Last updated: 2021-02-23
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Knit directory: fitnessGWAS/
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library(dplyr)
library(tibble)
library(clusterProfiler)
library(fgsea)
library(ggplot2)
library(readr)
library(glue)
library(stringr)
library(tidyr)
library(kableExtra)
library(purrr)
library(DT)
source("code/GO_and_KEGG_gsea.R")
# Make html tables:
my_data_table <- function(df){
datatable(
df, rownames=FALSE,
autoHideNavigation = TRUE,
extensions = c("Scroller", "Buttons"),
options = list(
dom = 'Bfrtip',
deferRender=TRUE,
scrollX=TRUE, scrollY=400,
scrollCollapse=TRUE,
buttons =
list('pageLength', 'colvis', 'csv', list(
extend = 'pdf',
pageSize = 'A4',
orientation = 'landscape',
filename = 'GWAS_enrichment')),
columnDefs = list( list(targets = c(8,10), visible = FALSE)),
pageLength = 50
)
)
}
options(stringsAsFactors = FALSE)
# Connect to the database of annotations
db <- DBI::dbConnect(RSQLite::SQLite(), "data/derived/annotations.sqlite3")The following uses custom code stored in code/GO_and_KEGG_gsea.R, which in turn relies on the fgsea package. This test involves ranking genes by some statistic (e.g. the ) and then searching for GO and KEGG terms that are over-represented among genes at the top or bottom of the list.
Here, we first calculate the average for each of the mixture assignment probabilities (i.e. equal effects, sexual anatagonism, female-limited, and male limited) for all the loci mapping to each gene, to obtain gene-level measures. The reults are then used for GO and KEGG enrichment tests using fgsea. The output of fgsea includes the “normalised enrichment score” (NES), which describes how much the pathway is over-represented at the top of this list (for positive NES), or over-represented at the bottom (negative NES), and there is an associated \(p\)-value for each NES (calculated by resampling).
In the tables below, one can click ‘column visibility’ then either ‘leadingEdge’ or ‘genes’ to see a list of genes that were annotated with the focal GO or KEGG term and also scored highly for the focal gene-level metric.
gene_means <- tbl(db, "univariate_lmm_results") %>%
select(SNP, starts_with("P_")) %>% select(-P_null) %>%
left_join(tbl(db, "variants") %>% select(SNP, FBID), by = "SNP") %>%
select(-SNP) %>%
filter(!is.na(FBID)) %>%
collect(n=Inf) %>%
group_by(FBID) %>%
summarise_all(mean) %>%
left_join(tbl(db, "genes") %>%
select(FBID, gene_name) %>%
collect(n=Inf), by = "FBID")
concordant_enrichment <- GO_and_KEGG_gsea(gene_means, column = "P_equal_effects")
female_specific_enrichment <- GO_and_KEGG_gsea(gene_means, column = "P_female_specific")
male_specific_enrichment <- GO_and_KEGG_gsea(gene_means, column = "P_male_specific")
sex_antag_enrichment <- GO_and_KEGG_gsea(gene_means, column = "P_sex_antag")concordant_enrichment %>%
filter(Test_type == "KEGG") %>%
my_data_table()female_specific_enrichment %>%
filter(Test_type == "KEGG") %>%
my_data_table()male_specific_enrichment %>%
filter(Test_type == "KEGG") %>%
my_data_table()sex_antag_enrichment %>%
