1 Methamphetamine IVSA

Male C57BL/6J mice underwent methamphetamine intravenous self-administration (METH IVSA). The striatum was dissected, and microglia were isolated immediately following the last maintenance session (Maintenance and Saline) or following 21 days of forced home-cage abstinence (Abstinence). The samples were then processed for RNA-sequencing. The subsequent analysis is the result of this RNA-sequencing experiment.

2 DESeq2 Analysis

3 Plot Estimates and Gather Results

## Subset for significant genes only

results_sig <- subset(res, padj < 0.05)
results_sig2 <- subset(res2, padj < 0.05)
results_sig3 <- subset(res3, padj < 0.05)

4 Looking at Differentially Expressed Genes

We can generate a matrix to identify where our differentially expressed genes lie between each group based on an alpha value of 0.05.

## Matrix Visualizaattion
x <- vsDEGMatrix(
  data = dds, padj = 0.05, d.factor = "condition",
  type = "deseq", title = TRUE, legend = TRUE, grid = TRUE
)

5 Data Visualization

level_order <- c("Saline", "Maintenance", "Abstinence") # this vector might be useful for other plots/analyses

normalized_counts <- counts(dds, normalized = TRUE)

####################################### HSPA8

d2 <- plotCounts(dds,
  gene = "Hspd1|Hspd1", intgroup = "condition",
  returnData = TRUE
)

a <- ggplot(d2, aes(x = factor(condition, level = level_order), y = count, color = condition)) +
  geom_point(position = position_jitter(w = 0.1, h = 0)) +
  theme_bw() +
  ggtitle(
    label = "Hspd1 Expression",
    subtitle = "Normalized Gene Expression"
  )

a

####################################### HSP90ab1


d2 <- plotCounts(dds,
  gene = "Cirbp|Cirbp", intgroup = "condition",
  returnData = TRUE
)

a <- ggplot(d2, aes(x = factor(condition, level = level_order), y = count, color = condition)) +
  geom_point(position = position_jitter(w = 0.1, h = 0)) +
  theme_bw() +
  ggtitle(
    label = "Cirbp Expression",
    subtitle = "Normalized Gene Expression"
  )

a

####################################### Dnajb1


d2 <- plotCounts(dds,
  gene = "Fus|Fus", intgroup = "condition",
  returnData = TRUE
)

a <- ggplot(d2, aes(x = factor(condition, level = level_order), y = count, color = condition)) +
  geom_point(position = position_jitter(w = 0.1, h = 0)) +
  theme_bw() +
  ggtitle(
    label = "Fus Expression",
    subtitle = "Normalized Gene Expression"
  )

a

####################################### Dnajb1


d2 <- plotCounts(dds,
  gene = "Cryab|Cryab", intgroup = "condition",
  returnData = TRUE
)

a <- ggplot(d2, aes(x = factor(condition, level = level_order), y = count, color = condition)) +
  geom_point(position = position_jitter(w = 0.1, h = 0)) +
  theme_bw() +
  ggtitle(
    label = "Cryab Expression",
    subtitle = "Normalized Gene Expression"
  )

a

####################################### Dnajb1


d2 <- plotCounts(dds,
  gene = "Nr1d1|Nr1d1", intgroup = "condition",
  returnData = TRUE
)

a <- ggplot(d2, aes(x = factor(condition, level = level_order), y = count, color = condition)) +
  geom_point(position = position_jitter(w = 0.1, h = 0)) +
  theme_bw() +
  ggtitle(
    label = "Nr1d1 Expression",
    subtitle = "Normalized Gene Expression"
  )

a

####################################### Dnajb1


d2 <- plotCounts(dds,
  gene = "Syt11|Syt11", intgroup = "condition",
  returnData = TRUE
)

a <- ggplot(d2, aes(x = factor(condition, level = level_order), y = count, color = condition)) +
  geom_point(position = position_jitter(w = 0.1, h = 0)) +
  theme_bw() +
  ggtitle(
    label = "Syt11 Expression",
    subtitle = "Normalized Gene Expression"
  )

a

ressig1 <- as.data.frame(results_sig)
ressig2 <- as.data.frame(results_sig2)
ressig3 <- as.data.frame(results_sig3)

ressig1$Comparison <- "Maintenance vs Saline"
ressig2$Comparison <- "Abstinence vs Saline"
ressig3$Comparison <- "Abstinence vs Maintenance"

ressig1_highlfc_pos <- ressig1[ressig1$log2FoldChange >= 1.3, ]
ressig1_highlfc_neg <- ressig1[ressig1$log2FoldChange <= -1.3, ]
ressig2_highlfc_pos <- ressig2[ressig2$log2FoldChange >= 1.3, ]
ressig2_highlfc_neg <- ressig2[ressig2$log2FoldChange <= -1.3, ]
ressig3_highlfc_pos <- ressig3[ressig3$log2FoldChange >= 1.3, ]
ressig3_highlfc_neg <- ressig3[ressig3$log2FoldChange <= -1.3, ]

