Luciferase on HeLa cells - statistical analysis in R

library(tidyverse)

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library(scales) # scientific format

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library(coin) # Approximative Two-Sample Fisher-Pitman Permutation Test

## Loading required package: survival
## 
## Attaching package: 'coin'
## 
## The following object is masked from 'package:scales':
## 
##     pvalue

library(ggpubr)
library(broom)
library(car)

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library(gtsummary)  
library(gt)

1 Data

data from Luciferase42
42-HeLa_Dual-Glo_1-26-2023_10-34-21 AM.xml
42-HeLa_Dual-Glo_1-26-2023_10-34-21 AM.csv

1.1 Luciferase = Assayed Luciferase Levels

Input = (
  "
60226   61051.7 66876.5 63589.5 63154.9 50962.8 55427   75472.8 71021.3 4249.85 70619.6 2421.28
48795.7 55075.2 53277.1 50819.1 53345.4 56443.3 65771.7 66978.2 69013.1 3680.64 1566.12 2107.21
60512.6 71297.1 77161.8 79652.1 70548.1 83508.5 97500.8 80641.5 84564.8 4618    2496.29 64830.9
44968.9 37731.8 43665.4 39291.4 3004.42 46096.6 49983.1 47138.2 62740.5 3152.47 56180   2672.34
56730.9 55696.4 59950.5 57197.4 88169.9 70541.1 89618.9 73322.8 82763.7 4710.04 1931.18 59933.4
45198.8 38547.7 41822.1 42741.7 50140.9 44617.4 62920.5 64911.4 61763.8 3294.51 1393.09 64251.4
53246.9 51343.6 45468   45739.1 2986.42 62682.1 72998.6 70233.1 72270.7 4382.9  1706.14 70032.8
35852.3 35955.7 38397.2 34042.4 4469.94 42973.6 47442.6 51043.2 50613.1 3449.56 1075.05 49453.7
"
)
luciferase = read.delim(textConnection(Input), header = FALSE)

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

1.2 Renilla = Assayed Renilla Levels

Input = (
  "
1472410 1562440 1687790 1524190 1610360 2012250 1473980 1911120 1770930 119287  1859870 81448.6
1544910 1802320 1714390 1557590 1711720 1718070 2002650 1886280 2109530 130234  51273.3 78355.5
1219240 1549310 1746110 1621560 1472070 1588340 1862590 1715340 1824380 100317  53486.1 1456070
1488500 1151550 1555550 1256780 71388.7 1531720 1810250 1703350 2166170 104416  2244400 97044.6
473446  495631  484832  494818  593864  555759  646673  536634  592993  36610.9 13402.4 529804
563558  490290  523060  587296  683813  584700  657799  714926  692377  35828.2 15697.6 659306
519301  529884  525803  498938  31775.4 586286  671262  634343  679095  40432.7 15395.1 618741
492362  589801  583858  631868  72923.1 571305  735373  649126  672333  46740.5 14423.8 633089
"
)
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
  )) + 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 12 rows containing non-finite values (`stat_boxplot()`).

## Warning: Removed 12 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
  )) + 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
  )) + 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 12 rows containing non-finite values (`stat_boxplot()`).

## Warning: Removed 12 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
  )) + 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 18 rows containing non-finite values (`stat_boxplot()`).

## Warning: Removed 18 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            0.967  0.855  Shapiro-Wilk normality test
## 2 delNR5A1+NR5A1      0.896  0.142  Shapiro-Wilk normality test
## 3 Reference           0.971  0.905  Shapiro-Wilk normality test
## 4 Reference+NR5A1     0.925  0.399  Shapiro-Wilk normality test
## 5 Variant_1           0.850  0.0954 Shapiro-Wilk normality test
## 6 Variant_1+NR5A1     0.901  0.224  Shapiro-Wilk normality test
## 7 Variant_2           0.934  0.493  Shapiro-Wilk normality test
## 8 Variant_2+NR5A1     0.919  0.307  Shapiro-Wilk normality test

p_value <- min(shapiro_test_result$p.value)
p_value

## [1] 0.09539483

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 = 2.4295, df = 3, p-value = 0.4882

