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Rmd baf2fd4 Evgenii O. Tretiakov 2023-04-05 Start workflowr project.

1: READ VICTOR’s FILE

Rep <- read.table(
    here(data_dir, "Homo_sapiens_triangles.txt"),
    sep = "\t",
    header = FALSE
)
names(Rep) <- c(
    "DelStart",
    "DelEnd",
    "DelLength",
    "MasterRepeat",
    "AlternRepeats1",
    "AlternRepeats2",
    "AlternRepeats3",
    "AlternRepeats4"
)

MasterRepeat is a repeat with arms located close to given deletion breakpoints AlternativeRepeats1-4 are repeats where: first arm == arm1 of Master; first arm == arm2 of Master; second arm == arm1 of Master; second arm == arm2 of Master; mb_del == realised deletions (exist in MitoBreak); non_del - non realised deletions == don’t exist in MitoBreak

2: filter out deletions within major arc:

  • OH: 110-441
  • OL: 5721-5781
nrow(Rep)
[1] 880
Rep <- Rep[Rep$DelStart > 5781 &
    Rep$DelStart < 16569 &
    Rep$DelEnd > 5781 &
    Rep$DelEnd < 16569, ]
nrow(Rep)
[1] 703

3: concatenate all alternative deletions

Rep$AllAlternRepeats <- paste(
    Rep$AlternRepeats1,
    Rep$AlternRepeats2,
    Rep$AlternRepeats3,
    Rep$AlternRepeats4,
    sep = ","
)
Rep$AllAlternRepeats <- gsub("\\,\\[\\]\\,", ",", Rep$AllAlternRepeats)
Rep$AllAlternRepeats <- gsub("^\\[", "", Rep$AllAlternRepeats)
Rep$AllAlternRepeats <- gsub("\\]$", "", Rep$AllAlternRepeats)

4: prepare dataset for analysis of realized and nonrealized deletions line by line

Rep$CenterOfRealizedRepeats <- 0
Rep$CenterOfNonRealizedRepeats <- 0

Rep$LengthOfRealizedRepeats <- 0
Rep$LengthOfNonRealizedRepeats <- 0

Rep$StartOfRealizedRepeats <- 0
Rep$StartOfNonRealizedRepeats <- 0

Rep$EndOfRealizedRepeats <- 0
Rep$EndOfNonRealizedRepeats <- 0

for (i in (1:nrow(Rep))) {
    # i = 1
    # format of data to get a dataset of master and all alternative repeats for each deletion (for each line of the dataset)
    temp <- Rep[i, ]
    AltRep <- unlist(strsplit(temp$AllAlternRepeats, "\\]\\,\\["))
    AltRep <- AltRep[AltRep != ""]
    AltRep <- data.frame(AltRep)
    names(AltRep) <- c("WholeLine")
    if (nrow(AltRep) > 0) {
        AltRep$RepeatType <- "alternative"
        AltRep$WholeLine <- as.character(AltRep$WholeLine)

        MasterRepeat <- as.character(temp$MasterRepeat)
        MasterRepeat <- gsub("^\\[", "", MasterRepeat)
        MasterRepeat <- gsub("\\]$", "", MasterRepeat)

        MasterRepeat <- paste(MasterRepeat, "mb_del", sep = " ")
        MasterRepeat <- data.frame(MasterRepeat)
        names(MasterRepeat) <- c("WholeLine")
        MasterRepeat$RepeatType <- "master"