temp_sigdegshm <- rbind(ressig1_highlfc_pos, ressig1_highlfc_neg)
temp_sigdegshm2 <- rbind(temp_sigdegshm, ressig2_highlfc_pos)
temp_sigdegshm3 <- rbind(temp_sigdegshm2, ressig2_highlfc_neg)
temp_sigdegshm4 <- rbind(temp_sigdegshm3, ressig3_highlfc_pos)
temp_sigdegshm5 <- rbind(temp_sigdegshm4, ressig3_highlfc_neg)

temp_sigdegshm6 <- temp_sigdegshm5[temp_sigdegshm5$lfcSE <= 1.50, ]

dup_rows <- duplicated(temp_sigdegshm6$symbol)
data_dedup <- temp_sigdegshm6[!dup_rows, ]

rows <- rownames(data_dedup)

select <- order(rowMeans(counts(dds, normalized = TRUE)),
  decreasing = TRUE
)

mat <- assay(ddsMat_rlog)[select, ]

data_mod <- mat[rownames(mat) %in% rows, ]

matrownames_data_mod <- data.frame(rownames(data_mod))

matrownames_data_mod <- matrownames_data_mod %>% separate(rownames.data_mod., c("id", "symbol"), sep = "([|])")


samples <- as.tibble(read.csv("data/sample_info_final.csv", sep = ","))

annot_col <- samples %>%
  column_to_rownames("samplename") %>%
  as.data.frame()

ann_colors <- list(
  condition = as.factor(c(Saline = "slateblue2", Maintenance = "limegreen", Abstinence = "violetred2")),
  Group = as.factor(c(Saline = "slateblue2", Maintenance = "limegreen", Abstinence = "violetred2"))
)

pheatmap::pheatmap(
  mat = data_mod,
  color = colorRampPalette(rev(brewer.pal(11, "RdYlBu")))(155),
  scale = "row", # Scale genes to Z-score (how many standard deviations),
  annotation_col = annot_col,
  # annotation_colors = ann_colors,# Change the default colors of the annotations
  fontsize = 3, # Make fonts smaller
  cellwidth = 15, # Make the cells wider
  cellheight = 3, # Make the cells wider
  show_colnames = T,
  cluster_rows = T,
  cluster_cols = T,
  annotation_legend = T,
  legend = T,
  show_rownames = T,
  clustering_distance_rows = "correlation",
  clustering_method = "ward.D",
  main = "Heatmap of Significant DEGs -- Unsupervised Clustering",
  cluster_row_slices = TRUE,
  show_row_dend = FALSE,
  cutree_rows = 4,
  cutree_cols = 3,
  treeheight_row = 10,
  treeheight_col = 10,
  # kmeans_k = 4,
  # display_numbers = TRUE,
  # filename = "plots/MIVSA_Heatmap_DEGs_Final.pdf",
  legend_labels = c("Saline", "Maintenance", "Abstinence"),
  labels_row = matrownames_data_mod$symbol
)

pheatmap::pheatmap(
  mat = data_mod,
  color = colorRampPalette(rev(brewer.pal(11, "RdYlBu")))(155),
  scale = "row", # Scale genes to Z-score (how many standard deviations),
  annotation_col = annot_col,
  fontsize = 8, # Make fonts smaller
  cellwidth = 15, # Make the cells wider
  cellheight = 3, # Make the cells wider
  show_colnames = F,
  cluster_rows = T,
  cluster_cols = F,
  annotation_legend = T,
  legend = T,
  show_rownames = F,
  clustering_distance_rows = "correlation",
  clustering_method = "ward.D",
  main = "Heatmap of Significant DEGs -- Supervised Clustering",
  cluster_row_slices = TRUE,
  show_row_dend = FALSE,
  cutree_rows = 4,
  cutree_cols = 3,
  gaps_col = c(5, 10),
  treeheight_row = 0,
  treeheight_col = 10,
  # filename = "plots/MIVSA_Heatmap_DEGs_Final_Unclustered_Nolabs.pdf",
  legend_labels = c("Saline", "Maintenance", "Abstinence")
)

pheatmap::pheatmap(
  mat = data_mod,
  color = colorRampPalette(rev(brewer.pal(11, "RdYlBu")))(155),
  scale = "row", # Scale genes to Z-score (how many standard deviations),
  annotation_col = annot_col,
  fontsize = 12, # Make fonts smaller
  show_colnames = F,
  cluster_rows = T,
  cluster_cols = F,
  annotation_legend = T,
  legend = T,
  show_rownames = F,
  clustering_distance_rows = "correlation",
  clustering_method = "ward.D",
  main = "Heatmap of Significant DEGs -- Supervised and Condensed",
  legend_labels = c("Saline", "Maintenance", "Abstinence"),
  cluster_row_slices = TRUE,
  show_row_dend = FALSE,
  gaps_col = c(5, 10),
  cutree_rows = 4,
  cutree_cols = 3,
  treeheight_row = 15,
  treeheight_col = 10
)

