BM_project_exp_3_double_imprinting

Libraries

library(tidyverse)
library(ggplot2)
library(ggbeeswarm)
library(plotrix)
library(car)
library(rstatix)
library(SimComp)
library(readxl)
library(dplyr)

Dataset

dataset_exp_3_double_imprint <- read_excel (path = "/mnt/c/Users/Toshiya Matsushima/OneDrive/R projects/BM_project/data analysis using Rstudio/BM_project_dataset.xlsx", sheet = "exp_3_double_imprint")
dataset_exp_3_double_imprint

dataset_exp_3_double_imprint_ctrl <- filter(dataset_exp_3_double_imprint, drug == "00_ctrl")
dataset_exp_3_double_imprint_vpa <- filter(dataset_exp_3_double_imprint, drug == "01_vpa")
dataset_exp_3_double_imprint_keta <- filter(dataset_exp_3_double_imprint, drug == "02_keta")
dataset_exp_3_double_imprint_tubo <- filter(dataset_exp_3_double_imprint, drug == "04_tubo")

dataset_exp_3_double_imprint_ctrl

dataset_exp_3_double_imprint_vpa

dataset_exp_3_double_imprint_keta

dataset_exp_3_double_imprint_tubo

NA

Dataset by group

dataset_exp_3_double_imprint %>% 
  group_by (drug) %>% 
  get_summary_stats(bm)

BM scores

figure 6a

fig <- ggplot(data = dataset_exp_3_double_imprint, mapping = aes(x = drug, y = bm))+
  geom_hline(yintercept=0, linetype = "dotted")+
  geom_boxplot()+
  geom_point(shape=16, size=3, colour="black")+
  # geom_beeswarm(shape=16, size=3, colour="black")+
  ylim(-600, 600)+
  theme_classic()
fig
ggsave(plot = fig, filename = "exp_3_bm_vs_drug.png", dpi = 300, height = 10, width = 7, units = "cm")

BM ANOVA

dataset_exp_3_double_imprint %>% 
  anova_test(bm ~ drug)

Coefficient covariances computed by hccm()

ANOVA Table (type II tests)

  Effect DFn DFd     F     p p<.05   ges
1   drug   3  43 3.755 0.018     * 0.208

BM linear fitting

fit_exp3_bm_drug <- lm(bm ~ drug, data = dataset_exp_3_double_imprint)
summary (fit_exp3_bm_drug)

Call:
lm(formula = bm ~ drug, data = dataset_exp_3_double_imprint)

Residuals:
    Min      1Q  Median      3Q     Max 
-497.58 -130.08   11.42  118.31  455.42 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   145.80      52.24   2.791  0.00781 **
drug01_vpa    -18.22      78.37  -0.232  0.81729   
drug02_keta  -172.30      82.61  -2.086  0.04296 * 
drug04_tubo  -235.00      82.61  -2.845  0.00678 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 202.3 on 43 degrees of freedom
Multiple R-squared:  0.2076,    Adjusted R-squared:  0.1523 
F-statistic: 3.755 on 3 and 43 DF,  p-value: 0.01758

car::Anova(fit_exp3_bm_drug)

Anova Table (Type II tests)

Response: bm
           Sum Sq Df F value  Pr(>F)  
drug       461236  3  3.7552 0.01758 *
Residuals 1760515 43                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

imprint (1, biological)

imprint (1, biological) figure 6b

fig <- ggplot(data = dataset_exp_3_double_imprint, mapping = aes(x = drug, y = imprint_1))+
  geom_hline(yintercept=0, linetype = "dotted")+
  geom_boxplot()+
  geom_point(shape=16, size=3, colour="black")+
  ylim(-600, 600)+
  theme_classic()
fig
ggsave(plot = fig, filename = "exp_3_imprint1_vs_drug.png", dpi = 300, height = 10, width = 7, units = "cm")

imprint (1, biological) ANOVA

dataset_exp_3_double_imprint %>% 
  anova_test(imprint_1 ~ drug)

Coefficient covariances computed by hccm()

ANOVA Table (type II tests)

