Luciferase on HEK293T cells - statistical analysis in R

library(tidyverse)
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library(gtsummary)  
library(gt)

1 Data

data from Luciferase32a
200307-32a_Dual-Glo_3-7-2020_11-45-17 AM.xml
200307-32a_Dual-Glo_3-7-2020_11-45-17 AM.csv

1.1 Luciferase = Assayed Luciferase Levels

Input = (
  "
932165  280694  291766  7579    326610  294131  213808  287660  274511  285368  295928  284701
248281  343599  342646  308856  313576  301555  339357  286528  298245  296319  308823  298451
279594  286938  313191  270324  299091  294240  290934  262438  281495  278678  262717  275721
217401  237318  287149  273201  35584.4 266744  252756  266391  260908  265575  264828  258921
1973580 914549  1056410 1019270 992366  972704  952885  946102  974248  1038220 973668  4800
540331  527855  541003  512053  34843   485106  501617  496061  515398  483273  514919  536478
395287  394912  425305  408337  427163  435909  412991  406536  412621  399348  395261  445417
461254  448295  517611  505456  497184  477564  483230  496222  501602  490351  461691  502010
"
)
luciferase = read.delim(textConnection(Input), header = FALSE)

# Stack
luciferase <-
  luciferase |>
  stack() |>
  pull(values)

1.2 Renilla = Assayed Renilla Levels

Input = (
  "
2109200 3280310 3035520 3152070 3485520 3257440 1280750 3172730 3042680 3127310 3817540 3271880
2255220 3213440 3273870 2915530 2771810 2660370 2951870 2622030 2822620 2794690 2891570 2877910
2585680 2896890 3005140 2696110 3080790 2648400 2777170 2600100 2891280 2886650 2577850 2774690
2610100 2913220 3095140 2828140 627127  2820150 2482040 2807710 2674760 2666920 2753210 2618330
2086170 2117440 2206910 2129310 2146500 2172140 2102720 1995050 2079730 2082990 2107500 2122020
1823620 1745900 1897230 1692220 142261  1735220 1734090 1791410 1821090 1671730 1722000 1827930
1612790 1737550 1814510 1658700 1903090 1851850 1831140 1741370 1625390 1768190 1760830 1900310
1873670 1795110 2021290 2060980 2087650 1850110 1858120 2057470 2077500 2018600 1999450 2129300
"
)
renilla = read.delim(textConnection(Input), header = FALSE)

# Stack
renilla <-
  renilla |>
  stack() |>
  pull(values)

1.3 Names = Experimental Conditions

Input = (
  "
Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference
Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1
Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2
delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1
Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1 Reference+NR5A1
Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1 Variant_1+NR5A1
Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1 Variant_2+NR5A1
delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1  delNR5A1+NR5A1"
)
names = read.delim(textConnection(Input), header = FALSE)

# Stack
names <-
  names |>
  stack() |>
  pull(values)

1.4 Reporter = Luciferase expressing vector

Input = (
  "
Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference
Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1
Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2
delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1
Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference   Reference
Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1   Variant_1
Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2   Variant_2
delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1    delNR5A1"
)
reporter = read.delim(textConnection(Input), header = FALSE)
# Stack
reporter <-
  reporter |>
  stack() |>
  pull(values)

1.5 Factor1 = transcription factor (NR5A1)

Input = (
  "
-   -   -   -   -   -   -   -   -   -   -   -
-   -   -   -   -   -   -   -   -   -   -   -
-   -   -   -   -   -   -   -   -   -   -   -
-   -   -   -   -   -   -   -   -   -   -   -
NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1
NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1
NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1
NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1   NR5A1
"
)
factor1 = read.delim(textConnection(Input), header = FALSE)

# Stack
factor1 <-
  factor1 |>
  stack() |>
  pull(values)

2 Data Organization

2.1 Generate the dataframe

level_order<-c("Reference",
      "Variant_1",
      "Variant_2",
      "delNR5A1",
      "Reference+NR5A1",
      "Variant_1+NR5A1",
      "Variant_2+NR5A1",
      "delNR5A1+NR5A1")

dataframe0 <-
  tibble(names,
         reporter,
         factor1,
         renilla,
         luciferase) |>
  mutate_if(is.character, as.factor) |>
  arrange(factor(names, levels = level_order))