# Gather Log-fold change and FDR-corrected pvalues from DESeq2 results
# Change pvalues to -log10 (1.3 = 0.05)
data <- data.frame(
  gene = (res$symbol),
  pval = -log10(res$padj),
  lfc = res$log2FoldChange
)

data2 <- data.frame(
  gene = (res2$symbol),
  pval = -log10(res2$padj),
  lfc = res2$log2FoldChange
)

data3 <- data.frame(
  gene = (res3$symbol),
  pval = -log10(res3$padj),
  lfc = res3$log2FoldChange
)


# Remove any rows that have NA as an entry
data <- na.omit(data)

data2 <- na.omit(data2)

data3 <- na.omit(data3)

# Color the points which are up or down
## If fold-change > 0 and pvalue > 1.3 (Increased significant)
## If fold-change < 0 and pvalue > 1.3 (Decreased significant)

data <- dplyr::mutate(data, color = case_when(
  data$lfc > 0 & data$pval > 1.3 ~ "Increased in Maintenance",
  data$lfc < 0 & data$pval > 1.3 ~ "Decreased in Maintenance",
  data$pval < 1.3 ~ "Nonsignificant"
))

data2 <- dplyr::mutate(data2, color = case_when(
  data2$lfc > 0 & data2$pval > 1.3 ~ "Increased in Abstinence",
  data2$lfc < 0 & data2$pval > 1.3 ~ "Decreased in Abstinence",
  data2$pval < 1.3 ~ "Nonsignificant"
))

data3 <- dplyr::mutate(data3, color = case_when(
  data3$lfc > 0 & data3$pval > 1.3 ~ "Increased in Abstinence",
  data3$lfc < 0 & data3$pval > 1.3 ~ "Decreased in Abstinence",
  data3$pval < 1.3 ~ "Nonsignificant"
))



# Make a basic ggplot2 object with x-y values
vol <- ggplot(data, aes(x = lfc, y = pval, color = color))

vol2 <- ggplot(data2, aes(x = lfc, y = pval, color = color))

vol3 <- ggplot(data3, aes(x = lfc, y = pval, color = color))



# Add ggplot2 layers

y.axis.text <- element_text(face = "bold", color = "black", size = 7)
x.axis.text <- element_text(face = "bold", color = "black", size = 7)
plot.title.text <- element_text(face = "bold", color = "black", size = 9, hjust = 0.5)
legend.text <- element_text(face = "bold", color = "black", size = 5)
legend.title.text <- element_text(face = "bold", color = "black", size = 6)
axis.title <- element_text(face = "bold", color = "black", size = 6)

vol_final <- vol +
  ggtitle(label = "Maintenance vs Saline") +
  geom_point(size = 1.5, alpha = 0.8, na.rm = T) +
  scale_color_manual(
    name = "Directionality",
    values = c("Increased in Maintenance" = "#008B00", "Decreased in Maintenance" = "#CD4F39", "Nonsignificant" = "darkgray")
  ) +
  theme_bw(base_size = 14) + # change overall theme+
  theme(
    legend.position = "right",
    axis.text.y = y.axis.text,
    axis.text.x = x.axis.text,
    axis.title = axis.title,
    plot.title = plot.title.text,
    legend.text = legend.text,
    legend.title = legend.title.text,
    legend.key.size = unit(0.5, "cm"), # change legend key size
    legend.key.height = unit(0.5, "cm"), # change legend key height
    legend.key.width = unit(0.5, "cm")
  ) + # change legend key width
  xlab(expression(log[2]("Fold Change"))) + # Change X-Axis label
  ylab(expression(-log[10]("adjusted p-value"))) + # Change Y-Axis label
  geom_hline(yintercept = 1.3, colour = "darkgrey") + # Add p-adj value cutoff line
  scale_y_continuous(trans = "log1p") # Scale y-axis due to large p-values

vol2_final <- vol2 +
  ggtitle(label = "Abstinence vs Saline") +
  geom_point(size = 1.5, alpha = 0.8, na.rm = T) +
  scale_color_manual(
    name = "Directionality",
    values = c("Increased in Abstinence" = "#008B00", "Decreased in Abstinence" = "#CD4F39", "Nonsignificant" = "darkgray")
  ) +
  theme_bw(base_size = 14) + # change overall theme
  theme(
    legend.position = "right",
    axis.text.y = y.axis.text,
    axis.text.x = x.axis.text,
    axis.title = axis.title,
    plot.title = plot.title.text,
    legend.text = legend.text,
    legend.title = legend.title.text,
    legend.key.size = unit(0.5, "cm"), # change legend key size
    legend.key.height = unit(0.5, "cm"), # change legend key height
    legend.key.width = unit(0.5, "cm")
  ) + # change legend key width
  xlab(expression(log[2]("Fold Change"))) + # Change X-Axis label
  ylab(expression(-log[10]("adjusted p-value"))) + # Change Y-Axis label
  geom_hline(yintercept = 1.3, colour = "darkgrey") + # Add p-adj value cutoff line
  scale_y_continuous(trans = "log1p") # Scale y-axis due to large p-values

