Reactome Analysis (Robertson Lab)
Ha Tran
22/08/2021
Last updated: 2025-11-27
Checks: 7 0
Knit directory: 5_gd_Tcell/1_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | d519e7f | Ha Tran | 2024-12-03 | Build site. |
| Rmd | 9fc0156 | Ha Tran | 2024-12-03 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| html | 5dce909 | Ha Tran | 2024-11-07 | Build site. |
| html | d71eeb4 | Ha Tran | 2024-10-16 | Build site. |
| Rmd | 5f0c7a1 | Ha Tran | 2024-10-16 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| html | ae93fcc | tranmanhha135 | 2024-02-02 | Build site. |
| Rmd | fbcdd69 | tranmanhha135 | 2024-02-02 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| html | 2c24612 | tranmanhha135 | 2022-10-13 | build website |
| Rmd | 324032b | tranmanhha135 | 2022-10-11 | resize images |
| html | 11a5cf4 | tranmanhha135 | 2022-10-03 | build wedsite |
| Rmd | 1101367 | tranmanhha135 | 2022-10-02 | Completed functional enrichment for all comparison |
| html | 1101367 | tranmanhha135 | 2022-10-02 | Completed functional enrichment for all comparison |
| html | 68585df | tranmanhha135 | 2022-09-20 | build webste |
| Rmd | 192d010 | tranmanhha135 | 2022-09-20 | functional enrichment with new dataset |
| html | 192d010 | tranmanhha135 | 2022-09-20 | functional enrichment with new dataset |
| Rmd | 0df047f | tranmanhha135 | 2022-09-08 | minor changes to build and publish |
| html | 0df047f | tranmanhha135 | 2022-09-08 | minor changes to build and publish |
| html | 54e0166 | Ha Manh Tran | 2022-01-01 | Build site. |
| html | 32454d5 | Ha Manh Tran | 2022-01-01 | Build site. |
| Rmd | c667dd0 | Ha Manh Tran | 2022-01-01 | workflowr::wflow_publish(files = here::here(c("1_analysis/index.Rmd", |
| Rmd | 3b268fc | Ha Tran | 2021-12-09 | multiple FC, reactome, big clean up. |
| html | 3b268fc | Ha Tran | 2021-12-09 | multiple FC, reactome, big clean up. |
| Rmd | 7247e9c | Ha Tran | 2021-11-30 | additional KEGG heatmap, pathview, reactome |
| html | 7247e9c | Ha Tran | 2021-11-30 | additional KEGG heatmap, pathview, reactome |
# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)
library(VennDiagram)
# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(pander)
library(cowplot)
library(pheatmap)
library(DT)
library(extrafont)
# Custom ggplot
library(ggbiplot)
library(ggrepel)
# Bioconductor packages:
library(edgeR)
library(limma)
library(Glimma)
library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)
library(ReactomePA)
library(pandoc)
library(knitr)
opts_knit$set(progress = FALSE, verbose = FALSE)
opts_chunk$set(warning=FALSE, message=FALSE, echo=FALSE)Reactome
Reactome database provides curated information about biological pathways, including molecular events and reactions within cells. It focuses on human biology and is widely used for pathway analysis and functional interpretation of high-throughput data.
KEGG and Reactome both include approximately the same number of genes. The difference lies in KEGG’s use of broader terms, while Reactome employs similar terms but with multiple detailed entries.
In the Reactome database, terms are organized hierarchically based on the classification of biological pathways. The organization follows a tree-like structure, where terms represent different levels of granularity in understanding molecular events and reactions within cells
Visualisation
The following visualisations are Reactome enrichment analysis performed with set of DE genes. IMPORTANTLY, these Reactome terms are significantly enriched with P value < 0.05.
