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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(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 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.
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:
For data exploratory purposes, the following visualisations are KEGG enrichment analysis 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.
Dot plot: illustrates the top 7 enriched KEGG pathways
Table: list of all the significant KEGG pathways
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
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 4.3.2 (2023-10-31)
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_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Australia/Adelaide
tzcode source: system (glibc)
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] knitr_1.45 pandoc_0.2.0 pathview_1.40.0
[4] enrichplot_1.20.3 org.Mm.eg.db_3.17.0 AnnotationDbi_1.62.2
[7] IRanges_2.34.1 S4Vectors_0.38.2 Biobase_2.60.0
[10] BiocGenerics_0.46.0 clusterProfiler_4.8.3 Glimma_2.10.0
[13] edgeR_3.42.4 limma_3.56.2 ggrepel_0.9.4
[16] ggbiplot_0.55 scales_1.3.0 plyr_1.8.9
[19] ggpubr_0.6.0 ggplotify_0.1.2 extrafont_0.19
[22] DT_0.31 pheatmap_1.0.12 cowplot_1.1.2
[25] viridis_0.6.4 viridisLite_0.4.2 pander_0.6.5
[28] kableExtra_1.3.4 data.table_1.14.10 KEGGREST_1.40.1
[31] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[34] purrr_1.0.2 tidyr_1.3.0 ggplot2_3.4.4
[37] tidyverse_2.0.0 reshape2_1.4.4 tibble_3.2.1
[40] readr_2.1.4 magrittr_2.0.3 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] splines_4.3.2 later_1.3.2
[3] bitops_1.0-7 polyclip_1.10-6
[5] graph_1.78.0 XML_3.99-0.16
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] rprojroot_2.0.4 lattice_0.22-5
[11] MASS_7.3-60 crosstalk_1.2.1
[13] backports_1.4.1 sass_0.4.8
[15] rmarkdown_2.25 jquerylib_0.1.4
[17] yaml_2.3.8 httpuv_1.6.13
[19] DBI_1.2.0 RColorBrewer_1.1-3
[21] abind_1.4-5 zlibbioc_1.46.0
[23] rvest_1.0.3 GenomicRanges_1.52.1
[25] ggraph_2.1.0 RCurl_1.98-1.13
[27] yulab.utils_0.1.2 rappdirs_0.3.3
[29] tweenr_2.0.2 git2r_0.33.0
[31] GenomeInfoDbData_1.2.10 tidytree_0.4.6
[33] svglite_2.1.3 codetools_0.2-19
[35] DelayedArray_0.26.7 DOSE_3.26.2
[37] xml2_1.3.6 ggforce_0.4.1
[39] tidyselect_1.2.0 aplot_0.2.2
[41] farver_2.1.1 matrixStats_1.2.0
[43] webshot_0.5.5 jsonlite_1.8.8
[45] ellipsis_0.3.2 tidygraph_1.3.0
[47] systemfonts_1.0.5 tools_4.3.2
[49] treeio_1.24.3 Rcpp_1.0.11
[51] glue_1.6.2 gridExtra_2.3
[53] Rttf2pt1_1.3.12 here_1.0.1
[55] xfun_0.41 DESeq2_1.40.2
[57] qvalue_2.32.0 MatrixGenerics_1.12.3
[59] GenomeInfoDb_1.36.4 withr_2.5.2
[61] fastmap_1.1.1 fansi_1.0.6
[63] digest_0.6.33 timechange_0.2.0
[65] R6_2.5.1 gridGraphics_0.5-1
[67] colorspace_2.1-0 GO.db_3.17.0
[69] RSQLite_2.3.4 utf8_1.2.4
[71] generics_0.1.3 graphlayouts_1.0.2
[73] httr_1.4.7 htmlwidgets_1.6.4
[75] S4Arrays_1.0.6 scatterpie_0.2.1
[77] whisker_0.4.1 pkgconfig_2.0.3
[79] gtable_0.3.4 blob_1.2.4
[81] workflowr_1.7.1 XVector_0.40.0
[83] shadowtext_0.1.2 htmltools_0.5.7
[85] carData_3.0-5 fgsea_1.26.0
[87] ggupset_0.3.0 png_0.1-8
[89] ggfun_0.1.3 rstudioapi_0.15.0
[91] tzdb_0.4.0 curl_5.2.0
[93] nlme_3.1-163 org.Hs.eg.db_3.17.0
[95] cachem_1.0.8 parallel_4.3.2
[97] HDO.db_0.99.1 pillar_1.9.0
[99] vctrs_0.6.5 promises_1.2.1
[101] car_3.1-2 extrafontdb_1.0
[103] Rgraphviz_2.44.0 KEGGgraph_1.60.0
[105] evaluate_0.23 cli_3.6.2
[107] locfit_1.5-9.8 compiler_4.3.2
[109] rlang_1.1.2 crayon_1.5.2
[111] ggsignif_0.6.4 labeling_0.4.3
[113] fs_1.6.3 stringi_1.8.3
[115] BiocParallel_1.34.2 munsell_0.5.0
[117] Biostrings_2.68.1 lazyeval_0.2.2
[119] GOSemSim_2.26.1 Matrix_1.6-3
[121] hms_1.1.3 patchwork_1.1.3
[123] bit64_4.0.5 highr_0.10
[125] SummarizedExperiment_1.30.2 igraph_1.6.0
[127] broom_1.0.5 memoise_2.0.1
[129] bslib_0.6.1 ggtree_3.8.2
[131] fastmatch_1.1-4 bit_4.0.5
[133] downloader_0.4 gson_0.1.0
[135] ape_5.7-1