vol3_final <- vol3 +
  ggtitle(label = "Abstinence vs Maintenance") +
  geom_point(size = 1.5, alpha = 0.8, na.rm = T) +
  scale_color_manual(
    name = "Directionality",
    values = c("Increased in Abstinence" = "#008B00", "Decreased in Abstinence" = "#CD4F39", "Nonsignificant" = "darkgray")
  ) +
  theme_bw(base_size = 14) + # change overall theme
  theme(
    legend.position = "right",
    axis.text.y = y.axis.text,
    axis.text.x = x.axis.text,
    axis.title = axis.title,
    plot.title = plot.title.text,
    legend.text = legend.text,
    legend.title = legend.title.text,
    legend.key.size = unit(0.5, "cm"), # change legend key size
    legend.key.height = unit(0.5, "cm"), # change legend key height
    legend.key.width = unit(0.5, "cm")
  ) + # change legend key width
  xlab(expression(log[2]("fold change"))) + # Change X-Axis label
  ylab(expression(-log[10]("adjusted p-value"))) + # Change Y-Axis label
  geom_hline(yintercept = 1.3, colour = "darkgrey") + # Add p-adj value cutoff line
  scale_y_continuous(trans = "log1p") # Scale y-axis due to large p-values

ggarrange(vol_final, vol2_final, vol3_final, labels = c("A", "B", "C"))

6 Pathway Analysis

# Remove any genes that have greater than 1.5 LFC Standard Error
results_sig_entrez <- subset(results_sig, lfcSE <= 1.5)
results_sig_entrez2 <- subset(results_sig2, lfcSE <= 1.5)
results_sig_entrez3 <- subset(results_sig3, lfcSE <= 1.5)

# Remove any genes that have greater than adj.pvalue 0.05
results_sig_entrez <- subset(results_sig_entrez, padj <= 0.05)
results_sig_entrez2 <- subset(results_sig_entrez2, padj <= 0.05)
results_sig_entrez3 <- subset(results_sig_entrez3, padj <= 0.05)

# Remove any genes that do not have any entrez identifiers
results_sig_entrez <- subset(results_sig_entrez, is.na(entrez) == FALSE)
results_sig_entrez2 <- subset(results_sig_entrez2, is.na(entrez) == FALSE)
results_sig_entrez3 <- subset(results_sig_entrez3, is.na(entrez) == FALSE)

# Create a matrix of gene log2 fold changes
gene_matrix <- results_sig_entrez$log2FoldChange
gene_matrix2 <- results_sig_entrez2$log2FoldChange
gene_matrix3 <- results_sig_entrez3$log2FoldChange

# Add the entrezID's as names for each logFC entry
names(gene_matrix) <- results_sig_entrez$entrez
names(gene_matrix2) <- results_sig_entrez2$entrez
names(gene_matrix3) <- results_sig_entrez3$entrez


go <- enrichGO(
  gene = names(gene_matrix),
  OrgDb = "org.Mm.eg.db",
  readable = T,
  universe = gene_matrix_background,
  ont = "ALL",
  pAdjustMethod = "fdr",
  pvalueCutoff = 0.05,
  qvalueCutoff = 0.10
)

go2 <- enrichGO(
  gene = names(gene_matrix2),
  OrgDb = "org.Mm.eg.db",
  readable = T,
  universe = gene_matrix_background,
  ont = "ALL",
  pAdjustMethod = "fdr",
  pvalueCutoff = 0.05,
  qvalueCutoff = 0.10
)

go3 <- enrichGO(
  gene = names(gene_matrix3),
  OrgDb = "org.Mm.eg.db",
  readable = T,
  universe = gene_matrix_background,
  ont = "ALL",
  pAdjustMethod = "fdr",
  pvalueCutoff = 0.05,
  qvalueCutoff = 0.10
)

kable(head(go, 10), caption = "Top 10 GO Pathways for Maintenance vs Saline", format = "html", align = "l", booktabs = TRUE) %>%
  kable_styling(html_font = "Arial", font_size = 10, bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  scroll_box(width = "1000px", height = "500px")

kable(head(go2, 10), caption = "Top 10 GO Pathways for Abstinence vs Saline", format = "html", align = "l", booktabs = TRUE) %>%
  kable_styling(html_font = "Arial", font_size = 10, bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  scroll_box(width = "1000px", height = "500px")

kable(head(go3, 10), caption = "Top 10 GO Pathways for Abstinence vs Maintenance", format = "html", align = "l", booktabs = TRUE) %>%
  kable_styling(html_font = "Arial", font_size = 10, bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  scroll_box(width = "1000px", height = "500px")

