- Reactome
- Visualisation
- Export Data
Reactome analysis
Ha M. Tran
05-02-2024
Last updated: 2024-02-08
Checks: 7 0
Knit directory: Treg_uNK/1_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8da2e31 | tranmanhha135 | 2024-02-08 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| html | d8d23ee | tranmanhha135 | 2024-01-13 | im on holiday |
| html | 36aeb85 | Ha Manh Tran | 2024-01-13 | Build site. |
| Rmd | a957cff | Ha Manh Tran | 2024-01-13 | workflowr::wflow_publish(here::here("1_analysis/*Rmd")) |
| Rmd | c78dfac | tranmanhha135 | 2024-01-12 | remote from ipad |
| Rmd | 221e2fa | tranmanhha135 | 2024-01-10 | fixed error |
| Rmd | 7d64f82 | tranmanhha135 | 2024-01-09 | completed ORA GO |
| Rmd | 05fa0b3 | tranmanhha135 | 2024-01-06 | added description |
# 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 significantly below FDR < 0.1 without FC threshold (TREAT). IMPORTANTLY, these Reactome terms are significantly enriched with FDR < 0.1.
Dot plot: illustrates the top enriched Reactome pathways
- Generatio= 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 significantly below FDR < 0.1 without FC threshold (TREAT). IMPORTANTLY, significant Reactome pathways are significantly if FDR < 0.1
Dot plot: illustrates the enriched Reactome pathways
- Generatio= 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
Export Data
The following are exported:
reactome_all.xlsx - This spreadsheet contains all Reactome pathways
reactome_sig.xlsx - This spreadsheet contains all significant (FDR < 0.1) Reactome 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 ReactomePA_1.44.0
[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] extrafont_0.19 DT_0.31 pheatmap_1.0.12
[22] cowplot_1.1.2 pander_0.6.5 kableExtra_1.3.4
[25] VennDiagram_1.7.3 futile.logger_1.4.3 lubridate_1.9.3
[28] forcats_1.0.0 stringr_1.5.1 purrr_1.0.2
[31] tidyr_1.3.0 ggplot2_3.4.4 tidyverse_2.0.0
[34] reshape2_1.4.4 tibble_3.2.1 readr_2.1.4
[37] 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 ggplotify_0.1.2
[5] polyclip_1.10-6 graph_1.78.0
[7] lifecycle_1.0.4 rprojroot_2.0.4
[9] lattice_0.22-5 MASS_7.3-60
[11] crosstalk_1.2.1 sass_0.4.8
[13] rmarkdown_2.25 jquerylib_0.1.4
[15] yaml_2.3.8 httpuv_1.6.13
[17] DBI_1.2.0 RColorBrewer_1.1-3
[19] abind_1.4-5 zlibbioc_1.46.0
[21] rvest_1.0.3 GenomicRanges_1.52.1
[23] ggraph_2.1.0 RCurl_1.98-1.13
[25] yulab.utils_0.1.2 rappdirs_0.3.3
[27] tweenr_2.0.2 git2r_0.33.0
[29] GenomeInfoDbData_1.2.10 tidytree_0.4.6
[31] reactome.db_1.84.0 svglite_2.1.3
[33] codetools_0.2-19 DelayedArray_0.26.7
[35] DOSE_3.26.2 xml2_1.3.6
[37] ggforce_0.4.1 tidyselect_1.2.0
[39] aplot_0.2.2 farver_2.1.1
[41] viridis_0.6.4 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] ragg_1.2.7 treeio_1.24.3
[51] Rcpp_1.0.11 glue_1.6.2
[53] gridExtra_2.3 Rttf2pt1_1.3.12
[55] here_1.0.1 xfun_0.41
[57] DESeq2_1.40.2 qvalue_2.32.0
[59] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.4
[61] withr_2.5.2 formatR_1.14
[63] fastmap_1.1.1 fansi_1.0.6
[65] digest_0.6.33 timechange_0.2.0
[67] R6_2.5.1 gridGraphics_0.5-1
[69] textshaping_0.3.7 colorspace_2.1-0
[71] GO.db_3.17.0 RSQLite_2.3.4
[73] utf8_1.2.4 generics_0.1.3
[75] data.table_1.14.10 graphlayouts_1.0.2
[77] httr_1.4.7 htmlwidgets_1.6.4
[79] S4Arrays_1.0.6 scatterpie_0.2.1
[81] graphite_1.46.0 whisker_0.4.1
[83] pkgconfig_2.0.3 gtable_0.3.4
[85] blob_1.2.4 workflowr_1.7.1
[87] XVector_0.40.0 shadowtext_0.1.2
[89] htmltools_0.5.7 fgsea_1.26.0
[91] ggupset_0.3.0 png_0.1-8
[93] ggfun_0.1.3 lambda.r_1.2.4
[95] rstudioapi_0.15.0 tzdb_0.4.0
[97] nlme_3.1-163 cachem_1.0.8
[99] parallel_4.3.2 HDO.db_0.99.1
[101] pillar_1.9.0 vctrs_0.6.5
[103] promises_1.2.1 extrafontdb_1.0
[105] evaluate_0.23 cli_3.6.2
[107] locfit_1.5-9.8 compiler_4.3.2
[109] futile.options_1.0.1 rlang_1.1.2
[111] crayon_1.5.2 labeling_0.4.3
[113] fs_1.6.3 writexl_1.4.2
[115] stringi_1.8.3 viridisLite_0.4.2
[117] BiocParallel_1.34.2 munsell_0.5.0
[119] Biostrings_2.68.1 lazyeval_0.2.2
[121] GOSemSim_2.26.1 Matrix_1.6-3
[123] hms_1.1.3 patchwork_1.1.3
[125] bit64_4.0.5 KEGGREST_1.40.1
[127] highr_0.10 SummarizedExperiment_1.30.2
[129] igraph_1.6.0 memoise_2.0.1
[131] bslib_0.6.1 ggtree_3.8.2
[133] fastmatch_1.1-4 bit_4.0.5
[135] downloader_0.4 gson_0.1.0
[137] ape_5.7-1