Gene ontology (GO) analysis
Ha M. Tran
08-01-2024
Last updated: 2024-01-10
Checks: 6 1
Knit directory: 140_treg_uNK/1_analysis/
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# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)
# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(DT)
library(extrafont)
library(VennDiagram)
# Custom ggplot
library(gridExtra)
library(ggbiplot)
library(ggrepel)
library(rrvgo)
library(d3treeR)
library(plotly)
library(GOSemSim)
library(data.table)
# Bioconductor packages:
library(edgeR)
library(limma)
library(Glimma)
library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)
library(patchwork)
library(pandoc)
library(knitr)
opts_knit$set(progress = FALSE, verbose = FALSE)
opts_chunk$set(warning=FALSE, message=FALSE, echo=FALSE)Gene ontology (GO) Analysis
Functional enrichment analysis is a method used to identify biological functions or processes overrepresented in a set of genes or proteins.
Gene Ontology (GO) is a standardized system for annotating genes and their products with terms from a controlled vocabulary, organized into three main categories: Molecular Function, Biological Process, and Cellular Component.
Biological Process (BP): Describes the larger, coordinated biological events or processes in which a gene product is involved. This category represents a series of molecular events that contribute to a specific function.
Molecular Function (MF): Describes the specific molecular activities that a gene product performs, such as catalytic or binding activities.
Cellular Component (CC): Describes the location or structure within the cell where a gene product is active, such as the nucleus, cytoplasm, or membrane.
Each of these three main categories is further organized into a hierarchical structure with more specific terms. The terms become more specialized as you move down the hierarchy (ontology level). Comparing a gene list to a reference database offers critical insights into the biological significance of gene expression changes.
Visualisations
The following visualisations are GO enrichment analysis performed with set of DE genes significantly below FDR 0.1 without FC threshold (TREAT). IMPORTANTLY, these GO terms are all significantly enriched (FDR <0.05)
Dot plot: illustrates the top 25 enriched GO terms.
- \(Gene ratio =\) the number of significant DE gene in the term / the total of number of genes in the term. Indicated by the size
- The shapes represents the three main GO categories, either BP, MP, or CC
Table: list of all the significant GO terms
Upset: illustrate the overlap of gene between different functional terms
Semantic similarity plots - GO specific
Due to the hierarchical structure of Gene Ontologies, the enriched sets generated often exhibit redundancy and pose challenges in interpretation. The subsequent analyses and visualizations seek to alleviate this redundancy in GO sets by grouping comparable terms based on their semantic similarity. The underlying concept behind measuring semantic similarity is grounded in the idea that genes sharing similar functions should possess analogous annotation vocabulary and exhibit close relationships within the ontology structure.
NOTE: the following semantic similarity analyses are performed using Graph-based method (Wang et al. 2007)
Dendrogram plot: performs hierarchical clustering on the semantic similarity of GO terms.
- NOTE: to maintain readability, only the top 50 most significant GO terms are clustered. These clusters are then divided into 9 clades and labeled using the top 4 high-frequency words.
Scatter plot: illustrates the UMAP space between semantically similar significant GO terms
- Distances represent the similarity between terms,
- Size represents the significance (in \(-\log_{10}FDR\)))
- NOTE: to maintain reability, only the top 15 most significant parent terms are labeled. Parent terms are the most significant term in a particular cluster
Treemap plot: Visualise the of hierarchical structures of semantically similar GO terms.
- The terms are colored based on their parent term,
- The size of the term is proportional to the significance.
