# Chapter 21 Analyses for Figure 2 of “ASK1 inhibits browning of white adipose tissue in obesity”

## 21.1 Setup

Some plots in this document require the file “ggplotsci.R” within the “R” folder, within the project folder.

```
::opts_chunk$set(echo = TRUE,
knitrwarning=FALSE,
message=FALSE)
# wrangling packages
library(here)
library(janitor)
library(readxl)
library(data.table)
library(stringr)
# analysis packages
library(emmeans)
library(car) # qqplot, spreadlevel
library(DHARMa)
# graphing packages
library(ggsci)
library(ggpubr)
library(ggforce)
library(cowplot)
library(lazyWeave) #pvalstring
<- here::here
here <- "data"
data_path
<- here("R", "ggplotsci.R")
ggplotsci_path source(ggplotsci_path)
```

## 21.2 Data source

Data source: ASK1 inhibits browning of white adipose tissue in obesity

This chunk assigns the path to the Excel data file for all panels of Figure 2. The data for each panel are in a single sheet in the Excel file named “Source Date_Figure 2.”

```
<- "ASK1 inhibits browning of white adipose tissue in obesity"
data_folder <- "41467_2020_15483_MOESM4_ESM.xlsx"
file_name <- here(data_path, data_folder, file_name)
file_path
<- "Source Date_Figure 2" fig_2_sheet
```

## 21.3 control the color palette

```
<- pal_okabe_ito[5:6]
fig_2_palette # fig_2_palette <- pal_okabe_ito
#fig_2_palette <- pal_nature_mod
```

## 21.4 useful functions

A function to import longitudinal data from Fig 2

```
# function to read in parts of 2b
<- function(range_2){
import_fig_2_part <- read_excel(file_path,
fig_2_part sheet = fig_2_sheet,
range = range_2,
col_names = TRUE) %>%
data.table()
<- colnames(fig_2_part)[1]
group setnames(fig_2_part, old = group, new = "treatment")
:= as.character(treatment)] # this was read as logical
fig_2_part[, treatment := group] # assign treatment group
fig_2_part[, treatment := paste(group, .I)]
fig_2_part[, mouse_id return(fig_2_part)
}
```

```
# script to compute various area under the curves (AUC) using trapezoidal method
# le Floch's "incremental" auc substracts the baseline value from all points.
# This can create some elements with negative area if post-baseline values are less
# than baseline value.
# Some researchers "correct" this by setting any(y - ybar < 0 to zero. Don't do this.
<- function(x, y, method="auc", average = FALSE){
auc # method = "auc", auc computed using trapezoidal calc
# method = "iauc" is an incremental AUC of Le Floch
# method = "pos_iauc" is a "positive" incremental AUC of Le Floch but not Wolever
# method = "post_0_auc" is AUC of post-time0 values
# if average then divide area by duration
if(method=="iauc"){y <- y - y[1]}
if(method=="pos_iauc"){y[y < 0] <- 0}
if(method=="post_0_auc"){
<- x[-1]
x <- y[-1]
y
}<- length(x)
n <- 0
area for(i in 2:n){
<- area + (x[i] - x[i-1])*(y[i-1] + y[i])
area
}<- area/2
value if(average == TRUE){
<- value/(x[length(x)] - x[1])
value
}return(value)
}
```

## 21.5 figure 2b – effect of ASK1 deletion on growth (body weight)

### 21.5.1 figure 2b – import

```
<- c("A21:N41", "A43:N56", "A58:N110", "A112:N170")
range_list <- data.table(NULL)
fig_2b_wide for(range_i in range_list){
<- import_fig_2_part(range_i)
part <- rbind(fig_2b_wide,
fig_2b_wide
part)
}
<- melt(fig_2b_wide,
fig_2b id.vars = c("treatment", "mouse_id"),
variable.name = "week",
value.name = "body_weight")
:= as.numeric(as.character(week))]
fig_2b[, week c("ask1", "diet") := tstrsplit(treatment, " ", fixed=TRUE)]
fig_2b[, := factor(week)] fig_2b[, week_f
```

### 21.5.2 figure 2b – exploratory plots

```
qplot(x = week,
y = body_weight,
data = fig_2b,
color = treatment) +
facet_grid(ask1~diet)
```

- no obvious outliers
- reduced growth rate at bigger size

```
qplot(x = week,
y = body_weight,
data = fig_2b,
color = treatment) +
geom_smooth()
```

- loess smooth. Growth in ASK1F/F + HFD clearly greater than other three treatment combinations.

