Experiment

Rationale

The aim of the experiment is to determine patch initiation rates in Ede1 internal domain deletion mutants. We looked at the patch density and lifetimes of late coat protein Sla1 in Ede1 mutants lacking all or some of the central region. This notebook is devoted to the Sla1 patch lifetime.

Acquisition and replicates

All lifetime estimates come from movies acquired on the Olympus IX83 with a 150x/1.45 objective. The illumination was CoolLED’s pE-300 lamp with a GFP filter cube. Camera was Hammamatsu’s ImageEMX2 EMCCD.

The first dataset (#0 in this notebook) was acquired with 25% power and 200 ms exposure times. For the three subsequent datasets, the lamp power was reduced to 15%, and exposure was increased to 500 ms. Despite this difference, the result from the first, exploratory dataset looks in line with the rest of the repeats, so I decided to include it in the analysis.

Processing and data extraction

All images were background subtracted using ImageJ rolling ball algorithm with 80 px radius, and normalized to correct for photobleaching. Individual cells were cropped out and median-filtered image (6px disk brush) was subtracted. ParticleTracker from the MOSAIC Suite was used to track the spots. Individual tracks were manually selected based on the quality of the tracking.

Another notebook was used to gather all output into tidy data frames with no further modifications.

List of strains used

strain ede1
MKY0140 wt
MKY3770 pq
MKY3776 cc
MKY3782 pqcc
MKY0654 delta

Per-dataset summary

Basic summary of each dataset: exact sample sizes, mean, sd, se, median, MAD.

ede1 dataset n lifetime_mean lifetime_sd lifetime_se lifetime_median lifetime_mad
wt 0 49 22.51184 6.264182 0.8948831 22.080 5.455968
wt 1 92 24.98370 6.769539 0.7057732 23.750 6.671700
wt 2 61 22.90164 6.952709 0.8902032 22.000 4.447800
wt 3 82 22.23171 6.700007 0.7398920 21.500 5.559750
pq 0 58 19.09397 4.435555 0.5824168 18.630 3.409980
pq 1 94 20.31915 5.703635 0.5882851 20.000 5.930400
pq 2 60 19.57500 7.816662 1.0091268 19.000 7.042350
pq 3 77 18.33766 5.593959 0.6374908 17.500 5.930400
cc 0 69 17.68667 4.399831 0.5296774 17.020 2.727984
cc 1 95 18.50526 5.158908 0.5292928 17.500 5.189100
cc 2 62 19.00806 5.240520 0.6655467 18.750 4.818450
cc 3 79 16.89241 6.307134 0.7096080 17.000 5.189100
pqcc 0 66 16.55652 4.510219 0.5551695 15.755 3.921477
pqcc 1 100 18.39000 5.278401 0.5278401 17.000 5.189100
pqcc 2 62 16.19355 5.228669 0.6640416 16.000 4.818450
pqcc 3 74 15.73649 5.200252 0.6045170 14.500 4.447800
delta 0 61 15.34213 4.225024 0.5409589 14.720 4.432974
delta 1 100 18.09000 4.979443 0.4979443 18.000 5.189100
delta 2 61 17.10656 5.922496 0.7582979 16.000 5.189100
delta 3 78 15.27564 4.611285 0.5221251 15.000 5.189100

Plots

SuperPlots

Large, coloured points represent mean values from each independent experiment. The center line and range represent the mean +/- SD, calculated based on the experimental averages.

Beeswarm points represent all underlying observations, shaped according to the dataset.

Violin instead of beeswarm to summarize all observations:

With significance

Let’s add significance stars based on Tukey’s test.

Like with density, this view is getting complicated even though it’s only half of the comparisons.

We can simplify it down to binary comparisons at a given α level (here, α = 0.95). We can reject the null of group mean equality at this level for groups which do not share any letters between them.

Hypothesis tests

Assumptions

ANOVA and similar parametric tests assume that the errors are normally distributed, with homogeneous variances, and that the samples are independent. We will test the null hypothesis that mean Sla1 lifetime is the same across different Ede1 strains.

We will use repeat-level data for the tests to account for experimental variability. Also, even if the populations are skewed (as it seems from the plots), the sample means should still be normally distributed (according to the Central Limit Theorem).

Normality

From the plots it looks like the underlying data is not perfectly normal with some skew. We can check the normality of residuals used in the model later, but it might still be interesting to know how normal the underlying data is overall.

