Figure 2: WT maximum amplitudes and decay rate constants

Manuscript figure 2 uses three subfigures to show WT maximum amplitude and decay rate constant variation over LPR5, LPR10, and LPR50 for the H2O condition (figure 2B); WT maximum amplitude variation over LPR5 for H2O, D2O, and H2O>D2O conditions (figure 2D); and WT maximum amplitude and decay rate constant variation over LPR5, LPR10, and LPR50 for D2O experiments (figure 2F).

The Dataset

We process the NeRh50_Table_S1_raw_data_used_in_figures.csv dataset to obtain the data for this analysis (Table 1).

# Show interactive tables of data
DT::datatable(dfms$dfm_fig2_amp)
DT::datatable(dfms$dfm_fig2_k)
# Write datasets to file (commented out for website)
#readr::write_csv(dfms$dfm_fig2_amp, "assets/data/01_fig2_amplitude.csv")
#readr::write_csv(dfms$dfm_fig2_k, "assets/data/01_fig2_rate.csv")

Table 1: Measurements for WT across H2O, D2O and D2O>H2O conditions at varying levels of LPR

(a) Maximum amplitude

(b) Decay rate

We visualise the datasets in the style of manuscript figure 2 in Figure 1.

p1b_amp <- ggplot2::ggplot(dfms$dfm_fig2_amp |> dplyr::filter(Condition == "H2O"), 
                           ggplot2::aes(x=LPR, y=Value, color=LPR)) +
  ggplot2::geom_boxplot(outlier.shape = NA) +
  ggplot2::geom_jitter() +
  ggplot2::labs(y="Maximum amplitude (nA)") +
  ggplot2::ylim(0, 3.5) +
  ggplot2::theme_minimal()

p1b_k <- ggplot2::ggplot(dfms$dfm_fig2_k |> dplyr::filter(Condition == "H2O"), 
                         ggplot2::aes(x=LPR, y=Value, color=LPR)) +
  ggplot2::geom_boxplot(outlier.shape = NA) +
  ggplot2::geom_jitter() +
  ggplot2::labs(y="Decay rate constant k (/s)") +
  ggplot2::ylim(0, 105) +
  ggplot2::theme_minimal()

p1d_amp <- ggplot2::ggplot(dfms$dfm_fig2_amp |> dplyr::filter(LPR == "LPR5"), 
                           ggplot2::aes(x=Condition, y=Value, color=Condition)) +
  ggplot2::geom_boxplot(outlier.shape = NA) +
  ggplot2::geom_jitter() +
  ggplot2::labs(y="Maximum amplitude (nA)") +
  ggplot2::ylim(0, 3.5) +
  ggplot2::theme_minimal() +
  ggplot2::facet_wrap(~LPR)

p1d_k <- ggplot2::ggplot(dfms$dfm_fig2_k |> dplyr::filter(LPR == "LPR5"), 
                         ggplot2::aes(x=Condition, y=Value, color=Condition)) +
  ggplot2::geom_boxplot(outlier.shape = NA) +
  ggplot2::geom_jitter() +
  ggplot2::labs(y="Decay rate constant k (/s)") +
  ggplot2::ylim(0, 105) +
  ggplot2::theme_minimal() +
  ggplot2::facet_wrap(~LPR)

p1f_amp <- ggplot2::ggplot(dfms$dfm_fig2_amp |> dplyr::filter(Condition == "D2O"), 
                           ggplot2::aes(x=LPR, y=Value, color=LPR)) +
  ggplot2::geom_boxplot(outlier.shape = NA) +
  ggplot2::geom_jitter() +
  ggplot2::labs(y="Maximum amplitude (nA)") +
  ggplot2::ylim(0, 3.5) +
  ggplot2::theme_minimal()

p1f_k <- ggplot2::ggplot(dfms$dfm_fig2_k |> dplyr::filter(Condition == "D2O"), 
                         ggplot2::aes(x=LPR, y=Value, color=LPR)) +
  ggplot2::geom_boxplot(outlier.shape = NA) +
  ggplot2::geom_jitter() +
  ggplot2::labs(y="Decay rate constant k (/s)") +
  ggplot2::ylim(0, 105) +
  ggplot2::theme_minimal()

ggpubr::ggarrange(p1b_amp, p1b_k, p1d_amp, p1d_k, p1f_amp, p1f_k,
                  ncol=2, nrow=3,
                  labels=c("B", "", "D", "", "F", ""),
                  legend="none")

Figure 1: WT measurements across three LPRs and three experimental conditions, representing figures 2B, 2D, and 2F from the draft manuscript.