  Effect DFn DFd     F     p p<.05   ges
1   drug   3  43 2.596 0.065       0.153

imprint (1, biological) linear fitting

fit_exp3_imprint_1 <- lm(imprint_1 ~ drug, data = dataset_exp_3_double_imprint)
summary (fit_exp3_imprint_1)

Call:
lm(formula = imprint_1 ~ drug, data = dataset_exp_3_double_imprint)

Residuals:
   Min     1Q Median     3Q    Max 
-485.1 -156.6   26.4  174.4  406.2 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)    37.13      59.77   0.621   0.5377  
drug01_vpa   -213.05      89.66  -2.376   0.0220 *
drug02_keta  -201.53      94.51  -2.132   0.0387 *
drug04_tubo   -60.33      94.51  -0.638   0.5266  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 231.5 on 43 degrees of freedom
Multiple R-squared:  0.1533,    Adjusted R-squared:  0.09428 
F-statistic: 2.596 on 3 and 43 DF,  p-value: 0.06465

car::Anova(fit_exp3_imprint_1)

Anova Table (Type II tests)

Response: imprint_1
           Sum Sq Df F value  Pr(>F)  
drug       417382  3   2.596 0.06465 .
Residuals 2304481 43                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

imprint (1, biological) linear fitting with BM, figure 6d

fit_exp3_imprint_1_bm <- lm(imprint_1 ~ drug * bm, data = dataset_exp_3_double_imprint)
summary (fit_exp3_imprint_1_bm)

Call:
lm(formula = imprint_1 ~ drug * bm, data = dataset_exp_3_double_imprint)

Residuals:
    Min      1Q  Median      3Q     Max 
-405.51 -121.52   18.37  130.59  408.40 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -128.6864    72.0850  -1.785 0.082007 .  
drug01_vpa       -1.7390    99.4170  -0.017 0.986134    
drug02_keta     -36.8073    98.1233  -0.375 0.709609    
drug04_tubo     104.6133   103.3479   1.012 0.317660    
bm                1.1373     0.3286   3.462 0.001317 ** 
drug01_vpa:bm    -1.4939     0.4161  -3.591 0.000911 ***
drug02_keta:bm   -1.1786     0.4706  -2.505 0.016545 *  
drug04_tubo:bm   -1.1471     0.5003  -2.293 0.027323 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 208.6 on 39 degrees of freedom
Multiple R-squared:  0.3764,    Adjusted R-squared:  0.2645 
F-statistic: 3.363 on 7 and 39 DF,  p-value: 0.00668

car::Anova(fit_exp3_imprint_1_bm)

Anova Table (Type II tests)

Response: imprint_1
           Sum Sq Df F value   Pr(>F)   
drug       408946  3  3.1321 0.036347 * 
bm          23074  1  0.5302 0.470892   
drug:bm    584028  3  4.4730 0.008583 **
Residuals 1697379 39                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

imprint (2, artifact)

imprint (2, artifact) figure 6c

fig <- ggplot(data = dataset_exp_3_double_imprint, mapping = aes(x = drug, y = imprint_2))+
  geom_hline(yintercept=0, linetype = "dotted")+
  geom_boxplot()+
  geom_point(shape=16, size=3, colour="black")+
  ylim(-600, 600)+
  theme_classic()
fig
ggsave(plot = fig, filename = "exp_3_imprint2_vs_drug.png", dpi = 300, height = 10, width = 7, units = "cm")

imprint (2, artifact) ANOVA

dataset_exp_3_double_imprint %>% 
  anova_test(imprint_2 ~ drug)

Coefficient covariances computed by hccm()

ANOVA Table (type II tests)

  Effect DFn DFd     F     p p<.05   ges
1   drug   3  43 2.693 0.058       0.158

imprint (2, artifact) linear fitting

fit_exp3_imprint_2 <- lm(imprint_2 ~ drug, data = dataset_exp_3_double_imprint)
summary (fit_exp3_imprint_2)

Call:
lm(formula = imprint_2 ~ drug, data = dataset_exp_3_double_imprint)