2.2 Exclude Renilla outliers by the Inter Quartile Range (IQR) method

boxplot(renilla ~ names, dataframe0)

dataframe1 <- dataframe0 |>
  group_by(names) |>
  mutate(
    IQR = IQR(renilla, na.rm = TRUE),
    Outlier_upper = quantile(renilla, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
    Outlier_lower = quantile(renilla, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
    renilla_wo_extremes = if_else(renilla <= Outlier_lower | renilla >= Outlier_upper, NA, renilla))

boxplot(renilla_wo_extremes ~ names, dataframe1)

2.3 New column containing the Luc/Ren Ratio Computation from Luc and Ren Columns

dataframe2 <-
  dataframe1 |> mutate(Luc_Ren = luciferase / renilla_wo_extremes)

2.4 Initial Data Exploration Through Scatterplots

2.4.1 Renilla

ggplot(dataframe2,
       aes(
         x = factor(names, level = level_order),
         y = renilla_wo_extremes,
         colour = reporter,
         group = names,
       )) +
  geom_boxplot() +
  geom_jitter(width = 0.2) +
  theme(axis.text.x = element_text(
    vjust = 1,
    hjust = 1,
    size = 10,
    angle = 30,
    family = "helvetica"
  )) + theme(
    panel.grid.major = element_line(colour = "gray85"),
    panel.grid.minor = element_line(colour = "gray90"),
    panel.background = element_rect(fill = NA)
  ) +
  scale_y_continuous(labels = scientific)
## Warning: Removed 10 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 10 rows containing missing values (`geom_point()`).

2.4.2 Luciferase

ggplot(dataframe2,
       aes(
         x = factor(names, level = level_order),
         y = luciferase,
         colour = reporter,
         group = names
       )) +
  geom_boxplot() +
  geom_jitter(width = 0.2) +
  theme(axis.text.x = element_text(
    vjust = 1,
    hjust = 1,
    size = 10,
    angle = 30,
    family = "helvetica"
  )) + theme(
    panel.grid.major = element_line(colour = "gray85"),
    panel.grid.minor = element_line(colour = "gray90"),
    panel.background = element_rect(fill = NA)
  ) +
  scale_y_continuous(labels = scientific)

2.5 luciferase/renilla

ggplot(dataframe2,
       aes(
         x = factor(names, level = level_order),
         y = Luc_Ren,
         colour = reporter,
         group = names
       )) +
  geom_boxplot() +
  geom_jitter(width = 0.2) +
  theme(axis.text.x = element_text(
    vjust = 1,
    hjust = 1,
    size = 10,
    angle = 30,
    family = "helvetica"
  )) + theme(
    panel.grid.major = element_line(colour = "gray85"),
    panel.grid.minor = element_line(colour = "gray90"),
    panel.background = element_rect(fill = NA)
  ) +
  scale_y_continuous(labels = scientific)
## Warning: Removed 10 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 10 rows containing missing values (`geom_point()`).

2.6 Remove the outliers from Luciferase/Renilla by the IQR method

boxplot(Luc_Ren ~ names, dataframe2)

dataframe3 <- dataframe2 |>
  group_by(names) |>
  mutate(
    IQR = IQR(Luc_Ren, na.rm = TRUE),
    Outlier_upper = quantile(Luc_Ren, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
    Outlier_lower = quantile(Luc_Ren, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
    Luc_Ren_wo_outliers = if_else(Luc_Ren <= Outlier_lower | Luc_Ren >= Outlier_upper, NA, Luc_Ren),
        )

boxplot(Luc_Ren_wo_outliers ~ names, dataframe3)

2.7 Calculate the relative ratio response (RRR)

see:
RRR = (well_value - mean_neg))/(mean_pos-mean_neg)