If we do a formal test (Shapiro-Wilkes):

ede1 n p.value
wt 284 2.5e-06
pq 289 0.0e+00
cc 305 0.0e+00
pqcc 302 2.2e-06
delta 300 5.3e-06

Shapiro-Wilkes rejects the normality of the data in each group. That is about expected with a large sample size, but it probably also reflects an actual skew in lifetimes.

Q-Q plots:

All datasets do indeed look heavy-tailed. Histograms:

Homoscedasticity

4 points per group is probably enough to assess whether the variance is similar in the repeat-level data. Levene’s test:

df1 df2 statistic p
4 15 0.3414199 0.8457886

Levene’s cannot reject the null here (variance does not differ between groups).

One-way ANOVA

term df sumsq meansq statistic p.value
ede1 4 118.74850 29.687125 23.27211 2.8e-06
Residuals 15 19.13478 1.275652 NA NA

Diagnostic plots

Again, the variance looks homogeneous enough. There is a definite departure from normality as well, although I am not sure how concerning it really is.

Post-hoc test (Tukey)

term group1 group2 null.value estimate conf.low conf.high p.adj p.adj.signif
ede1 wt pq 0 -3.8257756 -6.291916 -1.3596350 1.89e-03 **
ede1 wt cc 0 -5.1341199 -7.600261 -2.6679793 9.59e-05 ****
ede1 wt pqcc 0 -6.4380823 -8.904223 -3.9719417 6.80e-06 ****
ede1 wt delta 0 -6.7036374 -9.169778 -4.2374968 4.10e-06 ****
ede1 pq cc 0 -1.3083443 -3.774485 1.1577963 4.97e-01 ns
ede1 pq pqcc 0 -2.6123067 -5.078447 -0.1461661 3.55e-02 *
ede1 pq delta 0 -2.8778618 -5.344002 -0.4117212 1.88e-02 *
ede1 cc pqcc 0 -1.3039623 -3.770103 1.1621783 5.01e-01 ns
ede1 cc delta 0 -1.5695175 -4.035658 0.8966231 3.28e-01 ns
ede1 pqcc delta 0 -0.2655551 -2.731696 2.2005855 9.97e-01 ns

All mutants are significantly different than wild-type, but not necessarily between themselves. Most notably, we do not have enough power to say if ∆CC is different from ∆PQ, ∆PQCC or ede1∆. At the same time, ∆PQ difference from ∆PQCC and ede1∆ reaches the significance threshold.

Overall summary

Summary statistics for all experiments, derived from mean values of N independent repeats.

Final estimates

Final estimates with lower / upper 95% confidence intervals and a comparison to wild type (in %). half_ci is just the error for writing CI ranges in the format mean +/- error.

ede1 mean lower upper proc_wt half_ci
wt 23.157 21.171 25.143 100 1.986
pq 19.331 18.007 20.656 83 1.325
cc 18.023 16.543 19.503 78 1.480
pqcc 16.719 14.868 18.570 72 1.851
delta 16.454 14.255 18.652 71 2.199

Conclusions

  1. All mutations cause a significant reduction in Sla1 patch lifetime from wild type
  2. Ede1∆PQCC is indistinguishable from full Ede1 deletion, causes ~30% reduction in lifetime
  3. Individual PQ / CC deletions have intermediate defects

More statistics

ede1 N mean sd se median mad
wt 4 23.157 1.248 0.624 22.707 0.497
pq 4 19.331 0.832 0.416 19.334 0.908
cc 4 18.023 0.930 0.465 18.096 0.980
pqcc 4 16.719 1.163 0.582 16.375 0.608
delta 4 16.454 1.382 0.691 16.224 1.357

More statistics (observation-level)

It might be useful to also look at observation-level summary. The experimental means can be used to determine true population mean (because of the CLT), but if the population is really skewed, median and quartiles are particularly useful information which cannot be accurately assessed from only 4 points.

ede1 n mean sd se median mad 25% 75%
wt 284 23.315 6.774 0.402 22.50 5.930 19.0 27.035
pq 289 19.391 5.981 0.352 18.63 5.382 15.5 22.500
cc 305 18.005 5.376 0.308 17.50 4.448 14.5 21.000
pqcc 302 16.888 5.182 0.298 16.00 5.189 13.0 20.353
delta 300 16.600 5.087 0.294 16.00 5.189 13.0 19.608