        AllRep <- rbind(MasterRepeat, AltRep)
        ReturnFifth <- function(x) {
            unlist(strsplit(x, " "))[5]
        }
        AllRep$RealisedRepeat <- apply(as.matrix(AllRep$WholeLine), 1, FUN = ReturnFifth)
        ReturnFirst <- function(x) {
            as.numeric(unlist(strsplit(x, " "))[1])
        }
        AllRep$RepStart <- apply(as.matrix(AllRep$WholeLine), 1, FUN = ReturnFirst)
        ReturnSecond <- function(x) {
            as.numeric(unlist(strsplit(x, " "))[2])
        }
        AllRep$RepEnd <- apply(as.matrix(AllRep$WholeLine), 1, FUN = ReturnSecond)
        AllRep <- AllRep[AllRep$RepStart > 5781 &
            AllRep$RepStart < 16569 &
            AllRep$RepEnd > 5781 &
            AllRep$RepEnd < 16569, ]
        if (nrow(AllRep) > 0) {
            AllRep$Center <- (AllRep$RepEnd - AllRep$RepStart) / 2 + AllRep$RepStart
            Rep$CenterOfRealizedRepeats[i] <- mean(AllRep[AllRep$RealisedRepeat == "mb_del", ]$Center)
            Rep$CenterOfNonRealizedRepeats[i] <- mean(AllRep[AllRep$RealisedRepeat == "non_del", ]$Center)

            AllRep$Length <- AllRep$RepEnd - AllRep$RepStart
            Rep$LengthOfRealizedRepeats[i] <- mean(AllRep[AllRep$RealisedRepeat == "mb_del", ]$Length)
            Rep$LengthOfNonRealizedRepeats[i] <- mean(AllRep[AllRep$RealisedRepeat == "non_del", ]$Length)

            Rep$StartOfNonRealizedRepeats[i] <- mean(AllRep[AllRep$RealisedRepeat == "non_del", ]$RepStart)
            Rep$StartOfRealizedRepeats[i] <- mean(AllRep[AllRep$RealisedRepeat == "mb_del", ]$RepStart)

            Rep$EndOfNonRealizedRepeats[i] <- mean(AllRep[AllRep$RealisedRepeat == "non_del", ]$RepEnd)
            Rep$EndOfRealizedRepeats[i] <- mean(AllRep[AllRep$RealisedRepeat == "mb_del", ]$RepEnd)

            if (i == 1) {
                FinalAllRep <- AllRep
            }
            if (i > 1) {
                FinalAllRep <- rbind(FinalAllRep, AllRep)
            }
        }
    }
}
nrow(Rep) # 703
[1] 703
Rep <- Rep[Rep$CenterOfRealizedRepeats > 0, ]
nrow(Rep) # 643
[1] 643
Rep <- Rep[Rep$CenterOfNonRealizedRepeats > 0, ] # it means there is at least one extra (third) arm to analyze
nrow(Rep) # 643
[1] 643
Rep <- Rep[!is.na(Rep$LengthOfRealizedRepeat), ] # 618
nrow(Rep)
[1] 618

5: Copare position of repeats with realized deletions vs rest repeats

## center is a bit higher in realized repeats
wilcox.test(Rep$CenterOfRealizedRepeats,
    Rep$CenterOfNonRealizedRepeats,
    paired = TRUE
) # significant

    Wilcoxon signed rank test with continuity correction

data:  Rep$CenterOfRealizedRepeats and Rep$CenterOfNonRealizedRepeats
V = 114853, p-value = 1.506e-05
alternative hypothesis: true location shift is not equal to 0
t.test(Rep$CenterOfRealizedRepeats,
    Rep$CenterOfNonRealizedRepeats,
    paired = TRUE
) # significant

    Paired t-test

data:  Rep$CenterOfRealizedRepeats and Rep$CenterOfNonRealizedRepeats
t = 4.619, df = 617, p-value = 4.696e-06
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
 162.6072 403.1435
sample estimates:
mean difference 
       282.8754 
summary(Rep$CenterOfRealizedRepeats)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   8590   10634   11334   11500   12409   15647 
summary(Rep$CenterOfNonRealizedRepeats)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   7021   10292   11290   11217   12196   15889 
boxplot(Rep$CenterOfRealizedRepeats,
    Rep$CenterOfNonRealizedRepeats,
    notch = TRUE,
    names = c(
        "CenterOfRealizedRepeats",
        "CenterOfNonRealizedRepeats"
    )
)