Relative Response ratio (Promega - Dual-Glo® Luciferase Assay System, Instructions for use of Products E2920, E2940 and E2980)

https://www.promega.com/-/media/files/resources/protocols/technical-manuals/0/dual-glo-luciferase-assay-system-protocol.pdf

dataframe4 <- dataframe3 |>
  group_by(names) |>
  mutate(mean = mean(Luc_Ren_wo_outliers, na.rm = TRUE)) |>
  ungroup() |>
  mutate(
    mean_neg = min(mean),
    mean_pos = max(mean),
    RRR = (Luc_Ren_wo_outliers - mean_neg) / (mean_pos - mean_neg),
    RRRp = RRR * 100,
    block = "a") |>
  mutate_if(is.character, as.factor)

mean_pos = mean of the positive reference

mean_neg = mean of the negative reference

RRR = Relative Response Ratio

RRRp = Relative Response Ratio in percent

block = necessary for the Approximative Two-Sample Fisher-Pitman Permutation Test, it allows the stratification (it has to be a factor)

ggplot(dataframe4,
       aes(
         x = factor(names, level = level_order),
         y = RRRp,
         colour = reporter,
         group = names
       )) +
  geom_boxplot() +
  geom_jitter(width = 0.2) +
  theme(axis.text.x = element_text(
    vjust = 1,
    hjust = 1,
    size = 10,
    angle = 30,
    family = "helvetica"
  )) + theme(
    panel.grid.major = element_line(colour = "gray85"),
    panel.grid.minor = element_line(colour = "gray90"),
    panel.background = element_rect(fill = NA)
  )  +
  ylim(-10,110)
## Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 15 rows containing missing values (`geom_point()`).

3 Statistical tests

3.1 Normality test

shapiro_test_result <- dataframe4 |> 
  group_by(names) |> 
  do(tidy(shapiro.test(.$RRRp)))

print(shapiro_test_result)
## # A tibble: 8 × 4
## # Groups:   names [8]
##   names           statistic p.value method                     
##   <fct>               <dbl>   <dbl> <chr>                      
## 1 delNR5A1            0.986  0.989  Shapiro-Wilk normality test
## 2 delNR5A1+NR5A1      0.959  0.773  Shapiro-Wilk normality test
## 3 Reference           0.959  0.802  Shapiro-Wilk normality test
## 4 Reference+NR5A1     0.983  0.976  Shapiro-Wilk normality test
## 5 Variant_1           0.884  0.172  Shapiro-Wilk normality test
## 6 Variant_1+NR5A1     0.950  0.638  Shapiro-Wilk normality test
## 7 Variant_2           0.919  0.280  Shapiro-Wilk normality test
## 8 Variant_2+NR5A1     0.877  0.0810 Shapiro-Wilk normality test
p_value <- min(shapiro_test_result$p.value)
p_value
## [1] 0.08097895
if (p_value < 0.05) {
  cat("The overall p-value obtained from the Bartlett test is less than 0.05, suggesting evidence supporting the rejection of the null hypothesis of homogeneity of variances.\n")
} else {
  cat("The overall p-value obtained from the Bartlett test is greater than or equal to 0.05, indicating insufficient evidence to reject the null hypothesis of homogeneity of variances.\n")
}
## The overall p-value obtained from the Bartlett test is greater than or equal to 0.05, indicating insufficient evidence to reject the null hypothesis of homogeneity of variances.

3.2 Homogeneity of variances

The conditions with or without the addition of the transcription factor NR5A1 exhibited clearly distinct variances. As a result, a direct comparison between these conditions is not informative.
To assess the comparability of only conditions with NR5A1 added, both Bartlett and Levene tests were conducted.

dataframe4b <- dataframe4 |>
  filter(factor1 == "NR5A1")