Downloads

• Download 01_fig2_amplitude.csv
• Download 01_fig2_rate.csv

The Questions

We have multiple questions for this dataset. Within each of the six plots we wish to present the effects of the parameters plotted along the x-axis, for each measurement.

The Models

We address these questions using a separate linear model for each set of figure data. For figures 2B and 2F we treat maximum amplitude and decay rate constant as dependent variables, and fit these to the LPR explanatory variable (Value ~ LPR). For figure 2D we proceed in a similar way, but fit to the Condition explanatory variable (Value ~ Condition).

model1b_amp_lm <- lm(Value ~ LPR, data=dfms$dfm_fig2_amp |> dplyr::filter(Condition == "H2O"))
model1b_k_lm <- lm(Value ~ LPR, data=dfms$dfm_fig2_k |> dplyr::filter(Condition == "H2O"))

model1d_lpr5_amp_lm <- lm(Value ~ Condition, data=dfms$dfm_fig2_amp |> dplyr::filter(LPR == "LPR5"))
model1d_lpr5_k_lm <- lm(Value ~ Condition, data=dfms$dfm_fig2_k |> dplyr::filter(LPR == "LPR5"))

model1f_amp_lm <- lm(Value ~ LPR, data=dfms$dfm_fig2_amp |> dplyr::filter(Condition == "D2O"))
model1f_k_lm <- lm(Value ~ LPR, data=dfms$dfm_fig2_k |> dplyr::filter(Condition == "D2O"))

performance::check_model(model1b_amp_lm, size_title=6)
performance::check_model(model1b_k_lm, size_title=6)
performance::check_model(model1d_lpr5_amp_lm, size_title=6)
performance::check_model(model1d_lpr5_k_lm, size_title=6)
performance::check_model(model1f_amp_lm, size_title=6)
performance::check_model(model1f_k_lm, size_title=6)

(a) Figure 2B(i)

(b) Figure 2B(ii)

(c) Figure 2D(i)

(d) Figure 2D(ii)

(e) Figure 2F(i)

(f) Figure 2F(ii)

Figure 2: Diagnostic plots for linear model fits of Value ~ LPR and Max_amplitude ~ Condition

There are no obvious departures from assumptions in Figure 2, although some of the diagnostic plots do have odd spline fits. We extract the contrasts in Table 2.

EMM <- emmeans::emmeans(model1b_amp_lm, ~ LPR)
confint(emmeans::contrast(EMM, "pairwise", adjust="sidak")) |>
  kableExtra::kbl(digits=3) |>
  kableExtra::kable_styling(
    bootstrap_options = c("hover", "responsive")
  )
EMM <- emmeans::emmeans(model1b_k_lm, ~ LPR)
confint(emmeans::contrast(EMM, "pairwise", adjust="sidak")) |>
  kableExtra::kbl(digits=3) |>
  kableExtra::kable_styling(
    bootstrap_options = c("hover", "responsive")
  )
EMM <- emmeans::emmeans(model1d_lpr5_amp_lm, ~ Condition)
confint(emmeans::contrast(EMM, "pairwise", adjust="sidak")) |>
  kableExtra::kbl(digits=3) |>
  kableExtra::kable_styling(
    bootstrap_options = c("hover", "responsive")
  )
EMM <- emmeans::emmeans(model1d_lpr5_k_lm, ~ Condition)
confint(emmeans::contrast(EMM, "pairwise", adjust="sidak")) |>
  kableExtra::kbl(digits=3) |>
  kableExtra::kable_styling(
    bootstrap_options = c("hover", "responsive")
  )
EMM <- emmeans::emmeans(model1f_amp_lm, ~ LPR)
confint(emmeans::contrast(EMM, "pairwise", adjust="sidak")) |>
  kableExtra::kbl(digits=3) |>
  kableExtra::kable_styling(
    bootstrap_options = c("hover", "responsive")
  )
EMM <- emmeans::emmeans(model1f_k_lm, ~ LPR)
confint(emmeans::contrast(EMM, "pairwise", adjust="sidak")) |>
  kableExtra::kbl(digits=3) |>
  kableExtra::kable_styling(
    bootstrap_options = c("hover", "responsive")
  )

Table 2: Contrasts for model fits, with Sidak adjustment.

