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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)
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.
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.
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.
Scatter plot: illustrates the UMAP space between semantically similar significant GO terms
Treemap plot: Visualise the of hierarchical structures of semantically similar GO terms.
I recommend reading through the full list of significant GO terms and selecting the most biologically relevant for better visualisation
Interactive scatter
3D Interactive scatter
[[1]]
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.pl
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.pl
.pl
Interactive Scatter
.pl
3D scatter
.pl
.pl
.pl
.pl
.pl
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Interactive Scatter
.pl
3D scatter
.pl
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NULL
The following are exported:
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
[3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Australia.utf8
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] 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_4.3.1 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_0.21-8 crosstalk_1.2.1
[13] sass_0.4.8 rmarkdown_2.25
[15] jquerylib_0.1.4 yaml_2.3.7
[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_0.1.1 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_4.3.1
[59] treeio_1.24.3 Rcpp_1.0.11
[61] glue_1.6.2 Rttf2pt1_1.3.12
[63] here_1.0.1 xfun_0.39
[65] DESeq2_1.40.2 qvalue_2.32.0
[67] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.4
[69] withr_2.5.2 formatR_1.14
[71] fastmap_1.1.1 ggh4x_0.2.7
[73] fansi_1.0.6 openssl_2.1.1
[75] digest_0.6.33 gridGraphics_0.5-1
[77] timechange_0.2.0 R6_2.5.1
[79] mime_0.12 colorspace_2.1-0
[81] GO.db_3.17.0 RSQLite_2.3.4
[83] utf8_1.2.4 generics_0.1.3
[85] graphlayouts_1.0.2 httr_1.4.7
[87] htmlwidgets_1.6.4 S4Arrays_1.0.6
[89] scatterpie_0.2.1 whisker_0.4.1
[91] pkgconfig_2.0.3 gtable_0.3.4
[93] blob_1.2.4 workflowr_1.7.1
[95] XVector_0.40.0 shadowtext_0.1.2
[97] fgsea_1.26.0 ggupset_0.3.0
[99] png_0.1-8 wordcloud_2.6
[101] ggfun_0.1.3 lambda.r_1.2.4
[103] rstudioapi_0.15.0 tzdb_0.4.0
[105] nlme_3.1-164 cachem_1.0.8
[107] parallel_4.3.1 HDO.db_0.99.1
[109] treemap_2.4-4 pillar_1.9.0
[111] vctrs_0.6.5 slam_0.1-50
[113] promises_1.2.1 xtable_1.8-4
[115] extrafontdb_1.0 evaluate_0.23
[117] cli_3.6.1 locfit_1.5-9.8
[119] compiler_4.3.1 futile.options_1.0.1
[121] rlang_1.1.1 crayon_1.5.2
[123] labeling_0.4.3 fs_1.6.3
[125] stringi_1.8.3 viridisLite_0.4.2
[127] gridBase_0.4-7 BiocParallel_1.34.2
[129] munsell_0.5.0 Biostrings_2.68.1
[131] lazyeval_0.2.2 Matrix_1.6-4
[133] hms_1.1.3 bit64_4.0.5
[135] KEGGREST_1.40.1 shiny_1.8.0
[137] highr_0.10 SummarizedExperiment_1.30.2
[139] igraph_1.6.0 memoise_2.0.1
[141] bslib_0.6.1 ggtree_3.8.2
[143] fastmatch_1.1-4 bit_4.0.5
[145] downloader_0.4 gson_0.1.0
[147] ape_5.7-1