RNA_DGE_Comp_Species_Ensemble

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Last updated: 2026-02-20

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Knit directory: CrossSpecies_CM_Diff_RNA/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/RNA_DGE_Comp_Species_Ensemble.Rmd) and HTML (docs/RNA_DGE_Comp_Species_Ensemble.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	5c4d94c	John D. Hurley	2026-02-20	Final_CorrelationHeatMapEdits
html	0cb123a	John D. Hurley	2026-02-18	Build site.
Rmd	888d262	John D. Hurley	2026-02-18	Block Naming Duplication
Rmd	fce8415	John D. Hurley	2026-02-18	Typo
Rmd	b8dfde3	John D. Hurley	2026-02-18	MovedClustersToCormotif
Rmd	5d20f97	John D. Hurley	2026-02-18	WebsiteUpdates_DGE_Species
Rmd	c85837b	John D. Hurley	2026-02-18	SiteUpDate_CorrelationHeatMap
html	b0ab413	John D. Hurley	2026-01-28	Build site.
Rmd	c45c0b6	John D. Hurley	2026-01-28	Erorr in workflowr publish
Rmd	4174688	John D. Hurley	2026-01-28	Website updates
Rmd	e801b17	John D. Hurley	2026-01-28	Duplicate Code block titles
Rmd	92e63cc	John D. Hurley	2026-01-28	DGE for website fixes
Rmd	6c3cce3	John D. Hurley	2026-01-28	Cormotif Species
Rmd	085c1db	John D. Hurley	2026-01-28	Finalizing CorHeatMap

---

generate_volcano_plot <- function(toptable, title) {
  
  # #check for entrezid 
  # if(!"Entrez_ID" %in% colnames(toptable)) stop("Entrez_ID col not present")
  # 
  #make significance labels
  
  toptable <- toptable %>% 
    mutate(Significance = case_when(
      logFC > 0 & adj.P.Val < 0.05 ~ "Upregulated",
      logFC < 0 & adj.P.Val < 0.05 ~ "Downregulated",
      TRUE  ~ "Not Significant"
    ))
  
  #factor significance
  toptable$Significance <- factor(
    toptable$Significance, 
    levels = c("Upregulated", 
               "Not Significant", 
               "Downregulated")
  )
  
  #count genes in each category
  upgenes <- sum(toptable$Significance == "Upregulated")
  nsgenes <- sum(toptable$Significance == "Not Significant")
  downgenes <- sum(toptable$Significance == "Downregulated")
  
  
  #labels for legend
  legend_lab <- c(
    paste0("Upregulated: ", upgenes),
    paste0("Not Significant: ", nsgenes),
    paste0("Downregulated: ", downgenes)
  )
  
  #colors 
  color_map <- c("Upregulated" = "blue",
                 "Not Significant" = "grey30",
                 "Downregulated" = "red")
  
  #generate volcano plots
  p <- ggplot(toptable, aes(x = logFC, 
                            y = -log10(P.Value),
                            color = Significance)) +
    geom_point_rast(alpha = 0.8, size = 3) +
    scale_color_manual(values = color_map, 
                       labels = legend_lab,
                       breaks = c("Upregulated",
                                  "Not Significant", 
                                  "Downregulated"))  +
    xlim(-20,20) +
    labs(title = title, 
         x = expression("log"[2]*"FC"),
         y = expression("-log"[10]*"p-value")) +
    # theme_custom() +
    theme(legend.position = "right")
  
  return(p)
}

plot_cluster_fc <- function(gene_vector, cluster_name, top_table) {
  
  # Filter TopTable for the genes in this cluster
  df <- top_table %>% dplyr::filter(Gene %in% gene_vector) %>%
    mutate(Comparisons = factor(Comparisons, levels = c(
      "HC_D0","HC_D2","HC_D5","HC_D15","HC_D30","All_Genes","All_DEGs"
    )))
  
  # Optional: print dimensions and unique genes
  cat(cluster_name, ":", dim(df)[1], "rows,", length(unique(df$Gene)), "unique genes\n")
  
  # Create boxplot
  p <- ggplot(df, aes(x = Comparisons, y = logFC)) +
    geom_boxplot(aes(fill = Comparisons)) +
    guides(fill = guide_legend(title = "Comparisons")) +
    theme_bw() +
    xlab(" ") +
    ylab("logFC") +
    ggtitle(paste0(cluster_name, " RNA FC Comparisons")) +
    theme(
      plot.title = element_text(size = rel(1.5), hjust = 0.5),
      axis.title = element_text(size = 15, colour = "black"),
      strip.background = element_rect(fill = "#CAD899"),
      axis.text.x = element_text(size = 8, colour = "white", angle = 15),
      strip.text.x = element_text(size = 12, colour = "black", face = "bold")
    )
  
  return(p)
}

plot_cluster_fc_sig <- function(gene_vector, cluster_name, top_table) {

  df <- top_table %>%
    dplyr::filter(Gene %in% gene_vector) %>%
    mutate(Comparisons = factor(Comparisons, levels = c(
      "HC_D0","HC_D2","HC_D5","HC_D15","HC_D30"
    )))

  cat(cluster_name, ":", nrow(df), "rows,",
      length(unique(df$Gene)), "unique genes\n")