Residuals:
    Min      1Q  Median      3Q     Max 
-807.75  -84.13   33.87  168.45  387.25 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   487.13      68.66   7.095 9.38e-09 ***
drug01_vpa   -277.38     102.99  -2.693   0.0100 *  
drug02_keta  -194.93     108.56  -1.796   0.0796 .  
drug04_tubo   -91.23     108.56  -0.840   0.4053    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 265.9 on 43 degrees of freedom
Multiple R-squared:  0.1582,    Adjusted R-squared:  0.09942 
F-statistic: 2.693 on 3 and 43 DF,  p-value: 0.0579

car::Anova(fit_exp3_imprint_2)

Anova Table (Type II tests)

Response: imprint_2
           Sum Sq Df F value Pr(>F)  
drug       571217  3  2.6927 0.0579 .
Residuals 3040582 43                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

imprint (2, artifact) linear fitting with BM

fit_exp3_imprint_2_bm <- lm(imprint_2 ~ drug*bm, data = dataset_exp_3_double_imprint)
summary (fit_exp3_imprint_2_bm)

Call:
lm(formula = imprint_2 ~ drug * bm, data = dataset_exp_3_double_imprint)

Residuals:
    Min      1Q  Median      3Q     Max 
-734.26 -109.08   36.03  127.18  622.30 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     470.3160    93.4620   5.032 1.13e-05 ***
drug01_vpa     -320.8333   128.8993  -2.489   0.0172 *  
drug02_keta    -179.5913   127.2220  -1.412   0.1660    
drug04_tubo    -103.0923   133.9960  -0.769   0.4463    
bm                0.1153     0.4260   0.271   0.7880    
drug01_vpa:bm     0.3570     0.5394   0.662   0.5120    
drug02_keta:bm   -0.1710     0.6101  -0.280   0.7807    
drug04_tubo:bm   -0.4368     0.6486  -0.673   0.5046    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 270.5 on 39 degrees of freedom
Multiple R-squared:   0.21, Adjusted R-squared:  0.06819 
F-statistic: 1.481 on 7 and 39 DF,  p-value: 0.2026

car::Anova(fit_exp3_imprint_2_bm)

Anova Table (Type II tests)

Response: imprint_2
           Sum Sq Df F value  Pr(>F)  
drug       567241  3  2.5844 0.06698 .
bm          33363  1  0.4560 0.50348  
drug:bm    153844  3  0.7009 0.55722  
Residuals 2853376 39                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Correlations among BM, imprint_1 and imprint_2

imprint_1 vs BM scores, figure 6d

fig <- ggplot(data = dataset_exp_3_double_imprint, mapping = aes(x=bm, y=imprint_1, color=drug))+
  geom_point(shape=16, size=3)+
  geom_vline(xintercept=0, linetype = "dotted")+
  geom_hline(yintercept=0, linetype = "dotted")+
  geom_smooth(method = "lm")+
  xlim(-600, 600)+
  ylim(-600, 600)+
  theme_classic()
fig

`geom_smooth()` using formula 'y ~ x'

ggsave(plot = fig, filename = "exp_3_bm_imprint_1.png", dpi = 300, height = 10, width = 13, units = "cm")

`geom_smooth()` using formula 'y ~ x'

Correlation (Spearman) between imprint_1 and BM

cor_test(dataset_exp_3_double_imprint_ctrl, bm, imprint_1, method = "spearman")

cor_test(dataset_exp_3_double_imprint_vpa, bm, imprint_1, method = "spearman")

cor_test(dataset_exp_3_double_imprint_keta, bm, imprint_1, method = "spearman")

cor_test(dataset_exp_3_double_imprint_tubo, bm, imprint_1, method = "spearman")

imprint_2 vs BM, figure 6e

fig <- ggplot(data = dataset_exp_3_double_imprint, mapping = aes(x=bm, y=imprint_2, color=drug))+
  geom_point(shape=16, size=3)+
  geom_vline(xintercept=0, linetype = "dotted")+
  geom_hline(yintercept=0, linetype = "dotted")+
  geom_smooth(method = "lm")+
  xlim(-600, 600)+
  ylim(-600, 600)+
  theme_classic()
fig

`geom_smooth()` using formula 'y ~ x'

ggsave(plot = fig, filename = "exp_3_bm_imprint_2.png", dpi = 300, height = 10, width = 13, units = "cm")

`geom_smooth()` using formula 'y ~ x'