## 21.6 Figure 2c – Effect of ASK1 deletion on final body weight

### 21.6.1 Figure 2c – import

```
<- "A173:BD176"
range_2c <- c("ASK1F/F chow", "ASK1Δadipo chow", "ASK1F/F HFD", "ASK1Δadipo HFD")
y_cols <- read_excel(file_path,
fig_2c_import sheet = fig_2_sheet,
range = range_2c,
col_names = FALSE) %>%
transpose(make.names=1) %>%
data.table() %>%
melt(measure.vars = y_cols,
variable.name = "treatment",
value.name = "body_weight_gain") %>%
na.omit()
:= paste(treatment, .I, sep = "_"),
fig_2c_import[, mouse_id = treatment] by
```

### 21.6.2 Figure 2c – check own computation of weight change v imported value

Note that three cases are missing from fig_2c import that are in fig_2b

```
# change colnames to char
<- copy(fig_2b_wide)
fig_2c <- unique(fig_2b[, week])
weeks setnames(fig_2c,
old = colnames(fig_2c),
new = c("treatment", paste0("week_", weeks), "mouse_id"))
:= week_12 - week_0]
fig_2c[, weight_gain <- fig_2c[, .SD, .SDcols = c("treatment",
fig_2c "week_0",
"week_12",
"weight_gain")]
:= paste(treatment, .I, sep = "_"),
fig_2c[, mouse_id = treatment]
by
c("ask1", "diet") := tstrsplit(treatment, " ", fixed=TRUE)]
fig_2c[,
<- merge(fig_2c,
fig_2c_check
fig_2c_import,by = c("mouse_id"),
all = TRUE)
#View(fig_2c_check)
```

### 21.6.3 Figure 2c – exploratory plots

```
qplot(x = treatment,
y = weight_gain,
data = fig_2c)
```

- no obvious outliers
- variance increases with mean, as expected from growth, suggests a multiplicative model. But start with simple lm.

### 21.6.4 Figure 2c – fit the model: m1 (lm)

`<- lm(weight_gain ~ week_0 + ask1*diet, data = fig_2c) fig_2c_m1 `

### 21.6.5 Figure 2c – check the model: m1

```
# check normality assumption
set.seed(1)
qqPlot(fig_2c_m1, id=FALSE)
```

`spreadLevelPlot(fig_2c_m1, id=FALSE)`

```
##
## Suggested power transformation: 0.06419448
```

- QQ indicates possible right skew but especially left side is squashed toward mean
- spread-level indicates variance increases with mean

For p-value, this may not be too severe but for intervals, best to account for this. Try gamma with log link (which makes biological sense for growth)

### 21.6.6 Figure 2c – fit the model: m2 (gamma glm)

```
<- glm(weight_gain ~ week_0 + ask1*diet,
fig_2c_m2 family = Gamma(link = "log"),
data = fig_2c)
```

### 21.6.7 Figure 2c – check the model, m2

```
set.seed(1)
<- simulateResiduals(fig_2c_m2, n=250)
fig_2c_m2_sim plot(fig_2c_m2_sim)
```

- well behaved QQ and spread-level

### 21.6.8 Figure 2c – inference from the model

```
<- cbind(coef(summary(fig_2c_m2)),
coef_table exp(confint(fig_2c_m2))) %>%
data.table(keep.rownames = TRUE)
:=exp(Estimate)]
coef_table[, Estimate::kable(coef_table, digits = c(0,2,2,2,4,2,2)) knitr
```

rn | Estimate | Std. Error | t value | Pr(>|t|) | 2.5 % | 97.5 % |
---|---|---|---|---|---|---|