  # Global test across comparisons
  kw <- kruskal.test(logFC ~ Comparisons, data = df)
  
  pw_kw <-pairwise.wilcox.test(
    df$logFC,
    df$Comparisons,
    p.adjust.method = "BH")
  
  print(pw_kw)

  p <- ggplot(df, aes(x = Comparisons, y = logFC)) +
    geom_boxplot(aes(fill = Comparisons)) +
    theme_bw() +
    xlab(" ") +
    ylab("logFC") +
    ggtitle(
      paste0(
        cluster_name,
        " RNA FC Comparisons\nKruskal–Wallis p = ",
        signif(kw$p.value, 3)
      )
    ) +
    theme(
      plot.title = element_text(size = rel(1.3), hjust = 0.5),
      axis.title = element_text(size = 15),
      axis.text.x = element_text(size = 8, angle = 15)
    )

  return(p)


}

plot_cluster_absfc <- function(gene_vector, cluster_name, top_table) {
  
  # Filter TopTable for the genes in this cluster
  df <- top_table %>% dplyr::filter(Gene %in% gene_vector) %>%
    mutate(Comparisons = factor(Comparisons, levels = c(
      "HC_D0","HC_D2","HC_D5","HC_D15","HC_D30"
    )))
  
  # Optional: print dimensions and unique genes
  cat(cluster_name, ":", dim(df)[1], "rows,", length(unique(df$Gene)), "unique genes\n")
  
  # Create boxplot
  p <- ggplot(df, aes(x = Comparisons, y = AbsFC)) +
    geom_boxplot(aes(fill = Comparisons)) +
    guides(fill = guide_legend(title = "Comparisons")) +
    theme_bw() +
    xlab(" ") +
    ylab("AbsFC") +
    ggtitle(paste0(cluster_name, " RNA AbsFC Comparisons")) +
    theme(
      plot.title = element_text(size = rel(1.5), hjust = 0.5),
      axis.title = element_text(size = 15, colour = "black"),
      strip.background = element_rect(fill = "#CAD899"),
      axis.text.x = element_text(size = 8, colour = "white", angle = 15),
      strip.text.x = element_text(size = 12, colour = "black", face = "bold")
    )
  
  return(p)
}

plot_cluster_absfc_sig <- function(gene_vector, cluster_name, top_table) {

  df <- top_table %>%
    dplyr::filter(Gene %in% gene_vector) %>%
    mutate(Comparisons = factor(Comparisons, levels = c(
      "HC_D0","HC_D2","HC_D5","HC_D15","HC_D30"
    )))

  cat(cluster_name, ":", nrow(df), "rows,",
      length(unique(df$Gene)), "unique genes\n")
  
  # Global test across comparisons
  kw <- kruskal.test(AbsFC ~ Comparisons, data = df)
  
  pw_kw <-pairwise.wilcox.test(
    df$AbsFC,
    df$Comparisons,
    p.adjust.method = "BH")
  
  print(pw_kw)

  p <- ggplot(df, aes(x = Comparisons, y = AbsFC)) +
    geom_boxplot(aes(fill = Comparisons)) +
    theme_bw() +
    xlab(" ") +
    ylab("AbslogFC") +
    ggtitle(
      paste0(
        cluster_name,
        " RNA AbsFC Comparisons\nKruskal–Wallis p = ",
        signif(kw$p.value, 3)
      )
    ) +
    theme(
      plot.title = element_text(size = rel(1.3), hjust = 0.5),
      axis.title = element_text(size = 15),
      axis.text.x = element_text(size = 8, angle = 15)
    )

  return(p)


}

Filt_RMG0_RNA_fc_NoD4_NoRep <- Filt_RMG0_RNA_fc_NoD4 %>% 
  dplyr::select(-(contains("R")))

RNA_Metadata_NoD4_NoRep <- RNA_Metadata %>% 
  dplyr::filter(Timepoint != "Day4") %>% 
  dplyr::slice(-43:-46)
rownames(RNA_Metadata_NoD4_NoRep) <- colnames(Filt_RMG0_RNA_fc_NoD4_NoRep)
# saveRDS(RNA_Metadata_NoD4_NoRep,"data/DGE/Species/RNA_Metadata_NoD4_NoRep.RDS")

RNA_Metadata_NoD4_NoRep$dgelist <- c(rep(c("Human_Day0","Human_Day2","Human_Day5","Human_Day15","Human_Day30"),7),
                                     rep(c("Chimp_Day0","Chimp_Day2","Chimp_Day5","Chimp_Day15","Chimp_Day30"),7))
#create DGEList object
dge <- DGEList(counts = Filt_RMG0_RNA_fc_NoD4_NoRep)
dge$samples$group <- factor(RNA_Metadata_NoD4_NoRep$dgelist)
dge <- calcNormFactors(dge, method = "TMM")
dge$samples