RepReal <- Rep %>%
    select(
        Realized = CenterOfRealizedRepeats,
        NonRealized = CenterOfNonRealizedRepeats
    ) %>%
    gather(., Realized, NonRealized,
        key = "Deletion",
        value = "CenterOfRepeats"
    )

pltViolRepCenterRelease <- ggstatsplot::ggbetweenstats(
    data = RepReal,
    x = Deletion,
    y = CenterOfRepeats,
    notch = TRUE,
    # show notched box plot
    mean.ci = TRUE,
    # whether to display confidence interval for means
    k = 5,
    # number of decimal places for statistical results
    # outlier.tagging = TRUE, # whether outliers need to be tagged
    # outlier.label = ContactZone, # variable to be used for the outlier tag
    xlab = "Realisation of deletion",
    # label for the x-axis variable
    ylab = "Center of Repeats",
    # label for the y-axis variable
    title = "The effect of repeats' position on deletion realisation",
    # title text for the plot
    ggtheme = ggthemes::theme_fivethirtyeight(),
    # choosing a different theme
    ggstatsplot.layer = FALSE,
    # turn off `ggstatsplot` theme layer
    package = "wesanderson",
    # package from which color palette is to be taken
    palette = "Royal1",
    # choosing a different color palette
    messages = TRUE
)
# Note: Shapiro-Wilk Normality Test for Center of Repeats: p-value = 0.003
# Note: Bartlett's test for homogeneity of variances for factor Realisation of deletion: p-value = < 0.001
cowplot::save_plot(
    plot = pltViolRepCenterRelease,
    base_height = 8,
    base_asp = 1.618,
    file = normalizePath(
        file.path(plots_dir, "violin_rep_center_release.pdf")
    )
)

pltViolRepCenterRelease

6: Copare length of repeats with realized deletions vs rest repeats

## deletion is longer in realized repeats
wilcox.test(
    Rep$LengthOfRealizedRepeats,
    Rep$LengthOfNonRealizedRepeats,
    paired = TRUE
) # significant

    Wilcoxon signed rank test with continuity correction

data:  Rep$LengthOfRealizedRepeats and Rep$LengthOfNonRealizedRepeats
V = 175286, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
t.test(
    Rep$LengthOfRealizedRepeats,
    Rep$LengthOfNonRealizedRepeats,
    paired = TRUE
) # significant

    Paired t-test

data:  Rep$LengthOfRealizedRepeats and Rep$LengthOfNonRealizedRepeats
t = 24.481, df = 617, p-value < 2.2e-16
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
 1837.842 2158.412
sample estimates:
mean difference 
       1998.127 
summary(Rep$LengthOfRealizedRepeats) # 5643  mean
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     18    4420    5501    5643    7066    9682 
summary(Rep$LengthOfNonRealizedRepeats) # 3450 Median 3645 mean
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     12    2767    3450    3645    4489    8950 
summary(Rep$LengthOfRealizedRepeats - Rep$LengthOfNonRealizedRepeats) # 1998 mean
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  -4890     585    2116    1998    3335    7416 
boxplot(Rep$LengthOfRealizedRepeats,
    Rep$LengthOfNonRealizedRepeats,
    notch = TRUE,
    names = c(
        "LengthOfRealizedRepeats",
        "LengthOfNonRealizedRepeats"
    )
)

RepReal <-
    Rep %>%
    select(
        Realized = LengthOfRealizedRepeats,
        NonRealized = LengthOfNonRealizedRepeats
    ) %>%
    gather(., Realized, NonRealized,
        key = "Deletion",
        value = "DistanceBetweenRepeats"
    ) %>%
    full_join(., RepReal)

