BM_project_exp_2_IMI

Libraries

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

Dataset

dataset_exp_2_bm_imprint <- read_excel (path = "/mnt/c/Users/Toshiya Matsushima/OneDrive/R projects/BM_project/data analysis using Rstudio/BM_project_dataset.xlsx", sheet = "exp_2_3_bm_imprint")
dataset_exp_2_bm_imprint_ctrl_imi <- filter(dataset_exp_2_bm_imprint, drug == "00_ctrl"| drug == "07_imi")
dataset_exp_2_bm_imprint_imi <- filter(dataset_exp_2_bm_imprint, drug == "07_imi")
dataset_exp_2_bm_imprint_imi_1 <- filter(dataset_exp_2_bm_imprint, label == "07_imi_1")
dataset_exp_2_bm_imprint_imi_2 <- filter(dataset_exp_2_bm_imprint, label == "07_imi_2")
dataset_exp_2_bm_imprint_imi_3 <- filter(dataset_exp_2_bm_imprint, label == "07_imi_3")
dataset_exp_2_bm_imprint_imi_4 <- filter(dataset_exp_2_bm_imprint, label == "07_imi_4")

dataset_exp_2_bm_imprint_ctrl_imi

dataset_exp_2_bm_imprint_imi

dataset_exp_2_bm_imprint_imi_1

dataset_exp_2_bm_imprint_imi_2

dataset_exp_2_bm_imprint_imi_3

dataset_exp_2_bm_imprint_imi_4

BM scores of imidacloprid (figure 4a)

Statistics of BM

  dataset_exp_2_bm_imprint_imi %>% 
  group_by (label) %>% 
  get_summary_stats(bm)

figure 4a; BM scores of imidacloprid data

fig <- ggplot(dataset_exp_2_bm_imprint_imi, mapping = aes(x=label, y=bm))+
  geom_hline(yintercept = 0, linetype="dotted")+
    geom_hline(yintercept=136.3089, linetype = "solid", color ="blue", size = 1)+     #average of the control data
    geom_hline(yintercept=38.65567, linetype = "solid", color ="darkred", size = 1)+  #5% level below the control average
    geom_hline(yintercept=-5.85836, linetype = "solid", color ="red", size = 1)+      #1% level below the control average
  geom_boxplot()+
  geom_quasirandom(shape=16, size=3, colour="black")+
  ylim(-400, 600)+
  theme_classic()
fig
ggsave(plot=fig, filename = "exp_3_imi_bm.png", dpi=300, height=10, width=7, units="cm")

linear regression analysis

fit_exp_3_bm <- lm (bm ~ label, data = dataset_exp_2_bm_imprint_ctrl_imi)
summary (fit_exp_3_bm)

Call:
lm(formula = bm ~ label, data = dataset_exp_2_bm_imprint_ctrl_imi)

Residuals:
    Min      1Q  Median      3Q     Max 
-434.31 -136.31    4.69  122.10  402.69 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     136.31      15.34   8.884 7.85e-16 ***
label07_imi_1   -46.90      44.03  -1.065  0.28829    
label07_imi_2  -128.62      41.94  -3.067  0.00251 ** 
label07_imi_3  -132.81      55.95  -2.374  0.01871 *  
label07_imi_4  -155.71      55.95  -2.783  0.00598 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 170.2 on 174 degrees of freedom
Multiple R-squared:  0.1002,    Adjusted R-squared:  0.0795 
F-statistic: 4.843 on 4 and 174 DF,  p-value: 0.0009977

car::Anova(fit_exp_3_bm)

Anova Table (Type II tests)

Response: bm
           Sum Sq  Df F value    Pr(>F)    
label      560892   4   4.843 0.0009977 ***
Residuals 5037911 174                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

imprint scores of imidacloprid (figure 4a)

statistics of imprint scores

dataset_exp_2_bm_imprint_ctrl_imi %>% 
  group_by(label) %>% 
  get_summary_stats(imprint)

figure 4b; imprint scores of imidacloprid data

fig <- ggplot(dataset_exp_2_bm_imprint_imi, mapping = aes(x=label, y=imprint))+
  geom_hline(yintercept = 0, linetype="dotted")+
    geom_hline(yintercept=419.5203, linetype = "solid", color ="blue", size = 1)+     #average of the control data
    geom_hline(yintercept=284.19273, linetype = "solid", color ="darkred", size = 1)+  #5% level below the control average
    geom_hline(yintercept=217.9088, linetype = "solid", color ="red", size = 1)+      #1% level below the control average
  geom_boxplot()+
  geom_quasirandom(shape=16, size=3, colour="black")+
  ylim(-400, 600)+
  theme_classic()
fig