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 greater than or equal to 0.05, indicating insufficient evidence to reject 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  0.9606  0.421
##       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 Kruskal-Wallis rank sum test

library(dunn.test)
bb <- dunn.test(dataframe4$RRRp, g = dataframe4$names, kw = T, list = T, method = "bh")

##   Kruskal-Wallis rank sum test
## 
## data: x and group
## Kruskal-Wallis chi-squared = 77.4383, df = 7, p-value = 0
## 
## 
##                            Comparison of x by group                            
##                              (Benjamini-Hochberg)                              
## Col Mean-|
## Row Mean |   delNR5A1   delNR5A1   Referenc   Referenc   Variant_   Variant_
## ---------+------------------------------------------------------------------
## delNR5A1 |  -3.972807
##          |    0.0001*
##          |
## Referenc |  -1.624848   2.104570
##          |     0.0695     0.0309
##          |
## Referenc |  -6.759258  -3.024443  -4.838239
##          |    0.0000*    0.0029*    0.0000*
##          |
## Variant_ |  -0.691265   2.929527   0.841944   5.549015
##          |     0.2447    0.0037*     0.2073    0.0000*
##          |
## Variant_ |  -4.667717  -0.890126  -2.849288   2.043452  -3.622429
##          |    0.0000*     0.2091    0.0044*     0.0338    0.0004*
##          |
## Variant_ |  -2.595633   1.224336  -0.878838   4.067897  -1.713765   2.024444
##          |    0.0088*     0.1405     0.2043    0.0001*     0.0606     0.0334
##          |
## Variant_ |  -5.836005  -1.988718  -3.911672   1.063900  -4.664221  -1.027640
##          |    0.0000*     0.0344    0.0001*     0.1749    0.0000*     0.1774
## Col Mean-|
## Row Mean |   Variant_
## ---------+-----------
## Variant_ |  -3.099725
##          |    0.0025*
## 
## 
## List of pairwise comparisons: Z statistic (adjusted p-value)
## -------------------------------------------------------
## delNR5A1 - delNR5A1+NR5A1         : -3.972807 (0.0001)*
## delNR5A1 - Reference              : -1.624848 (0.0695)
## delNR5A1+NR5A1 - Reference        :  2.104570 (0.0309)
## delNR5A1 - Reference+NR5A1        : -6.759258 (0.0000)*
## delNR5A1+NR5A1 - Reference+NR5A1  : -3.024443 (0.0029)*
## Reference - Reference+NR5A1       : -4.838239 (0.0000)*
## delNR5A1 - Variant_1              : -0.691265 (0.2447)
## delNR5A1+NR5A1 - Variant_1        :  2.929527 (0.0037)*
## Reference - Variant_1             :  0.841944 (0.2073)
## Reference+NR5A1 - Variant_1       :  5.549015 (0.0000)*
## delNR5A1 - Variant_1+NR5A1        : -4.667717 (0.0000)*
## delNR5A1+NR5A1 - Variant_1+NR5A1  : -0.890126 (0.2091)
## Reference - Variant_1+NR5A1       : -2.849288 (0.0044)*
## Reference+NR5A1 - Variant_1+NR5A1 :  2.043452 (0.0338)
## Variant_1 - Variant_1+NR5A1       : -3.622429 (0.0004)*
## delNR5A1 - Variant_2              : -2.595633 (0.0088)*
## delNR5A1+NR5A1 - Variant_2        :  1.224336 (0.1405)
## Reference - Variant_2             : -0.878838 (0.2043)
## Reference+NR5A1 - Variant_2       :  4.067897 (0.0001)*
## Variant_1 - Variant_2             : -1.713765 (0.0606)
## Variant_1+NR5A1 - Variant_2       :  2.024444 (0.0334)
## delNR5A1 - Variant_2+NR5A1        : -5.836005 (0.0000)*
## delNR5A1+NR5A1 - Variant_2+NR5A1  : -1.988718 (0.0344)
## Reference - Variant_2+NR5A1       : -3.911672 (0.0001)*
## Reference+NR5A1 - Variant_2+NR5A1 :  1.063900 (0.1749)
## Variant_1 - Variant_2+NR5A1       : -4.664221 (0.0000)*
## Variant_1+NR5A1 - Variant_2+NR5A1 : -1.027640 (0.1774)
## Variant_2 - Variant_2+NR5A1       : -3.099725 (0.0025)*
## 
## alpha = 0.05
## Reject Ho if p <= alpha/2