(Intercept) | 10.87 | 0.35 | 6.83 | 0.0000 | 5.53 | 21.50 |

week_0 | 0.99 | 0.01 | -0.84 | 0.3997 | 0.96 | 1.02 |

ask1ASK1Δadipo | 1.03 | 0.11 | 0.24 | 0.8138 | 0.83 | 1.28 |

dietHFD | 1.56 | 0.08 | 5.45 | 0.0000 | 1.33 | 1.82 |

ask1ASK1Δadipo:dietHFD | 0.79 | 0.13 | -1.93 | 0.0551 | 0.61 | 1.00 |

```
<- emmeans(fig_2c_m2, specs = c("diet", "ask1"),
fig_2c_m2_emm type = "response")
<- contrast(fig_2c_m2_emm,
fig_2c_m2_pairs method = "revpairwise",
simple = "each",
combine = TRUE,
adjust = "none") %>%
summary(infer = TRUE)
```

` fig_2c_m2_emm`

```
## diet ask1 response SE df asymp.LCL asymp.UCL
## chow ASK1F/F 8.19 0.568 Inf 7.15 9.39
## HFD ASK1F/F 12.75 0.548 Inf 11.72 13.87
## chow ASK1Δadipo 8.41 0.721 Inf 7.11 9.95
## HFD ASK1Δadipo 10.28 0.424 Inf 9.48 11.14
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale
```

` fig_2c_m2_pairs`

```
## ask1 diet contrast ratio SE df asymp.LCL asymp.UCL
## ASK1F/F . HFD / chow 1.556 0.1263 Inf 1.328 1.825
## ASK1Δadipo . HFD / chow 1.222 0.1168 Inf 1.013 1.474
## . chow ASK1Δadipo / (ASK1F/F) 1.026 0.1127 Inf 0.828 1.273
## . HFD ASK1Δadipo / (ASK1F/F) 0.806 0.0485 Inf 0.716 0.907
## z.ratio p.value
## 5.453 <.0001
## 2.097 0.0360
## 0.236 0.8134
## -3.587 0.0003
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale
## Tests are performed on the log scale
```

- within ASK1 Cn, HFD mean is 1.6X chow mean
- within ASK1 KO, HFD mean is 1.2X chow mean
- within chow, ASK1 KO mean is 1.0X ASK1 Cn mean
- within HFD, ASK1 KO mean is 0.86X ASK1 Cn mean

```
# same as interaction effect in coefficient table
contrast(fig_2c_m2_emm, interaction = "pairwise", by = NULL) %>%
summary(infer = TRUE)
```

```
## diet_pairwise ask1_pairwise ratio SE df asymp.LCL asymp.UCL
## chow / HFD (ASK1F/F) / ASK1Δadipo 0.785 0.0982 Inf 0.614 1
## z.ratio p.value
## -1.934 0.0531
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale
## Tests are performed on the log scale
```

- the reduction in weight gain in the ASK1 KO mice compared to ASK1 CN is 0.785X. Notice that p > 0.05.

### 21.6.9 Figure 2c – plot the model

```
<- summary(fig_2c_m2_emm) %>%
fig_2c_m2_emm_dt
data.table<- data.table(fig_2c_m2_pairs)
fig_2c_m2_pairs_dt := pvalString(p.value)]
fig_2c_m2_pairs_dt[ , p_pretty
<- 0.8 # separation between groups
dodge_width # get x positions of brackets for p-values
# requires looking at table and mentally figuring out
# Chow is at x = 1 and HFD is at x = 2
:= c(1-dodge_width/4,
fig_2c_m2_pairs_dt[, group1 1+dodge_width/4,
1-dodge_width/4,
2-dodge_width/4)]
:= c(2-dodge_width/4,
fig_2c_m2_pairs_dt[, group2 2+dodge_width/4,
1+dodge_width/4,
2+dodge_width/4)]
<- position_dodge(width = dodge_width)
pd <- ggplot(data = fig_2c,
fig_2c_gg aes(x = diet,
y = weight_gain,
color = ask1)) +
# points
geom_sina(alpha = 0.5,
position = pd) +
# plot means and CI
geom_errorbar(data = fig_2c_m2_emm_dt,
aes(y = response,
ymin = asymp.LCL,
ymax = asymp.UCL,
color = ask1),
width = 0,
position = pd
+
)
geom_point(data = fig_2c_m2_emm_dt,
aes(y = response,
color = ask1),
size = 3,
position = pd
+
)
# plot p-values (y positions are adjusted by eye)
stat_pvalue_manual(fig_2c_m2_pairs_dt,
label = "p_pretty",
y.position=c(28.5, 31, 26, 26),
tip.length = 0.01) +
# aesthetics
ylab("Weight Gain") +
scale_color_manual(values=fig_2_palette,
name = NULL) +
theme_pubr() +
theme(legend.position="top") +
theme(axis.title.x=element_blank()) +
NULL
fig_2c_gg
```