9 Session Info

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

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

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

other attached packages:
 [1] GOplot_1.0.2                             
 [2] gridExtra_2.3                            
 [3] ggdendro_0.1.23                          
 [4] BiocParallel_1.32.6                      
 [5] viridis_0.6.3                            
 [6] viridisLite_0.4.2                        
 [7] ggbeeswarm_0.7.2                         
 [8] PoiClaClu_1.0.2.1                        
 [9] data.table_1.14.8                        
[10] DEGreport_1.34.0                         
[11] ggrepel_0.9.3                            
[12] conflicted_1.2.0                         
[13] regionReport_1.32.0                      
[14] kableExtra_1.3.4                         
[15] vidger_1.18.0                            
[16] tables_0.9.17                            
[17] knitr_1.43                               
[18] Glimma_2.8.0                             
[19] Mus.musculus_1.3.1                       
[20] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
[21] OrganismDbi_1.40.0                       
[22] GenomicFeatures_1.50.4                   
[23] gplots_3.1.3                             
[24] lubridate_1.9.2                          
[25] forcats_1.0.0                            
[26] stringr_1.5.0                            
[27] purrr_1.0.1                              
[28] readr_2.1.4                              
[29] tibble_3.2.1                             
[30] tidyverse_2.0.0                          
[31] statmod_1.5.0                            
[32] tweeDEseqCountData_1.36.0                
[33] edgeR_3.40.2                             
[34] limma_3.54.2                             
[35] GOSemSim_2.24.0                          
[36] apeglm_1.20.0                            
[37] ggpubr_0.6.0                             
[38] SPIA_2.50.0                              
[39] KEGGgraph_1.58.3                         
[40] ggnewscale_0.4.9                         
[41] enrichplot_1.18.4                        
[42] tidyr_1.3.0                              
[43] WGCNA_1.72-1                             
[44] fastcluster_1.2.3                        
[45] dynamicTreeCut_1.63-1                    
[46] pathview_1.38.0                          
[47] gage_2.48.0                              
[48] dplyr_1.1.2                              
[49] topGO_2.50.0                             
[50] SparseM_1.81                             
[51] graph_1.76.0                             
[52] GO.db_3.16.0                             
[53] RColorBrewer_1.1-3                       
[54] genefilter_1.80.3                        
[55] pheatmap_1.0.12                          
[56] ggsci_3.0.0                              
[57] tximport_1.26.1                          
[58] org.Mm.eg.db_3.16.0                      
[59] AnnotationDbi_1.60.2                     
[60] DOSE_3.24.2                              
[61] ReactomePA_1.42.0                        
[62] biomaRt_2.54.1                           
[63] clusterProfiler_4.6.2                    
[64] ggplot2_3.4.2                            
[65] DESeq2_1.38.3                            
[66] SummarizedExperiment_1.28.0              
[67] Biobase_2.58.0                           
[68] MatrixGenerics_1.10.0                    
[69] matrixStats_1.0.0                        
[70] GenomicRanges_1.50.2                     
[71] GenomeInfoDb_1.34.9                      
[72] IRanges_2.32.0                           
[73] S4Vectors_0.36.2                         
[74] BiocGenerics_0.44.0                      
[75] gprofiler2_0.2.2                         
[76] workflowr_1.7.0                          