bartlett_test_result <- bartlett.test(RRRp ~ names, data = dataframe4b)
print(bartlett_test_result)
## 
##  Bartlett test of homogeneity of variances
## 
## data:  RRRp by names
## Bartlett's K-squared = 8.8015, df = 3, p-value = 0.03205
p_value <- bartlett_test_result$p.value
if (p_value < 0.05) {
  cat("The overall p-value obtained from the Bartlett test is less than 0.05, suggesting evidence supporting the rejection of the null hypothesis of homogeneity of variances.\n")
} else {
  cat("The overall p-value obtained from the Bartlett test is greater than or equal to 0.05, indicating insufficient evidence to reject the null hypothesis of homogeneity of variances.\n")
}
## The overall p-value obtained from the Bartlett test is less than 0.05, suggesting evidence supporting the rejection of the null hypothesis of homogeneity of variances.
levene_test_result <- leveneTest(RRRp ~ names, data = dataframe4b)
print(levene_test_result)
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  3   1.847 0.1546
##       39
p_value <- levene_test_result$`Pr(>F)`[1]
if (p_value < 0.05) {
  cat("The overall p-value obtained from the Levene test is less than 0.05, suggesting evidence supporting the rejection of the null hypothesis of homogeneity of variances.\n")
} else {
  cat("The overall p-value obtained from the Levene test is greater than or equal to 0.05, indicating insufficient evidence to reject the null hypothesis of homogeneity of variances.\n")
}
## The overall p-value obtained from the Levene test is greater than or equal to 0.05, indicating insufficient evidence to reject the null hypothesis of homogeneity of variances.
dataframe5 <- dataframe4[dataframe4$factor1 == "NR5A1", ]
dataframe5$Other <- unname(vapply(as.character(dataframe5$names), FUN = function(x)  if(x == "Reference+NR5A1") x else "Other", FUN.VALUE = ""))
ggplot(dataframe5,
       aes(
         x = factor(names, level = level_order),
         y = RRRp,
         colour = reporter,
         group = names
       )) +
  geom_boxplot() +
  geom_jitter(width = 0.2) +
  theme(axis.text.x = element_text(
    vjust = 1,
    hjust = 1,
    size = 10,
    angle = 30
  )) + theme(
    panel.grid.major = element_line(colour = "gray85"),
    panel.grid.minor = element_line(colour = "gray90"),
    panel.background = element_rect(fill = NA)
  ) +
  scale_y_continuous(labels = scientific) +
  xlab(NULL)
## Warning: Removed 5 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 5 rows containing missing values (`geom_point()`).

3.3 Wilcoxon rank sum exact test

wilcox.test(RRRp~Other, data = dataframe5)
## 
##  Wilcoxon rank sum exact test
## 
## data:  RRRp by Other
## W = 0, p-value = 1.379e-08
## alternative hypothesis: true location shift is not equal to 0

3.4 Kruskal-Wallis rank sum test

library(dunn.test)
bb <- dunn.test(dataframe5$RRRp, g = dataframe5$names, kw = T, list = T, method = "bh")
##   Kruskal-Wallis rank sum test
## 
## data: x and group
## Kruskal-Wallis chi-squared = 35.8827, df = 3, p-value = 0
## 
## 
##                            Comparison of x by group                            
##                              (Benjamini-Hochberg)                              
## Col Mean-|
## Row Mean |   delNR5A1   Referenc   Variant_
## ---------+---------------------------------
## Referenc |  -4.056706
##          |    0.0001*
##          |
## Variant_ |  -2.623347   1.628239
##          |    0.0065*     0.0621
##          |
## Variant_ |   1.463076   5.365322   4.054264
##          |     0.0717    0.0000*    0.0001*
## 
## 
## List of pairwise comparisons: Z statistic (adjusted p-value)
## -------------------------------------------------------
## delNR5A1+NR5A1 - Reference+NR5A1  : -4.056706 (0.0001)*
## delNR5A1+NR5A1 - Variant_1+NR5A1  : -2.623347 (0.0065)*
## Reference+NR5A1 - Variant_1+NR5A1 :  1.628239 (0.0621)
## delNR5A1+NR5A1 - Variant_2+NR5A1  :  1.463076 (0.0717)
## Reference+NR5A1 - Variant_2+NR5A1 :  5.365322 (0.0000)*
## Variant_1+NR5A1 - Variant_2+NR5A1 :  4.054264 (0.0001)*
## 
## alpha = 0.05
## Reject Ho if p <= alpha/2

4 End results

4.1 Plot

ggplot(dataframe4,
       aes(
         x = factor(names, level = level_order),
         y = RRRp,
         colour = reporter,
         group = names
       )) +
  geom_boxplot() +
  geom_jitter(width = 0.2) +
  theme(axis.text.x = element_text(
    vjust = 1,
    hjust = 1,
    size = 10,
    angle = 30
  )) + theme(
    panel.grid.major = element_line(colour = "gray85"),
    panel.grid.minor = element_line(colour = "gray90"),
    panel.background = element_rect(fill = NA)
  ) +
  scale_y_continuous(labels = scientific) +
  xlab(NULL)
## Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 15 rows containing missing values (`geom_point()`).

