JoVE MIA Generation Protocol

Author

Sydney Camfield

Published

February 13, 2026

Load Packages

knitr::opts_chunk$set(warning = FALSE)
library(tidyverse)
library(ggplot2)
library(cowplot)
library(dplyr)
library(tidyr)
library(ggpubr)
library(rstatix)
library(svglite)

Load Data

data <- read.csv("Combined_data.csv") 
tnfa <- read.csv("TNFa.csv")

clean <- data |>
  filter(
    Trt_Stage == 9.5,
    Lost_Litter != "",
    between(X24_loss, -4, 10),
    Mat_IL6 != "") |>
  mutate(
    gain_48   = E12 - E10,
    plug_gain = E9 - E0.5,
    change_E9 = E9 - E9,
    change_E10 = E10 - E9,
    change_E12 = E12 - E9,
    Success = ifelse(Lost_Litter == "no", "Litter", "No Litter"),
    Vendor = case_when(Vendor %in% c("J", "JN") ~ "J", Vendor == "R" ~ "R", TRUE ~ Vendor))

data_long <- clean %>%
  pivot_longer(
    cols = c(change_E9, change_E10, change_E12),
    names_to = "Stage",
    values_to = "Weight")

data_long <- data_long %>%
  mutate(Stage = factor(Stage, levels = c("change_E9", "change_E10", "change_E12")))

Figure 2: Maternal IL-6

Figure 2A

clean |>
  ggplot(aes(x = factor(Group, levels = c("CON", "MIA")), y = log2(Mat_IL6 + 1), fill = Group)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("CON" = "grey", "MIA" = "tomato2")) +
  geom_jitter(size = 1, width = 0.15) +
  labs(title = "Maternal IL-6", x = NULL, y = "Log transformed IL-6 (pg/mL)") +
  theme_cowplot() +
  geom_hline(yintercept = 10, linetype = "dashed", color = "gray20", linewidth = 0.2)

Stats 2A

con <- clean |> filter(Group == "CON") |> mutate(Log2 = log2(Mat_IL6 + 1))
shapiro.test(con$Log2)


    Shapiro-Wilk normality test

data:  con$Log2
W = 0.72505, p-value = 2.005e-07

mia <- clean |> filter(Group == "MIA") |> mutate(Log2 = log2(Mat_IL6 + 1))
shapiro.test(mia$Log2)


    Shapiro-Wilk normality test

data:  mia$Log2
W = 0.8283, p-value = 1.654e-10

clean |> 
  wilcox_test(Mat_IL6 ~ Group, paired = FALSE)

# A tibble: 1 × 7
  .y.     group1 group2    n1    n2 statistic        p
* <chr>   <chr>  <chr>  <int> <int>     <dbl>    <dbl>
1 Mat_IL6 CON    MIA       41   120       223 3.36e-18

m_only <- clean |> filter(Group == "MIA")

total <- nrow(m_only)
below <- sum(m_only$Mat_IL6 < 1000)
above <- sum(m_only$Mat_IL6 > 1000)

data.frame(
  group = c("<1000", ">1000"),
  count = c(below, above),
  percent = c(below, above) / total * 100)

  group count  percent
1 <1000    37 30.83333
2 >1000    83 69.16667

Figure 2B

tnfa %>%
  mutate(TNFa = pmax(TNFa, 0)) %>%
  mutate(IL6 = pmax(IL6, 0)) %>%
ggplot(aes(x = TNFa, y = IL6)) +
  geom_point(size = 1, position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.1)) +
  geom_smooth(method = "lm", color = "grey40", linewidth = 0.7) +
  labs(title = "Maternal TNF-a vs IL-6", x = "TNF-a (pg/mL)", y = "IL-6 (pg/mL)") +
  theme_cowplot()

`geom_smooth()` using formula = 'y ~ x'

Stats 2B

shapiro.test(tnfa$IL6)


    Shapiro-Wilk normality test

data:  tnfa$IL6
W = 0.73188, p-value = 1.285e-07

shapiro.test(tnfa$TNFa)


