In Mortendall/Lipodomics: What the Package Does (One Line, Title Case)

volcano_plot <-  ggplot(lipodomics_results$Treatment_in_WT, aes(x = logFC, y = -log10(adj.P.Val))) +
  geom_point()

volcano_plot

ggsave(volcano_plot, filename = here( "data/figures/NAD_terms.png"), scale = 2.5)

candidates <- lipodomics_results[["KO_in_Control"]]
candidates <- candidates %>% 
  filter(adj.P.Val<0.05)

candidates_matrix <- count_matrix
candidates_matrix<-candidates_matrix %>% 
  filter(rownames(candidates_matrix)%in%candidates$rn)

  annotation <- as.data.frame(setup$Group)
  colnames(annotation)<-"Group"
  rownames(annotation) <- setup$ID

  heatmap <- pheatmap(candidates_matrix,
                      treeheight_col = 0,
                      treeheight_row = 0,
                      scale = "row",
                      legend = T,
                      na_col = "white",
                      Colv = NA,
                      na.rm = T,
                      cluster_cols = F,
                      show_rownames = T,
                      fontsize_row = 8,
                      fontsize_col = 8,
                      cellwidth = 8,
                      cellheight = 8,
                      annotation_col = annotation
  )


  candidates <- lipodomics_results[["Treatment_in_WT"]]
candidates <- candidates %>% 
  filter(adj.P.Val<0.05)

candidates_matrix <- count_matrix
candidates_matrix<-candidates_matrix %>% 
  filter(rownames(candidates_matrix)%in%candidates$rn)

  annotation <- as.data.frame(setup$Group)
  colnames(annotation)<-"Group"
  rownames(annotation) <- setup$ID

  heatmap <- pheatmap(candidates_matrix,
                      treeheight_col = 0,
                      treeheight_row = 0,
                      scale = "row",
                      legend = T,
                      na_col = "white",
                      Colv = NA,
                      na.rm = T,
                      cluster_cols = F,
                      show_rownames = T,
                      fontsize_row = 8,
                      fontsize_col = 8,
                      cellwidth = 8,
                      cellheight = 8,
                      annotation_col = annotation
  )

  TAG_data <- count_matrix %>% 
    filter(rownames(count_matrix)=="TAG")


  sig_lipids <- test_lim_group %>% 
    dplyr::filter(p.adj<0.05) %>% 
    dplyr::select(Lipid)
  candidate_lipids <- count_matrix_lim_groups %>% 
    dplyr::filter(rownames(count_matrix_lim_groups) %in% sig_lipids$Lipid)

  annotation <- as.data.frame(setup$Group)
  colnames(annotation)<-"Group"
  rownames(annotation) <- setup$ID

  heatmap <- pheatmap(candidate_lipids,
                      treeheight_col = 0,
                      treeheight_row = 0,
                      scale = "row",
                      legend = T,
                      na_col = "white",
                      Colv = NA,
                      na.rm = T,
                      cluster_cols = F,
                      show_rownames = T,
                      fontsize_row = 8,
                      fontsize_col = 8,
                      cellwidth = 8,
                      cellheight = 8,
                      annotation_col = annotation
  )
  ggsave(
    heatmap,
    filename = here::here("data/significantly_altered_lipids_heatmap.png"),
    units = "cm",
    width = 14,
    height = 10
  )
  heatmap_all <- pheatmap(count_matrix_lim_groups,
                      treeheight_col = 0,
                      treeheight_row = 0,
                      scale = "row",
                      legend = T,
                      na_col = "white",
                      Colv = NA,
                      na.rm = T,
                      cluster_cols = F,
                      show_rownames = T,
                      fontsize_row = 8,
                      fontsize_col = 8,
                      cellwidth = 8,
                      cellheight = 8,
                      annotation_col = annotation
  )

  ggsave(
    heatmap_all,
    filename = here::here("data/all_lipids_heatmap.png"),
    units = "cm",
    width = 14,
    height = 10
  )

summary_lipids <- candidate_lipids %>% 
  dplyr::mutate(Lipids = rownames(candidate_lipids)) %>% 
  tidyr::pivot_longer(-Lipids, 
               names_to = "ID",
                 values_to = "molar_percentage")

setup$ID <- as.character(setup$ID)
  colnames(summary_lipids) <- as.character(colnames(summary_lipids))
  setup_merge <- setup %>%
    dplyr::select(ID, Group)
  summary_lipids <-
    right_join(summary_lipids,
               setup_merge,
               by = "ID",
               keep = F)


  summary_lipids <- summary_lipids %>% 
    dplyr::select(-ID) %>% 
    group_by(Group, Lipids) %>% 
    get_summary_stats(molar_percentage, type = "mean_se")

  column_order <- c("WT_Control", "KO_Control", "WT_NR", "KO_NR")

  summary_lipids<- summary_lipids %>%
  dplyr::mutate(Group = factor(Group,levels = column_order))
 PI_plot<-ggplot(subset(summary_lipids, Lipids == "PI"), aes(x = Group, y = mean, fill = Group))+
  geom_col()+
   geom_errorbar(aes(ymin = mean - se,
                    ymax = mean + se,
                    width = 0.1))+
   ylab("Molar %")+
   theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        legend.position = "none",
        axis.text.x=element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        axis.title.y = element_text(size = 14))+
   ggtitle("PI")+
   xlab("")


    HexCer_plot<-ggplot(subset(summary_lipids, Lipids == "HexCer"), aes(x = Group, y = mean, fill = Group))+
  geom_col()+
   geom_errorbar(aes(ymin = mean - se,
                    ymax = mean + se,
                    width = 0.1))+
      ylab("Molar %")+
   theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        legend.position = "none",
        axis.text.x=element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        axis.title.y = element_text(size = 14))+
   ggtitle("HexCer")+
   xlab("")

    LPE_plot<-ggplot(subset(summary_lipids, Lipids == "LPE"), aes(x = Group, y = mean, fill = Group))+
  geom_col()+
   geom_errorbar(aes(ymin = mean - se,
                    ymax = mean + se,
                    width = 0.1))+
      ylab("Molar %")+
   theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        legend.position = "none",
        axis.text.x=element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        axis.title.y = element_text(size = 14))+
   ggtitle("LPE")+
   xlab("")

     PE_plot<-ggplot(subset(summary_lipids, Lipids == "PE O-"), aes(x = Group, y = mean, fill = Group))+
  geom_col()+
   geom_errorbar(aes(ymin = mean - se,
                    ymax = mean + se,
                    width = 0.1))+
      ylab("Molar %")+
   theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        legend.position = "none",
        axis.text.x=element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        axis.title.y = element_text(size = 14))+
   ggtitle("PE O-")+
   xlab("")

     PG_plot<-ggplot(subset(summary_lipids, Lipids == "PG"), aes(x = Group, y = mean, fill = Group))+
  geom_col()+
   geom_errorbar(aes(ymin = mean - se,
                    ymax = mean + se,
                    width = 0.1))+
      ylab("Molar %")+
   theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        legend.position = "none",
        axis.text.x=element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        axis.title.y = element_text(size = 14))+
   ggtitle("PG")+
   xlab("")

    PS_plot<-ggplot(subset(summary_lipids, Lipids == "PS"), aes(x = Group, y = mean, fill = Group))+
  geom_col()+
   geom_errorbar(aes(ymin = mean - se,
                    ymax = mean + se,
                    width = 0.1))+
      ylab("Molar %")+
   theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        legend.position = "none",
        axis.text.x=element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        axis.title.y = element_text(size = 14))+
   ggtitle("PS")+
   xlab("")