Over-representation and enrichment analysis: GO and KEGG
EBI Cancer Genomics & Transcriptomics 2024
Alexey Larionov
27 June 2024
Summary
Over-representation and Gene Sets Enrichment analyses are used for functional interpretation of differentially expressed genes lists.
Start section
Install and load libraries
#install.packages("readr")
#install.packages("dplyr")
#install.packages("stringi")
#BiocManager::install("org.Hs.eg.db")
#BiocManager::install("clusterProfiler")
#BiocManager::install("ggkegg")
# load packages
library(readr)
library(dplyr)
library(stringi)
library(org.Hs.eg.db)
library(clusterProfiler)
library(ggkegg)Clean environment
# Time stamp
Sys.time()## [1] "2024-06-26 16:19:50 BST"rm(list=ls())
graphics.off()1. Import and prepare data
1.1 Import DESeq2 results
These results were generated during the previous step of the practical session.
base_folder <- "C:/Users/alexe/OneDrive - Cranfield University/External/ebi/short_reads"
deseq2_output <- file.path(base_folder,"data","deseq_gene_lists","DESeq2_FDR_0.1_any_FC_genes.txt")
# Import all genes ranked by p-values from DeSeq2 analysis
# readr::read_tsv provides additional functionality when reading tsv files
genes_raw.df <- readr::read_tsv(file = deseq2_output)
# Explore data
class(genes_raw.df)## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"dim(genes_raw.df)## [1] 25030 5head(genes_raw.df)## # A tibble: 6 × 5
## gene_id gene_name baseMean log2FoldChange padj
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ENSG00000130294.18 KIF1A 42588. 11.1 6.83e-72
## 2 ENSG00000075340.23 ADD2 6056. 9.97 6.37e-53
## 3 ENSG00000105971.15 CAV2 9130. -8.35 5.25e-51
## 4 ENSG00000163682.17 RPL9 30771. -5.20 5.33e-49
## 5 ENSG00000167644.12 C19orf33 1720. -9.05 6.38e-40
## 6 ENSG00000069188.17 SDK2 1197. 8.99 1.55e-39tail(genes_raw.df)## # A tibble: 6 × 5
## gene_id gene_name baseMean log2FoldChange padj
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ENSG00000151725.12 CENPU 4333. 0.000153 1.00
## 2 ENSG00000270424.1 ENSG00000270424 11.5 -0.000535 1.00
## 3 ENSG00000224186.8 PITX1-AS1 269. -0.000167 1.00
## 4 ENSG00000143753.14 DEGS1 3148. 0.0000166 1.00
## 5 ENSG00000132763.15 MMACHC 1167. 0.0000149 1.00
## 6 ENSG00000129315.12 CCNT1 5978. -0.000000280 1.00# Clean-up
rm(deseq2_output)1.2 Add Entrez ID
Entrez ID will be needed for KEGG analysis. Note that some Ensembl IDs can not be mapped to Entrez IDs. This is an expected limitation of converting between the different Gene IDs.