    Shapiro-Wilk normality test

data:  tnfa$TNFa
W = 0.84932, p-value = 4.197e-05

cor.test(tnfa$IL6, tnfa$TNFa, method = "spearman")


    Spearman's rank correlation rho

data:  tnfa$IL6 and tnfa$TNFa
S = 2344, p-value = 1.883e-12
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.8348144

Figure 3: Weights

Figure 3A

ggplot(clean |> filter(Cohort != "1"),
       aes(x = Group, y = plug_gain, fill = Success)) +
  geom_boxplot(position = position_dodge(0.8), outlier.shape = NA) +
  geom_jitter(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.8), size = 1) +
  scale_fill_manual(values = c("#00b4d8", "grey")) +
  scale_y_continuous(limits = c(-1, 6)) +
  theme_cowplot()

Stats 3A

litter <- clean |> filter(Cohort != "1", Success == "Litter")
no <- clean |> filter(Cohort != "1", Success == "No Litter")

shapiro.test(litter$plug_gain)


    Shapiro-Wilk normality test

data:  litter$plug_gain
W = 0.98384, p-value = 0.5147

shapiro.test(no$plug_gain)


    Shapiro-Wilk normality test

data:  no$plug_gain
W = 0.96831, p-value = 0.1091

clean %>%
  t_test(plug_gain ~ Success)

# A tibble: 1 × 8
  .y.       group1 group2       n1    n2 statistic    df           p
* <chr>     <chr>  <chr>     <int> <int>     <dbl> <dbl>       <dbl>
1 plug_gain Litter No Litter    83    77      5.54  148. 0.000000135

litter %>%
  summarise(
    mean = mean(plug_gain, na.rm = TRUE),
    se = sd(plug_gain, na.rm = TRUE) / sqrt(n()))

      mean        se
1 2.482609 0.1102187

no %>%
  summarise(
    mean = mean(plug_gain, na.rm = TRUE),
    se = sd(plug_gain, na.rm = TRUE) / sqrt(n()))

      mean        se
1 1.443548 0.1659488

Figure 3B

data_long %>%
  filter(Cohort != "1", E12 != "", Success == "Litter") %>%
  ggplot(aes(x = Stage, y = Weight, fill = Group)) + 
    geom_boxplot(outlier.shape = NA) +
  geom_jitter(
  position = position_jitterdodge(jitter.width = 0.1, dodge.width = 0.75),
  size = 1) +
  scale_fill_manual(values = c("grey", "tomato2")) +
  scale_x_discrete(labels = c("change_E9" = "E9", "change_E10" = "E10", "change_E12" = "E12")) + 
  theme_cowplot()

Stats 3B

L_CON_change_E10 <- data_long |>
  filter(Group == "CON", Stage == "change_E10", Success == "Litter")
shapiro.test(L_CON_change_E10$Weight)


    Shapiro-Wilk normality test

data:  L_CON_change_E10$Weight
W = 0.92529, p-value = 0.03687

L_CON_change_E12 <- data_long |>
  filter(Group == "CON", Stage == "change_E12", Success == "Litter")

L_MIA_change_E10 <- data_long |>
  filter(Group == "MIA", Stage == "change_E10", Success == "Litter")

L_MIA_change_E12 <- data_long |>
  filter(Group == "MIA", Stage == "change_E12", Success == "Litter")
shapiro.test(L_MIA_change_E12$Weight)


    Shapiro-Wilk normality test

data:  L_MIA_change_E12$Weight
W = 0.97903, p-value = 0.5252

wilcox.test(L_MIA_change_E10$Weight, L_CON_change_E10$Weight)


    Wilcoxon rank sum test with continuity correction

data:  L_MIA_change_E10$Weight and L_CON_change_E10$Weight
W = 337, p-value = 1.43e-05
alternative hypothesis: true location shift is not equal to 0

t.test(L_MIA_change_E12$Weight, L_CON_change_E12$Weight)