### 21.6.10 Figure 2c – report

Results could be reported using either:

(This is inconsistent with plot, if using this, the plot should reverse what factor is on the x-axis and what factor is the grouping (color) variable) Mean weight gain in ASK1F/F mice on HFD was 1.56 (95% CI: 1.33, 1.82, \(p < 0.0001\)) times that of ASK1F/F mice on chow while mean weight gain in ASK1Δadipo mice on HFD was only 1.22 (95% CI: 1.01, 1.47, \(p = 0.036\)) times that of ASK1Δadipo mice on chow. This reduction in weight gain in ASK1Δadipo mice compared to ASK1F/F control mice was 0.79 times (95% CI; 0.61, 1.00, \(p = 0.0531\)).

(This is consistent with the plot in that its comparing difference in the grouping factor within each level of the factor on the x-axis) Mean weight gain in ASK1Δadipo mice on chow was trivially larger (1.03 times) than that in ASK1F/F mice on chow (95% CI: 0.83, 1.27, \(p = 0.81\)) while mean weight gain in ASK1Δadipo mice on HFD was smaller (0.81 times) than that in ASK1F/F control mice on HFD (95% CI: 0.72 , 0.91, \(p = 0.0003\)). This reduction in weight gain in ASK1Δadipo mice compared to ASK1Δadipo mice is 0.79 times (95% CI; 0.61, 1.00, \(p = 0.0531\)).

**note to research team**. The big difference in *p*-values between weight difference on chow and weight difference on HFD might lead one to believe there is a “difference in this difference.” Using a *p*-value = effect strategy, this is not supported.

## 21.7 Figure 2d – Effect of ASK1 KO on glucose tolerance (whole curve)

### 21.7.1 Figure 2d – Import

```
<- c("A179:H189", "A191:H199", "A201:H214", "A216:H230")
range_list <- data.table(NULL)
fig_2d_wide for(range_i in range_list){
<- import_fig_2_part(range_i)
part <- rbind(fig_2d_wide,
fig_2d_wide
part)
}c("ask1", "diet") := tstrsplit(treatment, " ", fixed=TRUE)]
fig_2d_wide[,
# melt
<- melt(fig_2d_wide,
fig_2d id.vars = c("treatment",
"ask1",
"diet",
"mouse_id"),
variable.name = "time",
value.name = "glucose")
:= as.numeric(as.character(time))]
fig_2d[, time
# for plot only (not analysis!)
<- 2
shift == "ASK1F/F chow", time_x := time - shift*1.5]
fig_2d[treatment == "ASK1Δadipo chow", time_x := time - shift*.5]
fig_2d[treatment == "ASK1F/F HFD", time_x := time + shift*.5]
fig_2d[treatment == "ASK1Δadipo HFD", time_x := time + shift*1.5] fig_2d[treatment
```

### 21.7.2 Figure 2d – exploratory plots

```
qplot(x = time_x, y = glucose, color = treatment, data = fig_2d) +
geom_line(aes(group = mouse_id), alpha = 0.3)
```

* no obvious unplausible outliers but two mice w/ high values in “F/F HFD” * similar at time zero (initial effect is trivial)

use AUC conditional on time 0 glucose

```
qplot(x = time,
y = glucose,
data = fig_2d,
color = treatment) +
geom_smooth()
```

### 21.7.3 Figure 2d – fit the model

### 21.7.4 Figure 2d – check the model

### 21.7.5 Figure 2d – inference

### 21.7.6 Figure 2d – plot the model

## 21.8 Figure 2e – Effect of ASK1 deletion on glucose tolerance (summary measure)

The researchers did create a table to import but this analysis uses the mean post-baseline glucose amount as the response instead of the area under the curve of over the full 120 minutes. This mean is computed as the post-baseline area under the curve divided by the duration of time of the post-baseline measures (105 minutes). This analysis will use fig_2d_wide since there is only one a single Y variable per mouse.

