14 GO富集分析
GO（Gene Ontology）是一个在生物信息学中广泛使用的概念，用于描述基因和基因产物的功能、它们所处的细胞位置以及它们参与的生物过程。GO项目是一个协作性的国际努力，旨在建立和维护一个适用于各种物种的、结构化的、可控制的词汇表（本体论），用以描述生物学的各个方面。具体来说，GO分为三个主要方面：
分子功能（Molecular Function, MF）：描述基因产物在分子水平上的活动，如催化活性或结合能力。生物过程（Biological Process, BP）：描述生物学上发生的一系列事件，如细胞生长、代谢过程或信号传导。细胞组件（Cellular Component, CC）：描述基因产物在细胞内的位置，如细胞核、线粒体或细胞膜。
14.1 加载R包
使用rm(list = ls())来清空环境中的所有变量。
14.2 导入数据
14.3 所需函数
get_DEGs获得不同类型的差异基因列表；get_ORA基于Over-Representative analysis (ORA)方法功能富集分析；使用
clusterProfiler::enrichGO(Yu 等 2012)做GO分析。
get_DEGs <- function(
dat,
lg2fc_cutoff = 0.5,
pval_cutoff = 0.05,
qval_cutoff = 0.05) {
# group_names
group_names <- gsub("\\d+_", "", unlist(strsplit(dat$Block[1], " vs ")))
colnames(dat)[5] <- "lg2fc"
colnames(dat)[6] <- "pval"
colnames(dat)[7] <- "qval"
# enrichment by beta and Pvalue AdjustedPvalue
dat[which(dat$lg2fc > lg2fc_cutoff &
dat$pval < pval_cutoff &
dat$qval < qval_cutoff),
"EnrichedDir"] <- group_names[2]
dat[which(dat$lg2fc < -lg2fc_cutoff &
dat$pval < pval_cutoff &
dat$qval < qval_cutoff),
"EnrichedDir"] <- group_names[1]
dat[which(abs(dat$lg2fc) <= lg2fc_cutoff |
dat$pval >= pval_cutoff |
dat$qval >= qval_cutoff),
"EnrichedDir"] <- "Nonsignif"
# dat status
dat$EnrichedDir <- factor(dat$EnrichedDir,
levels = c(group_names[2], "Nonsignif", group_names[1]))
df_status <- table(dat$EnrichedDir) %>% data.frame() %>%
stats::setNames(c("Group", "Number"))
grp1_number <- with(df_status, df_status[Group %in% group_names[1], "Number"])
grp2_number <- with(df_status, df_status[Group %in% group_names[2], "Number"])
nsf_number <- with(df_status, df_status[Group %in% "Nonsignif", "Number"])
legend_label <- c(paste0(group_names[1], " (", grp1_number, ")"),
paste0("Nonsignif", " (", nsf_number, ")"),
paste0(group_names[2], " (", grp2_number, ")"))
res_up <- dat_signif %>% # enriched in 1st group
dplyr::filter(EnrichedDir == group_names[1]) %>%
dplyr::mutate(Status = "Up_regulated")
res_down <- dat_signif %>% # enriched in 2st group
dplyr::filter(EnrichedDir == group_names[2]) %>%
dplyr::mutate(Status = "Down_regulated")
res <- list(all = dat_signif,
up = res_up,
down = res_down)
return(res)
}
get_ORA <- function(
genelist,
genetype = c("all", "up", "down"),
ORAtype = c("GO", "KEGG"),
showcase = 10,
group_names = grp_names) {
res <- list(fit = fit,
result = fit_result,
pl = pl)
return(res)
}14.4 运行
- 设置对应参数，运行
get_DEGs
- 使用ORA方法计算所有差异基因的富集GO term结果
All_ORA_GO <- get_ORA(