    Welch Two Sample t-test

data:  L_MIA_change_E12$Weight and L_CON_change_E12$Weight
t = -2.3173, df = 42.25, p-value = 0.0254
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.21656702 -0.08406634
sample estimates:
mean of x mean of y 
 2.297959  2.948276

Figure 3C

data_long %>%
  filter(Cohort != "1", Group == "CON") %>%
  ggplot(aes(x = Stage, y = Weight, fill = Success)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(width = 0.2, size = 1, alpha = 0.8) +
  scale_fill_manual(values = c("#00b4d8", "grey")) +
  scale_x_discrete(labels = c("change_E9" = "E9", "change_E10" = "E10", "change_E12" = "E12")) +
    facet_wrap(~ Success) +
  theme_cowplot()

Stats 3C

L_CON_change_E10 <- data_long |>
  filter(Group == "CON", Stage == "change_E10", Success == "Litter")
shapiro.test(L_CON_change_E10$Weight)


    Shapiro-Wilk normality test

data:  L_CON_change_E10$Weight
W = 0.92529, p-value = 0.03687

L_CON_change_E10 <- data_long |>
  filter(Group == "CON", Stage == "change_E10", Success == "Litter") |> pull(Weight)
wilcox.test(L_CON_change_E10, mu = 0)


    Wilcoxon signed rank test with continuity correction

data:  L_CON_change_E10
V = 347, p-value = 0.001077
alternative hypothesis: true location is not equal to 0

length(L_CON_change_E10)

[1] 30

NL_CON_change_E10 <- data_long |>
  filter(Group == "CON", Stage == "change_E10", Success == "No Litter")
shapiro.test(NL_CON_change_E10$Weight)


    Shapiro-Wilk normality test

data:  NL_CON_change_E10$Weight
W = 0.88642, p-value = 0.1254

NL_CON_change_E10 <- data_long |>
  filter(Group == "CON", Stage == "change_E10", Success == "No Litter") |> pull(Weight)
t.test(NL_CON_change_E10, mu = 0)


    One Sample t-test

data:  NL_CON_change_E10
t = 0.1541, df = 10, p-value = 0.8806
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -0.4894297  0.5621570
sample estimates:
 mean of x 
0.03636364

length(NL_CON_change_E10)

[1] 11

L_CON_change_E12 <- data_long |>
  filter(Group == "CON", Stage == "change_E12", Success == "Litter")
shapiro.test(L_CON_change_E12$Weight)


    Shapiro-Wilk normality test

data:  L_CON_change_E12$Weight
W = 0.95329, p-value = 0.2227

L_CON_change_E12 <- data_long |>
  filter(Group == "CON", Stage == "change_E12", Success == "Litter") |> pull(Weight)
t.test(L_CON_change_E12, mu = 0)


    One Sample t-test

data:  L_CON_change_E12
t = 11.749, df = 28, p-value = 2.449e-12
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 2.434248 3.462303
sample estimates:
mean of x 
 2.948276

length(L_CON_change_E12)

[1] 30

NL_CON_change_E12 <- data_long |>
  filter(Group == "CON", Stage == "change_E12", Success == "No Litter")
shapiro.test(NL_CON_change_E12$Weight)


    Shapiro-Wilk normality test

data:  NL_CON_change_E12$Weight
W = 0.94227, p-value = 0.6335

NL_CON_change_E12 <- data_long |>
  filter(Group == "CON", Stage == "change_E12", Success == "No Litter") |> pull(Weight)
t.test(NL_CON_change_E12, mu = 0 )


    One Sample t-test

data:  NL_CON_change_E12
t = -0.070557, df = 7, p-value = 0.9457
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -1.294265  1.219265
sample estimates:
mean of x 
  -0.0375

length(NL_CON_change_E12)

[1] 11

wilcox.test(NL_CON_change_E10, L_CON_change_E10)


