GO Analysis

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Last updated: 2023-09-16

Checks: 7 0

Knit directory: Mouse_endometrial_transcriptome_2023/1_analysis/

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Ignored files:
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  .gitignore
    Untracked:  0_data/RDS_objects/DT.rds

Unstaged changes:
    Modified:   0_data/RDS_objects/dge.rds
    Modified:   0_data/RDS_objects/enrichGO.rds
    Modified:   0_data/RDS_objects/enrichGO_sig.rds
    Modified:   0_data/RDS_objects/fc.rds
    Modified:   0_data/RDS_objects/lfc.rds
    Modified:   0_data/RDS_objects/lmTreat.rds
    Modified:   0_data/RDS_objects/lmTreat_all.rds
    Modified:   0_data/RDS_objects/lmTreat_sig.rds
    Modified:   0_data/RDS_objects/pub.rds
    Deleted:    1_analysis/mmu04060.pv.png
    Deleted:    1_analysis/mmu04151.pv.png
    Deleted:    1_analysis/mmu04270.pv.png
    Deleted:    1_analysis/mmu04510.pv.png
    Deleted:    1_analysis/mmu04640.pv.png
    Deleted:    1_analysis/mmu04670.pv.png
    Modified:   2_plots/de/pval_1.05.svg
    Modified:   2_plots/de/pval_1.1.svg
    Modified:   2_plots/de/pval_1.5.svg
    Modified:   2_plots/go/bp_dot_1.05.svg
    Modified:   2_plots/go/bp_dot_1.5.svg
    Modified:   2_plots/go/cc_dot_1.05.svg
    Modified:   2_plots/go/cc_dot_1.5.svg
    Modified:   2_plots/go/mf_dot_1.5.svg
    Modified:   2_plots/go/upset_1.05.svg
    Modified:   2_plots/go/upset_1.1.svg
    Modified:   2_plots/go/upset_1.5.svg
    Modified:   2_plots/ipa/Cell-To-Cell Signaling.svg
    Modified:   2_plots/ipa/diseaseAndFunction.svg
    Modified:   2_plots/ipa/pathways.svg
    Modified:   2_plots/kegg/kegg_dot_1.05.svg
    Modified:   2_plots/kegg/kegg_dot_1.1.svg
    Modified:   2_plots/kegg/kegg_dot_1.5.svg
    Modified:   2_plots/kegg/upset_kegg_1.05.svg
    Modified:   2_plots/kegg/upset_kegg_1.1.svg
    Modified:   2_plots/kegg/upset_kegg_1.5.svg
    Modified:   2_plots/qc/PCA_IntvsCont.svg
    Modified:   2_plots/qc/counts_after_filtering_3_3.svg
    Modified:   2_plots/qc/counts_before_after_filtering_3_3.svg
    Modified:   2_plots/qc/counts_before_filtering.svg
    Modified:   2_plots/qc/library_size.svg
    Modified:   2_plots/reactome/react_dot_1.05.svg
    Modified:   2_plots/reactome/react_dot_1.1.svg
    Modified:   2_plots/reactome/react_dot_1.5.svg
    Modified:   2_plots/reactome/upset_react_1.05.svg
    Modified:   2_plots/reactome/upset_react_1.1.svg
    Modified:   2_plots/reactome/upset_react_1.5.svg
    Modified:   3_output/enrichGO_sig.xlsx
    Modified:   3_output/enrichKEGG_all.xlsx
    Modified:   3_output/enrichKEGG_sig.xlsx
    Modified:   3_output/lmTreat_all.xlsx
    Modified:   3_output/lmTreat_fc1.5_voom2_all_fdr.xlsx
    Modified:   3_output/lmTreat_sig.xlsx
    Modified:   3_output/reactome_all.xlsx
    Modified:   3_output/reactome_sig.xlsx