                 group lib.size norm.factors
H28126_D0   Human_Day0 25932966    1.0496515
H28126_D2   Human_Day2 16307187    1.0711273
H28126_D5   Human_Day5 12085267    1.0445905
H28126_D15 Human_Day15 14451707    0.9894752
H28126_D30 Human_Day30 13411160    0.8533304
H17_D0      Human_Day0 12100627    0.9998587
H17_D2      Human_Day2  7735466    1.1195784
H17_D5      Human_Day5 13218704    1.0490514
H17_D15    Human_Day15 23020627    1.0219939
H17_D30    Human_Day30 14484164    0.8028698
H78_D0      Human_Day0 17271130    1.1057177
H78_D2      Human_Day2 17996336    1.0618670
H78_D5      Human_Day5 11595332    1.0645316
H78_D15    Human_Day15 10335012    0.9912379
H78_D30    Human_Day30 12500044    0.7905653
H20682_D0   Human_Day0 12098542    1.0515114
H20682_D2   Human_Day2  8796211    1.0642338
H20682_D5   Human_Day5 14503271    1.0527575
H20682_D15 Human_Day15 15791894    0.9758863
H20682_D30 Human_Day30 13461509    0.8528200
H22422_D0   Human_Day0 20440576    0.9944732
H22422_D2   Human_Day2 14838910    1.0566006
H22422_D5   Human_Day5 10907743    1.0225115
H22422_D15 Human_Day15 15168583    0.9582386
H22422_D30 Human_Day30 15804750    0.8691100
H21792_D0   Human_Day0 10664025    1.0943173
H21792_D2   Human_Day2 13902708    1.1051554
H21792_D5   Human_Day5 27139690    1.0715092
H21792_D15 Human_Day15 13059206    0.9711326
H21792_D30 Human_Day30 11476245    0.8685525
H24280_D0   Human_Day0 13110354    0.9724776
H24280_D2   Human_Day2 13373790    1.0723900
H24280_D5   Human_Day5 10998176    0.9793593
H24280_D15 Human_Day15 11398361    1.0029455
H24280_D30 Human_Day30  9240969    0.8196602
C3649_D0    Chimp_Day0 12362301    1.0885058
C3649_D2    Chimp_Day2 15320271    1.0656161
C3649_D5    Chimp_Day5 15077601    1.1092666
C3649_D15  Chimp_Day15 12193785    1.1458739
C3649_D30  Chimp_Day30 15078429    0.8009392
C4955_D0    Chimp_Day0 10296889    1.1308602
C4955_D2    Chimp_Day2 17054948    1.0633736
C4955_D5    Chimp_Day5 10840478    1.0796001
C4955_D15  Chimp_Day15 18693180    1.0028659
C4955_D30  Chimp_Day30 13238998    0.7818647
C3651_D0    Chimp_Day0 15922479    1.0501976
C3651_D2    Chimp_Day2 12300292    1.1312951
C3651_D5    Chimp_Day5 20234297    1.0499510
C3651_D15  Chimp_Day15  8876621    0.9274931
C3651_D30  Chimp_Day30 13436497    0.7268620
C40210_D0   Chimp_Day0 14352669    1.0386225
C40210_D2   Chimp_Day2  9014514    1.1470563
C40210_D5   Chimp_Day5  9386102    1.0700081
C40210_D15 Chimp_Day15  8160685    0.9414793
C40210_D30 Chimp_Day30 15173940    0.8257046
C8861_D0    Chimp_Day0  7071000    1.1748857
C8861_D2    Chimp_Day2 12368759    1.1332137
C8861_D5    Chimp_Day5 13171748    1.1242551
C8861_D15  Chimp_Day15 14882942    1.1183146
C8861_D30  Chimp_Day30  9269576    0.7552249
C40280_D0   Chimp_Day0 17712680    1.0615566
C40280_D2   Chimp_Day2  9954155    1.1163790
C40280_D5   Chimp_Day5  8982698    1.0982065
C40280_D15 Chimp_Day15  9515327    1.0407279
C40280_D30 Chimp_Day30 13459189    0.8195138
C3647_D0    Chimp_Day0 11139654    1.1253799
C3647_D2    Chimp_Day2 12750800    1.1019634
C3647_D5    Chimp_Day5 20390838    1.0649082
C3647_D15  Chimp_Day15 16054523    0.9676347
C3647_D30  Chimp_Day30 13897133    0.7387200

#dim(dge)
# saveRDS(dge, "data/DGE/Species/RNA_dge_matrix.RDS")
#dge$samples

design1 <- model.matrix(~ 0 + RNA_Metadata_NoD4_NoRep$dgelist)
colnames(design1) <- gsub("RNA_Metadata_NoD4_NoRep\\$dgelist", "", colnames(design1))
View(design1)

corfit <- duplicateCorrelation(object = dge$counts, design = design1, block = RNA_Metadata_NoD4_NoRep$Individual)
v <- voom(dge, design1, block = RNA_Metadata_NoD4_NoRep$Individual, correlation = corfit$consensus.correlation, plot = TRUE)

Past versions of RNASeq_DGE_Species_Data_Model-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

fit <- lmFit(v, design1, block = RNA_Metadata_NoD4_NoRep$Individual, correlation = corfit$consensus.correlation)

contrast_matrix <- makeContrasts(
  HC_D0 = Chimp_Day0 - Human_Day0,
  HC_D2 = Chimp_Day2 - Human_Day2,
  HC_D5 = Chimp_Day5 - Human_Day5,
  HC_D15 = Chimp_Day15 - Human_Day15,
  HC_D30 = Chimp_Day30 - Human_Day30,
  levels = design1)

fit2 <- contrasts.fit(fit,contrast_matrix)
fit2 <- eBayes(fit2)

plotSA(fit2, main = "RNA Model")

Past versions of RNASeq_DGE_Species_Data_Model-2.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

results_summary <- decideTests(fit2,
                               adjust.method = "BH",
                               p.value = 0.05)
summary(results_summary)

       HC_D0 HC_D2 HC_D5 HC_D15 HC_D30
Down    3371  1944  2070   2578   3127
NotSig  8269 10744 10684   9768   8848
Up      3198  2150  2084   2492   2863

# saveRDS(fit2,"data/DGE/Species/fit2.RDS")

#Create Human TopTable. using listapply, then you can pull specific comp using the $
TopTables_RNA_names <- colnames(fit2$coefficients) 

# colnames(fit2$coefficients)

TopTable_RNA_All <- do.call(
  rbind,
  lapply(TopTables_RNA_names,function(ct) {
    
    tt <- topTable(
      fit2,
      coef = ct,
      number = Inf,
      sort.by = "p"
    )
    
    #add identifiers
    tt$Gene <- rownames(tt)
    tt$Comparisons <- ct
    
    tt
  })
)