    Wilcoxon rank sum test with continuity correction

data:  NL_CON_change_E10 and L_CON_change_E10
W = 104, p-value = 0.07475
alternative hypothesis: true location shift is not equal to 0

t.test(NL_CON_change_E12, L_CON_change_E12)


    Welch Two Sample t-test

data:  NL_CON_change_E12 and L_CON_change_E12
t = -5.08, df = 10.34, p-value = 0.0004305
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -4.289543 -1.682009
sample estimates:
mean of x mean of y 
-0.037500  2.948276

c_only <- data_long |> filter(Cohort != "1", Group == "CON")

total <- nrow(c_only)
litter <- sum(c_only$Success == "Litter")
no_litter <- sum(c_only$Success == "No Litter")

data.frame(
  group = c("Litter", "No"),
  count = c(litter, no_litter),
  percent = c(litter, no_litter) / total * 100)

   group count  percent
1 Litter    72 70.58824
2     No    30 29.41176

Figure 3D

data_long %>%
  filter(Cohort != "1", E12 != "", Group == "MIA") %>%
  ggplot(aes(x = Stage, y = Weight, fill = Success)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(width = 0.2, size = 1, alpha = 0.8) +
  scale_fill_manual(values = c("#00b4d8", "grey")) +
  scale_x_discrete(labels = c("change_E9" = "E9", "change_E10" = "E10", "change_E12" = "E12")) + 
    facet_wrap(~ Success) +
  theme_cowplot()

Stats 3D

L_MIA_change_E10 <- data_long |>
  filter(Group == "MIA", Stage == "change_E10", Success == "Litter")
shapiro.test(L_MIA_change_E10$Weight)


    Shapiro-Wilk normality test

data:  L_MIA_change_E10$Weight
W = 0.90168, p-value = 0.0003688

L_MIA_change_E10 <- data_long |>
  filter(Group == "MIA", Stage == "change_E10", Success == "Litter") |> pull(Weight)
wilcox.test(L_MIA_change_E10, mu = 0)


    Wilcoxon signed rank test with continuity correction

data:  L_MIA_change_E10
V = 347, p-value = 0.001116
alternative hypothesis: true location is not equal to 0

length(L_MIA_change_E10)

[1] 53

NL_MIA_change_E10 <- data_long |>
  filter(Group == "MIA", Stage == "change_E10", Success == "No Litter")
shapiro.test(NL_MIA_change_E10$Weight)


    Shapiro-Wilk normality test

data:  NL_MIA_change_E10$Weight
W = 0.90663, p-value = 0.0001011

NL_MIA_change_E10 <- data_long |>
  filter(Group == "MIA", Stage == "change_E10", Success == "No Litter") |> pull(Weight)
wilcox.test(NL_MIA_change_E10, mu = 0)


    Wilcoxon signed rank test with continuity correction

data:  NL_MIA_change_E10
V = 402.5, p-value = 4.245e-06
alternative hypothesis: true location is not equal to 0

length(NL_MIA_change_E10)

[1] 67

L_MIA_change_E12 <- data_long |>
  filter(Group == "MIA", Stage == "change_E12", Success == "Litter")
shapiro.test(L_MIA_change_E12$Weight)


    Shapiro-Wilk normality test

data:  L_MIA_change_E12$Weight
W = 0.97903, p-value = 0.5252

L_MIA_change_E12 <- data_long |>
  filter(Group == "MIA", Stage == "change_E12", Success == "Litter") |> pull(Weight)
t.test(L_MIA_change_E12, mu = 0)


    One Sample t-test

data:  L_MIA_change_E12
t = 18.289, df = 48, p-value < 2.2e-16
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 2.045334 2.550584
sample estimates:
mean of x 
 2.297959

length(L_MIA_change_E12)

[1] 53

NL_MIA_change_E12 <- data_long |>
  filter(Group == "MIA", Stage == "change_E12", Success == "No Litter")
shapiro.test(NL_MIA_change_E12$Weight)