### 21.8.1 Figure 2e – message the data

```
# AUC of post-baseline values
# do this after melt as we don't need this in long format)
<- fig_2d_wide
fig_2e := get("0")]
fig_2e[, glucose_0
<- c(0, 15, 30, 45, 60, 90, 120)
times <- as.character(times)
time_cols <- fig_2e[, .SD, .SDcols = time_cols]
Y := apply(Y, 1, auc,
fig_2e[, glucose_mean x=times,
method = "post_0_auc",
average = TRUE)]
```

### 21.8.2 Figure 2e – exploratory plots

`qplot(x = treatment, y = glucose_mean, data = fig_2e)`

### 21.8.3 Figure 2e – fit the model

`<- lm(glucose_mean ~ glucose_0 + ask1*diet, data = fig_2e) fig_2e_m1 `

### 21.8.4 Figure 2e – check the model

```
# check normality assumption
set.seed(1)
qqPlot(fig_2e_m1, id=FALSE)
```

`spreadLevelPlot(fig_2e_m1, id=FALSE)`

```
##
## Suggested power transformation: -0.4035073
```

### 21.8.5 Figure 2e – inference from the model

```
<- coef(summary(fig_2e_m1))
fig_2e_m1_coef fig_2e_m1_coef
```

```
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6835026 1.2556730 6.9154169 2.460045e-08
## glucose_0 1.2194720 0.2390919 5.1004329 8.592596e-06
## ask1ASK1Δadipo -0.3488511 0.8759124 -0.3982716 6.925479e-01
## dietHFD 4.2782121 0.7908612 5.4095613 3.185930e-06
## ask1ASK1Δadipo:dietHFD -2.7503448 1.1288320 -2.4364518 1.937783e-02
```

```
<- emmeans(fig_2e_m1, specs = c("diet", "ask1"))
fig_2e_m1_emm <- contrast(fig_2e_m1_emm,
fig_2e_m1_pairs method = "revpairwise",
simple = "each",
combine = TRUE,
adjust = "none") %>%
summary(infer = TRUE)
```

` fig_2e_m1_emm`

```
## diet ask1 emmean SE df lower.CL upper.CL
## chow ASK1F/F 14.7 0.587 40 13.5 15.9
## HFD ASK1F/F 18.9 0.520 40 17.9 20.0
## chow ASK1Δadipo 14.3 0.659 40 13.0 15.7
## HFD ASK1Δadipo 15.9 0.493 40 14.9 16.8
##
## Confidence level used: 0.95
```

` fig_2e_m1_pairs`

```
## ask1 diet contrast estimate SE df lower.CL upper.CL
## ASK1F/F . HFD - chow 4.278 0.791 40 2.680 5.88
## ASK1Δadipo . HFD - chow 1.528 0.824 40 -0.138 3.19
## . chow ASK1Δadipo - (ASK1F/F) -0.349 0.876 40 -2.119 1.42
## . HFD ASK1Δadipo - (ASK1F/F) -3.099 0.715 40 -4.544 -1.65
## t.ratio p.value
## 5.410 <.0001
## 1.853 0.0712
## -0.398 0.6925
## -4.335 0.0001
##
## Confidence level used: 0.95
```

### 21.8.6 Figure 2e – plot the model

```
<- ggplot_the_response(
fig_2e_gg
fig_2e_m1,
fig_2e_m1_emm,
fig_2e_m1_pairs,wrap_col=NULL,
y_label = "Post-baseline glucose (mmol per l)",
contrast_rows = "all",
palette = pal_okabe_ito_4,
)
fig_2e_gg
```

## 21.9 Figure 2f – Effect of ASK1 deletion on glucose infusion rate

### 21.9.1 Figure 2f – import

```
<- "A239:I240"
range_2f <- c("ASK1F/F", "ASK1Δadipo")
treatment_levels <- read_excel(file_path,
fig_2f sheet = fig_2_sheet,
range = range_2f,
col_names = FALSE) %>%
transpose(make.names=1) %>%
data.table() %>%
melt(measure.vars = treatment_levels,
variable.name = "treatment",
value.name = "glucose_infusion_rate") %>%
na.omit()
:= factor(treatment, treatment_levels)] fig_2f[, treatment
```