genelist = df_DEGs$all,
genetype = "all",
ORAtype = "GO",
showcase = 10)
All_ORA_GO$pl结果：该图片展示了基于基因本体论（Gene Ontology, GO）的生物信息学分析结果，主要关注细胞过程、分子功能和细胞组件的多个方面。分析结果显示，在肝细胞癌（HCC）相关的基因中，有几个关键的生物过程和分子功能显著富集。
14.5 输出结果
14.6 总结
通过进行GO（Gene Ontology）富集分析，能够深入理解差异基因在细胞内的功能角色，这一分析为后续关于肝细胞癌（HCC）的研究奠定了坚实的生物学基础。
系统信息
R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Asia/Shanghai
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] org.Hs.eg.db_3.18.0 AnnotationDbi_1.66.0 IRanges_2.36.0
[4] S4Vectors_0.40.2 Biobase_2.62.0 BiocGenerics_0.48.1
[7] clusterProfiler_4.10.1 lubridate_1.9.3 forcats_1.0.0
[10] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[13] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[16] ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] DBI_1.2.2 bitops_1.0-7 gson_0.1.0
[4] shadowtext_0.1.3 gridExtra_2.3 rlang_1.1.3
[7] magrittr_2.0.3 DOSE_3.28.2 compiler_4.3.3
[10] RSQLite_2.3.6 png_0.1-8 vctrs_0.6.5
[13] reshape2_1.4.4 pkgconfig_2.0.3 crayon_1.5.2
[16] fastmap_1.1.1 XVector_0.42.0 ggraph_2.2.1
[19] utf8_1.2.4 HDO.db_0.99.1 rmarkdown_2.26
[22] tzdb_0.4.0 enrichplot_1.22.0 bit_4.0.5
[25] xfun_0.43 zlibbioc_1.48.2 cachem_1.0.8
[28] aplot_0.2.2 GenomeInfoDb_1.38.8 jsonlite_1.8.8
[31] blob_1.2.4 BiocParallel_1.36.0 tweenr_2.0.3
[34] parallel_4.3.3 R6_2.5.1 RColorBrewer_1.1-3
[37] stringi_1.8.4 GOSemSim_2.28.1 Rcpp_1.0.12
[40] knitr_1.46 Matrix_1.6-5 splines_4.3.3
[43] igraph_2.0.3 timechange_0.3.0 tidyselect_1.2.1
[46] viridis_0.6.5 qvalue_2.34.0 rstudioapi_0.16.0
[49] yaml_2.3.8 codetools_0.2-19 lattice_0.22-6
[52] plyr_1.8.9 treeio_1.26.0 withr_3.0.0
[55] KEGGREST_1.42.0 evaluate_0.23 gridGraphics_0.5-1
[58] scatterpie_0.2.2 polyclip_1.10-6 Biostrings_2.70.3
[61] ggtree_3.10.1 pillar_1.9.0 BiocManager_1.30.23
[64] renv_1.0.0 ggfun_0.1.4 generics_0.1.3
[67] RCurl_1.98-1.14 hms_1.1.3 tidytree_0.4.6
[70] munsell_0.5.1 scales_1.3.0 glue_1.7.0
[73] lazyeval_0.2.2 tools_4.3.3 data.table_1.15.4
[76] fgsea_1.28.0 fs_1.6.4 graphlayouts_1.1.1
[79] fastmatch_1.1-4 tidygraph_1.3.1 cowplot_1.1.3
[82] grid_4.3.3 ape_5.8 colorspace_2.1-0
[85] nlme_3.1-164 patchwork_1.2.0 GenomeInfoDbData_1.2.11
[88] ggforce_0.4.2 cli_3.6.2 fansi_1.0.6
[91] viridisLite_0.4.2 gtable_0.3.5 yulab.utils_0.1.4
[94] digest_0.6.35 ggplotify_0.1.2 ggrepel_0.9.5
[97] htmlwidgets_1.6.4 farver_2.1.1 memoise_2.0.1
[100] htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7
[103] GO.db_3.19.1 bit64_4.0.5 MASS_7.3-60.0.1