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These are the previous versions of the repository in which changes were made to the R Markdown (1_analysis/go.Rmd) and HTML (docs/go.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	18c6463	Ha Manh Tran	2023-09-16	Added KEGG copyright permission and changed kable
html	b44640a	Ha Manh Tran	2023-01-23	Build site.
Rmd	eb10bef	Ha Manh Tran	2023-01-22	workflowr::wflow_publish(here::here("1_analysis/*.Rmd"))
html	8c9178d	tranmanhha135	2023-01-21	added png
html	d578f46	Ha Manh Tran	2023-01-21	Build site.
html	159f352	tranmanhha135	2023-01-21	adding for pathview
Rmd	4d51a4e	tranmanhha135	2023-01-20	test png
html	4d51a4e	tranmanhha135	2023-01-20	test png
html	691cf34	Ha Manh Tran	2023-01-20	Build site.
Rmd	7f6bab2	Ha Manh Tran	2023-01-20	workflowr::wflow_publish(here::here("1_analysis/*.Rmd"))
Rmd	b6cf190	tranmanhha135	2023-01-19	quick commit
Rmd	3119fad	tranmanhha135	2022-11-05	build website
html	3119fad	tranmanhha135	2022-11-05	build website

---

Data Setup

# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)

# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(DT)

# Custom ggplot
library(gridExtra)
library(ggbiplot)
library(ggrepel)


# Bioconductor packages:
library(edgeR)
library(limma)
library(Glimma)
library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)

theme_set(theme_minimal())
pub <- readRDS(here::here("0_data/RDS_objects/pub.rds"))
DT <- readRDS(here::here("0_data/RDS_objects/DT.rds"))

Import DGElist Data

DGElist object containing the raw feature count, sample metadata, and gene metadata, created in the Set Up stage.

# load DGElist previously created in the set up
dge <- readRDS(here::here("0_data/RDS_objects/dge.rds"))
fc <- readRDS(here::here("0_data/RDS_objects/fc.rds"))
lfc <- readRDS(here::here("0_data/RDS_objects/lfc.rds"))
lmTreat <- readRDS(here::here("0_data/RDS_objects/lmTreat.rds"))
lmTreat_sig <- readRDS(here::here("0_data/RDS_objects/lmTreat_sig.rds"))

GO Analysis

goSummaries is a package created by Dr Stephen Pederson for filtering GO terms based on ontology level.

# circumvent rerunning of lengthy analysis.
enrichGO <- readRDS(here::here("0_data/RDS_objects/enrichGO.rds"))
enrichGO_sig <- readRDS(here::here("0_data/RDS_objects/enrichGO_sig.rds"))

# download go summaries and set the minimum ontology level
goSummaries <- url("https://uofabioinformaticshub.github.io/summaries2GO/data/goSummaries.RDS") %>%
  readRDS()
minPath <- 3

enrichGO=list()
enrichGO_sig <- list()
for (i in 1:length(fc)) {
  x <- fc[i] %>% as.character()
  # find enriched GO terms
  enrichGO[[x]] <- clusterProfiler::enrichGO(
    gene = lmTreat_sig[[x]]$entrezid,
    OrgDb = org.Mm.eg.db,
    keyType = "ENTREZID",
    ont = "ALL",
    pAdjustMethod = "fdr",
    pvalueCutoff = 0.05
  )

  # bind to goSummaries to elminate go terms with ontology levels 1 and 2.
  enrichGO_sig[[x]] <- enrichGO[[x]] %>% 
    clusterProfiler::setReadable(OrgDb = org.Mm.eg.db, keyType = "auto")

  enrichGO_sig[[x]] <- enrichGO_sig[[x]] %>%
    as.data.frame() %>%
    rownames_to_column("id") %>%
    left_join(goSummaries) %>%
    dplyr::filter(shortest_path >= minPath) %>%
    column_to_rownames("id")