3.4 Wilcoxon rank sum exact test

wilcox.test(RRRp~Other, data = dataframe5)

## 
##  Wilcoxon rank sum exact test
## 
## data:  RRRp by Other
## W = 2, p-value = 4.172e-09
## alternative hypothesis: true location shift is not equal to 0

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	473446.0	56730.90	0.02144612	0.17026626	0.08448180	473446.0	0.11982549	0.11982549	0.12747532	0.02903769	0.1274753	0.9222876	92.22876	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	495631.0	55696.40	0.02144612	0.17026626	0.08448180	495631.0	0.11237473	0.11237473	0.12747532	0.02903769	0.1274753	0.8465974	84.65974	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	484832.0	59950.50	0.02144612	0.17026626	0.08448180	484832.0	0.12365211	0.12365211	0.12747532	0.02903769	0.1274753	0.9611611	96.11611	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	494818.0	57197.40	0.02144612	0.17026626	0.08448180	494818.0	0.11559280	0.11559280	0.12747532	0.02903769	0.1274753	0.8792889	87.92889	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	593864.0	88169.90	0.02144612	0.17026626	0.08448180	593864.0	0.14846817	0.14846817	0.12747532	0.02903769	0.1274753	1.2132604	121.32604	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	555759.0	70541.10	0.02144612	0.17026626	0.08448180	555759.0	0.12692750	0.12692750	0.12747532	0.02903769	0.1274753	0.9944349	99.44349	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	646673.0	89618.90	0.02144612	0.17026626	0.08448180	646673.0	0.13858457	0.13858457	0.12747532	0.02903769	0.1274753	1.1128557	111.28557	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	536634.0	73322.80	0.02144612	0.17026626	0.08448180	536634.0	0.13663465	0.13663465	0.12747532	0.02903769	0.1274753	1.0930471	109.30471	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	592993.0	82763.70	0.02144612	0.17026626	0.08448180	592993.0	0.13956944	0.13956944	0.12747532	0.02903769	0.1274753	1.1228607	112.28607	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	36610.9	4710.04	0.02144612	0.17026626	0.08448180	NA	NA	NA	0.12747532	0.02903769	0.1274753	NA	NA	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	13402.4	1931.18	0.02144612	0.17026626	0.08448180	NA	NA	NA	0.12747532	0.02903769	0.1274753	NA	NA	a	Reference+NR5A1
Reference+NR5A1	Reference	NR5A1	529804.0	59933.40	0.02144612	0.17026626	0.08448180	529804.0	0.11312372	0.11312372	0.12747532	0.02903769	0.1274753	0.8542062	85.42062	a	Reference+NR5A1
Variant_1+NR5A1	Variant_1	NR5A1	563558.0	45198.80	0.01351059	0.11066317	0.05662083	563558.0	0.08020257	0.08020257	0.08342983	0.02903769	0.1274753	0.5197695	51.97695	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	490290.0	38547.70	0.01351059	0.11066317	0.05662083	490290.0	0.07862224	0.07862224	0.08342983	0.02903769	0.1274753	0.5037155	50.37155	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	523060.0	41822.10	0.01351059	0.11066317	0.05662083	523060.0	0.07995660	0.07995660	0.08342983	0.02903769	0.1274753	0.5172708	51.72708	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	587296.0	42741.70	0.01351059	0.11066317	0.05662083	587296.0	0.07277710	0.07277710	0.08342983	0.02903769	0.1274753	0.4443363	44.43363	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	683813.0	50140.90	0.01351059	0.11066317	0.05662083	683813.0	0.07332546	0.07332546	0.08342983	0.02903769	0.1274753	0.4499069	44.99069	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	584700.0	44617.40	0.01351059	0.11066317	0.05662083	584700.0	0.07630819	0.07630819	0.08342983	0.02903769	0.1274753	0.4802077	48.02077	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	657799.0	62920.50	0.01351059	0.11066317	0.05662083	657799.0	0.09565308	0.09565308	0.08342983	0.02903769	0.1274753	0.6767269	67.67269	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	714926.0	64911.40	0.01351059	0.11066317	0.05662083	714926.0	0.09079457	0.09079457	0.08342983	0.02903769	0.1274753	0.6273707	62.73707	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	692377.0	61763.80	0.01351059	0.11066317	0.05662083	692377.0	0.08920545	0.08920545	0.08342983	0.02903769	0.1274753	0.6112272	61.12272	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	35828.2	3294.51	0.01351059	0.11066317	0.05662083	NA	NA	NA	0.08342983	0.02903769	0.1274753	NA	NA	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	15697.6	1393.09	0.01351059	0.11066317	0.05662083	NA	NA	NA	0.08342983	0.02903769	0.1274753	NA	NA	a	Other