(a) Figure 2B(i): Maximum amplitude, H2O

contrast	estimate	SE	df	lower.CL	upper.CL
LPR5 - LPR10	0.762	0.157	12	0.328	1.197
LPR5 - LPR50	1.410	0.167	12	0.947	1.873
LPR10 - LPR50	0.648	0.174	12	0.166	1.129

(b) Figure 2B(ii): Decay rate, H2O

contrast	estimate	SE	df	lower.CL	upper.CL
LPR5 - LPR10	23.482	7.882	12	1.646	45.317
LPR5 - LPR50	43.994	8.402	12	20.718	67.271
LPR10 - LPR50	20.512	8.732	12	-3.677	44.702

(c) Figure 2D(i): Maximum amplitude, LPR5

contrast	estimate	SE	df	lower.CL	upper.CL
H2O - D2O	0.832	0.140	14	0.454	1.210
H2O - D2O>H2O	0.147	0.162	14	-0.292	0.585
D2O - D2O>H2O	-0.685	0.157	14	-1.111	-0.259

(d) Figure 2D(ii): Decay rate, LPR5

contrast	estimate	SE	df	lower.CL	upper.CL
H2O - D2O	-2.128	6.675	14	-20.210	15.954
H2O - D2O>H2O	10.351	7.744	14	-10.628	31.331
D2O - D2O>H2O	12.479	7.520	14	-7.892	32.851

(e) Figure 2F(i): Maximum amplitude, D2O

contrast	estimate	SE	df	lower.CL	upper.CL
LPR5 - LPR10	0.232	0.084	14	0.006	0.458
LPR5 - LPR50	0.713	0.094	14	0.458	0.968
LPR10 - LPR50	0.481	0.097	14	0.218	0.743

(f) Figure 2F(ii): Decay rate, D2O

contrast	estimate	SE	df	lower.CL	upper.CL
LPR5 - LPR10	33.607	4.709	14	20.849	46.366
LPR5 - LPR50	51.345	5.306	14	36.972	65.718
LPR10 - LPR50	17.738	5.464	14	2.935	32.540

The following contrasts are indicated not to include zero in their 95% confidence interval (estimates in Table 2), which we interpret as indicating an effect due to differing lipid-protein ratio, or differing solvent condition:

• WT Maximum amplitude, H2O: LPR5-LPR10 (0.762nA [0.328,1.20]), LPR10-LPR50 (0.648nA [0.166, 1.13]), LPR5-LPR50 (1.41nA [0.947,1.87])
• WT Decay rate, H2O: LPR5-LPR10 (23.5.s [1.65,45.3]), LPR5-LPR50 (44.0/s [20.7,67.3])
• WT Maximum amplitude, LPR5: H2O-D2O (0.832nA [0.454,1.21]), D2O-D2O>H2O (-0.685nA [-1.11,-0.259])
• WT Decay rate, LPR5: no contrasts show significant difference
• WT Maximum amplitude, D2O: LPR5-LPR10 (0.232nA [0.006,0.458]), LPR10-LPR50 (0.481nA [0.218,0.743]), LPR5-LPR50 (0.713nA [0.458,0.968])
• WT Decay rate, D2O: LPR5-LPR10 (33.6/s [20.8,46.4]), LPR5-LPR50 (51.3/s [37.0,65.7]), LPR10-LPR50 (17.7/s [2.94,32.5])

Figure

We can plot these differences in the style of the manuscript as in Figure 3

# Get p-values, accounting for heteroscedasticity and multiple testing
# Also generate group1 and group2 columns
EMM_amp_b <- as.data.frame(pairs(emmeans::emmeans(model1b_amp_lm, ~ LPR),
                               adjust="sidak")) |>
  tidyr::separate(contrast, into=c("group1", "group2"), sep=" - ") |>
  dplyr::mutate(y.position=2.5) |>
  rstatix::add_significance()