Warning: Removed 1 rows containing non-finite values (stat_boxplot).
Warning: Removed 1 rows containing missing values (position_quasirandom).

ggsave(plot=fig, filename = "exp_3_imi_imprint.png", dpi=300, height=10, width=7, units="cm")

Warning: Removed 1 rows containing non-finite values (stat_boxplot).
Warning: Removed 1 rows containing missing values (position_quasirandom).

linear model analysis

fit_exp_3_imprint <- lm (imprint ~ label, data = dataset_exp_2_bm_imprint_ctrl_imi)
summary (fit_exp_3_imprint)

Call:
lm(formula = imprint ~ label, data = dataset_exp_2_bm_imprint_ctrl_imi)

Residuals:
    Min      1Q  Median      3Q     Max 
-947.52  -79.52   79.24  157.98  276.00 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     419.52      20.85  20.117   <2e-16 ***
label07_imi_1    18.24      59.84   0.305   0.7608    
label07_imi_2  -101.52      57.01  -1.781   0.0767 .  
label07_imi_3   -62.62      76.05  -0.823   0.4114    
label07_imi_4  -103.62      76.05  -1.363   0.1748    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 231.3 on 174 degrees of freedom
Multiple R-squared:  0.03001,   Adjusted R-squared:  0.007709 
F-statistic: 1.346 on 4 and 174 DF,  p-value: 0.2549

car::Anova(fit_exp_3_imprint)

Anova Table (Type II tests)

Response: imprint
           Sum Sq  Df F value Pr(>F)
label      287920   4  1.3457 0.2549
Residuals 9307070 174               

#Correlations between BM scores and imprint scores ## x-y plotting of imprint vs BM

fig <- ggplot(data = dataset_exp_2_bm_imprint_imi, mapping = aes(x=bm, y=imprint, colour=label))+
  scale_color_nejm()+
  geom_point(shape=16, size=3)+
  scale_color_manual(values = c("black", "red", "blue", "orange"))+
  geom_vline(xintercept=0, linetype = "dotted")+
  geom_hline(yintercept=0, linetype = "dotted")+
  geom_smooth(method = "lm")+
  xlim(-400, 600)+
  ylim(-400, 600)+
  theme_classic()

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

fig

`geom_smooth()` using formula 'y ~ x'
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing missing values (geom_point).

ggsave(plot = fig, filename = "exp_3_imi_imprint_bm.png", dpi = 300, height = 10, width = 13, units = "cm")

`geom_smooth()` using formula 'y ~ x'
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing missing values (geom_point).

Correlation (Spearman) between BM and imprint scores

cor_test(dataset_exp_2_bm_imprint_imi_1, bm, imprint, method = "spearman")

cor_test(dataset_exp_2_bm_imprint_imi_2, bm, imprint, method = "spearman")

cor_test(dataset_exp_2_bm_imprint_imi_3, bm, imprint, method = "spearman")

cor_test(dataset_exp_2_bm_imprint_imi_4, bm, imprint, 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    LC_MESSAGES=C.UTF-8    LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8
[12] LC_IDENTIFICATION=C   

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

other attached packages:
 [1] ggsci_2.9        plotrix_3.8-2    ggbeeswarm_0.6.0 readxl_1.3.1     SimComp_3.3      rstatix_0.7.0    car_3.0-12       carData_3.0-5    plotly_4.10.0    forcats_0.5.1    stringr_1.4.0    dplyr_1.0.7      purrr_0.3.4      readr_2.1.1      tidyr_1.1.4     
[16] 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      utf8_1.2.2           R6_2.5.1             vipor_0.4.5          DBI_1.1.1            lazyeval_0.2.2       mgcv_1.8-31         
[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        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         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           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          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           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