I recommend reading through the full list of significant GO terms and selecting the most biologically relevant for better visualisation
DT vs PBS
Table
Scatter plot
| Version | Author | Date |
|---|---|---|
| 762020e | tranmanhha135 | 2024-01-09 |
Interactive scatter
3D Interactive scatter
Treg vs PBS
Table
.pl
Scatter
.pl
| Version | Author | Date |
|---|---|---|
| 762020e | tranmanhha135 | 2024-01-09 |
Interactive Scatter
.pl3D scatter
.plParent term
.pl
Treemap
interactive_treemap(reducedTerms[[1]][[i]])
| Version | Author | Date |
|---|---|---|
| 762020e | tranmanhha135 | 2024-01-09 |
Treg vs DT
Table
.pl
Scatter
.pl
| Version | Author | Date |
|---|---|---|
| 762020e | tranmanhha135 | 2024-01-09 |
Interactive Scatter
.pl3D scatter
.plParent term
.pl
Treemap
interactive_treemap(reducedTerms[[1]][[i]])
| Version | Author | Date |
|---|---|---|
| 762020e | tranmanhha135 | 2024-01-09 |
Combined
Venn diagram
#### Dot plot
Parent term Venn
NULL
Parent term dot
Biological processes parent terms
Export Data
The following are exported:
- GO.xlsx - This spreadsheet contains all significantly enriched GO terms. NOTE:
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] htmltools_0.5.7 knitr_1.45 pandoc_0.2.0
[4] patchwork_1.1.3 enrichplot_1.20.3 org.Mm.eg.db_3.17.0
[7] AnnotationDbi_1.62.2 IRanges_2.34.1 S4Vectors_0.38.2
[10] Biobase_2.60.0 BiocGenerics_0.46.0 clusterProfiler_4.8.3
[13] Glimma_2.10.0 edgeR_3.42.4 limma_3.56.2
[16] data.table_1.14.10 GOSemSim_2.26.1 plotly_4.10.3
[19] d3treeR_0.1 rrvgo_1.12.2 ggrepel_0.9.4
[22] ggbiplot_0.55 scales_1.3.0 plyr_1.8.9
[25] gridExtra_2.3 VennDiagram_1.7.3 futile.logger_1.4.3
[28] extrafont_0.19 DT_0.31 kableExtra_1.3.4
[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] ggplotify_0.1.2 bitops_1.0-7
[5] polyclip_1.10-6 XML_3.99-0.16
[7] lifecycle_1.0.4 rprojroot_2.0.4
[9] MASS_7.3-60 NLP_0.2-1
[11] lattice_0.22-5 crosstalk_1.2.1
[13] sass_0.4.8 rmarkdown_2.25
[15] jquerylib_0.1.4 yaml_2.3.8
[17] httpuv_1.6.13 askpass_1.2.0
[19] reticulate_1.34.0 cowplot_1.1.2
[21] DBI_1.2.0 RColorBrewer_1.1-3
[23] abind_1.4-5 zlibbioc_1.46.0
[25] rvest_1.0.3 GenomicRanges_1.52.1
[27] ggraph_2.1.0 RCurl_1.98-1.13
[29] yulab.utils_0.1.2 rappdirs_0.3.3
[31] tweenr_2.0.2 git2r_0.33.0
[33] GenomeInfoDbData_1.2.10 data.tree_1.1.0
[35] tm_0.7-11 tidytree_0.4.6
[37] pheatmap_1.0.12 umap_0.2.10.0
[39] RSpectra_0.16-1 svglite_2.1.3
[41] gridSVG_1.7-5 codetools_0.2-19
[43] DelayedArray_0.26.7 ggforce_0.4.1
[45] DOSE_3.26.2 xml2_1.3.6
[47] tidyselect_1.2.0 aplot_0.2.2
[49] farver_2.1.1 viridis_0.6.4
[51] matrixStats_1.2.0 webshot_0.5.5
[53] jsonlite_1.8.8 ellipsis_0.3.2
[55] tidygraph_1.3.0 systemfonts_1.0.5
[57] ggnewscale_0.4.9 tools_4.3.2
[59] ragg_1.2.7 treeio_1.24.3
[61] Rcpp_1.0.11 glue_1.6.2
[63] Rttf2pt1_1.3.12 here_1.0.1
[65] xfun_0.41 DESeq2_1.40.2
[67] qvalue_2.32.0 MatrixGenerics_1.12.3
[69] GenomeInfoDb_1.36.4 withr_2.5.2
[71] formatR_1.14 fastmap_1.1.1
[73] ggh4x_0.2.7 fansi_1.0.6
[75] openssl_2.1.1 digest_0.6.33
[77] gridGraphics_0.5-1 timechange_0.2.0
[79] R6_2.5.1 mime_0.12
[81] textshaping_0.3.7 colorspace_2.1-0
[83] GO.db_3.17.0 RSQLite_2.3.4
[85] utf8_1.2.4 generics_0.1.3
[87] graphlayouts_1.0.2 httr_1.4.7
[89] htmlwidgets_1.6.4 S4Arrays_1.0.6
[91] scatterpie_0.2.1 whisker_0.4.1
[93] pkgconfig_2.0.3 gtable_0.3.4
[95] blob_1.2.4 workflowr_1.7.1
[97] XVector_0.40.0 shadowtext_0.1.2
[99] fgsea_1.26.0 ggupset_0.3.0
[101] png_0.1-8 wordcloud_2.6
[103] ggfun_0.1.3 lambda.r_1.2.4
[105] rstudioapi_0.15.0 tzdb_0.4.0
[107] nlme_3.1-163 cachem_1.0.8
[109] parallel_4.3.2 HDO.db_0.99.1
[111] treemap_2.4-4 pillar_1.9.0
[113] vctrs_0.6.5 slam_0.1-50
[115] promises_1.2.1 xtable_1.8-4
[117] extrafontdb_1.0 evaluate_0.23
[119] cli_3.6.2 locfit_1.5-9.8
[121] compiler_4.3.2 futile.options_1.0.1
[123] rlang_1.1.2 crayon_1.5.2
[125] labeling_0.4.3 fs_1.6.3
[127] stringi_1.8.3 viridisLite_0.4.2
[129] gridBase_0.4-7 BiocParallel_1.34.2
[131] munsell_0.5.0 Biostrings_2.68.1
[133] lazyeval_0.2.2 Matrix_1.6-3
[135] hms_1.1.3 bit64_4.0.5
[137] KEGGREST_1.40.1 shiny_1.8.0
[139] highr_0.10 SummarizedExperiment_1.30.2
[141] igraph_1.6.0 memoise_2.0.1
[143] bslib_0.6.1 ggtree_3.8.2
[145] fastmatch_1.1-4 bit_4.0.5
[147] downloader_0.4 gson_0.1.0
[149] ape_5.7-1