KEGG analysis
Ha M. Tran
09-01-2024
Last updated: 2024-08-01
Checks: 6 1
Knit directory: 5_Treg_uNK/1_analysis/
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# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)
library(KEGGREST)
library(data.table)
library(KEGGREST)
# Visualisation:
library(VennDiagram)
library(kableExtra)
library(ggplot2)
library(grid)
library(pander)
library(viridis)
library(cowplot)
library(pheatmap)
library(DT)
library(extrafont)
# Custom ggplot
library(ggplotify)
library(ggpubr)
library(ggbiplot)
library(ggrepel)
# Bioconductor packages:
library(edgeR)
library(limma)
library(Glimma)
library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)
library(pathview)
library(pandoc)
library(knitr)
opts_knit$set(progress = FALSE, verbose = FALSE)
opts_chunk$set(warning=FALSE, message=FALSE, echo=FALSE)KEGG Analysis
KEGG pathway images reproduced by permission from Kanehisa Laboratories, September 2023
KEGG analysis, or Kyoto Encyclopedia of Genes and Genomes analysis, is a method that involves the mapping of molecular datasets, such as DE genes or proteins, to the reference pathways in the KEGG database. The KEGG database provides a comprehensive resource for understanding the molecular interaction and reaction networks within biological systems. KEGG pathways encompass a wide range of biological processes, including metabolism, cellular processes, environmental information processing, and human diseases.
More information about KEGG
The KEGG database is organized into several classes:
Pathway Maps (PATH): This class includes diagrams of molecular interactions and reactions in various biological pathways. Each pathway map is associated with specific biological processes or functions.
BRITE (B): BRITE is a hierarchical classification of biological entities, such as genes, proteins, and compounds. It provides a functional hierarchy and relationships between different biological components.
Module (M): Modules are sets of manually defined functional units, which can represent functional modules of genes or proteins in specific pathways.
Orthology (KO): The Orthology class provides information about orthologous gene groups, which are genes in different species that evolved from a common ancestral gene. This class is particularly useful for comparative genomics.
For this analysis, only KEGG pathway database will be used. This database is further sub-categorised into several classes:
- Metabolism
- Genetic Information Processing
- Environmental Information Processing
- Cellular Processes
- Organismal Systems
- Immune system (i.e. T cell receptor signaling pathway , Th1 and Th2 cell differentiation & etc.)
- Endocrine system (i.e. Estrogen signaling pathway , Progesterone-mediated oocyte maturation & etc.)
- Human Diseases
- Drug Development
Visualisation
The following visualisations are KEGG enrichment analysis performed with set of DE genes significantly below FDR < 0.1 without FC threshold (TREAT). IMPORTANTLY, these KEGG terms are significantly enriched with FDR < 0.2.
Dot plot: illustrates the top 7 enriched KEGG pathways
- \(Gene ratio =\) the number of significant DE gene in the term / the total of number of genes in the term as indicated by the size
Table: list of all the significant KEGG pathways
- NOTE: To keep this a readable table, the full pathway description were removed, check the exported Excel spreadsheet for full details on pathways class, descriptions, related pathways, and references
Upset: illustrate the overlap of gene between different pathways
I recommend reading through the full list of significant KEGG pathways and selecting the most biologically relevant for better visualisation
DT vs veh
Dot plot
Table
DT+Treg vs veh
Dot plot
Table
DT+Treg vs DT
Dot plot
Table
Pathway specific heatmaps
Here the top 4 most interesting KEGG pathways were selected for further visualisations.
These visualisations are similarly performed with set of DE genes significantly below FDR < 0.1 or < 0.05 with and without FC threshold (TREAT). IMPORTANTLY, these KEGG terms are all significantly enriched but only with P-value < 0.05 and no P-value correction method.
Heatmap: illustrates the expression of genes in specific KEGG pathways.
- The genes (rows) and samples (columns) are heirachirally clusterd based on their expression profile.
Table: list of all the significant DE genes in the specified KEGG pathway
Pathview: maps gene expression data onto the specified KEGG pathways, allowing users to see where genes in their dataset are located within specific pathways, and potential upstream and downstream elements.