Session info

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] multcompView_0.1-8 knitr_1.33         rstatix_0.7.0      broom_0.7.6       
##  [5] ggsignif_0.6.1     ggbeeswarm_0.6.0   forcats_0.5.1      stringr_1.4.0     
##  [9] dplyr_1.0.6        purrr_0.3.4        readr_1.4.0        tidyr_1.1.3       
## [13] tibble_3.1.2       ggplot2_3.3.3      tidyverse_1.3.1   
## 
## loaded via a namespace (and not attached):
##  [1] fs_1.5.0            lubridate_1.7.10    RColorBrewer_1.1-2 
##  [4] httr_1.4.2          tools_4.1.0         backports_1.2.1    
##  [7] utf8_1.2.1          R6_2.5.0            rpart_4.1-15       
## [10] vipor_0.4.5         Hmisc_4.5-0         DBI_1.1.1          
## [13] colorspace_2.0-1    nnet_7.3-16         withr_2.4.2        
## [16] gridExtra_2.3       tidyselect_1.1.1    curl_4.3.1         
## [19] compiler_4.1.0      cli_2.5.0           rvest_1.0.0        
## [22] htmlTable_2.2.1     xml2_1.3.2          labeling_0.4.2     
## [25] checkmate_2.0.0     scales_1.1.1        digest_0.6.27      
## [28] foreign_0.8-81      rmarkdown_2.8       rio_0.5.26         
## [31] base64enc_0.1-3     jpeg_0.1-8.1        pkgconfig_2.0.3    
## [34] htmltools_0.5.1.1   dbplyr_2.1.1        highr_0.9          
## [37] htmlwidgets_1.5.3   rlang_0.4.11        readxl_1.3.1       
## [40] rstudioapi_0.13     farver_2.1.0        generics_0.1.0     
## [43] jsonlite_1.7.2      zip_2.2.0           car_3.0-10         
## [46] magrittr_2.0.1      Formula_1.2-4       Matrix_1.3-3       
## [49] Rcpp_1.0.6          munsell_0.5.0       fansi_0.5.0        
## [52] abind_1.4-5         lifecycle_1.0.0     stringi_1.6.2      
## [55] yaml_2.2.1          carData_3.0-4       grid_4.1.0         
## [58] crayon_1.4.1        lattice_0.20-44     haven_2.4.1        
## [61] splines_4.1.0       hms_1.1.0           pillar_1.6.1       
## [64] reprex_2.0.0        glue_1.4.2          evaluate_0.14      
## [67] latticeExtra_0.6-29 data.table_1.14.0   modelr_0.1.8       
## [70] vctrs_0.3.8         png_0.1-7           cellranger_1.1.0   
## [73] gtable_0.3.0        assertthat_0.2.1    xfun_0.23          
## [76] openxlsx_4.2.3      mime_0.10           survival_3.2-11    
## [79] beeswarm_0.3.1      cluster_2.1.2       ellipsis_0.3.2
---
title: "Sla1 lifetime in Ede1 internal deletion mutants"
date: "Last compiled on `r format(Sys.time(), '%B %d, %Y')`"
output: 
  html_document:
    code_download: TRUE
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message=FALSE, warning=FALSE,
                      dpi = 96, fig.width = 4, fig.height = 3)
```

```{r libs}
library(tidyverse)
library(ggbeeswarm)
library(ggsignif)
library(broom)
library(rstatix)
library(knitr)
library(multcompView)
```

```{r load}
# Load data generated by cleanup notebook
rm(list = ls())
load('data/sla1_lifetime.RData')
```

```{r theme}
# Custom ggplot2 theme
# --------------------

# minimal theme with border
# based on theme_linedraw without the grid lines
# also trying to remove all backgrounds and margins
# the aim is to make it as easy as possible to edit in illustrator

theme_clean <- function(base_size = 11, base_family = "",
                        base_line_size = base_size / 22,
                        base_rect_size = base_size / 22) {
  theme_linedraw(
    base_size = base_size,
    base_family = base_family,
    base_line_size = base_line_size,
    base_rect_size = base_rect_size
  ) %+replace%
    theme(
      # no grid and no backgrounds if I can help it
      legend.background =  element_blank(),
      panel.background = element_blank(),
      panel.grid = element_blank(),
      plot.background = element_blank(),
      plot.margin = margin(0, 0, 0, 0),
      complete = TRUE
    )
}