Variant_1+NR5A1	Variant_1	NR5A1	659306.0	64251.40	0.01351059	0.11066317	0.05662083	659306.0	0.09745308	0.09745308	0.08342983	0.02903769	0.1274753	0.6950126	69.50126	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	519301.0	53246.90	0.01313349	0.12827432	0.07574037	519301.0	0.10253572	0.10253572	0.10235920	0.02903769	0.1274753	0.7466457	74.66457	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	529884.0	51343.60	0.01313349	0.12827432	0.07574037	529884.0	0.09689592	0.09689592	0.10235920	0.02903769	0.1274753	0.6893526	68.93526	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	525803.0	45468.00	0.01313349	0.12827432	0.07574037	525803.0	0.08647345	0.08647345	0.10235920	0.02903769	0.1274753	0.5834737	58.34737	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	498938.0	45739.10	0.01313349	0.12827432	0.07574037	498938.0	0.09167291	0.09167291	0.10235920	0.02903769	0.1274753	0.6362935	63.62935	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	31775.4	2986.42	0.01313349	0.12827432	0.07574037	31775.4	0.09398528	0.09398528	0.10235920	0.02903769	0.1274753	0.6597842	65.97842	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	586286.0	62682.10	0.01313349	0.12827432	0.07574037	586286.0	0.10691386	0.10691386	0.10235920	0.02903769	0.1274753	0.7911220	79.11220	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	671262.0	72998.60	0.01313349	0.12827432	0.07574037	671262.0	0.10874830	0.10874830	0.10235920	0.02903769	0.1274753	0.8097575	80.97575	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	634343.0	70233.10	0.01313349	0.12827432	0.07574037	634343.0	0.11071786	0.11071786	0.10235920	0.02903769	0.1274753	0.8297657	82.97657	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	679095.0	72270.70	0.01313349	0.12827432	0.07574037	679095.0	0.10642208	0.10642208	0.10235920	0.02903769	0.1274753	0.7861261	78.61261	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	40432.7	4382.90	0.01313349	0.12827432	0.07574037	40432.7	0.10839988	0.10839988	0.10235920	0.02903769	0.1274753	0.8062181	80.62181	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	15395.1	1706.14	0.01313349	0.12827432	0.07574037	NA	NA	NA	0.10235920	0.02903769	0.1274753	NA	NA	a	Other
Variant_2+NR5A1	Variant_2	NR5A1	618741.0	70032.80	0.01313349	0.12827432	0.07574037	618741.0	0.11318597	0.11318597	0.10235920	0.02903769	0.1274753	0.8548386	85.48386	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	492362.0	35852.30	0.01152458	0.09252188	0.04642354	492362.0	0.07281695	0.07281695	0.06956795	0.02903769	0.1274753	0.4447411	44.47411	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	589801.0	35955.70	0.01152458	0.09252188	0.04642354	589801.0	0.06096243	0.06096243	0.06956795	0.02903769	0.1274753	0.3243144	32.43144	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	583858.0	38397.20	0.01152458	0.09252188	0.04642354	583858.0	0.06576462	0.06576462	0.06956795	0.02903769	0.1274753	0.3730985	37.30985	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	631868.0	34042.40	0.01152458	0.09252188	0.04642354	631868.0	0.05387581	0.05387581	0.06956795	0.02903769	0.1274753	0.2523235	25.23235	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	72923.1	4469.94	0.01152458	0.09252188	0.04642354	72923.1	0.06129663	0.06129663	0.06956795	0.02903769	0.1274753	0.3277094	32.77094	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	571305.0	42973.60	0.01152458	0.09252188	0.04642354	571305.0	0.07522007	0.07522007	0.06956795	0.02903769	0.1274753	0.4691537	46.91537	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	735373.0	47442.60	0.01152458	0.09252188	0.04642354	735373.0	0.06451501	0.06451501	0.06956795	0.02903769	0.1274753	0.3604041	36.04041	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	649126.0	51043.20	0.01152458	0.09252188	0.04642354	649126.0	0.07863373	0.07863373	0.06956795	0.02903769	0.1274753	0.5038322	50.38322	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	672333.0	50613.10	0.01152458	0.09252188	0.04642354	672333.0	0.07527981	0.07527981	0.06956795	0.02903769	0.1274753	0.4697606	46.97606	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	46740.5	3449.56	0.01152458	0.09252188	0.04642354	46740.5	0.07380238	0.07380238	0.06956795	0.02903769	0.1274753	0.4547518	45.47518	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	14423.8	1075.05	0.01152458	0.09252188	0.04642354	14423.8	0.07453306	0.07453306	0.06956795	0.02903769	0.1274753	0.4621746	46.21746	a	Other
delNR5A1+NR5A1	delNR5A1	NR5A1	633089.0	49453.70	0.01152458	0.09252188	0.04642354	633089.0	0.07811493	0.07811493	0.06956795	0.02903769	0.1274753	0.4985618	49.85618	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	-0.1	45.0	10.6	97.8	3.1	51.9	18.7	78.6
Mean	0.0	41.2	10.7	100.0	2.9	55.3	19.2	74.5
SD	2.2	8.1	1.3	12.9	0.9	9.2	3.0	8.9
Minimum	-4.1	25.2	8.7	84.7	1.5	44.4	15.4	58.3
Maximum	3.8	50.4	12.9	121.3	3.9	69.5	23.9	85.5
NA	1	0	3	2	4	2	2	1