loaded via a namespace (and not attached):
  [1] Hmisc_5.1-0                 svglite_2.1.1              
  [3] ps_1.7.5                    Rsamtools_2.14.0           
  [5] foreach_1.5.2               rprojroot_2.0.3            
  [7] crayon_1.5.2                MASS_7.3-60                
  [9] nlme_3.1-162                backports_1.4.1            
 [11] impute_1.72.3               rlang_1.1.1                
 [13] XVector_0.38.0              HDO.db_0.99.1              
 [15] irlba_2.3.5.1               callr_3.7.3                
 [17] filelock_1.0.2              rjson_0.2.21               
 [19] bit64_4.0.5                 glue_1.6.2                 
 [21] mixsqp_0.3-48               rngtools_1.5.2             
 [23] vipor_0.4.5                 parallel_4.2.1             
 [25] processx_3.8.2              DEFormats_1.26.0           
 [27] tidyselect_1.2.0            XML_3.99-0.14              
 [29] GenomicAlignments_1.34.1    xtable_1.8-4               
 [31] magrittr_2.0.3              evaluate_0.21              
 [33] bibtex_0.5.1                cli_3.6.1                  
 [35] zlibbioc_1.44.0             doRNG_1.8.6                
 [37] rstudioapi_0.14             whisker_0.4.1              
 [39] bslib_0.5.0                 rpart_4.1.19               
 [41] derfinderHelper_1.32.0      fastmatch_1.1-3            
 [43] BiocStyle_2.26.0            treeio_1.22.0              
 [45] xfun_0.39                   clue_0.3-64                
 [47] gson_0.1.0                  cluster_2.1.4              
 [49] caTools_1.18.2              tidygraph_1.2.3            
 [51] KEGGREST_1.38.0             logging_0.10-108           
 [53] ape_5.7-1                   Biostrings_2.66.0          
 [55] png_0.1-8                   reshape_0.8.9              
 [57] withr_2.5.0                 bitops_1.0-7               
 [59] ggforce_0.4.1               RBGL_1.74.0                
 [61] plyr_1.8.8                  coda_0.19-4                
 [63] bumphunter_1.40.0           pillar_1.9.0               
 [65] GlobalOptions_0.1.2         cachem_1.0.8               
 [67] fs_1.6.2                    GetoptLong_1.0.5           
 [69] graphite_1.44.0             vctrs_0.6.3                
 [71] generics_0.1.3              tools_4.2.1                
 [73] foreign_0.8-84              beeswarm_0.4.0             
 [75] munsell_0.5.0               tweenr_2.0.2               
 [77] fgsea_1.24.0                DelayedArray_0.24.0        
 [79] fastmap_1.1.1               compiler_4.2.1             
 [81] abind_1.4-5                 httpuv_1.6.11              
 [83] rtracklayer_1.58.0          plotly_4.10.2              
 [85] GenomeInfoDbData_1.2.9      lattice_0.21-8             
 [87] utf8_1.2.3                  later_1.3.1                
 [89] BiocFileCache_2.6.1         jsonlite_1.8.7             
 [91] GGally_2.1.2                scales_1.2.1               
 [93] tidytree_0.4.2              carData_3.0-5              
 [95] lazyeval_0.2.2              promises_1.2.0.1           
 [97] car_3.1-2                   doParallel_1.0.17          
 [99] checkmate_2.2.0             rmarkdown_2.23             
[101] cowplot_1.1.1               webshot_0.5.5              
[103] downloader_0.4              BSgenome_1.66.3            
[105] igraph_1.5.0                survival_3.5-5             
[107] numDeriv_2016.8-1.1         yaml_2.3.7                 
[109] systemfonts_1.0.4           ashr_2.2-54                
[111] SQUAREM_2021.1              htmltools_0.5.5            
[113] memoise_2.0.1               VariantAnnotation_1.44.1   
[115] BiocIO_1.8.0                locfit_1.5-9.8             
[117] graphlayouts_1.0.0          digest_0.6.32              
[119] rappdirs_0.3.3              knitrBootstrap_1.0.2       
[121] emdbook_1.3.13              RSQLite_2.3.1              
[123] yulab.utils_0.0.6           derfinder_1.32.0           
[125] blob_1.2.4                  preprocessCore_1.60.2      
[127] labeling_0.4.2              splines_4.2.1              
[129] Formula_1.2-5               RCurl_1.98-1.12            
[131] broom_1.0.5                 hms_1.1.3                  
[133] ConsensusClusterPlus_1.62.0 colorspace_2.1-0           
[135] base64enc_0.1-3             mnormt_2.1.1               
[137] BiocManager_1.30.21         shape_1.4.6                
[139] aplot_0.1.10                nnet_7.3-19                
[141] sass_0.4.6                  Rcpp_1.0.10                
[143] circlize_0.4.15             mvtnorm_1.2-2              
[145] fansi_1.0.4                 tzdb_0.4.0                 
[147] truncnorm_1.0-9             R6_2.5.1                   
[149] grid_4.2.1                  lifecycle_1.0.3            
[151] curl_5.0.1                  ggsignif_0.6.4             
[153] jquerylib_0.1.4             Matrix_1.5-4.1             
[155] qvalue_2.30.0               org.Hs.eg.db_3.16.0        
[157] iterators_1.0.14            RefManageR_1.4.0           
[159] htmlwidgets_1.6.2           markdown_1.7               
[161] polyclip_1.10-4             shadowtext_0.1.2           
[163] timechange_0.2.0            gridGraphics_0.5-1         
[165] reactome.db_1.82.0          ComplexHeatmap_2.14.0      
[167] rvest_1.0.3                 htmlTable_2.4.1            
[169] patchwork_1.1.2             bdsmatrix_1.3-6            
[171] invgamma_1.1                codetools_0.2-19           
[173] gtools_3.9.4                getPass_0.2-2              
[175] prettyunits_1.1.1           psych_2.3.6                
[177] dbplyr_2.3.2                gtable_0.3.3               
[179] DBI_1.1.3                   git2r_0.32.0               
[181] highr_0.10                  ggfun_0.1.1                
[183] httr_1.4.6                  KernSmooth_2.23-21         
[185] stringi_1.7.12              progress_1.2.2             
[187] reshape2_1.4.4              farver_2.1.1               
[189] annotate_1.76.0             Rgraphviz_2.42.0           
[191] ggtree_3.6.2                xml2_1.3.4                 
[193] bbmle_1.0.25                restfulr_0.0.15            
[195] geneplotter_1.76.0          ggplotify_0.1.1            
[197] bit_4.0.5                   scatterpie_0.2.1           
[199] ggraph_2.1.0                pkgconfig_2.0.3            
[201] rstatix_0.7.2               GenomicFiles_1.34.0        

