The solutions given below are just one way of obtaining the desired plots! There are probably several different ways you could code to get the same plots.

1 R and basics-1

For the exercises in the first two lab sessions: R introduction and the ggplot basics 1, all you need to do is to replace the filename raw_counts.txt with each of the different files to look at the differences between different normalization methods.

2 ggplot basics 2

Task   Plot 1:

gc_raw <- read.table(file = "data/counts_raw.txt", sep = "\t", header = T)
gc_filt <- read.table(file = "data/counts_filtered.txt", sep = "\t", header = T)
gc_vst <- read.table(file = "data/counts_vst.txt", sep = "\t", header = T)
gc_deseq <- read.table(file = "data/counts_deseq2.txt", sep = "\t", header = T)
md <- read.table("data/metadata.csv", header = T, sep = ";")

gene_counts_all <- 
  gc_raw %>% gather(Sample_ID, Raw, -Gene) %>%
  full_join(gc_filt %>% gather(Sample_ID, Filtered, -Gene), by = c("Gene", "Sample_ID")) %>%
  full_join(gc_vst %>% gather(Sample_ID, VST, -Gene), by = c("Gene", "Sample_ID")) %>%
  full_join(gc_deseq %>% gather(Sample_ID, DESeq2, -Gene), by = c("Gene", "Sample_ID")) %>%
  gather(Method, count, Raw:DESeq2) %>%
  filter(!is.na(count)) %>%
  full_join(md, by = "Sample_ID")

gene_counts_all$Time <- factor(gene_counts_all$Time, levels = c("t0","t2","t6","t24"))
gene_counts_all$Replicate <- factor(gene_counts_all$Replicate, levels = c("A","B","C"))
gene_counts_all$Method <- factor(gene_counts_all$Method, levels = c("Raw","Filtered","DESeq2","VST"))

gene_counts_all %>% 
  group_by(Time, Replicate, Method) %>% 
  summarise(mean=mean(log10(count +1)),se=se(log10(count +1))) %>%
  ggplot(aes(x= Time, y= mean, fill = Replicate)) + 
  geom_bar(position = position_dodge2(), stat = "identity") +
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), position = position_dodge2(.9, padding = .6)) +
  facet_wrap(~Method, scales = "free")

Task   Plot 2:

gene_counts_all %>% 
  group_by(Time, Replicate, Method) %>% 
  ggplot() +
  geom_boxplot(mapping = aes(x = Sample_Name, y = log10(count + 1), fill = Time)) +
  facet_wrap(~Method*Replicate, ncol = 3, scales = "free")

3 ggplot basics 3

gc_long %>% 
  group_by(Time, Replicate) %>% 
  summarise(mean=mean(log10(count +1)),se=se(log10(count +1))) %>%
  ggplot(aes(x=Time, y=mean, color = Replicate)) +
  facet_wrap(~Replicate) +
  geom_line(aes(group=1), stat= "identity", size = 2) +
  scale_x_discrete(limits= c("t0", "t2", "t24")) +
  scale_y_continuous(limits = c(0.4,0.8), breaks = seq(0.4,0.8,0.05)) +
  guides(color="none") +
  ylab(label = "mean(log10(count + 1))") +
  theme_light() +
  theme(axis.text = element_text(face="bold", size=12),
        axis.title = element_text(face="bold", color = "#C84DF9", size=14),
        axis.ticks = element_blank())

4 Combining plots

p4 <- ggplot(data=iris,mapping=aes(x=Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3, alpha = 0.6) +
  theme_classic(base_size = 12) +
  border() 

d1 <- ggplot(data=iris,mapping=aes(Sepal.Length, fill = Species)) +
  geom_density(alpha = 0.6) +
  theme_classic() +
  clean_theme() +
  theme(legend.position = "none")

d2 <- ggplot(data=iris,mapping=aes(Sepal.Width, fill = Species)) +
  geom_density(alpha = 0.6) +
  theme_classic() +
  clean_theme() +
  theme(legend.position = "none") +
  rotate()

ggarrange(d1, NULL, p4, d2, 
          ncol = 2, nrow = 2,  align = "hv", 
          widths = c(3, 1), heights = c(1, 3),
          common.legend = TRUE)