# Remove version number from ENSEMBL ids
genes_ensembl_no_version.df <- genes_raw.df %>%
mutate(gene_id = stringi::stri_replace_all(gene_id,regex = "\\.(\\d*)$", ""))
head(genes_ensembl_no_version.df)## # A tibble: 6 × 5
## gene_id gene_name baseMean log2FoldChange padj
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ENSG00000130294 KIF1A 42588. 11.1 6.83e-72
## 2 ENSG00000075340 ADD2 6056. 9.97 6.37e-53
## 3 ENSG00000105971 CAV2 9130. -8.35 5.25e-51
## 4 ENSG00000163682 RPL9 30771. -5.20 5.33e-49
## 5 ENSG00000167644 C19orf33 1720. -9.05 6.38e-40
## 6 ENSG00000069188 SDK2 1197. 8.99 1.55e-39tail(genes_ensembl_no_version.df)## # A tibble: 6 × 5
## gene_id gene_name baseMean log2FoldChange padj
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ENSG00000151725 CENPU 4333. 0.000153 1.00
## 2 ENSG00000270424 ENSG00000270424 11.5 -0.000535 1.00
## 3 ENSG00000224186 PITX1-AS1 269. -0.000167 1.00
## 4 ENSG00000143753 DEGS1 3148. 0.0000166 1.00
## 5 ENSG00000132763 MMACHC 1167. 0.0000149 1.00
## 6 ENSG00000129315 CCNT1 5978. -0.000000280 1.00# Get vector of all ENSEMBL IDs in our data set
ensembl_id_vector <- genes_ensembl_no_version.df$gene_id
# Using org.Hs.eg.db find ENTREZ IDs corresponding to our ENSEMBL IDs
ensembl_to_entrez.df <- clusterProfiler::bitr(geneID = ensembl_id_vector,
fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = "org.Hs.eg.db")
# Check result
head(ensembl_to_entrez.df)## ENSEMBL ENTREZID
## 1 ENSG00000130294 547
## 2 ENSG00000075340 119
## 3 ENSG00000105971 858
## 4 ENSG00000163682 6133
## 5 ENSG00000167644 64073
## 6 ENSG00000069188 54549# Rename the ENSEMBL ID column to "gene_id"
# to make it consistent with the source data
# (to allow for easy merging with the original dataframe)
ensembl_to_entrez.df <- ensembl_to_entrez.df %>%
dplyr::rename("gene_id" = ENSEMBL)
# Check result
head(ensembl_to_entrez.df)## gene_id ENTREZID
## 1 ENSG00000130294 547
## 2 ENSG00000075340 119
## 3 ENSG00000105971 858
## 4 ENSG00000163682 6133
## 5 ENSG00000167644 64073
## 6 ENSG00000069188 54549# Add a column with ENTREZ IDs to our dataset
genes_all.df <- genes_ensembl_no_version.df %>%
left_join(ensembl_to_entrez.df, by = "gene_id")
# Check result
dim(genes_all.df)## [1] 25575 6head(genes_all.df)## # A tibble: 6 × 6
## gene_id gene_name baseMean log2FoldChange padj ENTREZID
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 ENSG00000130294 KIF1A 42588. 11.1 6.83e-72 547
## 2 ENSG00000075340 ADD2 6056. 9.97 6.37e-53 119
## 3 ENSG00000105971 CAV2 9130. -8.35 5.25e-51 858
## 4 ENSG00000163682 RPL9 30771. -5.20 5.33e-49 6133
## 5 ENSG00000167644 C19orf33 1720. -9.05 6.38e-40 64073
## 6 ENSG00000069188 SDK2 1197. 8.99 1.55e-39 54549# Clean-up (remove unneeded interim data)
rm(genes_raw.df, genes_ensembl_no_version.df,
ensembl_to_entrez.df, ensembl_id_vector)2. GSEA
2.1 Order by fold change
During DESeq analysis, we sorted our gene list by adjusted p-value. Although it could be used for GSEA, it is more common to order genes by fold change, or by a combination of fold change and p-value, for instance: lfc / padj .
In the analysis below we will order genes by fold change.