pltViolRepFlankLengthRelease <-
    ggstatsplot::ggbetweenstats(
        data = RepReal,
        x = Deletion,
        y = DistanceBetweenRepeats,
        notch = TRUE,
        # show notched box plot
        mean.ci = TRUE,
        # whether to display confidence interval for means
        k = 5,
        # number of decimal places for statistical results
        # outlier.tagging = TRUE, # whether outliers need to be tagged
        # outlier.label = ContactZone, # variable to be used for the outlier tag
        xlab = "Realisation of deletion",
        # label for the x-axis variable
        ylab = "Distance between Repeats",
        # label for the y-axis variable
        title = "The effect of repeats' distance on deletion realisation",
        # title text for the plot
        ggtheme = ggthemes::theme_fivethirtyeight(),
        # choosing a different theme
        ggstatsplot.layer = FALSE,
        # turn off `ggstatsplot` theme layer
        package = "wesanderson",
        # package from which color palette is to be taken
        palette = "Royal1",
        # choosing a different color palette
        messages = TRUE
    )
# Note: Bartlett's test for homogeneity of variances for factor Realisation of deletion: p-value = < 0.001
cowplot::save_plot(
    plot = pltViolRepFlankLengthRelease,
    base_height = 8,
    base_asp = 1.618,
    file = normalizePath(
        file.path(plots_dir, "violin_rep_flanked_length_release.pdf")
    )
)

pltViolRepFlankLengthRelease

7: Start of repeats comparison for realized deletions vs rest repeats

## start and end in realized versus non-realized repeats
wilcox.test(Rep$StartOfRealizedRepeats,
    Rep$StartOfNonRealizedRepeats,
    paired = TRUE
) # significant

    Wilcoxon signed rank test with continuity correction

data:  Rep$StartOfRealizedRepeats and Rep$StartOfNonRealizedRepeats
V = 44922, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
t.test(Rep$StartOfRealizedRepeats,
    Rep$StartOfNonRealizedRepeats,
    paired = TRUE
) # significant

    Paired t-test

data:  Rep$StartOfRealizedRepeats and Rep$StartOfNonRealizedRepeats
t = -9.2587, df = 617, p-value < 2.2e-16
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
 -868.0955 -564.2809
sample estimates:
mean difference 
      -716.1882 
summary(Rep$StartOfRealizedRepeats) # mean 8678: 25%: 7401 - 75% 9764:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   5810    7401    8472    8678    9764   15638 
quantile(Rep$StartOfRealizedRepeats, 0.1) # 6465
 10% 
6465 
quantile(Rep$StartOfRealizedRepeats, 0.9) # 10954
  90% 
10954 
summary(Rep$StartOfNonRealizedRepeats) # mean 9394
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   5873    8330    9112    9394   10434   15718 
summary(Rep$StartOfNonRealizedRepeats - Rep$StartOfRealizedRepeats) # mean 716 => non realised start later (~700 bp): 9394 - 8678
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-6137.0  -303.1   393.0   716.2  1797.4  7416.5

8: End of repeats comparison for realized deletions vs rest repeats

wilcox.test(Rep$EndOfRealizedRepeats, Rep$EndOfNonRealizedRepeats, paired = TRUE) # significant

    Wilcoxon signed rank test with continuity correction

data:  Rep$EndOfRealizedRepeats and Rep$EndOfNonRealizedRepeats
V = 125093, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
t.test(Rep$EndOfRealizedRepeats, Rep$EndOfNonRealizedRepeats, paired = TRUE) # significant

    Paired t-test

data:  Rep$EndOfRealizedRepeats and Rep$EndOfNonRealizedRepeats
t = 18.41, df = 617, p-value < 2.2e-16
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
 1145.196 1418.682
sample estimates:
mean difference 
       1281.939 
summary(Rep$EndOfRealizedRepeats) # 14321 mean
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  10970   13696   14124   14321   15362   16274 
summary(Rep$EndOfNonRealizedRepeats) # 13039 mean
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   7451   12132   13270   13039   13992   16261 
summary(Rep$EndOfNonRealizedRepeats - Rep$EndOfRealizedRepeats) # mean -1281=> non realised end earlier (~1300 bp): 13039 - 14321
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-7334.5 -2198.6  -934.2 -1281.9     0.0  4890.0 
quantile(Rep$EndOfRealizedRepeats, 0.1) # 13286
    10% 
13286.6 
quantile(Rep$EndOfRealizedRepeats, 0.9) # 15863
    90% 
15863.4