    Shapiro-Wilk normality test

data:  NL_MIA_change_E12$Weight
W = 0.98077, p-value = 0.4282

NL_MIA_change_E12 <- data_long |>
  filter(Group == "MIA", Stage == "change_E12", Success == "No Litter") |> pull(Weight)
t.test(NL_MIA_change_E12, mu = 0 )


    One Sample t-test

data:  NL_MIA_change_E12
t = 3.1759, df = 62, p-value = 0.002328
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 0.2117642 0.9310930
sample estimates:
mean of x 
0.5714286

length(NL_MIA_change_E12)

[1] 67

wilcox.test(NL_MIA_change_E10, L_MIA_change_E10)


    Wilcoxon rank sum test with continuity correction

data:  NL_MIA_change_E10 and L_MIA_change_E10
W = 1289.5, p-value = 0.01023
alternative hypothesis: true location shift is not equal to 0

t.test(NL_MIA_change_E12, L_MIA_change_E12)


    Welch Two Sample t-test

data:  NL_MIA_change_E12 and L_MIA_change_E12
t = -7.8674, df = 104.97, p-value = 3.417e-12
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2.161666 -1.291395
sample estimates:
mean of x mean of y 
0.5714286 2.2979592

data_long |> filter(Group == "MIA", Success == "Litter", Stage == "change_E10") |>
  summarise(
    mean = mean(Weight, na.rm = TRUE),
    se = sd(Weight, na.rm = TRUE) / sqrt(n()))

# A tibble: 1 × 2
    mean    se
   <dbl> <dbl>
1 -0.545 0.144

data_long |> filter(Group == "MIA", Success == "No Litter", Stage == "change_E10") |>
  summarise(
    mean = mean(Weight, na.rm = TRUE),
    se = sd(Weight, na.rm = TRUE) / sqrt(n()))

# A tibble: 1 × 2
    mean    se
   <dbl> <dbl>
1 -0.922 0.167

data_long |> filter(Group == "MIA", Success == "Litter", Stage == "change_E12") |>
  summarise(
    mean = mean(Weight, na.rm = TRUE),
    se = sd(Weight, na.rm = TRUE) / sqrt(n()))

# A tibble: 1 × 2
   mean    se
  <dbl> <dbl>
1  2.30 0.121

data_long |> filter(Group == "MIA", Success == "No Litter", Stage == "change_E12") |>
  summarise(
    mean = mean(Weight, na.rm = TRUE),
    se = sd(Weight, na.rm = TRUE) / sqrt(n()))

# A tibble: 1 × 2
   mean    se
  <dbl> <dbl>
1 0.571 0.174

Figure 4: Variables affecting MIA

Figure 4A

clean |> 
  ggplot(aes(x = Group, y = E0.5, fill = Success )) + 
  geom_boxplot(outlier.shape = NA) + 
  geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.1)) + 
  theme_cowplot() + scale_fill_manual(values = c("#00b4d8", "grey")) + 
  labs(y = "starting weight (g)") + 
  ylim(15,25)

Stats 4A

a <- clean |> filter(Group == "CON", Success == "Litter")
shapiro.test(a$E0.5)


    Shapiro-Wilk normality test

data:  a$E0.5
W = 0.97274, p-value = 0.6164

nrow(a)

[1] 30

b <- clean |> filter(Group == "CON", Success == "No Litter")
shapiro.test(b$E0.5)


    Shapiro-Wilk normality test

data:  b$E0.5
W = 0.97519, p-value = 0.9336

nrow(b)

[1] 11

c <- clean |> filter(Group == "MIA", Success == "Litter")
shapiro.test(c$E0.5)


    Shapiro-Wilk normality test

data:  c$E0.5
W = 0.86029, p-value = 1.837e-05

nrow(c)

[1] 53

d <- clean |> filter(Group == "MIA", Success == "No Litter")
shapiro.test(d$E0.5)


    Shapiro-Wilk normality test

data:  d$E0.5
W = 0.98278, p-value = 0.4911

nrow(d)