Dot plot: illustrates the top enriched Reactome 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 Reactome 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 Reactome pathways and selecting the most biologically relevant for better visualisation
The following visualisations are Reactome enrichment analysis performed with set of DE genes. IMPORTANTLY, significant Reactome pathways are significantly if P value < 0.05
Dot plot: illustrates the enriched Reactome 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 Reactome pathways
Upset: illustrate the overlap of gene between different pathways
I recommend reading through the full list of significant Reactome pathways and selecting the most biologically relevant for more in-depth visualisation
INT vs CONT
Table
INT vs SVX_VAS
Table
SVX vs SVX_VAS
Table
VAS vs SVX_VAS
Table
SVX_VAS vs CONT
Table
INT vs VAS
Table
Export Data
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS 26.1
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_1.50 pandoc_0.2.0 ReactomePA_1.50.0
[4] enrichplot_1.26.6 org.Mm.eg.db_3.20.0 AnnotationDbi_1.68.0
[7] IRanges_2.40.1 S4Vectors_0.44.0 Biobase_2.66.0
[10] BiocGenerics_0.52.0 clusterProfiler_4.14.6 Glimma_2.16.0
[13] edgeR_4.4.2 limma_3.62.2 ggrepel_0.9.6
[16] ggbiplot_0.6.2 extrafont_0.19 DT_0.34.0
[19] pheatmap_1.0.13 cowplot_1.2.0 pander_0.6.6
[22] kableExtra_1.4.0 VennDiagram_1.7.3 futile.logger_1.4.3
[25] lubridate_1.9.4 forcats_1.0.0 stringr_1.5.2
[28] purrr_1.1.0 tidyr_1.3.1 ggplot2_4.0.0
[31] tidyverse_2.0.0 reshape2_1.4.4 tibble_3.3.0
[34] readr_2.1.5 magrittr_2.0.4 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] splines_4.4.1 later_1.4.4
[3] ggplotify_0.1.3 R.oo_1.27.1
[5] polyclip_1.10-7 graph_1.84.1
[7] lifecycle_1.0.4 rprojroot_2.1.1
[9] lattice_0.22-7 MASS_7.3-65
[11] crosstalk_1.2.2 sass_0.4.10
[13] rmarkdown_2.29 jquerylib_0.1.4
[15] yaml_2.3.10 httpuv_1.6.16
[17] ggtangle_0.0.7 DBI_1.2.3
[19] RColorBrewer_1.1-3 abind_1.4-8
[21] zlibbioc_1.52.0 GenomicRanges_1.58.0
[23] R.utils_2.13.0 ggraph_2.2.2
[25] yulab.utils_0.2.1 tweenr_2.0.3
[27] rappdirs_0.3.3 git2r_0.36.2
[29] GenomeInfoDbData_1.2.13 tidytree_0.4.6
[31] reactome.db_1.89.0 svglite_2.2.1
[33] codetools_0.2-20 DelayedArray_0.32.0
[35] DOSE_4.0.1 xml2_1.4.0
[37] ggforce_0.5.0 tidyselect_1.2.1
[39] aplot_0.2.9 UCSC.utils_1.2.0
[41] farver_2.1.2 viridis_0.6.5
[43] matrixStats_1.5.0 jsonlite_2.0.0
[45] tidygraph_1.3.1 systemfonts_1.2.3
[47] tools_4.4.1 ragg_1.5.0
[49] treeio_1.30.0 Rcpp_1.1.0
[51] glue_1.8.0 gridExtra_2.3
[53] Rttf2pt1_1.3.12 SparseArray_1.6.2
[55] here_1.0.2 xfun_0.53
[57] DESeq2_1.46.0 qvalue_2.38.0
[59] MatrixGenerics_1.18.1 GenomeInfoDb_1.42.3
[61] withr_3.0.2 formatR_1.14
[63] fastmap_1.2.0 digest_0.6.37
[65] timechange_0.3.0 R6_2.6.1
[67] gridGraphics_0.5-1 textshaping_1.0.3
[69] colorspace_2.1-1 GO.db_3.20.0
[71] RSQLite_2.4.3 R.methodsS3_1.8.2
[73] generics_0.1.4 data.table_1.17.8
[75] graphlayouts_1.2.2 httr_1.4.7
[77] htmlwidgets_1.6.4 S4Arrays_1.6.0
[79] graphite_1.52.0 whisker_0.4.1
[81] pkgconfig_2.0.3 gtable_0.3.6
[83] blob_1.2.4 workflowr_1.7.2
[85] S7_0.2.0 XVector_0.46.0
[87] htmltools_0.5.8.1 fgsea_1.32.4
[89] ggupset_0.4.1 scales_1.4.0
[91] png_0.1-8 ggfun_0.2.0
[93] lambda.r_1.2.4 rstudioapi_0.17.1
[95] tzdb_0.5.0 nlme_3.1-168
[97] cachem_1.1.0 parallel_4.4.1
[99] pillar_1.11.1 vctrs_0.6.5
[101] promises_1.3.3 extrafontdb_1.0
[103] evaluate_1.0.5 cli_3.6.5
[105] locfit_1.5-9.12 compiler_4.4.1
[107] futile.options_1.0.1 rlang_1.1.6
[109] crayon_1.5.3 labeling_0.4.3
[111] plyr_1.8.9 fs_1.6.6
[113] writexl_1.5.4 stringi_1.8.7
[115] viridisLite_0.4.2 BiocParallel_1.40.2
[117] Biostrings_2.74.1 lazyeval_0.2.2
[119] GOSemSim_2.32.0 Matrix_1.7-4
[121] hms_1.1.3 patchwork_1.3.2
[123] bit64_4.6.0-1 KEGGREST_1.46.0
[125] statmod_1.5.0 SummarizedExperiment_1.36.0
[127] igraph_2.1.4 memoise_2.0.1
[129] bslib_0.9.0 ggtree_3.14.0
[131] fastmatch_1.1-6 bit_4.6.0
[133] ape_5.8-1 gson_0.1.0