# TopTable_RNA_All$Comparisons
# saveRDS(TopTable_RNA_All,"data/DGE/Species/TopTable_RNA_all.RDS")
head(TopTable_RNA_All)

                    logFC    AveExpr        t      P.Value    adj.P.Val
ENSG00000174231  6.604341 5.99612713 52.85392 9.143730e-55 1.356747e-50
ENSG00000184983 11.237428 0.75630938 44.99139 2.230381e-50 1.654720e-46
ENSG00000272540  9.859124 0.05875218 39.97301 3.493933e-47 1.728099e-43
ENSG00000177370  9.890876 0.11451324 39.47648 7.575533e-47 2.252354e-43
ENSG00000231074 10.811687 0.89083894 39.47528 7.589817e-47 2.252354e-43
ENSG00000108953  5.382156 7.03619706 38.02789 7.632500e-46 1.887517e-42
                        B            Gene Comparisons
ENSG00000174231 112.74454 ENSG00000174231       HC_D0
ENSG00000184983  99.69885 ENSG00000184983       HC_D0
ENSG00000272540  93.52547 ENSG00000272540       HC_D0
ENSG00000177370  92.84590 ENSG00000177370       HC_D0
ENSG00000231074  92.94400 ENSG00000231074       HC_D0
ENSG00000108953  94.02074 ENSG00000108953       HC_D0

Comparisons_names <- unique(TopTable_RNA_All$Comparisons)
# saveRDS(Comparisons_names,"data/DGE/Species/Comparisons_names_Trajectory.RDS")
plot <- list()
for (n in Comparisons_names) {

plot[[n]] <- TopTable_RNA_All %>% 
  dplyr::filter(Comparisons == paste0(n)) %>% 
  generate_volcano_plot(.,n)
}

for (n in Comparisons_names) {
  print(plot[[n]])
        }

Past versions of RNASeq_DGE_Species_TopTable_VolcancoPlots-1.png

Version	Author	Date
b0ab413	John D. Hurley	2026-01-28

Past versions of RNASeq_DGE_Species_TopTable_VolcancoPlots-2.png

Version	Author	Date
b0ab413	John D. Hurley	2026-01-28

Past versions of RNASeq_DGE_Species_TopTable_VolcancoPlots-3.png

Version	Author	Date
b0ab413	John D. Hurley	2026-01-28

Past versions of RNASeq_DGE_Species_TopTable_VolcancoPlots-4.png

Version	Author	Date
b0ab413	John D. Hurley	2026-01-28

Past versions of RNASeq_DGE_Species_TopTable_VolcancoPlots-5.png

Version	Author	Date
b0ab413	John D. Hurley	2026-01-28

days <- c("D0", "D2", "D5", "D15", "D30")

RNA_sets <- map(
  days,
  ~ {
    df <- TopTable_RNA_All %>%
      filter(Comparisons == paste0("HC_", .x))

    list(
      DEGs = df %>% filter(adj.P.Val < 0.05),
      NonDEGs = df %>% filter(adj.P.Val >= 0.05)
    )
  }
)

names(RNA_sets) <- paste0("Day", sub("D", "", days), "_HC")

# saveRDS(RNA_sets,"data/DGE/Species/RNA_DEG_Non_DEG_sets_05.RDS")

all_genes <- unique(TopTable_RNA_All$Gene)
  
DEG_Lists <- list(
  Day0_DEGs_HC = RNA_sets$Day0_HC$DEGs,       # TopTable data.frame
  Day2_DEGs_HC = RNA_sets$Day2_HC$DEGs,
  Day5_DEGs_HC = RNA_sets$Day5_HC$DEGs,
  Day15_DEGs_HC = RNA_sets$Day15_HC$DEGs,
  Day30_DEGs_HC = RNA_sets$Day30_HC$DEGs
)

fit_list <- list()

for (name in names(DEG_Lists)) {

  # DEG gene vector
  deg_vector <- unique(DEG_Lists[[name]]$Gene)

  # Euler with containment
  fit_list[[name]] <- euler(list(
    AllGenes = all_genes,
    DEGs = deg_vector
  ))

  # Plot
  print(
    plot(
      fit_list[[name]],
      fills = list(
        fill = c("grey85", "firebrick"),
        alpha = 0.6
      ),
      labels = c("NonDEGs", "DEGs"),
      quantities = TRUE,
      main = paste0(name, " (p < 0.05)")
    )
  )
}

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.05_Eulers-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.05_Eulers-2.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.05_Eulers-3.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.05_Eulers-4.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.05_Eulers-5.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

####proportion bar plots####
#first factor the timepoints so that they are in the right order
df <- data.frame(
  Day = c("Day0", "Day2", "Day5", "Day15", "Day30"),
  DEGs = c(6569, 4094, 4145, 5070, 5990),
  Non_DEGs = c(8269, 10744, 10684, 9768, 8848)
)

# Convert to long format for ggplot
df_long <- df %>%
  pivot_longer(cols = c(DEGs, Non_DEGs),
               names_to = "Type",
               values_to = "Count") %>%
  group_by(Day) %>%
  mutate(Proportion = Count / sum(Count))  # calculate proportions

df_long$Day <- factor(df_long$Day,levels = c("Day0", "Day2", "Day5", "Day15", "Day30"),ordered = TRUE)

# df_long

ggplot(df_long, aes(x = Day, y = Proportion, fill = Type)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent_format()) +  # show % on y-axis
  scale_fill_manual(values = c("DEGs" = "#E41A1C", "Non_DEGs" = "#377EB8")) + # custom colors
  labs(y = "Proportion", x = "", fill = "",title = "Proportion of Species DEGs p < 0.05") +
  theme_minimal(base_size = 14)