genes_all.df <- genes_all.df %>%
arrange(desc(log2FoldChange))
# Check result
head(genes_all.df)## # A tibble: 6 × 6
## gene_id gene_name baseMean log2FoldChange padj ENTREZID
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 ENSG00000117154 IGSF21 930. 13.4 2.05e- 5 84966
## 2 ENSG00000101180 HRH3 696. 13.0 2.14e- 6 11255
## 3 ENSG00000149295 DRD2 2428. 12.9 4.32e- 6 1813
## 4 ENSG00000185518 SV2B 2208. 12.7 7.36e- 7 9899
## 5 ENSG00000286190 ENSG00000286190 1027. 12.6 6.57e-19 728392
## 6 ENSG00000286190 ENSG00000286190 1027. 12.6 6.57e-19 124900388tail(genes_all.df)## # A tibble: 6 × 6
## gene_id gene_name baseMean log2FoldChange padj ENTREZID
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 ENSG00000184995 IFNE 239. -11.3 4.13e- 4 338376
## 2 ENSG00000189052 CGB5 503. -11.3 7.28e- 4 93659
## 3 ENSG00000284981 UPK3BL2 350. -11.8 2.54e- 3 107983993
## 4 ENSG00000228437 LINC02474 410. -12.0 2.36e- 4 <NA>
## 5 ENSG00000138271 GPR87 836. -12.1 1.73e- 9 53836
## 6 ENSG00000087128 TMPRSS11E 493. -12.3 9.69e-10 289832.2 GO
The input data for Gene Set Enrichment Analysis (GSEA) should be provided as sorted vectors presenting some numeric feature, such as log fold changes, p-values or others. These vectors must be named by the gene IDs. However, different types of analysis may need different IDs. Thus, GO enrichment needs Ensembl IDs, while KEGG enrichment requires Entrez IDs as the gene names.
# Get the sorted log-fold change for GO GSEA
ensembl_gene_list <- genes_all.df$log2FoldChange
# Add Ensembl IDs as names for GO enrichment
names(ensembl_gene_list) <- genes_all.df$gene_id
# Carry out GO sets enrichment analysis
go_gsea <- gseGO(geneList = ensembl_gene_list,
OrgDb = org.Hs.eg.db,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
keyType = "ENSEMBL")
# Explore result
go_gsea.df <- as.data.frame(go_gsea)
colnames(go_gsea.df)## [1] "ID" "Description" "setSize" "enrichmentScore" "NES" "pvalue" "p.adjust" "qvalue" "rank" "leading_edge" "core_enrichment"go_gsea.df <- go_gsea.df %>%
dplyr::select(ID,Description,setSize,enrichmentScore,p.adjust) %>%
arrange(p.adjust)
head(go_gsea.df)## ID Description setSize enrichmentScore p.adjust
## GO:0030534 GO:0030534 adult behavior 119 0.6417390 8.177397e-08
## GO:0051607 GO:0051607 defense response to virus 267 -0.5149410 8.177397e-08
## GO:0140546 GO:0140546 defense response to symbiont 267 -0.5149410 8.177397e-08
## GO:0099003 GO:0099003 vesicle-mediated transport in synapse 201 0.5669958 8.177397e-08
## GO:0050804 GO:0050804 modulation of chemical synaptic transmission 390 0.5137496 8.177397e-08
## GO:0099177 GO:0099177 regulation of trans-synaptic signaling 391 0.5129046 8.177397e-08# Plot GO enrichment results
dotplot(go_gsea)
# Clean-up
rm(ensembl_gene_list)2.3 KEGG
KEGG enrichment analysis requires Entrez IDs as the gene names.