5 PCA

Eigenvalues <- gc_mds$eig
Variance <- Eigenvalues / sum(Eigenvalues) 
Variance1 <- 100 * signif(Variance[1], 3)
Variance2 <- 100 * signif(Variance[2], 3)

gc_mds_long <- gc_mds$points %>%
  as.data.frame() %>%
  rownames_to_column("Sample_ID") %>%
  full_join(md, by = "Sample_ID")

gc_mds_long$Sample_Name <- factor(gc_mds_long$Sample_Name, levels = c("t0_A","t0_B","t0_C","t2_A","t2_B","t2_C","t6_A","t6_B","t6_C","t24_A","t24_B","t24_C"))
gc_mds_long$Time <- factor(gc_mds_long$Time, levels = c("t0","t2","t6","t24"))
gc_mds_long$Replicate <- factor(gc_mds_long$Replicate, levels = c("A","B","C"))

ggplot(gc_mds_long, aes(x=V1, y=V2, color = Time)) +
  geom_point(size = 3, aes(shape = Replicate)) +
  xlab(paste("PCO1: ", Variance1, "%")) +
  ylab(paste("PCO2: ", Variance2, "%")) +
  geom_vline(xintercept = 0, linetype=2) +
  geom_hline(yintercept = 0, linetype=2) +
  theme_bw() +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank()) 

6 Session info

sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] gridExtra_2.3          shiny_1.7.1            ggimage_0.3.0         
##  [4] treeio_1.18.1          ggtree_3.2.1           pheatmap_1.0.12       
##  [7] leaflet_2.0.4.1        swemaps_1.0            mapdata_2.3.0         
## [10] maps_3.4.0             ggpubr_0.4.0           cowplot_1.1.1         
## [13] ggthemes_4.2.4         scales_1.1.1           ggrepel_0.9.1         
## [16] wesanderson_0.3.6      forcats_0.5.1          stringr_1.4.0         
## [19] purrr_0.3.4            readr_2.1.1            tidyr_1.1.4           
## [22] tibble_3.1.6           tidyverse_1.3.1        reshape2_1.4.4        
## [25] ggplot2_3.3.5          formattable_0.2.1      kableExtra_1.3.4      
## [28] dplyr_1.0.7            lubridate_1.8.0        yaml_2.2.1            
## [31] fontawesome_0.2.2.9000 captioner_2.2.3        bookdown_0.24         
## [34] knitr_1.37            
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_2.0-2        ggsignif_0.6.3          ellipsis_0.3.2         
##  [4] fs_1.5.2                aplot_0.1.2             rstudioapi_0.13        
##  [7] xaringan_0.22           farver_2.1.0            fansi_1.0.2            
## [10] xml2_1.3.3              splines_4.1.2           cachem_1.0.6           
## [13] jsonlite_1.7.3          broom_0.7.11            dbplyr_2.1.1           
## [16] compiler_4.1.2          httr_1.4.2              backports_1.4.1        
## [19] assertthat_0.2.1        Matrix_1.3-4            fastmap_1.1.0          
## [22] lazyeval_0.2.2          cli_3.1.0               later_1.3.0            
## [25] leaflet.providers_1.9.0 htmltools_0.5.2         tools_4.1.2            
## [28] gtable_0.3.0            glue_1.6.0              Rcpp_1.0.8             
## [31] carData_3.0-5           cellranger_1.1.0        jquerylib_0.1.4        
## [34] vctrs_0.3.8             ape_5.6-1               svglite_2.0.0          
## [37] nlme_3.1-153            crosstalk_1.2.0         xfun_0.29              
## [40] ps_1.6.0                rvest_1.0.2             mime_0.12              
## [43] lifecycle_1.0.1         rstatix_0.7.0           promises_1.2.0.1       
## [46] hms_1.1.1               parallel_4.1.2          RColorBrewer_1.1-2     
## [49] curl_4.3.2              ggfun_0.0.4             yulab.utils_0.0.4      
## [52] sass_0.4.0              stringi_1.7.6           highr_0.9              
## [55] tidytree_0.3.7          rlang_0.4.12            pkgconfig_2.0.3        
## [58] systemfonts_1.0.3       evaluate_0.14           lattice_0.20-45        
## [61] patchwork_1.1.1         htmlwidgets_1.5.4       labeling_0.4.2         
## [64] processx_3.5.2          tidyselect_1.1.1        plyr_1.8.6             
## [67] magrittr_2.0.1          R6_2.5.1                magick_2.7.3           
## [70] generics_0.1.1          DBI_1.1.2               pillar_1.6.4           
## [73] haven_2.4.3             withr_2.4.3             mgcv_1.8-38            
## [76] abind_1.4-5             modelr_0.1.8            crayon_1.4.2           
## [79] car_3.0-12              utf8_1.2.2              tzdb_0.2.0             
## [82] rmarkdown_2.11          grid_4.1.2              readxl_1.3.1           
## [85] callr_3.7.0             reprex_2.0.1            digest_0.6.29          
## [88] webshot_0.5.2           xtable_1.8-4            httpuv_1.6.5           
## [91] gridGraphics_0.5-1      munsell_0.5.0           viridisLite_0.4.0      
## [94] ggplotify_0.1.0         bslib_0.3.1