  # adjust go results, separate compound column, add FDR column, adjust the GeneRatio column
  enrichGO_sig[[x]] <- enrichGO_sig[[x]] %>%
    separate(col = BgRatio, sep = "/", into = c("Total", "Universe")) %>%
    dplyr::mutate(
      logFDR = -log(p.adjust, 10),
      GeneRatio = Count / as.numeric(Total)) %>% 
    dplyr::select(c("Description", "ontology", "GeneRatio", "pvalue", "p.adjust", "logFDR", "qvalue", "geneID", "Count"))
  
  # at the beginnning of a word (after 35 characters), add a newline. shorten the y axis for dot plot 
  # enrichGO_sig[[x]]$Description <- sub(pattern = "(.{1,35})(?:$| )", 
  #                                      replacement = "\\1\n", 
  #                                      x = enrichGO_sig[[x]]$Description)
  
  # # remove the additional newline at the end of the string
  # enrichGO_sig[[x]]$Description <- sub(pattern = "\n$", 
  #                                      replacement = "", 
  #                                      x = enrichGO_sig[[x]]$Description)
}

FC=1.05

# display the top 30 most sig
enrichGO_sig[[1]] %>% 
  dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>% 
  DT(., caption = "Significantly enriched GO terms")

  # kable(caption = "Significantly enriched GO terms") %>%
  # kable_styling(bootstrap_options = c("striped", "hover")) %>%
  # scroll_box(height = "600px")

Visualisation

bp_dot=list()
mf_dot=list()
cc_dot=list()
upset=list()
for (i in 1:length(fc)) {
  x <- fc[i] %>% as.character()
  
  # extract the enriched GO terms from each ontology
  bp <- enrichGO_sig[[x]] %>% dplyr::filter(ontology == "BP") 
  mf <- enrichGO_sig[[x]] %>% dplyr::filter(ontology == "MF")
  cc <- enrichGO_sig[[x]] %>% dplyr::filter(ontology == "CC")

  # bp dot plot, save
  bp_dot[[x]] <- ggplot(bp[1:15, ]) +
    geom_point(aes(x = GeneRatio, y = reorder(Description, GeneRatio), colour = logFDR, size = Count)) +
    scale_color_gradient(low = "dodgerblue3", high = "firebrick3", limits = c(0, NA)) +
    scale_size(range = c(.5,3)) +
    ggtitle("Biological Process") +
    ylab(label = "") +
    xlab(label = "Gene Ratio") +
    labs(color = expression("-log"[10] * "FDR"), size = "Gene Counts")
  ggsave(filename = paste0("bp_dot_", fc[i], ".svg"), plot = bp_dot[[x]] + pub, path = here::here("2_plots/go/"), 
         width = 200, height = 120, units = "mm")

  # mf dot plot, save
  mf_dot[[x]] <- ggplot(mf[1:15, ]) +
    geom_point(aes(x = GeneRatio, y = reorder(Description, GeneRatio), colour = logFDR, size = Count)) +
    scale_color_gradient(low = "dodgerblue3", high = "firebrick3", limits = c(0, NA)) +
    scale_size(range = c(.5,3)) +
    ggtitle("Molecular Function") +
    ylab(label = "") +
    xlab(label = "Gene Ratio") +
    labs(color = expression("-log"[10] * "FDR"), size = "Gene Counts")
  ggsave(filename = paste0("mf_dot_", fc[i], ".svg"), plot = mf_dot[[x]] + pub, path = here::here("2_plots/go/"), 
         width = 200, height = 120, units = "mm")