# Remove rows with missed entrez ids (appr 20% of rows)
filtered_genes_all.df <- genes_all.df %>% filter(!is.na(ENTREZID))
# Get the sorted log-fold changes for GSEA
entrez_gene_list <- filtered_genes_all.df$log2FoldChange
# Add ENTREZID as names for KEGG enrichment
names(entrez_gene_list) <- filtered_genes_all.df$ENTREZID
# Carry out KEGG pathways enrichment analysis
kegg_gsea <- gseKEGG(geneList = entrez_gene_list,
organism = 'hsa',
pvalueCutoff = 0.05)
# Explore result
enriched_kegg.df <- as.data.frame(kegg_gsea)
colnames(enriched_kegg.df)## [1] "ID" "Description" "setSize" "enrichmentScore" "NES" "pvalue" "p.adjust" "qvalue" "rank" "leading_edge" "core_enrichment"enriched_kegg.df <- enriched_kegg.df %>%
dplyr::select(ID,Description,setSize,enrichmentScore,p.adjust) %>%
arrange(p.adjust)
head(enriched_kegg.df)## ID Description setSize enrichmentScore p.adjust
## hsa04060 hsa04060 Cytokine-cytokine receptor interaction 179 -0.5128795 1.677375e-05
## hsa05323 hsa05323 Rheumatoid arthritis 67 -0.6477949 9.634365e-05
## hsa04725 hsa04725 Cholinergic synapse 94 0.6108013 1.537186e-04
## hsa04668 hsa04668 TNF signaling pathway 108 -0.5569894 1.537186e-04
## hsa05164 hsa05164 Influenza A 127 -0.5271519 1.537186e-04
## hsa04621 hsa04621 NOD-like receptor signaling pathway 141 -0.5035455 1.606471e-04# Plot enrichment results
dotplot(kegg_gsea)
# Clean-up
rm(filtered_genes_all.df, entrez_gene_list)3. Over-representation
3.1 Select Differentially Expressed Genes
Over-representation analysis requires a list of Differentially Expressed Genes (DEGs).
# Select DEGs with
# arbitrary criteria:
# padj < 0.01 and Fold Change > 2x
degs_0.01_2x_entrez.df <- genes_all.df %>%
filter(padj <= 0.01, abs(log2FoldChange) >= 1)
dim(degs_0.01_2x_entrez.df)## [1] 1415 6head(degs_0.01_2x_entrez.df)## # A tibble: 6 × 6
## gene_id gene_name baseMean log2FoldChange padj ENTREZID
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 ENSG00000117154 IGSF21 930. 13.4 2.05e- 5 84966
## 2 ENSG00000101180 HRH3 696. 13.0 2.14e- 6 11255
## 3 ENSG00000149295 DRD2 2428. 12.9 4.32e- 6 1813
## 4 ENSG00000185518 SV2B 2208. 12.7 7.36e- 7 9899
## 5 ENSG00000286190 ENSG00000286190 1027. 12.6 6.57e-19 728392
## 6 ENSG00000286190 ENSG00000286190 1027. 12.6 6.57e-19 124900388tail(degs_0.01_2x_entrez.df)## # A tibble: 6 × 6
## gene_id gene_name baseMean log2FoldChange padj ENTREZID
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 ENSG00000184995 IFNE 239. -11.3 4.13e- 4 338376
## 2 ENSG00000189052 CGB5 503. -11.3 7.28e- 4 93659
## 3 ENSG00000284981 UPK3BL2 350. -11.8 2.54e- 3 107983993
## 4 ENSG00000228437 LINC02474 410. -12.0 2.36e- 4 <NA>
## 5 ENSG00000138271 GPR87 836. -12.1 1.73e- 9 53836
## 6 ENSG00000087128 TMPRSS11E 493. -12.3 9.69e-10 289833.2 GO
# Create list of DEGs' Ensembl IDs
degs_ensembl_gene_list <- degs_0.01_2x_entrez.df$gene_id
# carry out GO over-representation analysis
go_ora <- enrichGO(gene = degs_ensembl_gene_list,
OrgDb = org.Hs.eg.db,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
keyType = "ENSEMBL")