Past versions of RNASeq_DGE_Species_TopTable_Subsetting0.5_barplot-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

days <- c("D0", "D2", "D5", "D15", "D30")

RNA_sets_0.1 <- map(
  days,
  ~ {
    df <- TopTable_RNA_All %>%
      filter(Comparisons == paste0("HC_", .x))

    list(
      DEGs = df %>% filter(adj.P.Val < 0.1),
      NonDEGs = df %>% filter(adj.P.Val >= 0.1)
    )
  }
)

names(RNA_sets_0.1) <- paste0("Day", sub("D", "", days), "_HC")

# saveRDS(RNA_sets_0.1,"data/DGE/Species/RNA_DEG_Non_DEG_sets_05.RDS")

all_genes <- unique(TopTable_RNA_All$Gene)
  
DEG_Lists <- list(
  Day0_DEGs_HC = RNA_sets_0.1$Day0_HC$DEGs,       # TopTable data.frame
  Day2_DEGs_HC = RNA_sets_0.1$Day2_HC$DEGs,
  Day5_DEGs_HC = RNA_sets_0.1$Day5_HC$DEGs,
  Day15_DEGs_HC = RNA_sets_0.1$Day15_HC$DEGs,
  Day30_DEGs_HC = RNA_sets_0.1$Day30_HC$DEGs
)

fit_list <- list()

for (name in names(DEG_Lists)) {

  # DEG gene vector
  deg_vector <- unique(DEG_Lists[[name]]$Gene)

  # Euler with containment
  fit_list[[name]] <- euler(list(
    AllGenes = all_genes,
    DEGs = deg_vector
  ))

  # Plot
  print(
    plot(
      fit_list[[name]],
      fills = list(
        fill = c("grey85", "firebrick"),
        alpha = 0.6
      ),
      labels = c("NonDEGs", "DEGs"),
      quantities = TRUE,
      main = paste0(name, " (p < 0.1)")
    )
  )
}

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.1_Eulers-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.1_Eulers-2.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.1_Eulers-3.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.1_Eulers-4.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_DEGs0.1_Eulers-5.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

####proportion bar plots####
#first factor the timepoints so that they are in the right order
df <- data.frame(
  Day = c("Day0", "Day2", "Day5", "Day15", "Day30"),
  DEGs = c(7539, 5012, 5216, 6103, 7040),
  Non_DEGs = c(7299, 9826, 9622, 8735, 7798)
)

# Convert to long format for ggplot
df_long <- df %>%
  pivot_longer(cols = c(DEGs, Non_DEGs),
               names_to = "Type",
               values_to = "Count") %>%
  group_by(Day) %>%
  mutate(Proportion = Count / sum(Count))  # calculate proportions

df_long$Day <- factor(df_long$Day,levels = c("Day0", "Day2", "Day5", "Day15", "Day30"),ordered = TRUE)

# df_long

ggplot(df_long, aes(x = Day, y = Proportion, fill = Type)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent_format()) +  # show % on y-axis
  scale_fill_manual(values = c("DEGs" = "#E41A1C", "Non_DEGs" = "#377EB8")) + # custom colors
  labs(y = "Proportion", x = "", fill = "", title = "Proportion of Species DEGs p < 0.1") +
  theme_minimal(base_size = 14)

Past versions of RNASeq_DGE_Species_TopTable_Subsetting0.1_barplot-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Gene_list <- list(
  All_genes  = all_genes,
  DEGs = deg_genes
)

cluster_plots <- lapply(names(Gene_list), function(clust_name) {
  plot_cluster_fc_sig(Gene_list[[clust_name]], clust_name, TopTable_RNA_All)
})

All_genes : 74190 rows, 14838 unique genes

    Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  df$logFC and df$Comparisons 

       HC_D0   HC_D2   HC_D5   HC_D15 
HC_D2  < 2e-16 -       -       -      
HC_D5  2.8e-10 0.00025 -       -      
HC_D15 3.7e-13 2.1e-05 0.61867 -      
HC_D30 2.7e-08 1.0e-08 0.06482 0.03293

P value adjustment method: BH 
DEGs : 58240 rows, 11648 unique genes

    Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  df$logFC and df$Comparisons 

       HC_D0   HC_D2   HC_D5   HC_D15 
HC_D2  < 2e-16 -       -       -      
HC_D5  1.2e-09 0.00561 -       -      
HC_D15 < 2e-16 0.06704 0.26136 -      
HC_D30 2.6e-11 0.00063 0.73944 0.03392

P value adjustment method: BH 

names(cluster_plots) <- names(Gene_list)

for (p in cluster_plots) print(p)

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_FC-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_FC-2.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

for (comp in Comparisons_names) {

  subset_df <- TopTable_RNA_All %>%
    dplyr::filter(Comparisons == comp)

  cat("\n", comp, "Average logFC:\n")
  cat(mean(subset_df$logFC, na.rm = TRUE), "\n")
}

 HC_D0 Average logFC:
-0.04176301 

 HC_D2 Average logFC:
0.1291112 

 HC_D5 Average logFC:
0.06418101 

 HC_D15 Average logFC:
0.1353744 

 HC_D30 Average logFC:
0.10031 

all_genes <- readRDS("data/DGE/Species/ExpressedGenes.RDS")
deg_genes <- readRDS("data/DGE/Species/DEG_AtLeastOne.RDS")

TopTable_RNA_All$AbsFC <- abs(TopTable_RNA_All$logFC)

Gene_list <- list(
  All_genes  = all_genes,
  DEGs = deg_genes
)

cluster_plots <- lapply(names(Gene_list), function(clust_name) {
  plot_cluster_absfc_sig(Gene_list[[clust_name]], clust_name, TopTable_RNA_All)
})