  # cc dot plot, save
  cc_dot[[x]] <- ggplot(cc[1:15, ]) +
    geom_point(aes(x = GeneRatio, y = reorder(Description, GeneRatio), colour = logFDR, size = Count)) +
    scale_color_gradient(low = "dodgerblue3", high = "firebrick3", limits = c(0, NA)) +
    scale_size(range = c(.5,3)) +
    ggtitle("Cellular Components") +
    ylab(label = "") +
    xlab(label = "Gene Ratio") +
    labs(color = expression("-log"[10] * "FDR"), size = "Gene Counts")
  ggsave(filename = paste0("cc_dot_", fc[i], ".svg"), plot = cc_dot[[x]] + pub, path = here::here("2_plots/go/"), 
         width = 200, height = 120, units = "mm")
  
  upset[[x]] <- upsetplot(x = enrichGO[[x]], 10)
  ggsave(filename = paste0("upset_", fc[i], ".svg"), plot = upset[[x]], path = here::here("2_plots/go/"), width = 250, height = 166, units = "mm")
}

Biological Process

bp_dot[[1]] 

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Molecular Function

mf_dot[[1]]

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Cellular Components

cc_dot[[1]]

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Upset

upset[[1]]

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

FC=1.1

# display the top 30 most sig
enrichGO_sig[[2]] %>% 
  dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
  DT(.,caption = "Significantly enriched GO terms")

  # kable(caption = "Significantly enriched GO terms") %>%
  # kable_styling(bootstrap_options = c("striped", "hover")) %>%
  # scroll_box(height = "600px")

Visualisation

Biological Process

bp_dot[[2]]

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Molecular Function

mf_dot[[2]]

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Cellular Components

cc_dot[[2]]

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Upset

upset[[2]]

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

FC=1.5

# display the top 30 most sig
enrichGO_sig[[3]] %>% 
  dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
  DT(.,caption = "Significantly enriched GO terms") 

  # kable(caption = "Significantly enriched GO terms") %>%
  # kable_styling(bootstrap_options = c("striped", "hover")) %>%
  # scroll_box(height = "600px")

Visualisation

Biological Process

bp_dot[[3]]

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Molecular Function

mf_dot[[3]]

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Cellular Components

cc_dot[[3]]

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
4d51a4e	tranmanhha135	2023-01-20
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Upset

upset[[3]]

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
d578f46	Ha Manh Tran	2023-01-21
159f352	tranmanhha135	2023-01-21
691cf34	Ha Manh Tran	2023-01-20
3119fad	tranmanhha135	2022-11-05

Export Data

# save to excel
writexl::write_xlsx(x = enrichGO_sig, here::here("3_output/enrichGO_sig.xlsx"))
saveRDS(object = enrichGO_sig,file = here::here("0_data/RDS_objects/enrichGO_sig.rds"))
saveRDS(object = enrichGO,file = here::here("0_data/RDS_objects/enrichGO.rds"))

Session information

sessionInfo()

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] enrichplot_1.20.1     org.Mm.eg.db_3.17.0   AnnotationDbi_1.62.2 
 [4] IRanges_2.34.1        S4Vectors_0.38.1      Biobase_2.60.0       
 [7] BiocGenerics_0.46.0   clusterProfiler_4.8.3 Glimma_2.10.0        
[10] edgeR_3.42.4          limma_3.56.2          ggrepel_0.9.3        
[13] ggbiplot_0.55         scales_1.2.1          plyr_1.8.8           
[16] gridExtra_2.3         DT_0.29               kableExtra_1.3.4     
[19] lubridate_1.9.2       forcats_1.0.0         stringr_1.5.0        
[22] purrr_1.0.1           tidyr_1.3.0           ggplot2_3.4.3        
[25] tidyverse_2.0.0       reshape2_1.4.4        tibble_3.2.1         
[28] readr_2.1.4           magrittr_2.0.3        dplyr_1.1.2          