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    coin_1.4-3     
##  [9] survival_3.5-7  scales_1.3.0    lubridate_1.9.3 forcats_1.0.0  
## [13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
## [17] tidyr_1.3.0     tibble_3.2.1    ggplot2_3.4.4   tidyverse_2.0.0
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.4         xfun_0.41            bslib_0.6.1         
##  [4] rstatix_0.7.2        lattice_0.22-5       tzdb_0.4.0          
##  [7] vctrs_0.6.5          tools_4.3.2          generics_0.1.3      
## [10] stats4_4.3.2         parallel_4.3.2       sandwich_3.1-0      
## [13] fansi_1.0.6          highr_0.10           pkgconfig_2.0.3     
## [16] Matrix_1.6-5         lifecycle_1.0.4      farver_2.1.1        
## [19] compiler_4.3.2       munsell_0.5.0        codetools_0.2-19    
## [22] htmltools_0.5.7      sass_0.4.8           yaml_2.3.8          
## [25] pillar_1.9.0         jquerylib_0.1.4      broom.helpers_1.14.0
## [28] MASS_7.3-60.0.1      cachem_1.0.8         abind_1.4-5         
## [31] multcomp_1.4-25      commonmark_1.9.0     tidyselect_1.2.0    
## [34] digest_0.6.34        mvtnorm_1.2-4        stringi_1.8.3       
## [37] labeling_0.4.3       splines_4.3.2        fastmap_1.1.1       
## [40] grid_4.3.2           colorspace_2.1-0     cli_3.6.2           
## [43] magrittr_2.0.3       utf8_1.2.4           TH.data_1.1-2       
## [46] libcoin_1.0-10       withr_3.0.0          backports_1.4.1     
## [49] timechange_0.3.0     rmarkdown_2.25       matrixStats_1.2.0   
## [52] ggsignif_0.6.4       zoo_1.8-12           modeltools_0.2-23   
## [55] hms_1.1.3            evaluate_0.23        knitr_1.45          
## [58] markdown_1.12        rlang_1.1.3          glue_1.7.0          
## [61] xml2_1.3.6           rstudioapi_0.15.0    jsonlite_1.8.8      
## [64] R6_2.5.1

Supplementary_Data_16

Denis Houzelstein

2024-02-21