gt(dataframe5)
names reporter factor1 renilla luciferase IQR Outlier_upper Outlier_lower renilla_wo_extremes Luc_Ren Luc_Ren_wo_outliers mean mean_neg mean_pos RRR RRRp block Other
Reference+NR5A1 Reference NR5A1 2086170 1973580 0.02697775 0.5165931 0.4086821 2086170 0.946030285 NA 0.4628465 0.09060474 0.4628465 NA NA a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2117440 914549 0.02697775 0.5165931 0.4086821 2117440 0.431912593 0.4319126 0.4628465 0.09060474 0.4628465 0.9168984 91.68984 a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2206910 1056410 0.02697775 0.5165931 0.4086821 NA NA NA 0.4628465 0.09060474 0.4628465 NA NA a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2129310 1019270 0.02697775 0.5165931 0.4086821 2129310 0.478685584 0.4786856 0.4628465 0.09060474 0.4628465 1.0425506 104.25506 a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2146500 992366 0.02697775 0.5165931 0.4086821 2146500 0.462318192 0.4623182 0.4628465 0.09060474 0.4628465 0.9985808 99.85808 a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2172140 972704 0.02697775 0.5165931 0.4086821 2172140 0.447809073 0.4478091 0.4628465 0.09060474 0.4628465 0.9596032 95.96032 a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2102720 952885 0.02697775 0.5165931 0.4086821 2102720 0.453167802 0.4531678 0.4628465 0.09060474 0.4628465 0.9739990 97.39990 a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 1995050 946102 0.02697775 0.5165931 0.4086821 NA NA NA 0.4628465 0.09060474 0.4628465 NA NA a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2079730 974248 0.02697775 0.5165931 0.4086821 2079730 0.468449270 0.4684493 0.4628465 0.09060474 0.4628465 1.0150515 101.50515 a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2082990 1038220 0.02697775 0.5165931 0.4086821 2082990 0.498427741 0.4984277 0.4628465 0.09060474 0.4628465 1.0955865 109.55865 a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2107500 973668 0.02697775 0.5165931 0.4086821 2107500 0.462001423 0.4620014 0.4628465 0.09060474 0.4628465 0.9977299 99.77299 a Reference+NR5A1
Reference+NR5A1 Reference NR5A1 2122020 4800 0.02697775 0.5165931 0.4086821 2122020 0.002261996 NA 0.4628465 0.09060474 0.4628465 NA NA a Reference+NR5A1
Variant_1+NR5A1 Variant_1 NR5A1 1823620 540331 0.01357464 0.3180218 0.2637232 1823620 0.296295829 0.2962958 0.2906128 0.09060474 0.4628465 0.5525740 55.25740 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1745900 527855 0.01357464 0.3180218 0.2637232 1745900 0.302339767 0.3023398 0.2906128 0.09060474 0.4628465 0.5688106 56.88106 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1897230 541003 0.01357464 0.3180218 0.2637232 1897230 0.285154146 0.2851541 0.2906128 0.09060474 0.4628465 0.5226427 52.26427 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1692220 512053 0.01357464 0.3180218 0.2637232 1692220 0.302592453 0.3025925 0.2906128 0.09060474 0.4628465 0.5694894 56.94894 a Other
Variant_1+NR5A1 Variant_1 NR5A1 142261 34843 0.01357464 0.3180218 0.2637232 NA NA NA 0.2906128 0.09060474 0.4628465 NA NA a Other
Variant_1+NR5A1 Variant_1 NR5A1 1735220 485106 0.01357464 0.3180218 0.2637232 1735220 0.279564551 0.2795646 0.2906128 0.09060474 0.4628465 0.5076266 50.76266 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1734090 501617 0.01357464 0.3180218 0.2637232 1734090 0.289268146 0.2892681 0.2906128 0.09060474 0.4628465 0.5336946 53.36946 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1791410 496061 0.01357464 0.3180218 0.2637232 1791410 0.276910925 0.2769109 0.2906128 0.09060474 0.4628465 0.5004979 50.04979 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1821090 515398 0.01357464 0.3180218 0.2637232 1821090 0.283016216 0.2830162 0.2906128 0.09060474 0.4628465 0.5168993 51.68993 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1671730 483273 0.01357464 0.3180218 0.2637232 1671730 0.289085558 0.2890856 0.2906128 0.09060474 0.4628465 0.5332041 53.32041 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1722000 514919 0.01357464 0.3180218 0.2637232 1722000 0.299023810 0.2990238 0.2906128 0.09060474 0.4628465 0.5599025 55.99025 a Other