# Set default theme
# -----------------
theme_set(theme_clean(base_size = 12, base_family = "Myriad Pro"))

# Create a ggsave wrapper
# -----------------------

# This way we can set a default size and device for all plots
my_ggsave <- function(filename, plot = last_plot(),
                      device = cairo_pdf, units = "mm",
                      width = 100, height = 80, ...){
  ggsave(filename = filename, plot = plot,
         device = device, units = units,
         height = height, width = width,  ...)
  }
```

```{r functions}
# couple of small functions for extracting comparisons
# into a format understandable by ggplot

#' Add letter group labels generated by Tukey's HSD
#'
#' This function performs ANOVA and Tukey's HSD,
#' extracts the letter labels and attaches them
#' to the original data.
#'
#' @param x: df or tibble, the data (long format!)
#' @param yvar: chr, name of the dependent variable
#' @param xvar: chr, name of the independent variable
#' @param alpha: dbl, confidence level passed on to Tukey's test
#'
add_tukey_labels <- function(x, yvar, xvar, alpha = 0.95){
  aov_form <- formula(paste(yvar, '~', xvar))
  anova <- aov(formula = aov_form, data = x)
  tukey <- TukeyHSD(anova, which = xvar, conf.level = alpha)
  # Extract labels and factor levels from Tukey post-hoc 
  x_labels <- as_tibble(
    multcompLetters4(anova, tukey)[[xvar]]$Letters,
    rownames = xvar
    )
  
  if (is.factor(x[[xvar]])) {
    x_labels[[xvar]] <- factor(
      x_labels[[xvar]], levels = levels(x[[xvar]])
    )
  }
  
  x_labels <- rename(x_labels, tukey_group = value)
  x <- left_join(x, x_labels, by = xvar)
  
  return(x)
  }


#' Extract comparisons from rstatix tidy tests
#' 
#' This function subsets the selected comparisons in a Tukey,
#' Dunn or similar test done by rstatix.
#' It converts groups to a list of vectors that can be passed to geom_signif
#'
#'
#' @param x: df or tibble, the comparison results
#' @param rows: integer vector, the rows with desired comparisons
extract_comparisons <- function(x, rows){
  x_subset <- x[rows,] %>%
    .[nrow(.):1,]
  
  x_comparisons <- x_subset %>%
    select(group1, group2) %>%
    t() %>%
    as.data.frame() %>%
    as.list
  x_annotations <- x_subset$p.adj.signif %>%
    as.vector()
  
  significance <- list(
    comparisons = x_comparisons,
    annotations = x_annotations
  )
  
  return(significance)
}
```

# {.tabset .tabset-pills}

## Experiment

### Rationale

The aim of the experiment is to determine patch initiation rates 
in Ede1 internal domain deletion mutants.
We looked at the patch density and lifetimes of late coat protein Sla1 
in Ede1 mutants lacking all or some of the central region.
This notebook is devoted to the Sla1 patch lifetime.

### Acquisition and replicates

All lifetime estimates come from movies 
acquired on the Olympus IX83 with a 150x/1.45 objective.
The illumination was CoolLED’s pE-300 lamp with a GFP filter cube. 
Camera was Hammamatsu's ImageEMX2 EMCCD.

The first dataset (#0 in this notebook) 
was acquired with 25% power and 200 ms exposure times. 
For the three subsequent datasets, 
  the lamp power was reduced to 15%, 
  and exposure was increased to 500 ms.
Despite this difference, the result from the first, exploratory dataset 
looks in line with the rest of the repeats,
so I decided to include it in the analysis.

### Processing and data extraction

All images were background subtracted 
using ImageJ rolling ball algorithm with 80 px radius, 
and normalized to correct for photobleaching.
Individual cells were cropped out 
and median-filtered image (6px disk brush) was subtracted. 
ParticleTracker from the MOSAIC Suite was used to track the spots.
Individual tracks were manually selected based on the quality of the tracking.

Another notebook was used to gather all output into tidy data frames
with no further modifications.

### List of strains used

```{r strains}
kable(strains)
```

## Per-dataset summary

Basic summary of each dataset: exact sample sizes, mean, sd, se, median, MAD.