Correlation (Spearman) between imprint_2 and BM

cor_test(dataset_exp_3_double_imprint_ctrl, bm, imprint_2, method = "spearman")

cor_test(dataset_exp_3_double_imprint_vpa, bm, imprint_2, method = "spearman")

cor_test(dataset_exp_3_double_imprint_keta, bm, imprint_2, method = "spearman")

cor_test(dataset_exp_3_double_imprint_tubo, bm, imprint_2, method = "spearman")

imprint_2 vs imprint_1

fig <- ggplot(data = dataset_exp_3_double_imprint, mapping = aes(x=imprint_2, y=imprint_1, color=drug))+
  geom_point(shape=16, size=3)+
  geom_vline(xintercept=0, linetype = "dotted")+
  geom_hline(yintercept=0, linetype = "dotted")+
  geom_smooth(method = "lm")+
  xlim(-600, 600)+
  ylim(-600, 600)+
  theme_classic()
fig

`geom_smooth()` using formula 'y ~ x'

ggsave(plot = fig, filename = "exp_3_imprint_2_imprint_1.png", dpi = 300, height = 10, width = 13, units = "cm")

`geom_smooth()` using formula 'y ~ x'

Correlation (Spearman) between imprint_2 and imprint_1

cor_test(dataset_exp_3_double_imprint, imprint_1, imprint_2, method = "spearman")

sessionInfo

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8   
 [6] LC_MESSAGES=C.UTF-8    LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           LC_TELEPHONE=C        
[11] LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggsci_2.9        pwr_1.3-0        plotly_4.10.0    readxl_1.3.1     SimComp_3.3      rstatix_0.7.0    car_3.0-12      
 [8] carData_3.0-5    plotrix_3.8-2    ggbeeswarm_0.6.0 forcats_0.5.1    stringr_1.4.0    dplyr_1.0.7      purrr_0.3.4     
[15] readr_2.1.1      tidyr_1.1.4      tibble_3.1.6     ggplot2_3.3.5    tidyverse_1.3.1 

loaded via a namespace (and not attached):
 [1] nlme_3.1-144         fs_1.5.2             lubridate_1.8.0      httr_1.4.2           tools_3.6.3          backports_1.4.1     
 [7] utf8_1.2.2           R6_2.5.1             vipor_0.4.5          mgcv_1.8-31          DBI_1.1.1            lazyeval_0.2.2      
[13] colorspace_2.0-2     withr_2.4.3          tidyselect_1.1.1     compiler_3.6.3       survPresmooth_1.1-11 mratios_1.4.2       
[19] cli_3.1.0            rvest_1.0.2          xml2_1.3.3           sandwich_3.0-1       labeling_0.4.2       scales_1.1.1        
[25] mvtnorm_1.1-3        digest_0.6.29        rmarkdown_2.11       pkgconfig_2.0.3      htmltools_0.5.2      dbplyr_2.1.1        
[31] fastmap_1.1.0        htmlwidgets_1.5.4    rlang_0.4.12         rstudioapi_0.13      jquerylib_0.1.4      generics_0.1.1      
[37] farver_2.1.0         zoo_1.8-9            jsonlite_1.7.2       magrittr_2.0.1       Matrix_1.2-18        Rcpp_1.0.7          
[43] munsell_0.5.0        fansi_0.5.0          abind_1.4-5          lifecycle_1.0.1      stringi_1.7.6        multcomp_1.4-18     
[49] yaml_2.2.1           MASS_7.3-51.5        grid_3.6.3           crayon_1.4.2         lattice_0.20-40      haven_2.4.3         
[55] splines_3.6.3        hms_1.1.1            knitr_1.37           pillar_1.6.4         codetools_0.2-16     reprex_2.0.1        
[61] glue_1.6.0           evaluate_0.14        data.table_1.14.2    modelr_0.1.8         vctrs_0.3.8          tzdb_0.2.0          
[67] cellranger_1.1.0     gtable_0.3.0         assertthat_0.2.1     xfun_0.29            broom_0.7.10         survival_3.1-8      
[73] viridisLite_0.4.0    beeswarm_0.4.0       TH.data_1.1-0        ellipsis_0.3.2