All_genes : 74190 rows, 14838 unique genes

    Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  df$AbsFC and df$Comparisons 

       HC_D0   HC_D2   HC_D5   HC_D15 
HC_D2  < 2e-16 -       -       -      
HC_D5  < 2e-16 0.46    -       -      
HC_D15 < 2e-16 3.9e-15 < 2e-16 -      
HC_D30 0.18    < 2e-16 < 2e-16 < 2e-16

P value adjustment method: BH 
DEGs : 58240 rows, 11648 unique genes

    Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  df$AbsFC and df$Comparisons 

       HC_D0  HC_D2  HC_D5  HC_D15
HC_D2  <2e-16 -      -      -     
HC_D5  <2e-16 0.23   -      -     
HC_D15 <2e-16 <2e-16 <2e-16 -     
HC_D30 0.22   <2e-16 <2e-16 <2e-16

P value adjustment method: BH 

names(cluster_plots) <- names(Gene_list)

for (p in cluster_plots) print(p)

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_AbsFC-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

Past versions of RNASeq_DGE_Species_TopTable_Subsetting_AbsFC-2.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

for (comp in Comparisons_names) {

  subset_df <- TopTable_RNA_All %>%
    dplyr::filter(Comparisons == comp)

  cat("\n", comp, "Average AbsFC:\n")
  cat(mean(subset_df$AbsFC, na.rm = TRUE), "\n")
}

 HC_D0 Average AbsFC:
0.7933367 

 HC_D2 Average AbsFC:
0.6770691 

 HC_D5 Average AbsFC:
0.665505 

 HC_D15 Average AbsFC:
0.7349379 

 HC_D30 Average AbsFC:
0.8157532 

# Your DEGs per day (already defined)

sets <- list(
  Day0_DEGs_HC = RNA_sets$Day0_HC$DEGs$Gene,       # TopTable data.frame
  Day2_DEGs_HC = RNA_sets$Day2_HC$DEGs$Gene,
  Day5_DEGs_HC = RNA_sets$Day5_HC$DEGs$Gene,
  Day15_DEGs_HC = RNA_sets$Day15_HC$DEGs$Gene,
  Day30_DEGs_HC = RNA_sets$Day30_HC$DEGs$Gene
)

# Create a 5-way Venn diagram
p <- ggVennDiagram(sets,
                   label_alpha = 0,       # makes background of labels transparent
                   label = "count") +     # show counts in intersections
  scale_fill_gradient(low = "skyblue", high = "navy") + # optional color gradient
  theme(legend.position = "none") +                     # hide legend
  ggtitle("5-way DEGs")

# Plot
print(p)

Past versions of RNASeq_DGE_Species_TopTable_Venn_ggplot_plot-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18
b0ab413	John D. Hurley	2026-01-28

# Helper function to get intersections
get_intersections <- function(sets_list) {
  set_names <- names(sets_list)
  n <- length(sets_list)
  result <- list()
  
  # Loop over all combination lengths (1 to n)
  for (k in 1:n) {
    combos <- combn(set_names, k, simplify = FALSE)
    for (combo in combos) {
      # Start with first set in combo
      genes <- sets_list[[combo[1]]]
      # Intersect with the rest
      if (length(combo) > 1) {
        for (s in combo[-1]) {
          genes <- intersect(genes, sets_list[[s]])
        }
      }
      # Remove genes that appear in other sets outside the combo (exclusive)
      other_sets <- setdiff(set_names, combo)
      if (length(other_sets) > 0) {
        for (s in other_sets) {
          genes <- setdiff(genes, sets_list[[s]])
        }
      }
      # Save if any genes remain
      if (length(genes) > 0) {
        result[[paste(combo, collapse = "&")]] <- genes
      }
    }
  }
  return(result)
}

# Compute all intersections
all_intersections <- get_intersections(sets)
# saveRDS(all_intersections,"data/DGE/Species/all_intersections.RDS")

# # Example: genes only in Day0
# all_intersections$Day0

# # Example: genes only in Day0 & Day2
# all_intersections$`Day0&Day2`

H_comp <- "HC_D0"
C_comp <- "HC_D2"

df_wide <- TopTable_RNA_All %>%
  filter(Comparisons %in% c(H_comp, C_comp)) %>%
  dplyr::select(Gene, Comparisons, logFC) %>%
  group_by(Gene, Comparisons) %>%
  summarize(logFC = mean(logFC), .groups = "drop") %>%
  pivot_wider(names_from = Comparisons, values_from = logFC) %>%
  filter(!is.na(.data[[H_comp]]), !is.na(.data[[C_comp]]))

# head(df_wide)
# nrow(df_wide)

fit <- lm(df_wide[[C_comp]] ~ df_wide[[H_comp]])
summary(fit)

Call:
lm(formula = df_wide[[C_comp]] ~ df_wide[[H_comp]])

Residuals:
    Min      1Q  Median      3Q     Max 
-6.2637 -0.4703 -0.1342  0.3374  6.5623 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       0.160214   0.006291   25.47   <2e-16 ***
df_wide[[H_comp]] 0.744745   0.004702  158.41   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7659 on 14836 degrees of freedom
Multiple R-squared:  0.6284,    Adjusted R-squared:  0.6284 
F-statistic: 2.509e+04 on 1 and 14836 DF,  p-value: < 2.2e-16

r2 <- summary(fit)$r.squared
pval <- summary(fit)$coefficients[2, "Pr(>|t|)"]