Variant_1+NR5A1 Variant_1 NR5A1 1827930 536478 0.01357464 0.3180218 0.2637232 1827930 0.293489357 0.2934894 0.2906128 0.09060474 0.4628465 0.5450346 54.50346 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1612790 395287 0.01204423 0.2558834 0.2077065 1612790 0.245095146 0.2450951 0.2341973 0.09060474 0.4628465 0.4150271 41.50271 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1737550 394912 0.01204423 0.2558834 0.2077065 1737550 0.227280942 0.2272809 0.2341973 0.09060474 0.4628465 0.3671706 36.71706 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1814510 425305 0.01204423 0.2558834 0.2077065 1814510 0.234391103 0.2343911 0.2341973 0.09060474 0.4628465 0.3862715 38.62715 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1658700 408337 0.01204423 0.2558834 0.2077065 1658700 0.246178935 0.2461789 0.2341973 0.09060474 0.4628465 0.4179386 41.79386 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1903090 427163 0.01204423 0.2558834 0.2077065 1903090 0.224457593 0.2244576 0.2341973 0.09060474 0.4628465 0.3595858 35.95858 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1851850 435909 0.01204423 0.2558834 0.2077065 1851850 0.235391095 0.2353911 0.2341973 0.09060474 0.4628465 0.3889579 38.89579 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1831140 412991 0.01204423 0.2558834 0.2077065 1831140 0.225537643 0.2255376 0.2341973 0.09060474 0.4628465 0.3624873 36.24873 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1741370 406536 0.01204423 0.2558834 0.2077065 1741370 0.233457565 0.2334576 0.2341973 0.09060474 0.4628465 0.3837636 38.37636 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1625390 412621 0.01204423 0.2558834 0.2077065 1625390 0.253859689 0.2538597 0.2341973 0.09060474 0.4628465 0.4385724 43.85724 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1768190 399348 0.01204423 0.2558834 0.2077065 1768190 0.225851294 0.2258513 0.2341973 0.09060474 0.4628465 0.3633299 36.33299 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1760830 395261 0.01204423 0.2558834 0.2077065 1760830 0.224474254 0.2244743 0.2341973 0.09060474 0.4628465 0.3596306 35.96306 a Other
Variant_2+NR5A1 Variant_2 NR5A1 1900310 445417 0.01204423 0.2558834 0.2077065 1900310 0.234391757 0.2343918 0.2341973 0.09060474 0.4628465 0.3862732 38.62732 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 1873670 461254 0.01089407 0.2676594 0.2240831 1873670 0.246176755 0.2461768 0.2454832 0.09060474 0.4628465 0.4179328 41.79328 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 1795110 448295 0.01089407 0.2676594 0.2240831 1795110 0.249731214 0.2497312 0.2454832 0.09060474 0.4628465 0.4274816 42.74816 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 2021290 517611 0.01089407 0.2676594 0.2240831 2021290 0.256079533 0.2560795 0.2454832 0.09060474 0.4628465 0.4445359 44.45359 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 2060980 505456 0.01089407 0.2676594 0.2240831 2060980 0.245250318 0.2452503 0.2454832 0.09060474 0.4628465 0.4154440 41.54440 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 2087650 497184 0.01089407 0.2676594 0.2240831 2087650 0.238154863 0.2381549 0.2454832 0.09060474 0.4628465 0.3963825 39.63825 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 1850110 477564 0.01089407 0.2676594 0.2240831 1850110 0.258127355 0.2581274 0.2454832 0.09060474 0.4628465 0.4500372 45.00372 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 1858120 483230 0.01089407 0.2676594 0.2240831 1858120 0.260063936 0.2600639 0.2454832 0.09060474 0.4628465 0.4552397 45.52397 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 2057470 496222 0.01089407 0.2676594 0.2240831 2057470 0.241180673 0.2411807 0.2454832 0.09060474 0.4628465 0.4045112 40.45112 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 2077500 501602 0.01089407 0.2676594 0.2240831 2077500 0.241445006 0.2414450 0.2454832 0.09060474 0.4628465 0.4052213 40.52213 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 2018600 490351 0.01089407 0.2676594 0.2240831 2018600 0.242916378 0.2429164 0.2454832 0.09060474 0.4628465 0.4091740 40.91740 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 1999450 461691 0.01089407 0.2676594 0.2240831 1999450 0.230909000 0.2309090 0.2454832 0.09060474 0.4628465 0.3769171 37.69171 a Other
delNR5A1+NR5A1 delNR5A1 NR5A1 2129300 502010 0.01089407 0.2676594 0.2240831 2129300 0.235762927 0.2357629 0.2454832 0.09060474 0.4628465 0.3899568 38.99568 a Other
 