loaded via a namespace (and not attached):
  [1] splines_4.3.1               later_1.3.1                
  [3] bitops_1.0-7                ggplotify_0.1.2            
  [5] polyclip_1.10-4             lifecycle_1.0.3            
  [7] rprojroot_2.0.3             lattice_0.21-8             
  [9] MASS_7.3-60                 crosstalk_1.2.0            
 [11] sass_0.4.7                  rmarkdown_2.24             
 [13] jquerylib_0.1.4             yaml_2.3.7                 
 [15] httpuv_1.6.11               cowplot_1.1.1              
 [17] DBI_1.1.3                   RColorBrewer_1.1-3         
 [19] abind_1.4-5                 zlibbioc_1.46.0            
 [21] rvest_1.0.3                 GenomicRanges_1.52.0       
 [23] ggraph_2.1.0                RCurl_1.98-1.12            
 [25] yulab.utils_0.0.9           tweenr_2.0.2               
 [27] git2r_0.32.0                GenomeInfoDbData_1.2.10    
 [29] tidytree_0.4.5              svglite_2.1.1              
 [31] codetools_0.2-19            DelayedArray_0.26.7        
 [33] DOSE_3.26.1                 xml2_1.3.5                 
 [35] ggforce_0.4.1               tidyselect_1.2.0           
 [37] aplot_0.2.1                 farver_2.1.1               
 [39] viridis_0.6.4               matrixStats_1.0.0          
 [41] webshot_0.5.5               jsonlite_1.8.7             
 [43] ellipsis_0.3.2              tidygraph_1.2.3            
 [45] systemfonts_1.0.4           tools_4.3.1                
 [47] treeio_1.24.3               ragg_1.2.5                 
 [49] Rcpp_1.0.11                 glue_1.6.2                 
 [51] xfun_0.39                   here_1.0.1                 
 [53] DESeq2_1.40.2               qvalue_2.32.0              
 [55] MatrixGenerics_1.12.3       GenomeInfoDb_1.36.3        
 [57] withr_2.5.0                 fastmap_1.1.1              
 [59] fansi_1.0.4                 digest_0.6.33              
 [61] timechange_0.2.0            R6_2.5.1                   
 [63] gridGraphics_0.5-1          textshaping_0.3.6          
 [65] colorspace_2.1-0            GO.db_3.17.0               
 [67] RSQLite_2.3.1               utf8_1.2.3                 
 [69] generics_0.1.3              data.table_1.14.8          
 [71] graphlayouts_1.0.0          httr_1.4.7                 
 [73] htmlwidgets_1.6.2           S4Arrays_1.0.6             
 [75] scatterpie_0.2.1            whisker_0.4.1              
 [77] pkgconfig_2.0.3             gtable_0.3.4               
 [79] blob_1.2.4                  workflowr_1.7.1            
 [81] XVector_0.40.0              shadowtext_0.1.2           
 [83] htmltools_0.5.5             fgsea_1.26.0               
 [85] ggupset_0.3.0               png_0.1-8                  
 [87] ggfun_0.1.3                 knitr_1.44                 
 [89] rstudioapi_0.15.0           tzdb_0.4.0                 
 [91] nlme_3.1-163                cachem_1.0.8               
 [93] parallel_4.3.1              HDO.db_0.99.1              
 [95] pillar_1.9.0                vctrs_0.6.3                
 [97] promises_1.2.0.1            evaluate_0.21              
 [99] cli_3.6.1                   locfit_1.5-9.8             
[101] compiler_4.3.1              rlang_1.1.1                
[103] crayon_1.5.2                labeling_0.4.3             
[105] fs_1.6.3                    writexl_1.4.2              
[107] stringi_1.7.12              viridisLite_0.4.2          
[109] BiocParallel_1.34.2         munsell_0.5.0              
[111] Biostrings_2.68.1           lazyeval_0.2.2             
[113] GOSemSim_2.26.1             Matrix_1.6-1               
[115] hms_1.1.3                   patchwork_1.1.3            
[117] bit64_4.0.5                 KEGGREST_1.40.0            
[119] SummarizedExperiment_1.30.2 igraph_1.5.1               
[121] memoise_2.0.1               bslib_0.5.1                
[123] ggtree_3.8.2                fastmatch_1.1-4            
[125] bit_4.0.5                   downloader_0.4             
[127] ape_5.7-1                   gson_0.1.0