```{r stats}
sla1_lifetime_stats <- sla1_lifetime %>%
  group_by(ede1, dataset) %>%
  summarise(n = n(),
            across(lifetime,
                   list(mean = mean, sd = sd, 
                        se = ~ sd(.x) / sqrt(n()),
                        median = median, mad = mad)),
            .groups = 'drop')

sla1_lifetime_stats %>% kable()
```

## Plots {.tabset}

```{r lifetime.scatter}
plot_blank <- ggplot(sla1_lifetime_stats,
                     aes(x = ede1, y = lifetime_mean)) +
                         #shape = dataset, fill = dataset))+
  labs(title = NULL, x = 'Ede1', y = "Sla1 lifetime (s)") +
  scale_y_continuous(breaks = scales::breaks_extended(6))+
  scale_shape_manual(values = c(21:25)) +
  scale_color_brewer(palette = 'Set2') +
  scale_fill_brewer(palette = 'Set2')

plot_scatter <- plot_blank +
  geom_quasirandom(inherit.aes = F, data = sla1_lifetime,
                   aes(x = ede1, y = lifetime,
                       shape = dataset,# colour = dataset
                       ),
                   colour = 'grey75',# shape = 1,
                   show.legend = F, size = 0.8
                   )

plot_violin <- plot_blank + 
  geom_violin(inherit.aes = F,
              data = sla1_lifetime, aes(x = ede1, y = lifetime),
              colour = 'grey75', fill = 'transparent'
              )
```

### SuperPlots

Large, coloured points represent mean values 
from each independent experiment.
The center line and range represent the mean +/- SD, 
calculated based on the experimental averages.

Beeswarm points represent all underlying observations, 
shaped according to the dataset.

```{r}
plot_super <- plot_scatter +
  geom_quasirandom(aes(shape = dataset, fill = dataset),
                   show.legend = F,
                   width = 0.3, size = 2)+
  stat_summary(fun = mean, geom = 'crossbar',
               width = 0.5, fatten = 1)+
  stat_summary(fun.data = 'mean_sdl',
               fun.args = list(mult = 1), 
               geom = 'errorbar', width = 0.2)
  
print(plot_super)
my_ggsave('figures/lifetime_super.pdf')
```

Violin instead of beeswarm to summarize all observations:

```{r}
plot_super_violin <- plot_violin +
  geom_quasirandom(aes(shape = dataset, fill = dataset),
                   show.legend = F,
                   width = 0.3, size = 2)+
  stat_summary(fun = mean, geom = 'crossbar',
               width = 0.5, fatten = 1)+
  stat_summary(fun.data = 'mean_sdl',
               fun.args = list(mult = 1),
               geom = 'errorbar', width = 0.2)

print(plot_super_violin)
my_ggsave('figures/lifetime_super_violin.pdf')
```

### With significance

Let's add significance stars based on Tukey's test.

```{r}
tukey <- tukey_hsd(sla1_lifetime_stats, lifetime_mean ~ ede1,
                   ordered = TRUE)

significance <- extract_comparisons(tukey, c(1:4, 10))

plot_super_signif <- plot_super +
  geom_signif(comparisons = significance$comparisons,
              annotations = significance$annotations,
              step_increase = 0.03,
              tip_length = 0.01, vjust = 0.8,
              margin_top = -0.1)
print(plot_super_signif)
my_ggsave('figures/lifetime_super_signif.pdf')
```

Like with density, this view is getting complicated
even though it's only half of the comparisons.

We can simplify it down to binary comparisons at a given α level (here, α = 0.95).
We can reject the null of group mean equality at this level
for groups which do not share any letters between them.

```{r}
sla1_lifetime_stats <- sla1_lifetime_stats %>%
  add_tukey_labels('lifetime_mean', 'ede1')
```

```{r}
plot_super_letters <- plot_super +
  geom_text(data = sla1_lifetime_stats,
            aes(label = tukey_group), y = Inf, vjust = 1.2)

print(plot_super_letters)
my_ggsave('figures/lifetime_super_letters.pdf')
```

## Hypothesis tests {.tabset}

### Assumptions

ANOVA and similar parametric tests 
assume that the errors are normally distributed,
with homogeneous variances,
and that the samples are independent.
We will test the null hypothesis that mean Sla1 lifetime is the same
across different Ede1 strains.

We will use repeat-level data for the tests
to account for experimental variability.
Also, even if the populations are skewed (as it seems from the plots),
the sample means should still be normally distributed
(according to the Central Limit Theorem). 

#### Normality

From the plots it looks like the underlying data
is not perfectly normal with some skew.
We can check the normality of residuals used in the model later, but it might
still be interesting to know how normal the underlying data is overall.

If we do a formal test (Shapiro-Wilkes):