# Save result to data frame
go_ora.df <- as.data.frame(go_ora)
head(go_ora.df)## ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count
## GO:0022843 GO:0022843 voltage-gated monoatomic cation channel activity 29/933 160/21059 8.914405e-11 7.693132e-08 6.728030e-08 ENSG00000177301/ENSG00000075043/ENSG00000153253/ENSG00000182674/ENSG00000138622/ENSG00000132639/ENSG00000151079/ENSG00000006071/ENSG00000148408/ENSG00000075429/ENSG00000173826/ENSG00000177272/ENSG00000183873/ENSG00000164588/ENSG00000055118/ENSG00000135519/ENSG00000171303/ENSG00000129159/ENSG00000171385/ENSG00000162687/ENSG00000184185/ENSG00000151067/ENSG00000196876/ENSG00000184408/ENSG00000153956/ENSG00000069431/ENSG00000121361/ENSG00000170745/ENSG00000175294 29
## GO:0005267 GO:0005267 potassium channel activity 24/933 128/21059 1.823836e-09 7.869853e-07 6.882582e-07 ENSG00000177301/ENSG00000163873/ENSG00000075043/ENSG00000182674/ENSG00000138622/ENSG00000132639/ENSG00000151079/ENSG00000006071/ENSG00000173826/ENSG00000177272/ENSG00000164588/ENSG00000055118/ENSG00000135519/ENSG00000171303/ENSG00000129159/ENSG00000171385/ENSG00000162687/ENSG00000184185/ENSG00000184408/ENSG00000105737/ENSG00000095209/ENSG00000069431/ENSG00000121361/ENSG00000170745 24
## GO:0005249 GO:0005249 voltage-gated potassium channel activity 21/933 104/21059 4.673300e-09 8.981298e-07 7.854596e-07 ENSG00000177301/ENSG00000075043/ENSG00000182674/ENSG00000138622/ENSG00000132639/ENSG00000151079/ENSG00000006071/ENSG00000173826/ENSG00000177272/ENSG00000164588/ENSG00000055118/ENSG00000135519/ENSG00000171303/ENSG00000129159/ENSG00000171385/ENSG00000162687/ENSG00000184185/ENSG00000184408/ENSG00000069431/ENSG00000121361/ENSG00000170745 21
## GO:0005244 GO:0005244 voltage-gated monoatomic ion channel activity 31/933 214/21059 6.230463e-09 8.981298e-07 7.854596e-07 ENSG00000177301/ENSG00000075043/ENSG00000153253/ENSG00000182674/ENSG00000138622/ENSG00000132639/ENSG00000151079/ENSG00000006071/ENSG00000148408/ENSG00000075429/ENSG00000173826/ENSG00000177272/ENSG00000183873/ENSG00000164588/ENSG00000055118/ENSG00000135519/ENSG00000171303/ENSG00000177098/ENSG00000129159/ENSG00000171385/ENSG00000162687/ENSG00000184185/ENSG00000151067/ENSG00000196876/ENSG00000184408/ENSG00000161509/ENSG00000153956/ENSG00000069431/ENSG00000121361/ENSG00000170745/ENSG00000175294 31
## GO:0022832 GO:0022832 voltage-gated channel activity 31/933 214/21059 6.230463e-09 8.981298e-07 7.854596e-07 ENSG00000177301/ENSG00000075043/ENSG00000153253/ENSG00000182674/ENSG00000138622/ENSG00000132639/ENSG00000151079/ENSG00000006071/ENSG00000148408/ENSG00000075429/ENSG00000173826/ENSG00000177272/ENSG00000183873/ENSG00000164588/ENSG00000055118/ENSG00000135519/ENSG00000171303/ENSG00000177098/ENSG00000129159/ENSG00000171385/ENSG00000162687/ENSG00000184185/ENSG00000151067/ENSG00000196876/ENSG00000184408/ENSG00000161509/ENSG00000153956/ENSG00000069431/ENSG00000121361/ENSG00000170745/ENSG00000175294 31
## GO:0022839 GO:0022839 monoatomic ion gated channel activity 42/933 354/21059 6.701677e-09 8.981298e-07 7.854596e-07 