[1] 67

clean |>
  filter(Group == "CON") |>
  t_test(E0.5 ~ Success)

# A tibble: 1 × 8
  .y.   group1 group2       n1    n2 statistic    df     p
* <chr> <chr>  <chr>     <int> <int>     <dbl> <dbl> <dbl>
1 E0.5  Litter No Litter    30    11      1.17  14.6 0.262

clean |>
  filter(Group == "MIA") |>
  wilcox_test(E0.5 ~ Success)

# A tibble: 1 × 7
  .y.   group1 group2       n1    n2 statistic     p
* <chr> <chr>  <chr>     <int> <int>     <dbl> <dbl>
1 E0.5  Litter No Litter    53    66     1892. 0.448

clean |>
  filter(Success == "Litter") |>
  wilcox_test(E0.5 ~ Group)

# A tibble: 1 × 7
  .y.   group1 group2    n1    n2 statistic     p
* <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>
1 E0.5  CON    MIA       30    53       874 0.457

clean |>
  filter(Success == "No Litter") |>
  t_test(E0.5 ~ Group)

# A tibble: 1 × 8
  .y.   group1 group2    n1    n2 statistic    df     p
* <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl>
1 E0.5  CON    MIA       11    66    -0.265  13.1 0.795

Figure 4B

#Vendor
clean_v <- clean |>
  mutate(Vendor_bin = case_when(Vendor == "J"  ~ "J", Vendor == "R"  ~ "R", TRUE ~ NA_character_),
    Success = ifelse(Lost_Litter == "no", "Litter", "No Litter")) |> 
  filter(!is.na(Vendor_bin)) |> 
  filter(Cohort != "1") |>
  filter(PIC != 'none')

plot_v <- clean_v |>
  group_by(Vendor_bin, Success) |>
  summarise(n = n(), .groups = "drop") |>
  group_by(Vendor_bin) |>
  mutate(percent = n / sum(n) * 100)

plot_v <- plot_v |>
  mutate(label = paste0(round(percent), "%"))

n_v <- clean_v |>
  group_by(Vendor_bin) |>
  summarise(n = n(), .groups = "drop")

plot_v |>
  group_by(Vendor_bin) |>
  ggplot(aes(x = Vendor_bin, y = percent, fill = Success)) +
  geom_bar(stat = "identity", color = "black", size = 0.6) +
  geom_text(aes(label = label), 
            position = position_stack(vjust = 0.5), 
            color = "white", size = 3) +
  scale_x_discrete(labels = c("J" = "Jackson", "R" = "Charles River")) +
  geom_text(data = n_v, aes(x = Vendor_bin, y = 105, label = paste0("n=", n)),
            inherit.aes = FALSE, size = 3) +
  scale_fill_manual(values = c("Litter" = "#00b4d8", "No Litter" = "grey")) +
  labs(x = "", y = "Percentage of Litter Success", fill = "") +
  theme_cowplot()

#poly(I:C)
clean_p <- clean |>
  mutate(PIC_bin = case_when(PIC == "lmw"  ~ "lmw", PIC == "sigma"  ~ "sigma", TRUE ~ NA_character_),
    Success = ifelse(Lost_Litter == "no", "Litter", "No Litter")) |> 
  filter(!is.na(PIC_bin)) |> 
  filter(Cohort != "1")

clean_p1 <- clean_p |>
  group_by(PIC_bin, Success) |>
  summarise(n = n(), .groups = "drop") |>
  group_by(PIC_bin) |>
  mutate(percent = n / sum(n) * 100)

plot_p <- clean_p1 |>
  mutate(label = paste0(round(percent), "%"))

n_p <- clean_p1 |>
  group_by(PIC_bin) |>
  summarise(n = n(), .groups = "drop")

plot_p |>
  group_by(PIC_bin) |>
  ggplot(aes(x = PIC_bin, y = percent, fill = Success)) +
  geom_bar(stat = "identity", color = "black") +
  geom_text(aes(label = label), 
            position = position_stack(vjust = 0.5), 
            color = "white", size = 3) +
  geom_text(data = plot_p |> group_by(PIC_bin) |> summarise(n = sum(n)), 
            aes(x = PIC_bin, y = 105, label = paste0("n=", n)),
            inherit.aes = FALSE, size = 3) +
  scale_fill_manual(values = c("Litter" = "#00b4d8", "No Litter" = "grey")) +
  labs(x = "", y = "Percentage of Litter Success", fill = "") +
  theme_classic()