9: Visualize it in terms of X(start) and Y(end)

plot(
    FinalAllRep[FinalAllRep$RealisedRepeat == "non_del", ]$RepStart,
    FinalAllRep[FinalAllRep$RealisedRepeat == "non_del", ]$RepEnd,
    pch = 16,
    col = "grey",
    xlim = c(5781, 16569),
    ylim = c(16569, 5781),
    xlab = "",
    ylab = ""
)
par(new = TRUE)
plot(
    FinalAllRep[FinalAllRep$RealisedRepeat == "mb_del", ]$RepStart,
    FinalAllRep[FinalAllRep$RealisedRepeat == "mb_del", ]$RepEnd,
    pch = 16,
    col = "red",
    xlim = c(5781, 16569),
    ylim = c(16569, 5781),
    xlab = "Start",
    ylab = "End"
)

# Visualise difference between density distributions of realized vs non-realized repeats.
sp <- ggplot(
  FinalAllRep,
  aes(
    RepStart,
    RepEnd
  )
) +
  aes(colour = RealisedRepeat) +
  geom_point() +
  scale_y_reverse() +
  theme_minimal(17) +
  xlab("5’") +
  ylab("3’") +
  scale_x_continuous(breaks = c(15000, 12000, 9000, 6000), position = "bottom") +
  scale_color_manual(
    values = c("red", "grey"),
    labels = c(
      "Realized repeats",
      "Non-realized repeats"
    )
  ) +
  theme(
    legend.position = "bottom",
    legend.title = element_blank(),
    legend.box = "horizontal"
  )

gg_realised <-
  ggExtra::ggMarginal(
    sp,
    type = "density",
    margins = "both",
    size = 3,
    groupColour = TRUE,
    groupFill = TRUE
  )

cowplot::save_plot(here(plots_dir, "real-vs-nonreal-repeats.svg"), gg_realised, base_asp = 0.868, base_height = 7)
wilcox.test(FinalAllRep[FinalAllRep$RealisedRepeat == "mb_del", ]$RepStart, FinalAllRep[FinalAllRep$RealisedRepeat == "non_del", ]$RepStart)

    Wilcoxon rank sum test with continuity correction

data:  FinalAllRep[FinalAllRep$RealisedRepeat == "mb_del", ]$RepStart and FinalAllRep[FinalAllRep$RealisedRepeat == "non_del", ]$RepStart
W = 1354700, p-value = 1.299e-07
alternative hypothesis: true location shift is not equal to 0
wilcox.test(FinalAllRep[FinalAllRep$RealisedRepeat == "mb_del", ]$RepEnd, FinalAllRep[FinalAllRep$RealisedRepeat == "non_del", ]$RepEnd)

    Wilcoxon rank sum test with continuity correction

data:  FinalAllRep[FinalAllRep$RealisedRepeat == "mb_del", ]$RepEnd and FinalAllRep[FinalAllRep$RealisedRepeat == "non_del", ]$RepEnd
W = 2151564, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
FinalAllRep$RealisedRepeat <- as.factor(FinalAllRep$RealisedRepeat)
summary(FinalAllRep$RealisedRepeat)
 mb_del non_del 
    902    3391

10: Enrichment of repeats realised as a deletion in the contact zone

StartA <- 6000 # 7000
StartB <- 9000 # 10000
EndA <- 13000
EndB <- 16000
R.In <- nrow(Rep[Rep$StartOfRealizedRepeats >= StartA &
    Rep$StartOfRealizedRepeats <= StartB &
    Rep$EndOfRealizedRepeats >= EndA &
    Rep$EndOfRealizedRepeats <= EndB, ])
R.Out <- nrow(Rep[(Rep$StartOfRealizedRepeats < StartA |
    Rep$StartOfRealizedRepeats > StartB) |
    (Rep$EndOfRealizedRepeats < EndA |
        Rep$EndOfRealizedRepeats > EndB), ])
R.In + R.Out # 324 + 294 = 618
[1] 618
NR.In <- nrow(Rep[Rep$StartOfNonRealizedRepeats >= StartA &
    Rep$StartOfNonRealizedRepeats <= StartB &
    Rep$EndOfNonRealizedRepeats >= EndA &
    Rep$EndOfNonRealizedRepeats <= EndB, ])
NR.Out <- nrow(Rep[(Rep$StartOfNonRealizedRepeats < StartA |
    Rep$StartOfNonRealizedRepeats > StartB) |
    (Rep$EndOfNonRealizedRepeats < EndA |
        Rep$EndOfNonRealizedRepeats > EndB), ])
NR.In + NR.Out # 79 + 598 = 618
[1] 618
X <- cbind(c(R.In, R.Out), c(NR.In, NR.Out))
fisher.test(X) # odds 7.5, p-value < 2.2e-16

