Sla1 lifetime in Ede1 internal deletion mutants

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

Source data

.csv

Download sla1_lifetime.csv

.RData

Download sla1_lifetime.RData

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