Antigen processing and presentation
Table
Pathview
Natural killer cell mediated cytotoxicity
Table
Pathview
Epstein-Barr virus infection
Table
Pathview
Export Data
The following are exported:
KEGG_all.xlsx - This spreadsheet contains all KEGG pathways
KEGG_sig.xlsx - This spreadsheet contains all significant (P value < 0.05) KEGG pathways
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Adelaide
tzcode source: internal
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] knitr_1.48 pandoc_0.2.0 pathview_1.44.0
[4] enrichplot_1.24.2 org.Mm.eg.db_3.19.1 AnnotationDbi_1.66.0
[7] IRanges_2.38.1 S4Vectors_0.42.1 Biobase_2.64.0
[10] BiocGenerics_0.50.0 clusterProfiler_4.12.2 Glimma_2.14.0
[13] edgeR_4.2.1 limma_3.60.4 ggrepel_0.9.5.9999
[16] ggbiplot_0.6.2 ggpubr_0.6.0 ggplotify_0.1.2
[19] extrafont_0.19 DT_0.33 pheatmap_1.0.12
[22] cowplot_1.1.3 viridis_0.6.5 viridisLite_0.4.2
[25] pander_0.6.5 kableExtra_1.4.0 VennDiagram_1.7.3
[28] futile.logger_1.4.3 data.table_1.15.4 KEGGREST_1.44.1
[31] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[34] purrr_1.0.2 tidyr_1.3.1 ggplot2_3.5.1
[37] tidyverse_2.0.0 reshape2_1.4.4 tibble_3.2.1
[40] readr_2.1.5 magrittr_2.0.3 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] fs_1.6.4 matrixStats_1.3.0
[3] bitops_1.0-8 HDO.db_0.99.1
[5] httr_1.4.7 RColorBrewer_1.1-3
[7] doParallel_1.0.17 Rgraphviz_2.48.0
[9] tools_4.4.1 backports_1.5.0
[11] utf8_1.2.4 R6_2.5.1
[13] lazyeval_0.2.2 GetoptLong_1.0.5
[15] withr_3.0.1 gridExtra_2.3
[17] cli_3.6.3 textshaping_0.4.0
[19] formatR_1.14 scatterpie_0.2.3
[21] labeling_0.4.3 sass_0.4.9
[23] KEGGgraph_1.64.0 systemfonts_1.1.0
[25] yulab.utils_0.1.5 gson_0.1.0
[27] ggupset_0.4.0 DOSE_3.30.2
[29] svglite_2.1.3 rstudioapi_0.16.0
[31] RSQLite_2.3.7 generics_0.1.3
[33] gridGraphics_0.5-1 shape_1.4.6.1
[35] crosstalk_1.2.1 car_3.1-2
[37] GO.db_3.19.1 Matrix_1.7-0
[39] fansi_1.0.6 abind_1.4-5
[41] lifecycle_1.0.4 whisker_0.4.1
[43] yaml_2.3.10 carData_3.0-5
[45] SummarizedExperiment_1.34.0 qvalue_2.36.0
[47] SparseArray_1.4.8 blob_1.2.4
[49] promises_1.3.0 crayon_1.5.3
[51] lattice_0.22-6 magick_2.8.4
[53] pillar_1.9.0 ComplexHeatmap_2.20.0
[55] fgsea_1.30.0 GenomicRanges_1.56.1
[57] rjson_0.2.21 codetools_0.2-20
[59] fastmatch_1.1-4 glue_1.7.0
[61] ggfun_0.1.5 vctrs_0.6.5
[63] png_0.1-8 treeio_1.28.0
[65] gtable_0.3.5 cachem_1.1.0
[67] xfun_0.46 S4Arrays_1.4.1
[69] tidygraph_1.3.1 iterators_1.0.14
[71] statmod_1.5.0 nlme_3.1-165
[73] ggtree_3.12.0 bit64_4.0.5
[75] GenomeInfoDb_1.40.1 rprojroot_2.0.4
[77] bslib_0.8.0 colorspace_2.1-1
[79] DBI_1.2.3 DESeq2_1.44.0
[81] tidyselect_1.2.1 bit_4.0.5
[83] compiler_4.4.1 extrafontdb_1.0
[85] curl_5.2.1 git2r_0.33.0
[87] graph_1.82.0 xml2_1.3.6
[89] DelayedArray_0.30.1 shadowtext_0.1.4
[91] scales_1.3.0 rappdirs_0.3.3
[93] digest_0.6.36 rmarkdown_2.27
[95] XVector_0.44.0 htmltools_0.5.8.1
[97] pkgconfig_2.0.3 MatrixGenerics_1.16.0
[99] highr_0.11 fastmap_1.2.0
[101] rlang_1.1.4 GlobalOptions_0.1.2
[103] htmlwidgets_1.6.4 UCSC.utils_1.0.0
[105] farver_2.1.2 jquerylib_0.1.4
[107] jsonlite_1.8.8 BiocParallel_1.38.0
[109] GOSemSim_2.30.0 RCurl_1.98-1.16
[111] GenomeInfoDbData_1.2.12 patchwork_1.2.0
[113] munsell_0.5.1 Rcpp_1.0.13
[115] ape_5.8 stringi_1.8.4
[117] ggraph_2.2.1 zlibbioc_1.50.0
[119] MASS_7.3-61 plyr_1.8.9
[121] org.Hs.eg.db_3.19.1 parallel_4.4.1
[123] Biostrings_2.72.1 graphlayouts_1.1.1
[125] splines_4.4.1 hms_1.1.3
[127] circlize_0.4.16 locfit_1.5-9.10
[129] igraph_2.0.3 ggsignif_0.6.4
[131] futile.options_1.0.1 XML_3.99-0.17
[133] evaluate_0.24.0 lambda.r_1.2.4
[135] tzdb_0.4.0 foreach_1.5.2
[137] tweenr_2.0.3 httpuv_1.6.15
[139] Rttf2pt1_1.3.12 polyclip_1.10-7
[141] clue_0.3-65 ggforce_0.4.2
[143] broom_1.0.6 tidytree_0.4.6
[145] rstatix_0.7.2 later_1.3.2
[147] ragg_1.3.2 aplot_0.2.3
[149] memoise_2.0.1 writexl_1.5.0
[151] cluster_2.1.6 workflowr_1.7.1
[153] timechange_0.3.0 here_1.0.1