    Fisher's Exact Test for Count Data

data:  X
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  5.615152 10.113074
sample estimates:
odds ratio 
  7.505642

Visualize it as mosaic plot

X <- data.frame(X)
names(X) <- c("R", "NR")
row.names(X) <- c("IN", "OUT")
mosaicplot(X, main = "")

Figure 2B:

gg_realised

Visualise difference between density distributions of realized vs non-realized repeats.


sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] ggpubr_0.6.0    here_1.0.1      lubridate_1.9.2 forcats_1.0.0  
 [5] stringr_1.5.0   dplyr_1.1.1     purrr_1.0.1     readr_2.1.4    
 [9] tidyr_1.3.0     tibble_3.2.1    ggplot2_3.4.2   tidyverse_2.0.0
[13] workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] fs_1.6.1               insight_0.19.1         httr_1.4.5            
 [4] rprojroot_2.0.3        tools_4.2.2            backports_1.4.1       
 [7] bslib_0.4.2            utf8_1.2.3             R6_2.5.1              
[10] statsExpressions_1.5.0 colorspace_2.1-0       withr_2.5.0           
[13] tidyselect_1.2.0       processx_3.8.0         compiler_4.2.2        
[16] git2r_0.31.0           textshaping_0.3.6      performance_0.10.2    
[19] cli_3.6.1              prismatic_1.1.1        labeling_0.4.2        
[22] bayestestR_0.13.0      sass_0.4.5             scales_1.2.1          
[25] mvtnorm_1.1-3          callr_3.7.3            pbapply_1.7-0         
[28] systemfonts_1.0.4      digest_0.6.31          svglite_2.1.1         
[31] rmarkdown_2.21         pkgconfig_2.0.3        htmltools_0.5.5       
[34] fastmap_1.1.1          highr_0.10             ggthemes_4.2.4        
[37] rlang_1.1.0            rstudioapi_0.14        shiny_1.7.4           
[40] farver_2.1.1           jquerylib_0.1.4        generics_0.1.3        
[43] jsonlite_1.8.4         car_3.1-2              magrittr_2.0.3        
[46] parameters_0.20.2      patchwork_1.1.2        Matrix_1.5-3          
[49] Rcpp_1.0.10            munsell_0.5.0          fansi_1.0.4           
[52] abind_1.4-5            lifecycle_1.0.3        stringi_1.7.12        
[55] whisker_0.4.1          yaml_2.3.7             carData_3.0-5         
[58] grid_4.2.2             paletteer_1.5.0        ggrepel_0.9.3         
[61] parallel_4.2.2         promises_1.2.0.1       miniUI_0.1.1.1        
[64] lattice_0.20-45        cowplot_1.1.1          hms_1.1.3             
[67] zeallot_0.1.0          knitr_1.42             ps_1.7.4              
[70] pillar_1.9.0           ggsignif_0.6.4         effectsize_0.8.3      
[73] glue_1.6.2             evaluate_0.20          getPass_0.2-2         
[76] renv_0.17.2            vctrs_0.6.1            tzdb_0.3.0            
[79] httpuv_1.6.9           MatrixModels_0.5-1     gtable_0.3.3          
[82] BayesFactor_0.9.12-4.4 rematch2_2.1.2         datawizard_0.7.1      
[85] cachem_1.0.7           ggExtra_0.10.0         xfun_0.38             
[88] mime_0.12              xtable_1.8-4           correlation_0.8.3     
[91] broom_1.0.4            coda_0.19-4            rstatix_0.7.2         
[94] later_1.3.0            ragg_1.2.5             ggstatsplot_0.11.0    
[97] timechange_0.2.0       ellipsis_0.3.2