Session information

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

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

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

other attached packages:
 [1] GOplot_1.0.2                             
 [2] gridExtra_2.3                            
 [3] ggdendro_0.1.23                          
 [4] BiocParallel_1.32.6                      
 [5] viridis_0.6.3                            
 [6] viridisLite_0.4.2                        
 [7] ggbeeswarm_0.7.2                         
 [8] PoiClaClu_1.0.2.1                        
 [9] data.table_1.14.8                        
[10] DEGreport_1.34.0                         
[11] ggrepel_0.9.3                            
[12] conflicted_1.2.0                         
[13] regionReport_1.32.0                      
[14] kableExtra_1.3.4                         
[15] vidger_1.18.0                            
[16] tables_0.9.17                            
[17] knitr_1.43                               
[18] Glimma_2.8.0                             
[19] Mus.musculus_1.3.1                       
[20] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
[21] OrganismDbi_1.40.0                       
[22] GenomicFeatures_1.50.4                   
[23] gplots_3.1.3                             
[24] lubridate_1.9.2                          
[25] forcats_1.0.0                            
[26] stringr_1.5.0                            
[27] purrr_1.0.1                              
[28] readr_2.1.4                              
[29] tibble_3.2.1                             
[30] tidyverse_2.0.0                          
[31] statmod_1.5.0                            
[32] tweeDEseqCountData_1.36.0                
[33] edgeR_3.40.2                             
[34] limma_3.54.2                             
[35] GOSemSim_2.24.0                          
[36] apeglm_1.20.0                            
[37] ggpubr_0.6.0                             
[38] SPIA_2.50.0                              
[39] KEGGgraph_1.58.3                         
[40] ggnewscale_0.4.9                         
[41] enrichplot_1.18.4                        
[42] tidyr_1.3.0                              
[43] WGCNA_1.72-1                             
[44] fastcluster_1.2.3                        
[45] dynamicTreeCut_1.63-1                    
[46] pathview_1.38.0                          
[47] gage_2.48.0                              
[48] dplyr_1.1.2                              
[49] topGO_2.50.0                             
[50] SparseM_1.81                             
[51] graph_1.76.0                             
[52] GO.db_3.16.0                             
[53] RColorBrewer_1.1-3                       
[54] genefilter_1.80.3                        
[55] pheatmap_1.0.12                          
[56] ggsci_3.0.0                              
[57] tximport_1.26.1                          
[58] org.Mm.eg.db_3.16.0                      
[59] AnnotationDbi_1.60.2                     
[60] DOSE_3.24.2                              
[61] ReactomePA_1.42.0                        
[62] biomaRt_2.54.1                           
[63] clusterProfiler_4.6.2                    
[64] ggplot2_3.4.2                            
[65] DESeq2_1.38.3                            
[66] SummarizedExperiment_1.28.0              
[67] Biobase_2.58.0                           
[68] MatrixGenerics_1.10.0                    
[69] matrixStats_1.0.0                        
[70] GenomicRanges_1.50.2                     
[71] GenomeInfoDb_1.34.9                      
[72] IRanges_2.32.0                           
[73] S4Vectors_0.36.2                         
[74] BiocGenerics_0.44.0                      
[75] gprofiler2_0.2.2                         
[76] workflowr_1.7.0                          

