Datasets
dataset_exp_5_brain_weight <- read_excel (path = "/mnt/c/Users/Toshiya Matsushima/OneDrive/R projects/BM_project/data analysis using Rstudio/BM_project_dataset.xlsx", sheet = "exp_5_brain_weight")
dataset_exp_6_neuron_glia <- read_excel (path = "/mnt/c/Users/Toshiya Matsushima/OneDrive/R projects/BM_project/data analysis using Rstudio/BM_project_dataset.xlsx", sheet = "exp_6_neuron_glia")
dataset_exp_5_brain_weight
dataset_exp_6_neuron_glia
Analysis of brain and body weight
absolute brain weight, linear fitting
fit_exp_5_abs_w_brain <- lm (w_brain ~ sex * drug, data = dataset_exp_5_brain_weight)
summary (fit_exp_5_abs_w_brain)
Call:
lm(formula = w_brain ~ sex * drug, data = dataset_exp_5_brain_weight)
Residuals:
Min 1Q Median 3Q Max
-0.41406 -0.03474 0.00594 0.04715 0.17926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.00174 0.01520 65.887 < 2e-16 ***
sex 0.03101 0.02135 1.453 0.148404
drug01_vpa -0.08410 0.02426 -3.467 0.000683 ***
drug02_keta -0.04889 0.02891 -1.691 0.092845 .
drug04_tubo -0.03094 0.04246 -0.729 0.467397
sex:drug01_vpa 0.02942 0.03266 0.901 0.369059
sex:drug02_keta 0.01340 0.03939 0.340 0.734245
sex:drug04_tubo 0.02419 0.06000 0.403 0.687336
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.08865 on 153 degrees of freedom
Multiple R-squared: 0.1514, Adjusted R-squared: 0.1126
F-statistic: 3.901 on 7 and 153 DF, p-value: 0.0006117
car::Anova(fit_exp_5_abs_w_brain)
Anova Table (Type II tests)
Response: w_brain
Sum Sq Df F value Pr(>F)
sex 0.07864 1 10.0058 0.001882 **
drug 0.14299 3 6.0645 0.000629 ***
sex:drug 0.00669 3 0.2838 0.837068
Residuals 1.20248 153
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
absolute body weight figure
dataset_exp_5_brain_weight %>%
ggplot(mapping = aes(x=drug, y=w_body, colour = factor(sex))) +
geom_boxplot() +
geom_quasirandom()+
theme_classic()
ggsave(filename = "exp_5_body_weight.png", dpi = 300, height = 10, width = 10, units = "cm")
absolute body size, linear fitting
fit_exp_5_abs_w_body <- lm (w_body ~ sex * drug, data = dataset_exp_5_brain_weight)
summary (fit_exp_5_abs_w_body)
Call:
lm(formula = w_body ~ sex * drug, data = dataset_exp_5_brain_weight)
Residuals:
Min 1Q Median 3Q Max
-7.5484 -2.0371 0.1629 1.8077 9.7864
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 39.65882 0.54955 72.166 <2e-16 ***
sex 0.17832 0.77161 0.231 0.818
drug01_vpa -1.34519 0.87678 -1.534 0.127
drug02_keta -0.06652 1.04492 -0.064 0.949
drug04_tubo 1.10118 1.53481 0.717 0.474
sex:drug01_vpa 0.85643 1.18040 0.726 0.469
sex:drug02_keta -1.36438 1.42373 -0.958 0.339
sex:drug04_tubo -1.91832 2.16856 -0.885 0.378
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.204 on 153 degrees of freedom
Multiple R-squared: 0.03717, Adjusted R-squared: -0.006877
F-statistic: 0.8439 on 7 and 153 DF, p-value: 0.5527
car::Anova(fit_exp_5_abs_w_body)
Anova Table (Type II tests)
Response: w_body
Sum Sq Df F value Pr(>F)
sex 0.31 1 0.0306 0.8615
drug 28.95 3 0.9396 0.4231
sex:drug 31.67 3 1.0281 0.3819
Residuals 1571.03 153
Analysis of cell counts
total cell number, ANOVA
dataset_exp_6_neuron_glia %>%
anova_test(cell_number ~ drug)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 drug 3 36 0.283 0.838 0.023
total cell number, lineaer fitting
fit_exp_6_cell_count <- lm(cell_number ~ sex * drug, data = dataset_exp_6_neuron_glia)
summary (fit_exp_6_cell_count)
Call:
lm(formula = cell_number ~ sex * drug, data = dataset_exp_6_neuron_glia)
Residuals:
Min 1Q Median 3Q Max
-16.337 -2.595 0.220 5.439 9.073
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.806 3.128 10.488 6.97e-12 ***
sex 3.626 4.424 0.820 0.418
drug01_vpa -0.599 4.424 -0.135 0.893
drug02_keta -1.412 4.424 -0.319 0.752
drug04_tubo 2.088 4.424 0.472 0.640
sex:drug01_vpa -2.719 6.256 -0.435 0.667
sex:drug02_keta -2.004 6.256 -0.320 0.751
sex:drug04_tubo -5.436 6.256 -0.869 0.391
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.994 on 32 degrees of freedom