# r2
# pval

# format(pval, scientific = TRUE, digits =3)

x_pos <- max(df_wide[[H_comp]]) * 0.95      # right side
x_neg <- min(df_wide[[H_comp]]) * 0.5      # left side

y_pos <- max(df_wide[[C_comp]]) * 0.95

ggplot(df_wide, aes(x = .data[[H_comp]], y = .data[[C_comp]])) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  annotate(
    "text",
    x = x_neg,
    y = y_pos,
    label = paste0(
      "R² = ", round(r2, 3),
      "\np = ", format(pval, scientific = TRUE, digits = 3)
    ),
    hjust = 1,
    vjust = 1
  ) +
  theme_bw() +
  labs(
    x = H_comp,
    y = C_comp,
    title = paste0(H_comp, " vs ", C_comp)
  )

Past versions of RNASeq_DGE_Clusters_FC_Quadrants_0_2-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

H_comp <- "HC_D2"
C_comp <- "HC_D5"

df_wide <- TopTable_RNA_All %>%
  filter(Comparisons %in% c(H_comp, C_comp)) %>%
  dplyr::select(Gene, Comparisons, logFC) %>%
  group_by(Gene, Comparisons) %>%
  summarize(logFC = mean(logFC), .groups = "drop") %>%
  pivot_wider(names_from = Comparisons, values_from = logFC) %>%
  filter(!is.na(.data[[H_comp]]), !is.na(.data[[C_comp]]))

# head(df_wide)
# nrow(df_wide)

fit <- lm(df_wide[[C_comp]] ~ df_wide[[H_comp]])
summary(fit)

Call:
lm(formula = df_wide[[C_comp]] ~ df_wide[[H_comp]])

Residuals:
    Min      1Q  Median      3Q     Max 
-6.0578 -0.2940  0.0238  0.3482  8.1083 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.040325   0.005812  -6.938 4.14e-12 ***
df_wide[[H_comp]]  0.809428   0.004602 175.891  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7043 on 14836 degrees of freedom
Multiple R-squared:  0.6759,    Adjusted R-squared:  0.6759 
F-statistic: 3.094e+04 on 1 and 14836 DF,  p-value: < 2.2e-16

r2 <- summary(fit)$r.squared
pval <- summary(fit)$coefficients[2, "Pr(>|t|)"]

# r2
# pval

# format(pval, scientific = TRUE, digits = 3)

library(ggplot2)

x_pos <- max(df_wide[[H_comp]]) * 0.95      # right side
x_neg <- min(df_wide[[H_comp]]) * 0.5      # left side

y_pos <- max(df_wide[[C_comp]]) * 0.95

ggplot(df_wide, aes(x = .data[[H_comp]], y = .data[[C_comp]])) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  annotate(
    "text",
    x = x_neg,
    y = y_pos,
    label = paste0(
      "R² = ", round(r2, 3),
      "\np = ", format(pval, scientific = TRUE, digits = 3)
    ),
    hjust = 1,
    vjust = 1
  ) +
  theme_bw() +
  labs(
    x = H_comp,
    y = C_comp,
    title = paste0(H_comp, " vs ", C_comp)
  )

Past versions of RNASeq_DGE_Clusters_FC_Quadrants_2_5-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

H_comp <- "HC_D5"
C_comp <- "HC_D15"

df_wide <- TopTable_RNA_All %>%
  filter(Comparisons %in% c(H_comp, C_comp)) %>%
  dplyr::select(Gene, Comparisons, logFC) %>%
  group_by(Gene, Comparisons) %>%
  summarize(logFC = mean(logFC), .groups = "drop") %>%
  pivot_wider(names_from = Comparisons, values_from = logFC) %>%
  filter(!is.na(.data[[H_comp]]), !is.na(.data[[C_comp]]))

# head(df_wide)
# nrow(df_wide)

fit <- lm(df_wide[[C_comp]] ~ df_wide[[H_comp]])
summary(fit)

Call:
lm(formula = df_wide[[C_comp]] ~ df_wide[[H_comp]])

Residuals:
    Min      1Q  Median      3Q     Max 
-5.7407 -0.4346 -0.0991  0.2863  7.6044 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       0.079014   0.006138   12.87   <2e-16 ***
df_wide[[H_comp]] 0.878150   0.004956  177.20   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7467 on 14836 degrees of freedom
Multiple R-squared:  0.6791,    Adjusted R-squared:  0.6791 
F-statistic: 3.14e+04 on 1 and 14836 DF,  p-value: < 2.2e-16

r2 <- summary(fit)$r.squared
pval <- summary(fit)$coefficients[2, "Pr(>|t|)"]

# r2
# pval

# format(pval, scientific = TRUE, digits = 3)

x_pos <- max(df_wide[[H_comp]]) * 0.95      # right side
x_neg <- min(df_wide[[H_comp]]) * 0.5      # left side

y_pos <- max(df_wide[[C_comp]]) * 0.95

ggplot(df_wide, aes(x = .data[[H_comp]], y = .data[[C_comp]])) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  annotate(
    "text",
    x = x_neg,
    y = y_pos,
    label = paste0(
      "R² = ", round(r2, 3),
      "\np = ", format(pval, scientific = TRUE, digits = 3)
    ),
    hjust = 1,
    vjust = 1
  ) +
  theme_bw() +
  labs(
    x = H_comp,
    y = C_comp,
    title = paste0(H_comp, " vs ", C_comp)
  )

Past versions of RNASeq_DGE_Clusters_FC_Quadrants_5_15-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