### 21.9.2 Figure 2f – exploratory plots

### 21.9.3 Figure 2f – fit the model

`<- lm(glucose_infusion_rate ~ treatment, data = fig_2f) fig_2f_m1 `

### 21.9.4 Figure 2f – check the model

### 21.9.5 Figure 2f – inference

```
<- summary(fig_2f_m1) %>%
fig_2f_m1_coef coef()
<- emmeans(fig_2f_m1, specs = "treatment")
fig_2f_m1_emm <- contrast(fig_2f_m1_emm,
fig_2f_m1_pairs method = "revpairwise") %>%
summary(infer = TRUE)
```

` fig_2f_m1_pairs`

```
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## ASK1Δadipo - (ASK1F/F) 18.9 6.3 12 5.18 32.6 3.000 0.0111
##
## Confidence level used: 0.95
```

### 21.9.6 Figure 2f – plot the model

```
<- summary(fig_2f_m1_emm) %>%
fig_2f_m1_emm_dt
data.table<- data.table(fig_2f_m1_pairs)
fig_2f_m1_pairs_dt := pvalString(p.value)]
fig_2f_m1_pairs_dt[ , p_pretty := 1]
fig_2f_m1_pairs_dt[, group1 := 2]
fig_2f_m1_pairs_dt[, group2
<- ggplot(data = fig_2f,
fig_2f_gg aes(x = treatment,
y = glucose_infusion_rate,
color = treatment)) +
# points
geom_sina(alpha = 0.5) +
# plot means and CI
geom_errorbar(data = fig_2f_m1_emm_dt,
aes(y = emmean,
ymin = lower.CL,
ymax = upper.CL,
color = treatment),
width = 0
+
)
geom_point(data = fig_2f_m1_emm_dt,
aes(y = emmean,
color = treatment),
size = 3
+
)
# plot p-values (y positions are adjusted by eye)
stat_pvalue_manual(fig_2f_m1_pairs_dt,
label = "p_pretty",
y.position=c(95),
tip.length = 0.01) +
# aesthetics
ylab("Glucose infusion rate") +
scale_color_manual(values=fig_2_palette,
name = NULL) +
theme_pubr() +
theme(legend.position="none") +
theme(axis.title.x=element_blank()) +
NULL
fig_2f_gg
```

## 21.10 Figure 2g – Effect of ASK1 deletion on tissue-specific glucose uptake

### 21.10.1 Figure 2g – import

```
<- c("A244:G247", "A250:H253")
range_list # import ASK1F/F
<- read_excel(file_path,
fig_2g_1 sheet = fig_2_sheet,
range = "A244:G247",
col_names = FALSE) %>%
transpose(make.names=1) %>%
data.table()
:= "ASK1F/F"]
fig_2g_1[, treatment
# import ASK1Δadipo
<- read_excel(file_path,
fig_2g_2 sheet = fig_2_sheet,
range = "A250:H253",
col_names = FALSE) %>%
transpose(make.names=1) %>%
data.table()
:= "ASK1Δadipo"]
fig_2g_2[, treatment
# combine
<- rbind(fig_2g_1, fig_2g_2) fig_2g
```

### 21.10.2 Figure 2g – exploratory plots

### 21.10.3 Figure 2g – fit the model

```
# a more sophisticated would be a mixed model to dampen noise
<- lm(ingWAT ~ treatment, data = fig_2g)
fig_2g_m1_ingWAT <- lm(epiWAT ~ treatment, data = fig_2g)
fig_2g_m1_epiWAT <- lm(Muscle ~ treatment, data = fig_2g)
fig_2g_m1_Muscle <- lm(BAT ~ treatment, data = fig_2g) fig_2g_m1_BAT
```