Stats 4B

clean_v |> 
  filter(Vendor == "J") |>
  nrow()

[1] 35

clean_v |> 
  filter(Vendor == "R") |>
  nrow()

[1] 63

clean_v |>
  group_by(Success, Vendor_bin) |>
  tally() |> spread(key = Success, value = n) |>
  select(Litter, `No Litter`) |>
  fisher.test()


    Fisher's Exact Test for Count Data

data:  select(spread(tally(group_by(clean_v, Success, Vendor_bin)), key = Success, value = n), Litter, `No Litter`)
p-value = 0.05631
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.9650207 6.2148706
sample estimates:
odds ratio 
  2.414653

clean_p |> 
  filter(PIC_bin == "lmw") |>
  nrow()

[1] 80

clean_p |> 
  filter(PIC_bin == "sigma") |>
  nrow()

[1] 18

clean_p %>%
  group_by(Success, PIC_bin) |>
  tally() |> spread(key = Success, value = n) |>
  select(Litter, `No Litter`) |>
  fisher.test()


    Fisher's Exact Test for Count Data

data:  select(spread(tally(clean_p %>% group_by(Success, PIC_bin)), key = Success, value = n), Litter, `No Litter`)
p-value = 0.1936
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.1401145 1.5015806
sample estimates:
odds ratio 
 0.4740377

Figure 4C

clean |>
  filter(Group == "MIA") |>
  ggplot(aes(x = Vendor, y = gain_48, fill = Success)) +
  geom_boxplot(outlier.shape = NA, position = position_dodge(width = 0.8)) +
  geom_point(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
    size = 1.5) +
  scale_fill_manual(values = c("#00b4d8", "grey")) +
  scale_x_discrete(labels = c("J" = "Jackson", "R" = "Charles River")) +
  theme_cowplot()

Stats 4C

e <- clean |> filter(Group == "MIA", Success == "Litter", Vendor == "J")
shapiro.test(e$gain_48)


    Shapiro-Wilk normality test

data:  e$gain_48
W = 0.95877, p-value = 0.3065

nrow(e)

[1] 29

f <- clean |> filter(Group == "MIA", Success == "No Litter", Vendor == "J")
shapiro.test(f$gain_48)


    Shapiro-Wilk normality test

data:  f$gain_48
W = 0.88361, p-value = 0.005753

nrow(f)

[1] 28

g <- clean |> filter(Group == "MIA", Success == "Litter", Vendor == "R")
shapiro.test(g$gain_48)


    Shapiro-Wilk normality test

data:  g$gain_48
W = 0.96264, p-value = 0.5978

nrow(g)

[1] 24

h <- clean |> filter(Group == "MIA", Success == "No Litter", Vendor == "R")
shapiro.test(h$gain_48)


    Shapiro-Wilk normality test

data:  h$gain_48
W = 0.9736, p-value = 0.5317

nrow(h)

[1] 39

clean |>
  filter(Vendor == "J") |>
  filter(Group == "MIA") |>
  wilcox_test(gain_48 ~ Success)

# A tibble: 1 × 7
  .y.     group1 group2       n1    n2 statistic         p
* <chr>   <chr>  <chr>     <int> <int>     <dbl>     <dbl>
1 gain_48 Litter No Litter    29    27      678. 0.0000028

clean |>
  filter(Vendor == "R") |>
  filter(Group == "MIA") |>
  t_test(gain_48 ~ Success)