ENSG00000177301/ENSG00000163873/ENSG00000075043/ENSG00000153253/ENSG00000182674/ENSG00000138622/ENSG00000132639/ENSG00000120251/ENSG00000151079/ENSG00000006071/ENSG00000148408/ENSG00000175344/ENSG00000075429/ENSG00000173826/ENSG00000160716/ENSG00000177272/ENSG00000130054/ENSG00000183873/ENSG00000164588/ENSG00000055118/ENSG00000152578/ENSG00000135519/ENSG00000171303/ENSG00000177098/ENSG00000129159/ENSG00000171385/ENSG00000162687/ENSG00000184185/ENSG00000151067/ENSG00000196876/ENSG00000184408/ENSG00000161509/ENSG00000119782/ENSG00000153956/ENSG00000105737/ENSG00000069431/ENSG00000121361/ENSG00000167895/ENSG00000102287/ENSG00000170745/ENSG00000103534/ENSG00000175294 42# Plot top results
dotplot(go_ora)
# Clean-up
rm(degs_ensembl_gene_list)3.3 KEGG
# Create list of Entrez gene IDs
degs_entrez_gene_list <- degs_0.01_2x_entrez.df$ENTREZID
# carry out pathway over-representation analysis
kegg_ora <- enrichKEGG(gene = degs_entrez_gene_list,
organism = 'hsa',
pvalueCutoff = 0.05,
pAdjustMethod = "BH")
# Save result to data frame
kegg_ora.df <- as.data.frame(kegg_ora)
head(kegg_ora.df)## category subcategory ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count
## hsa04512 Environmental Information Processing Signaling molecules and interaction hsa04512 ECM-receptor interaction 17/429 89/8842 9.999040e-07 0.0003029709 0.0002705004 9899/9900/1288/5649/3680/6382/6385/3339/3912/1101/3691/7057/3914/3909/3694/3371/3918 17
## hsa05146 Human Diseases Infectious disease: parasitic hsa05146 Amoebiasis 15/429 103/8842 1.221142e-04 0.0126009528 0.0112504599 1288/5579/384/5296/81/3912/9630/7097/2921/3576/3914/3909/2920/2769/3918 15
## hsa04510 Cellular Processes Cellular community - eukaryotes hsa04510 Focal adhesion 23/429 203/8842 1.247619e-04 0.0126009528 0.0112504599 57144/1288/596/5649/5579/3680/5296/2317/81/824/3912/1101/3691/7057/3914/3909/1956/1950/3694/857/3371/858/3918 23
## hsa04080 Environmental Information Processing Signaling molecules and interaction hsa04080 Neuroactive ligand-receptor interaction 34/429 368/8842 2.120014e-04 0.0160591026 0.0143379863 11255/1813/117/2899/1394/2891/2914/1139/1141/2912/554/4985/2893/1395/6751/1132/2905/2901/7425/2696/5021/5734/718/5724/2564/5618/9002/8698/5028/147/3269/154/2150/5029 34
## hsa04514 Environmental Information Processing Signaling molecules and interaction hsa04514 Cell adhesion molecules 18/429 157/8842 5.793526e-04 0.0302853477 0.0270395496 4684/26047/9379/23705/9378/114798/83700/5789/23114/57502/1002/57863/3680/6382/6385/3108/3383/1001 18
## hsa04024 Environmental Information Processing Signal transduction hsa04024 cAMP signaling pathway 23/429 226/8842 5.997099e-04 0.0302853477 0.0270395496 1813/117/108/1394/10021/84152/2891/56670/482/816/2893/1395/4878/6751/775/5296/2905/2696/5021/338442/2353/27198/154 23# Plot top results
dotplot(kegg_ora)
# Clean-up
rm(degs_entrez_gene_list)4. Plot a KEGG pathway
TNF signalling (pid=hsa04668) has been found as one of the top enriched KEGG pathways in GSEA analysis. The ggkegg::rawMap function can be used to plot the KEGG pathway diagram with our enriched genes highlighted.