EMM_k_b <- as.data.frame(pairs(emmeans::emmeans(model1b_k_lm, ~ LPR),
                               adjust="sidak")) |>
  tidyr::separate(contrast, into=c("group1", "group2"), sep=" - ") |>
  dplyr::mutate(y.position=85) |>
  rstatix::add_significance()

EMM_lpr5_amp_d <- as.data.frame(pairs(emmeans::emmeans(model1d_lpr5_amp_lm, ~ Condition),
                               adjust="sidak")) |>
  tidyr::separate(contrast, into=c("group1", "group2"), sep=" - ") |>
  dplyr::mutate(y.position=2.5) |>
  rstatix::add_significance() |>
  dplyr::mutate(LPR="LPR5")

EMM_lpr5_k_d <- as.data.frame(pairs(emmeans::emmeans(model1d_lpr5_k_lm, ~ Condition),
                               adjust="sidak")) |>
  tidyr::separate(contrast, into=c("group1", "group2"), sep=" - ") |>
  dplyr::mutate(y.position=85) |>
  rstatix::add_significance() |>
  dplyr::mutate(LPR="LPR5")

EMM_amp_f <- as.data.frame(pairs(emmeans::emmeans(model1f_amp_lm, ~ LPR),
                               adjust="sidak")) |>
  tidyr::separate(contrast, into=c("group1", "group2"), sep=" - ") |>
  dplyr::mutate(y.position=2.5) |>
  rstatix::add_significance()

EMM_k_f <- as.data.frame(pairs(emmeans::emmeans(model1f_k_lm, ~ LPR),
                               adjust="sidak")) |>
  tidyr::separate(contrast, into=c("group1", "group2"), sep=" - ") |>
  dplyr::mutate(y.position=85) |>
  rstatix::add_significance()

p1b_amp_annot <- p1b_amp +
  ggpubr::stat_pvalue_manual(EMM_amp_b,
                             label="p = {scales::pvalue(p.value)}",
                             size=3, step.increase=0.1)

p1b_k_annot <- p1b_k +
  ggpubr::stat_pvalue_manual(EMM_k_b, 
                             label="p = {scales::pvalue(p.value)}", 
                             size=3, step.increase=0.1)

p1d_amp_annot <- p1d_amp +
  ggpubr::stat_pvalue_manual(EMM_lpr5_amp_d,
                             label="p = {scales::pvalue(p.value)}",
                             size=3, step.increase=0.1)

p1d_k_annot <- p1d_k +
  ggpubr::stat_pvalue_manual(EMM_lpr5_k_d,
                             label="p = {scales::pvalue(p.value)}",
                             size=3, step.increase=0.1)

p1f_amp_annot <- p1f_amp +
  ggpubr::stat_pvalue_manual(EMM_amp_f,
                             label="p = {scales::pvalue(p.value)}",
                             size=3, step.increase=0.1)

p1f_k_annot <- p1f_k +
  ggpubr::stat_pvalue_manual(EMM_k_f, 
                             label="p = {scales::pvalue(p.value)}", 
                             size=3, step.increase=0.1)

p_comp <- ggpubr::ggarrange(p1b_amp_annot, p1b_k_annot, p1d_amp_annot,
                            p1d_k_annot, p1f_amp_annot, p1f_k_annot,
                            ncol=2, nrow=3,
                            labels=c("B", "", "D", "", "F", ""),
                            legend="none")
p_comp

# Write figures to file (commented out for webpage)
#ggplot2::ggsave("assets/images/fig02_WT_across_three_conditions_and_LPRs.pdf", p_comp)
#ggplot2::ggsave("assets/images/fig02_WT_across_three_conditions_and_LPRs.png", p_comp)

(a) Figure 2B(i)

Figure 3: WT measurements across three LPRs and three experimental conditions, representing figures 2B, 2D, and 2F from the draft manuscript. , with Sidak-adjusted p-values indicated

Downloads

• Download as .pdf
• Download as .png