loaded via a namespace (and not attached):
  [1] Hmisc_5.1-0                 svglite_2.1.1              
  [3] ps_1.7.5                    Rsamtools_2.14.0           
  [5] foreach_1.5.2               rprojroot_2.0.3            
  [7] crayon_1.5.2                MASS_7.3-60                
  [9] nlme_3.1-162                backports_1.4.1            
 [11] impute_1.72.3               rlang_1.1.1                
 [13] XVector_0.38.0              HDO.db_0.99.1              
 [15] irlba_2.3.5.1               callr_3.7.3                
 [17] filelock_1.0.2              rjson_0.2.21               
 [19] bit64_4.0.5                 glue_1.6.2                 
 [21] mixsqp_0.3-48               rngtools_1.5.2             
 [23] vipor_0.4.5                 parallel_4.2.1             
 [25] processx_3.8.2              DEFormats_1.26.0           
 [27] tidyselect_1.2.0            XML_3.99-0.14              
 [29] GenomicAlignments_1.34.1    xtable_1.8-4               
 [31] magrittr_2.0.3              evaluate_0.21              
 [33] bibtex_0.5.1                cli_3.6.1                  
 [35] zlibbioc_1.44.0             doRNG_1.8.6                
 [37] rstudioapi_0.14             whisker_0.4.1              
 [39] bslib_0.5.0                 rpart_4.1.19               
 [41] derfinderHelper_1.32.0      fastmatch_1.1-3            
 [43] BiocStyle_2.26.0            treeio_1.22.0              
 [45] xfun_0.39                   clue_0.3-64                
 [47] gson_0.1.0                  cluster_2.1.4              
 [49] caTools_1.18.2              tidygraph_1.2.3            
 [51] KEGGREST_1.38.0             logging_0.10-108           
 [53] ape_5.7-1                   Biostrings_2.66.0          
 [55] png_0.1-8                   reshape_0.8.9              
 [57] withr_2.5.0                 bitops_1.0-7               
 [59] ggforce_0.4.1               RBGL_1.74.0                
 [61] plyr_1.8.8                  coda_0.19-4                
 [63] bumphunter_1.40.0           pillar_1.9.0               
 [65] GlobalOptions_0.1.2         cachem_1.0.8               
 [67] fs_1.6.2                    GetoptLong_1.0.5           
 [69] graphite_1.44.0             vctrs_0.6.3                
 [71] generics_0.1.3              tools_4.2.1                
 [73] foreign_0.8-84              beeswarm_0.4.0             
 [75] munsell_0.5.0               tweenr_2.0.2               
 [77] fgsea_1.24.0                DelayedArray_0.24.0        
 [79] fastmap_1.1.1               compiler_4.2.1             
 [81] abind_1.4-5                 httpuv_1.6.11              
 [83] rtracklayer_1.58.0          plotly_4.10.2              
 [85] GenomeInfoDbData_1.2.9      lattice_0.21-8             
 [87] utf8_1.2.3                  later_1.3.1                
 [89] BiocFileCache_2.6.1         jsonlite_1.8.7             
 [91] GGally_2.1.2                scales_1.2.1               
 [93] tidytree_0.4.2              carData_3.0-5              
 [95] lazyeval_0.2.2              promises_1.2.0.1           
 [97] car_3.1-2                   doParallel_1.0.17          
 [99] checkmate_2.2.0             rmarkdown_2.23             
[101] cowplot_1.1.1               webshot_0.5.5              
[103] downloader_0.4              BSgenome_1.66.3            
[105] igraph_1.5.0                survival_3.5-5             
[107] numDeriv_2016.8-1.1         yaml_2.3.7                 
[109] systemfonts_1.0.4           ashr_2.2-54                
[111] SQUAREM_2021.1              htmltools_0.5.5            
[113] memoise_2.0.1               VariantAnnotation_1.44.1   
[115] BiocIO_1.8.0                locfit_1.5-9.8             
[117] graphlayouts_1.0.0          digest_0.6.32              
[119] rappdirs_0.3.3              knitrBootstrap_1.0.2       
[121] emdbook_1.3.13              RSQLite_2.3.1              
[123] yulab.utils_0.0.6           derfinder_1.32.0           
[125] blob_1.2.4                  preprocessCore_1.60.2      
[127] labeling_0.4.2              splines_4.2.1              
[129] Formula_1.2-5               RCurl_1.98-1.12            
[131] broom_1.0.5                 hms_1.1.3                  
[133] ConsensusClusterPlus_1.62.0 colorspace_2.1-0           
[135] base64enc_0.1-3             mnormt_2.1.1               
[137] BiocManager_1.30.21         shape_1.4.6                
[139] aplot_0.1.10                nnet_7.3-19                
[141] sass_0.4.6                  Rcpp_1.0.10                
[143] circlize_0.4.15             mvtnorm_1.2-2              
[145] fansi_1.0.4                 tzdb_0.4.0                 
[147] truncnorm_1.0-9             R6_2.5.1                   
[149] grid_4.2.1                  lifecycle_1.0.3            
[151] curl_5.0.1                  ggsignif_0.6.4             
[153] jquerylib_0.1.4             Matrix_1.5-4.1             
[155] qvalue_2.30.0               org.Hs.eg.db_3.16.0        
[157] iterators_1.0.14            RefManageR_1.4.0           
[159] htmlwidgets_1.6.2           markdown_1.7               
[161] polyclip_1.10-4             shadowtext_0.1.2           
[163] timechange_0.2.0            gridGraphics_0.5-1         
[165] reactome.db_1.82.0          ComplexHeatmap_2.14.0      
[167] rvest_1.0.3                 htmlTable_2.4.1            
[169] patchwork_1.1.2             bdsmatrix_1.3-6            
[171] invgamma_1.1                codetools_0.2-19           
[173] gtools_3.9.4                getPass_0.2-2              
[175] prettyunits_1.1.1           psych_2.3.6                
[177] dbplyr_2.3.2                gtable_0.3.3               
[179] DBI_1.1.3                   git2r_0.32.0               
[181] highr_0.10                  ggfun_0.1.1                
[183] httr_1.4.6                  KernSmooth_2.23-21         
[185] stringi_1.7.12              progress_1.2.2             
[187] reshape2_1.4.4              farver_2.1.1               
[189] annotate_1.76.0             Rgraphviz_2.42.0           
[191] ggtree_3.6.2                xml2_1.3.4                 
[193] bbmle_1.0.25                restfulr_0.0.15            
[195] geneplotter_1.76.0          ggplotify_0.1.1            
[197] bit_4.0.5                   scatterpie_0.2.1           
[199] ggraph_2.1.0                pkgconfig_2.0.3            
[201] rstatix_0.7.2               GenomicFiles_1.34.0