Multiple R-squared: 0.05307, Adjusted R-squared: -0.1541
F-statistic: 0.2562 on 7 and 32 DF, p-value: 0.9663
car::Anova(fit_exp_6_cell_count)
Anova Table (Type II tests)
Response: cell_number
Sum Sq Df F value Pr(>F)
sex 11.80 1 0.2412 0.6267
drug 38.04 3 0.2592 0.8542
sex:drug 37.89 3 0.2582 0.8549
Residuals 1565.45 32
neuron_glia ratio, ANOVA
dataset_exp_6_neuron_glia %>%
anova_test(neuron_ratio ~ drug)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 drug 3 36 3.842 0.017 * 0.243
neuron_glia ratio, lineaer fitting
fit_exp_6_neuron_glia_ratio <- lm(neuron_ratio ~ sex * drug, data = dataset_exp_6_neuron_glia)
summary (fit_exp_6_neuron_glia_ratio)
Call:
lm(formula = neuron_ratio ~ sex * drug, data = dataset_exp_6_neuron_glia)
Residuals:
Min 1Q Median 3Q Max
-0.084514 -0.022167 0.008942 0.027854 0.062633
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.522874 0.019104 27.370 <2e-16 ***
sex 0.017086 0.027017 0.632 0.5316
drug01_vpa -0.073903 0.027017 -2.735 0.0101 *
drug02_keta 0.008096 0.027017 0.300 0.7664
drug04_tubo -0.001942 0.027017 -0.072 0.9431
sex:drug01_vpa 0.025310 0.038208 0.662 0.5124
sex:drug02_keta -0.055518 0.038208 -1.453 0.1559
sex:drug04_tubo -0.017455 0.038208 -0.457 0.6509
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.04272 on 32 degrees of freedom
Multiple R-squared: 0.3429, Adjusted R-squared: 0.1991
F-statistic: 2.385 on 7 and 32 DF, p-value: 0.04411
car::Anova(fit_exp_6_neuron_glia_ratio)
Anova Table (Type II tests)
Response: neuron_ratio
Sum Sq Df F value Pr(>F)
sex 0.000267 1 0.1465 0.70445
drug 0.021550 3 3.9365 0.01692 *
sex:drug 0.008649 3 1.5799 0.21343
Residuals 0.058394 32
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Analysis of behavioral data
BM, linear fitting
fit_exp_6_bm <- lm(bm ~ drug, data = dataset_exp_6_neuron_glia)
summary(fit_exp_6_bm)
Call:
lm(formula = bm ~ drug, data = dataset_exp_6_neuron_glia)
Residuals:
Min 1Q Median 3Q Max
-597.90 -90.98 32.63 112.93 341.10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 383.70 63.62 6.031 9.92e-07 ***
drug01_vpa -281.80 89.97 -3.132 0.003697 **
drug02_keta -352.50 89.97 -3.918 0.000441 ***
drug04_tubo -387.87 103.89 -3.733 0.000736 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 201.2 on 32 degrees of freedom
(4 observations deleted due to missingness)
Multiple R-squared: 0.3966, Adjusted R-squared: 0.34
F-statistic: 7.01 on 3 and 32 DF, p-value: 0.0009377
car::Anova(fit_exp_6_bm)
Anova Table (Type II tests)
Response: bm
Sum Sq Df F value Pr(>F)
drug 851197 3 7.0099 0.0009377 ***
Residuals 1295235 32
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
imprint, linear fitting
fit_exp_6_imprint <- lm(imprint ~ drug, data = dataset_exp_6_neuron_glia)
summary(fit_exp_6_imprint)
Call:
lm(formula = imprint ~ drug, data = dataset_exp_6_neuron_glia)
Residuals:
Min 1Q Median 3Q Max
-711.9 -182.7 25.2 181.9 652.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 536.80 85.99 6.242 5.39e-07 ***
drug01_vpa -763.10 121.61 -6.275 4.91e-07 ***
drug02_keta -323.90 121.61 -2.663 0.012 *
drug04_tubo -249.63 140.42 -1.778 0.085 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 271.9 on 32 degrees of freedom
(4 observations deleted due to missingness)
Multiple R-squared: 0.5588, Adjusted R-squared: 0.5175
F-statistic: 13.51 on 3 and 32 DF, p-value: 7.273e-06
car::Anova(fit_exp_6_imprint)
Anova Table (Type II tests)
Response: imprint
Sum Sq Df F value Pr(>F)
drug 2997271 3 13.511 7.273e-06 ***
Residuals 2366281 32
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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ZHJ1Zy5wbmciLCBkcGkgPSAzMDAsIGhlaWdodCA9IDEwLCB3aWR0aCA9IDcsIHVuaXRzID0gImNtIikKYGBgCgojIyBpbXByaW50LCBsaW5lYXIgZml0dGluZwpgYGB7cn0KZml0X2V4cF82X2ltcHJpbnQgPC0gbG0oaW1wcmludCB+IGRydWcsIGRhdGEgPSBkYXRhc2V0X2V4cF82X25ldXJvbl9nbGlhKQpzdW1tYXJ5KGZpdF9leHBfNl9pbXByaW50KQpjYXI6OkFub3ZhKGZpdF9leHBfNl9pbXByaW50KQpgYGAKCgojIHNlc3Npb25JbmZvCmBgYHtyfQpzZXNzaW9uSW5mbygpCmBgYA==