library(gtsummary)
dataframe4 |> tbl_summary(
  type = all_continuous() ~ "continuous2",
  include = c(names, RRRp),
  by = names,
  statistic = all_continuous() ~ c("{median}","{mean}", "{sd}", "{min}", "{max}"),
  digits = list(
              all_categorical() ~c(0,1),
              all_continuous() ~ c(1,1)),
  missing_text = "NA"
  )|>
  bold_labels() |>
  italicize_levels()
Characteristic delNR5A1, N = 12 delNR5A1+NR5A1, N = 12 Reference, N = 12 Reference+NR5A1, N = 12 Variant_1, N = 12 Variant_1+NR5A1, N = 12 Variant_2, N = 12 Variant_2+NR5A1, N = 12
RRRp







    Median 1.6 41.2 0.0 99.8 4.4 53.4 2.7 38.5
    Mean 1.7 41.6 0.0 100.0 4.9 53.7 3.0 38.6
    SD 0.8 2.4 0.9 5.4 1.1 2.4 1.2 2.6
    Minimum 0.6 37.7 -1.4 91.7 3.5 50.0 1.6 36.0
    Maximum 3.0 45.5 1.5 109.6 6.5 56.9 5.5 43.9
    NA 3 0 4 4 3 1 0 0
 
sessionInfo()
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.3.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Paris
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] dunn.test_1.3.5 gt_0.10.1       gtsummary_1.7.2 car_3.1-2      
##  [5] carData_3.0-5   broom_1.0.5     ggpubr_0.6.0    scales_1.3.0   
##  [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
## [13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.0     tibble_3.2.1   
## [17] ggplot2_3.4.4   tidyverse_2.0.0
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.8           utf8_1.2.4           generics_0.1.3      
##  [4] xml2_1.3.6           rstatix_0.7.2        stringi_1.8.3       
##  [7] hms_1.1.3            digest_0.6.34        magrittr_2.0.3      
## [10] evaluate_0.23        grid_4.3.2           timechange_0.3.0    
## [13] fastmap_1.1.1        broom.helpers_1.14.0 jsonlite_1.8.8      
## [16] backports_1.4.1      fansi_1.0.6          jquerylib_0.1.4     
## [19] abind_1.4-5          cli_3.6.2            rlang_1.1.3         
## [22] commonmark_1.9.0     munsell_0.5.0        withr_3.0.0         
## [25] cachem_1.0.8         yaml_2.3.8           tools_4.3.2         
## [28] tzdb_0.4.0           ggsignif_0.6.4       colorspace_2.1-0    
## [31] vctrs_0.6.5          R6_2.5.1             lifecycle_1.0.4     
## [34] pkgconfig_2.0.3      pillar_1.9.0         bslib_0.6.1         
## [37] gtable_0.3.4         glue_1.7.0           highr_0.10          
## [40] xfun_0.41            tidyselect_1.2.0     rstudioapi_0.15.0   
## [43] knitr_1.45           farver_2.1.1         htmltools_0.5.7     
## [46] labeling_0.4.3       rmarkdown_2.25       compiler_4.3.2      
## [49] markdown_1.12