Datasets
dataset_exp_1_ballistography <- read_excel (path = "/mnt/c/Users/Toshiya Matsushima/OneDrive/R projects/BM_project/data analysis using Rstudio/BM_project_dataset.xlsx", sheet = "exp_1_3_ballistography")
dataset_exp_1_ballistography_fig <- filter(dataset_exp_1_ballistography, fig_data == 1)
dataset_exp_1_ballistography_imi <- filter(dataset_exp_1_ballistography, drug %in% c("00_ctrl", "07_imi"))
dataset_exp_1_ballistography_bume_vu <- filter(dataset_exp_1_ballistography, drug %in% c("00_ctrl", "08_bume", "09_vu"))
dataset_exp_1_ballistography
dataset_exp_1_ballistography_fig
dataset_exp_1_ballistography_imi
dataset_exp_1_ballistography_bume_vu
Datasets statistics summary
dataset_exp_1_ballistography %>%
group_by(label) %>%
get_summary_stats(ratio)
dataset_exp_1_ballistography_fig %>%
group_by(label) %>%
get_summary_stats(ratio)
dataset_exp_1_ballistography_imi %>%
group_by(label) %>%
get_summary_stats(ratio)
dataset_exp_1_ballistography_bume_vu %>%
group_by(label) %>%
get_summary_stats(ratio)
Selected data for Fig.1C
fig <- ggplot(data = dataset_exp_1_ballistography_fig, mapping = aes(x=label, y=ratio)) +
geom_hline(yintercept=1, linetype = "dotted")+
geom_boxplot() +
geom_quasirandom(size = 3, shape = 16, colour = "black") +
theme_classic()+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
fig
ggsave(plot = fig, filename = "exp_1_ratio_label_selected.png", dpi = 300, height = 10, width = 10, units = "cm")
Selected data liner fitting
fit_exp_1_ballistography_selected <- lm(ratio ~ label, data = dataset_exp_1_ballistography_fig)
summary(fit_exp_1_ballistography_selected)
Call:
lm(formula = ratio ~ label, data = dataset_exp_1_ballistography_fig)
Residuals:
Min 1Q Median 3Q Max
-0.72375 -0.16201 -0.04892 0.17336 0.98976
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.18063 0.07899 14.947 < 2e-16 ***
label01_vpa_2 -0.56805 0.13099 -4.337 6.23e-05 ***
label02_keta_2 -0.57210 0.13099 -4.368 5.60e-05 ***
label03_mk801_2 -0.69986 0.13099 -5.343 1.80e-06 ***
label04_tubo_2 -0.67630 0.13099 -5.163 3.46e-06 ***
label05_mla_2 -0.56939 0.13099 -4.347 6.01e-05 ***
label06_dhbe_2 -0.78066 0.13099 -5.960 1.87e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2956 on 55 degrees of freedom
Multiple R-squared: 0.5006, Adjusted R-squared: 0.4461
F-statistic: 9.188 on 6 and 55 DF, p-value: 5.765e-07
car::Anova(fit_exp_1_ballistography_selected)
Anova Table (Type II tests)
Response: ratio
Sum Sq Df F value Pr(>F)
label 4.8154 6 9.1878 5.765e-07 ***
Residuals 4.8043 55
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
All data ANOVA
dataset_exp_1_ballistography %>%
anova_test(ratio ~ drug)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 drug 9 100 10.141 5.94e-11 * 0.477
All data multiple comparison (vs. control)
dataset_exp_1_ballistography %>% mutate(drug2=factor(drug)) -> dataset_exp_1_ballistography2
dataset_exp_1_ballistography2 <- data.frame(dataset_exp_1_ballistography2)
SimTestDiff(data = dataset_exp_1_ballistography2, grp = "drug2", resp = "ratio", base = 1, type = "Dunnett", covar.equal = F)
Test for differences of means of multiple endpoints
Assumption: Heterogeneous covariance matrices for the groups
Alternative hypotheses: True differences not equal to the margins
NANANA
Imidacloprid data for supplementary material Fig.S1B
fig <- ggplot(data = dataset_exp_1_ballistography_imi, mapping = aes(x=label, y=ratio)) +
geom_hline(yintercept=1, linetype = "dotted")+
geom_boxplot() +
geom_quasirandom(size = 3, shape = 16, colour = "black") +
theme_classic()+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
fig
ggsave(plot = fig, filename = "exp_1_ratio_label_imi.png", dpi = 300, height = 10, width = 6, units = "cm")
Imidacloprid data linear fitting
fit_exp_1_ballistography_selected <- lm(ratio ~ label, data = dataset_exp_1_ballistography_imi)
summary(fit_exp_1_ballistography_selected)
Call:
lm(formula = ratio ~ label, data = dataset_exp_1_ballistography_imi)
Residuals:
Min 1Q Median 3Q Max
-0.7238 -0.1692 -0.0065 0.2175 0.5436
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.18063 0.08043 14.679 2.17e-14 ***
label07_imi_1 -0.28187 0.13337 -2.113 0.043951 *
label07_imi_2 -0.59009 0.13337 -4.424 0.000143 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3009 on 27 degrees of freedom
Multiple R-squared: 0.4239, Adjusted R-squared: 0.3812
F-statistic: 9.934 on 2 and 27 DF, p-value: 0.0005842
car::Anova(fit_exp_1_ballistography_selected)
Anova Table (Type II tests)
Response: ratio
Sum Sq Df F value Pr(>F)
label 1.7993 2 9.9341 0.0005842 ***
Residuals 2.4452 27
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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