### 21.10.4 Figure 2g – check the model

### 21.10.5 Figure 2g – inference

```
<- function(m1){
fig_2g_infer <- emmeans(m1, specs = "treatment")
m1_emm <- contrast(m1_emm,
m1_pairs method = "revpairwise") %>%
summary(infer = TRUE)
return(list(emm = m1_emm,
pairs = m1_pairs))
}
<- data.table(NULL)
fig_2g_m1_emm_dt <- data.table(NULL)
fig_2g_m1_pairs_dt <- list(fig_2g_m1_ingWAT,
m1_list
fig_2g_m1_epiWAT,
fig_2g_m1_Muscle,
fig_2g_m1_BAT)<- c("ingWAT", "epiWAT", "Muscle", "BAT")
y_cols for(i in 1:length(y_cols)){
<- fig_2g_infer(m1_list[[i]])
m1_infer
<- summary(m1_infer$emm) %>%
m1_emm_dt
data.table<- rbind(fig_2g_m1_emm_dt,
fig_2g_m1_emm_dt data.table(tissue = y_cols[i], m1_emm_dt))
<- m1_infer$pairs %>%
m1_pairs_dt
data.table<- rbind(fig_2g_m1_pairs_dt,
fig_2g_m1_pairs_dt data.table(tissue = y_cols[i], m1_pairs_dt))
}
```

` fig_2g_m1_pairs_dt`

```
## tissue contrast estimate SE df lower.CL upper.CL
## 1: ingWAT ASK1Δadipo - (ASK1F/F) 3.595000 1.468289 10 0.32344725 6.866553
## 2: epiWAT ASK1Δadipo - (ASK1F/F) 1.390238 0.669957 11 -0.08432738 2.864804
## 3: Muscle ASK1Δadipo - (ASK1F/F) 2.694048 5.675468 11 -9.79757382 15.185669
## 4: BAT ASK1Δadipo - (ASK1F/F) 33.855000 28.715230 7 -34.04572935 101.755729
## t.ratio p.value
## 1: 2.4484273 0.03435010
## 2: 2.0751153 0.06222096
## 3: 0.4746829 0.64429728
## 4: 1.1789911 0.27691810
```

### 21.10.6 Figure 2g – plot the model

```
# melt fig_2g
<- melt(fig_2g,
fig_2g_long id.vars = "treatment",
variable.name = "tissue",
value.name = "glucose_uptake")
# change name of ASK1Δadipo label
== "ASK1Δadipo", treatment := "ASK1-/-adipo"]
fig_2g_long[treatment == "ASK1Δadipo", treatment := "ASK1-/-adipo"]
fig_2g_m1_emm_dt[treatment
:= pvalString(p.value)]
fig_2g_m1_pairs_dt[ , p_pretty := 1]
fig_2g_m1_pairs_dt[, group1 := 2]
fig_2g_m1_pairs_dt[, group2
<- function(tissue_i,
fig_2g_plot y_lab = FALSE, # title y-axis?
g_lab = FALSE # add group label?
){<- max(fig_2g_long[tissue == tissue_i, glucose_uptake], na.rm=TRUE)
y_max <- min(fig_2g_long[tissue == tissue_i, glucose_uptake], na.rm=TRUE)
y_min <- y_max + (y_max-y_min)*.05
y_pos
<- ggplot(data = fig_2g_long[tissue == tissue_i],
gg aes(x = treatment,
y = glucose_uptake,
color = treatment)) +
# points
geom_sina(alpha = 0.5) +
# plot means and CI
geom_errorbar(data = fig_2g_m1_emm_dt[tissue == tissue_i],
aes(y = emmean,
ymin = lower.CL,
ymax = upper.CL,
color = treatment),
width = 0
+
)
geom_point(data = fig_2g_m1_emm_dt[tissue == tissue_i],
aes(y = emmean,
color = treatment),
size = 3
+
)
# plot p-values (y positions are adjusted by eye)
stat_pvalue_manual(fig_2g_m1_pairs_dt[tissue == tissue_i],
label = "p_pretty",
y.position=c(y_pos),
tip.length = 0.01) +
# aesthetics
ylab("Glucose Uptake") +
scale_color_manual(values=fig_2_palette,
name = NULL) +
ggtitle(tissue_i)+
theme_pubr() +
theme(legend.position="top") +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
NULL
if(y_lab == FALSE){
<- gg + theme(axis.title.y = element_blank())
gg
}if(g_lab == FALSE){
<- gg + theme(legend.position="none")
gg
}
return(gg)
}
<- c("ingWAT", "epiWAT", "Muscle", "BAT")
y_cols <- get_legend(fig_2g_plot("ingWAT", y_lab = TRUE, g_lab = TRUE))
legend <- fig_2g_plot("ingWAT", y_lab = TRUE, )
gg1 <- fig_2g_plot("epiWAT")
gg2 <- fig_2g_plot("Muscle")
gg3 <- fig_2g_plot("BAT")
gg4 <- plot_grid(gg1, gg2, gg3, gg4,
top_gg nrow=1,
rel_widths = c(1.15, 1, 1.05, 1.1)) # by eye
plot_grid(top_gg, legend,
nrow=2,
rel_heights = c(1, 0.1))
```