# A tibble: 1 × 8
  .y.     group1 group2       n1    n2 statistic    df           p
* <chr>   <chr>  <chr>     <int> <int>     <dbl> <dbl>       <dbl>
1 gain_48 Litter No Litter    20    36      5.73  53.4 0.000000478

clean |>
  filter(Success == "Litter") |>
  filter(Group == "MIA") |>
  t_test(gain_48 ~ Vendor)

# A tibble: 1 × 8
  .y.     group1 group2    n1    n2 statistic    df       p
* <chr>   <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>
1 gain_48 J      R         29    20     -2.99  46.5 0.00445

clean |>
  filter(Success == "No Litter") |>
  filter(Group == "MIA") |>
  wilcox_test(gain_48 ~ Vendor)

# A tibble: 1 × 7
  .y.     group1 group2    n1    n2 statistic       p
* <chr>   <chr>  <chr>  <int> <int>     <dbl>   <dbl>
1 gain_48 J      R         27    36      252. 0.00115

Figure 4D

clean |>
  filter(Group == "MIA", PIC == "lmw") |>
  ggplot(aes(x = Vendor, y = log2(Mat_IL6 + 1))) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(width = 0.2, size = 1.5) +
  scale_x_discrete(labels = c("J" = "Jackson", "R" = "Charles River")) +
  theme_cowplot() +
  geom_hline(yintercept = 10, linetype = "dashed", color = "gray4", linewidth = 0.2)

Stats 4D

c3 <- clean |> filter(Group == "MIA", Vendor == "J", PIC == "lmw") |> mutate(log2_IL6 = log2(Mat_IL6 + 1))
shapiro.test(c3$log2_IL6)


    Shapiro-Wilk normality test

data:  c3$log2_IL6
W = 0.68912, p-value = 6e-07

nrow(c3)

[1] 32

c4 <- clean |> filter(Group == "MIA", Vendor == "R", PIC == "lmw") |> mutate(log2_IL6 = log2(Mat_IL6 + 1))
shapiro.test(c4$log2_IL6)


    Shapiro-Wilk normality test

data:  c4$log2_IL6
W = 0.84252, p-value = 1.146e-06

nrow(c4)

[1] 63

clean |>
  filter(Group == "MIA", PIC == "lmw") |>
  wilcox_test(Mat_IL6 ~ Vendor)

# A tibble: 1 × 7
  .y.     group1 group2    n1    n2 statistic        p
* <chr>   <chr>  <chr>  <int> <int>     <dbl>    <dbl>
1 Mat_IL6 J      R         32    63     1454. 0.000445

Figure 4E

clean |>
  filter(Group == "MIA") |>
  filter(Vendor == "J") |>
  ggplot(aes(x = PIC, y = log2(Mat_IL6 + 1))) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(width = 0.2, size = 1, alpha = 0.8) +
  theme_cowplot()

Stats 4E

c1 <- clean |> filter(Group == "MIA", Vendor == "J", PIC == "lmw") |> mutate(log2_IL6 = log2(Mat_IL6 + 1))
shapiro.test(c1$log2_IL6)


    Shapiro-Wilk normality test

data:  c1$log2_IL6
W = 0.68912, p-value = 6e-07

nrow(c1)

[1] 32

c2 <- clean |> filter(Group == "MIA", Vendor == "J", PIC == "sigma") |> mutate(log2_IL6 = log2(Mat_IL6 + 1))
shapiro.test(c2$log2_IL6)


    Shapiro-Wilk normality test

data:  c2$log2_IL6
W = 0.8099, p-value = 0.000334

nrow(c2)

[1] 25

clean |> 
  filter(Group == "MIA") |> 
  filter(Vendor == "J") |>
wilcox_test(Mat_IL6 ~ PIC)

# A tibble: 1 × 7
  .y.     group1 group2    n1    n2 statistic     p
* <chr>   <chr>  <chr>  <int> <int>     <dbl> <dbl>
1 Mat_IL6 lmw    sigma     32    25       353 0.454