(1 - t2$p.value)
cat(paste("$Z$ score for `PE(32:0)` to `PC(32:0)` is", format(z_score2, nsmall = 3)))
```
**Compute $Z_{A}$ for pathway `PS(32:0)` to `PE(32:0)` to `PC(32:0)`.**
Recall the formula is defined as:
![](figures/giz061_BioPAN_ZA_score.PNG)
where $k$ is 2 and $Z_{i}$ are the pathway scores `PS(32:0)` to `PE(32:0)` and `PE(32:0)` to `PC(32:0)` computed earlier.
```{r label=Zscore_suppressed_pathway, message=FALSE, warning=FALSE, results='asis'}
z_a <- (1/sqrt(2)) * (z_score1 + z_score2)
cat(paste("$Z_{A}$ is", format(z_a, nsmall = 3)))
```
With this settings,
![](figures/giz061_Pvalue_0_05.PNG)
Since $Z_{A} > 1.645$, the pathway is classified as suppressed.