## 21.11 Figure 2h

## 21.12 Figure 2i – Effect of ASK1 deletion on liver TG

```
<- "A265:G266"
range_2i <- c("ASK1F/F", "ASK1Δadipo")
treatment_levels <- read_excel(file_path,
fig_2i sheet = fig_2_sheet,
range = range_2i,
col_names = FALSE) %>%
transpose(make.names=1) %>%
data.table() %>%
melt(measure.vars = treatment_levels,
variable.name = "treatment",
value.name = "liver_tg") %>%
na.omit()
:= factor(treatment, treatment_levels)]
fig_2i[, treatment
# View(fig_2i)
```

### 21.12.1 Figure 2i – fit the model

`<- lm(liver_tg ~ treatment, data = fig_2i) fig_2i_m1 `

### 21.12.2 Figure 2i – check the model

```
set.seed(1)
qqPlot(fig_2i_m1, id=FALSE)
```

`spreadLevelPlot(fig_2i_m1, id=FALSE)`

```
##
## Suggested power transformation: 1.294553
```

- The QQ plot looks okay, in the sense that the observed data points (open circles) fall within the boundaries set by the dashed line.
- spread level looks pretty good
- Fit normal model

### 21.12.3 Figure 2i – inference

```
<- lm(liver_tg ~ treatment, data = fig_2i)
fig_2i_m1 <- cbind(coef(summary(fig_2i_m1)),
fig_2i_m1_coef confint(fig_2i_m1))
<- emmeans(fig_2i_m1, specs = "treatment")
fig_2i_m1_emm <- contrast(fig_2i_m1_emm,
fig_2i_m1_pairs method = "revpairwise") %>%
summary(infer = TRUE)
```

` fig_2i_m1_pairs`

```
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## ASK1Δadipo - (ASK1F/F) -21.6 7.05 10 -37.3 -5.9 -3.066 0.0119
##
## Confidence level used: 0.95
```

### 21.12.4 Figure 2i – plot the model

```
<- summary(fig_2i_m1_emm) %>%
fig_2i_m1_emm_dt
data.table<- data.table(fig_2i_m1_pairs)
fig_2i_m1_pairs_dt := pvalString(p.value)]
fig_2i_m1_pairs_dt[ , p_pretty := 1]
fig_2i_m1_pairs_dt[, group1 := 2]
fig_2i_m1_pairs_dt[, group2
<- ggplot(data = fig_2i,
fig_2i_gg aes(x = treatment,
y = liver_tg,
color = treatment)) +
# points
geom_sina(alpha = 0.5) +
# plot means and CI
geom_errorbar(data = fig_2i_m1_emm_dt,
aes(y = emmean,
ymin = lower.CL,
ymax = upper.CL,
color = treatment),
width = 0
+
)
geom_point(data = fig_2i_m1_emm_dt,
aes(y = emmean,
color = treatment),
size = 3
+
)
# plot p-values (y positions are adjusted by eye)
stat_pvalue_manual(fig_2i_m1_pairs_dt,
label = "p_pretty",
y.position=c(95),
tip.length = 0.01) +
# aesthetics
ylab("Liver TG (µmol per g liver)") +
scale_color_manual(values=fig_2_palette,
name = NULL) +
theme_pubr() +
theme(legend.position="none") +
theme(axis.title.x=element_blank()) +
NULL
fig_2i_gg
```

### 21.12.5 Figure 2i – report the model

Mean TG level in ASK1Δadipo mice on a high-fat diet was 21.6 µmol/g less than that in ASK1F/F mice on a high-fat diet (95% CI: -37.3, -5.9, \(p = 0.012\)) (Figure xxx).