filter(Test_type == "KEGG") %>%
my_data_table()concordant_enrichment %>%
filter(Test_type == "GO: Biological process") %>%
my_data_table()female_specific_enrichment %>%
filter(Test_type == "GO: Biological process") %>%
my_data_table()male_specific_enrichment %>%
filter(Test_type == "GO: Biological process") %>%
my_data_table()sex_antag_enrichment %>%
filter(Test_type == "GO: Biological process") %>%
my_data_table()concordant_enrichment %>%
filter(Test_type == "GO: Molecular function") %>%
my_data_table()female_specific_enrichment %>%
filter(Test_type == "GO: Molecular function") %>%
my_data_table()male_specific_enrichment %>%
filter(Test_type == "GO: Molecular function") %>%
my_data_table()sex_antag_enrichment %>%
filter(Test_type == "GO: Molecular function") %>%
my_data_table()concordant_enrichment %>%
filter(Test_type == "GO: Cellular component") %>%
my_data_table()female_specific_enrichment %>%
filter(Test_type == "GO: Cellular component") %>%
my_data_table()male_specific_enrichment %>%
filter(Test_type == "GO: Cellular component") %>%
my_data_table()sex_antag_enrichment %>%
filter(Test_type == "GO: Cellular component") %>%
my_data_table()
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DT_0.13 purrr_0.3.4 kableExtra_1.1.0
[4] tidyr_1.1.0 stringr_1.4.0 glue_1.4.2
[7] readr_1.3.1 ggplot2_3.3.2 fgsea_1.14.0
[10] clusterProfiler_3.16.0 tibble_3.0.1 dplyr_1.0.0
loaded via a namespace (and not attached):
[1] fs_1.4.1 enrichplot_1.8.1 bit64_0.9-7
[4] webshot_0.5.2 RColorBrewer_1.1-2 progress_1.2.2
[7] httr_1.4.1 rprojroot_1.3-2 tools_4.0.3
[10] backports_1.1.7 R6_2.4.1 DBI_1.1.0
[13] BiocGenerics_0.34.0 colorspace_1.4-1 withr_2.2.0
[16] tidyselect_1.1.0 gridExtra_2.3 prettyunits_1.1.1
[19] bit_1.1-15.2 compiler_4.0.3 git2r_0.27.1
[22] rvest_0.3.5 Biobase_2.48.0 scatterpie_0.1.4
[25] xml2_1.3.2 triebeard_0.3.0 scales_1.1.1
[28] ggridges_0.5.2 digest_0.6.25 rmarkdown_2.5
[31] DOSE_3.14.0 pkgconfig_2.0.3 htmltools_0.5.0
[34] dbplyr_1.4.4 htmlwidgets_1.5.1 rlang_0.4.6
[37] rstudioapi_0.11 RSQLite_2.2.0 gridGraphics_0.5-0
[40] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0
[43] crosstalk_1.1.0.1 BiocParallel_1.22.0 GOSemSim_2.14.0
[46] magrittr_2.0.1 ggplotify_0.0.5 GO.db_3.11.4
[49] Matrix_1.2-18 Rcpp_1.0.4.6 munsell_0.5.0
[52] S4Vectors_0.26.1 viridis_0.5.1 lifecycle_0.2.0
[55] stringi_1.5.3 yaml_2.2.1 ggraph_2.0.3
[58] MASS_7.3-53 plyr_1.8.6 qvalue_2.20.0
[61] grid_4.0.3 blob_1.2.1 parallel_4.0.3
[64] promises_1.1.0 ggrepel_0.8.2 DO.db_2.9
[67] crayon_1.3.4 lattice_0.20-41 graphlayouts_0.7.0
[70] cowplot_1.0.0 splines_4.0.3 hms_0.5.3
[73] knitr_1.30 pillar_1.4.4 igraph_1.2.5
[76] reshape2_1.4.4 stats4_4.0.3 fastmatch_1.1-0
[79] evaluate_0.14 downloader_0.4 BiocManager_1.30.10
[82] data.table_1.12.8 vctrs_0.3.0 tweenr_1.0.1
[85] httpuv_1.5.3.1 urltools_1.7.3 gtable_0.3.0
[88] polyclip_1.10-0 assertthat_0.2.1 xfun_0.19
[91] ggforce_0.3.1 europepmc_0.4 tidygraph_1.2.0
[94] later_1.0.0 viridisLite_0.3.0 rvcheck_0.1.8
[97] AnnotationDbi_1.50.0 memoise_1.1.0 IRanges_2.22.2
[100] workflowr_1.6.2 ellipsis_0.3.1