rawMap(kegg_gsea, pid = "hsa04668")
End section
# save image
save.image("Functional_analysis.RData")
# List objects
ls()## [1] "base_folder" "degs_0.01_2x_entrez.df" "enriched_kegg.df" "genes_all.df" "go_gsea" "go_gsea.df" "go_ora" "go_ora.df" "kegg_category" "kegg_gsea" "kegg_ora" "kegg_ora.df"# List libraries etc
sessionInfo()## R version 4.3.1 (2023-06-16 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8 LC_CTYPE=English_United Kingdom.utf8 LC_MONETARY=English_United Kingdom.utf8 LC_NUMERIC=C LC_TIME=English_United Kingdom.utf8
##
## time zone: Europe/London
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggkegg_1.0.13 tidygraph_1.3.1 igraph_2.0.3 XML_3.99-0.16.1 ggraph_2.2.1 ggplot2_3.5.1 clusterProfiler_4.10.1 org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1 IRanges_2.36.0 S4Vectors_0.40.2 Biobase_2.62.0 BiocGenerics_0.48.1 stringi_1.8.3 dplyr_1.1.4 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8 magrittr_2.0.3 magick_2.8.3 farver_2.1.1 rmarkdown_2.26 GlobalOptions_0.1.2 fs_1.6.4 zlibbioc_1.48.2 vctrs_0.6.5 memoise_2.0.1 RCurl_1.98-1.14 ggtree_3.10.1 htmltools_0.5.8 curl_5.2.1 gridGraphics_0.5-1 sass_0.4.9 bslib_0.7.0 plyr_1.8.9 cachem_1.0.8 lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.6-0 R6_2.5.1 fastmap_1.1.1 gson_0.1.0 GenomeInfoDbData_1.2.11 digest_0.6.33 aplot_0.2.2 enrichplot_1.22.0 colorspace_2.1-0 patchwork_1.2.0 RSQLite_2.3.6 labeling_0.4.3 filelock_1.0.3 fansi_1.0.6 httr_1.4.7 polyclip_1.10-6 compiler_4.3.1 bit64_4.0.5
## [42] withr_3.0.0 BiocParallel_1.36.0 viridis_0.6.5 DBI_1.2.2 highr_0.10 ggforce_0.4.2 MASS_7.3-60 rjson_0.2.21 HDO.db_0.99.1 tools_4.3.1 ape_5.8 scatterpie_0.2.3 glue_1.7.0 nlme_3.1-164 GOSemSim_2.28.1 grid_4.3.1 shadowtext_0.1.3 reshape2_1.4.4 snow_0.4-4 fgsea_1.28.0 generics_0.1.3 gtable_0.3.5 tzdb_0.4.0 tidyr_1.3.1 data.table_1.15.4 hms_1.1.3 utf8_1.2.4 XVector_0.42.0 ggrepel_0.9.5 pillar_1.9.0 stringr_1.5.1 vroom_1.6.5 yulab.utils_0.1.4 splines_4.3.1 tweenr_2.0.3 BiocFileCache_2.10.2 treeio_1.26.0 lattice_0.22-6 bit_4.0.5 tidyselect_1.2.1 GO.db_3.18.0
## [83] Biostrings_2.70.3 knitr_1.46 gridExtra_2.3 xfun_0.43 graphlayouts_1.1.1 lazyeval_0.2.2 ggfun_0.1.5 yaml_2.3.8 evaluate_0.23 codetools_0.2-20 tibble_3.2.1 qvalue_2.34.0 ggplotify_0.1.2 cli_3.6.1 munsell_0.5.1 jquerylib_0.1.4 Rcpp_1.0.12 GenomeInfoDb_1.38.8 dbplyr_2.5.0 png_0.1-8 parallel_4.3.1 blob_1.2.4 DOSE_3.28.2 bitops_1.0-7 viridisLite_0.4.2 tidytree_0.4.6 scales_1.3.0 purrr_1.0.2 crayon_1.5.2 GetoptLong_1.0.5 rlang_1.1.1 cowplot_1.1.3 fastmatch_1.1-4 KEGGREST_1.42.0# Time stamp
Sys.time()## [1] "2024-06-26 16:22:10 BST"