```{r}
sla1_lifetime %>%
  group_by(ede1) %>%
  summarise(n = n(),
            p.value = tidy(shapiro.test(lifetime))$p.value, 
            .groups = 'drop') %>%
  kable()
```

Shapiro-Wilkes rejects the normality of the data in each group.
That is about expected with a large sample size, but it
probably also reflects an actual skew in lifetimes.

Q-Q plots:

```{r fig.height=6, fig.width=8}
sla1_lifetime %>%
  ggplot(aes(sample = lifetime))+
  facet_wrap('ede1', scales = 'free')+          
  stat_qq(shape = 1)+
  stat_qq_line()
```

All datasets do indeed look heavy-tailed. Histograms:

```{r fig.height=6, fig.width=8}
sla1_lifetime %>%
  ggplot(aes(x = lifetime))+
  facet_wrap('ede1', scales = 'free')+          
  geom_histogram()
```

#### Homoscedasticity

4 points per group is probably enough to assess
whether the variance is similar in the repeat-level data.
Levene's test:

```{r}
sla1_lifetime_stats %>%
  levene_test(lifetime_mean ~ ede1) %>%
  kable()
```

Levene's cannot reject the null here (variance does not differ between groups).

### One-way ANOVA

```{r}
anova <- aov(lifetime_mean ~ ede1, data = sla1_lifetime_stats)
tidy(anova) %>% kable()
```

#### Diagnostic plots

```{r}
plot(anova)
```

Again, the variance looks homogeneous enough. 
There is a definite departure from normality as well, although
I am not sure how concerning it really is.

### Post-hoc test (Tukey)

```{r}
tukey %>%
  kable()
```

All mutants are significantly different than wild-type,
but not necessarily between themselves. 
Most notably, we do not have enough power to say
if ∆CC is different from ∆PQ, ∆PQCC or ede1∆.
At the same time, ∆PQ difference from ∆PQCC and ede1∆ reaches
the significance threshold.

## Overall summary

Summary statistics for all experiments, derived from *mean values*
of N independent repeats.

### Final estimates

Final estimates with lower / upper 95% confidence intervals and a comparison
to wild type (in %). `half_ci` is just the error for writing CI ranges
in the format mean +/- error.

```{r}
wt_mean <- sla1_lifetime_stats %>%
  filter(ede1 == 'wt') %>%
  pull(lifetime_mean) %>%
  mean()

lifetime_ci <- sla1_lifetime_stats %>%
  group_by(ede1)%>%
  summarise(mean_cl_normal(lifetime_mean)) %>%
  rename(mean = y, lower = ymin, upper = ymax) %>%
  mutate(proc_wt = round(100 * mean / wt_mean),
         half_ci = (upper - lower) / 2) 

kable(lifetime_ci, digits = 3)
```

### Conclusions

1. All mutations cause a significant reduction in Sla1 patch lifetime from wild type
2. Ede1∆PQCC is indistinguishable from full Ede1 deletion, 
  causes ~30% reduction in lifetime
3. Individual PQ / CC deletions have intermediate defects

### More statistics

```{r}
sla1_lifetime_stats %>%
  group_by(ede1)%>%
  summarise(N = n(),
            across(lifetime_mean,
                   list(mean = mean, sd = sd,
                        se = ~ sd(.x) / sqrt(n()),
                        median = median, mad = mad),
                       .names = '{.fn}'),
            .groups = 'drop') %>%
  kable(digits = 3)
```

### More statistics (observation-level)

It might be useful to also look at observation-level summary.
The experimental means can be used to determine
true population mean (because of the CLT),
but if the population is  really skewed,
median and quartiles are particularly useful information
which cannot be accurately assessed from only 4 points.

```{r}
sla1_lifetime %>%
  group_by(ede1)%>%
  summarise(n = n(),
            across(lifetime,
                   list(mean = mean, sd = sd,
                        se = ~ sd(.x) / sqrt(n()),
                        median = median, mad = mad
                        #, quant = ~quantile(.x, c(0.25, 0.5, 0.75))
                        ),
                       .names = '{.fn}'),
            pivot_wider(enframe(quantile(lifetime, c(0.25, 0.75)))),
            .groups = 'drop') %>%
  
  kable(digits = 3)
```

## Source data

### .csv

```{r echo=FALSE}
xfun::embed_file('data/sla1_lifetime.csv')
```

### .RData

```{r echo=FALSE}
xfun::embed_file('data/sla1_lifetime.RData')
```

## Session info

```{r session, message=TRUE}
sessionInfo()
```