H_comp <- "HC_D15"
C_comp <- "HC_D30"

df_wide <- TopTable_RNA_All %>%
  filter(Comparisons %in% c(H_comp, C_comp)) %>%
  dplyr::select(Gene, Comparisons, logFC) %>%
  group_by(Gene, Comparisons) %>%
  summarize(logFC = mean(logFC), .groups = "drop") %>%
  pivot_wider(names_from = Comparisons, values_from = logFC) %>%
  filter(!is.na(.data[[H_comp]]), !is.na(.data[[C_comp]]))

# head(df_wide)
# nrow(df_wide)

fit <- lm(df_wide[[C_comp]] ~ df_wide[[H_comp]])
summary(fit)

Call:
lm(formula = df_wide[[C_comp]] ~ df_wide[[H_comp]])

Residuals:
    Min      1Q  Median      3Q     Max 
-8.5173 -0.3344 -0.0048  0.3363  6.0096 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.014621   0.006633  -2.204   0.0275 *  
df_wide[[H_comp]]  0.848989   0.005006 169.611   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.8037 on 14836 degrees of freedom
Multiple R-squared:  0.6598,    Adjusted R-squared:  0.6597 
F-statistic: 2.877e+04 on 1 and 14836 DF,  p-value: < 2.2e-16

r2 <- summary(fit)$r.squared
pval <- summary(fit)$coefficients[2, "Pr(>|t|)"]

# r2
# pval

# format(pval, scientific = TRUE, digits = 3)

x_pos <- max(df_wide[[H_comp]]) * 0.95      # right side
x_neg <- min(df_wide[[H_comp]]) * 0.5      # left side

y_pos <- max(df_wide[[C_comp]]) * 0.95

ggplot(df_wide, aes(x = .data[[H_comp]], y = .data[[C_comp]])) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  annotate(
    "text",
    x = x_neg,
    y = y_pos,
    label = paste0(
      "R² = ", round(r2, 3),
      "\np = ", format(pval, scientific = TRUE, digits = 3)
    ),
    hjust = 1,
    vjust = 1
  ) +
  theme_bw() +
  labs(
    x = H_comp,
    y = C_comp,
    title = paste0(H_comp, " vs ", C_comp)
  )

Past versions of RNASeq_DGE_Clusters_FC_Quadrants_15_30-1.png

Version	Author	Date
0cb123a	John D. Hurley	2026-02-18

# git -> commit all changes
# git -> push
# wflow_publish("analysis/RNA_DGE_Comp_Species_Ensemble.Rmd")

Session information

sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggsignif_0.6.4       edgeR_4.6.3          limma_3.64.3        
 [4] ggrastr_1.0.2        ggVennDiagram_1.5.7  org.Hs.eg.db_3.21.0 
 [7] AnnotationDbi_1.70.0 IRanges_2.42.0       S4Vectors_0.46.0    
[10] Biobase_2.68.0       BiocGenerics_0.54.1  generics_0.1.4      
[13] eulerr_7.0.4         patchwork_1.3.2      ggfortify_0.4.19    
[16] ggrepel_0.9.6        readxl_1.4.5         lubridate_1.9.5     
[19] forcats_1.0.1        stringr_1.6.0        dplyr_1.1.4         
[22] purrr_1.2.1          readr_2.1.6          tidyr_1.3.2         
[25] tibble_3.3.1         ggplot2_4.0.2        tidyverse_2.0.0     
[28] workflowr_1.7.2     

loaded via a namespace (and not attached):
 [1] DBI_1.2.3               gridExtra_2.3           rlang_1.1.7            
 [4] magrittr_2.0.4          git2r_0.36.2            otel_0.2.0             
 [7] compiler_4.5.1          RSQLite_2.4.5           getPass_0.2-4          
[10] mgcv_1.9-4              png_0.1-8               callr_3.7.6            
[13] vctrs_0.7.1             polylabelr_1.0.0        pkgconfig_2.0.3        
[16] crayon_1.5.3            fastmap_1.2.0           XVector_0.48.0         
[19] labeling_0.4.3          promises_1.5.0          rmarkdown_2.30         
[22] tzdb_0.5.0              UCSC.utils_1.4.0        ps_1.9.1               
[25] ggbeeswarm_0.7.3        bit_4.6.0               xfun_0.56              
[28] cachem_1.1.0            GenomeInfoDb_1.44.3     jsonlite_2.0.0         
[31] blob_1.3.0              later_1.4.5             R6_2.6.1               
[34] bslib_0.10.0            stringi_1.8.7           RColorBrewer_1.1-3     
[37] jquerylib_0.1.4         cellranger_1.1.0        Rcpp_1.1.1             
[40] knitr_1.51              Matrix_1.7-4            httpuv_1.6.16          
[43] splines_4.5.1           timechange_0.4.0        tidyselect_1.2.1       
[46] rstudioapi_0.18.0       yaml_2.3.12             processx_3.8.6         
[49] lattice_0.22-7          withr_3.0.2             KEGGREST_1.48.1        
[52] S7_0.2.1                evaluate_1.0.5          polyclip_1.10-7        
[55] Biostrings_2.76.0       pillar_1.11.1           whisker_0.4.1          
[58] rprojroot_2.1.1         hms_1.1.4               scales_1.4.0           
[61] glue_1.8.0              tools_4.5.1             locfit_1.5-9.12        
[64] fs_1.6.6                grid_4.5.1              Cairo_1.7-0            
[67] nlme_3.1-168            GenomeInfoDbData_1.2.14 beeswarm_0.4.0         
[70] vipor_0.4.7             cli_3.6.5               gtable_0.3.6           
[73] sass_0.4.10             digest_0.6.39           farver_2.1.2           
[76] memoise_2.0.1           htmltools_0.5.9         lifecycle_1.0.5        
[79] httr_1.4.7              statmod_1.5.1           bit64_4.6.0-1