- Human Kidney Renal Clear Cell Carcinoma
- Positive
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Negative
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Human Head and Neck Squamous Carcinoma
- Positive
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Negative
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Human Lung Squamous Carcinoma
- Positive
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Negative
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Human Lung Adenocarcinoma
- Positive
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Negative
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Human Pancreas Carcinoma
- Positive
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
- Negative
- Barplots
- Dotplots
- Kinase-Concept Network
- Enrichment Map
Kinase Set Enrichment Analysis
Last updated: 2024-06-20
Checks: 6 1
Knit directory: PPP/
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library(clusterProfiler)clusterProfiler v4.12.0 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
If you use clusterProfiler in published research, please cite:
T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
Attaching package: 'clusterProfiler'The following object is masked from 'package:stats':
filterlibrary(ggplot2)
full_name <- c("Human Kidney Renal Clear Cell Carcinoma",
"Human Head and Neck Squamous Carcinoma",
"Human Lung Squamous Carcinoma",
"Human Lung Adenocarcinoma",
"Human Pancreas Carcinoma")
names <- c("kirc", "hnsc", "lusc", "luad", "paad")ksa <- read.gmt("data/gold_standard_CoPheeKSA.gmt")
i <- 1
for (name in names) {
cat("\n\n")
cat("# ", full_name[i], sep = "")
cat("\n\n")
i <- i + 1
dps <- read.csv(paste("output/DPS/", name, "_fc_0.05_kinases.csv",
sep = ""))
dps_plus <- dps[dps$Fold_Change > 0, ]
dps_neg <- dps[dps$Fold_Change < 0, ]
cat("\n\n")
cat("## Positive")
cat("\n\n")
dps <- dps_plus
dps <- dps[order(dps$Adjusted_P_Value, decreasing = TRUE), ]
gse <- enricher(gene = dps$Seq, TERM2GENE = ksa)
dir.create(paste("output/pho/", name, "/", sep = ""), showWarnings = FALSE)
write.table(gse@result, paste("output/pho/", name, "/KSEA.csv",
sep = ""))
cat("\n\n")
cat("### Barplots")
cat("\n\n")
print(mutate(gse, qscore = -log(p.adjust, base = 10)) %>%
barplot(x = "qscore") + ggtitle(full_name[i]))
cat("\n\n")
cat("### Dotplots")
cat("\n\n")
print(dotplot(gse, showCategory = 30) + ggtitle(full_name[i]))
cat("\n\n")
cat("### Kinase-Concept Network")
cat("\n\n")
print(cnetplot(gse, color.params = list(foldChange = dps$Fold_Change)) +
ggtitle(full_name[i]))
cat("\n\n")
cat("### Enrichment Map")
cat("\n\n")
print(emapplot(enrichplot::pairwise_termsim(gse)) + ggtitle(full_name[i]))
cat("\n\n")
cat("## Negative")
cat("\n\n")
dps <- dps_neg
dps <- dps[order(dps$Adjusted_P_Value, decreasing = TRUE), ]
gse <- enricher(gene = dps$Seq, TERM2GENE = ksa)
dir.create(paste("output/pho/", name, "/", sep = ""), showWarnings = FALSE)
write.table(gse@result, paste("output/pho/", name, "/KSEA.csv",
sep = ""))
cat("\n\n")
cat("### Barplots")
cat("\n\n")
print(mutate(gse, qscore = -log(p.adjust, base = 10)) %>%
barplot(x = "qscore") + ggtitle(full_name[i]))
cat("\n\n")
cat("### Dotplots")
cat("\n\n")
print(dotplot(gse, showCategory = 30) + ggtitle(full_name[i]))
cat("\n\n")
cat("### Kinase-Concept Network")
cat("\n\n")
print(cnetplot(gse, color.params = list(foldChange = dps$Fold_Change)) +
ggtitle(full_name[i]))
cat("\n\n")
cat("### Enrichment Map")
cat("\n\n")
print(emapplot(enrichplot::pairwise_termsim(gse)) + ggtitle(full_name[i]))
}Human Kidney Renal Clear Cell Carcinoma
Positive
Human Head and Neck Squamous Carcinoma
Positive
Barplots
Negative
Dotplots
Human Lung Squamous Carcinoma
Positive
Kinase-Concept Network
Negative
Human Lung Adenocarcinoma
Positive
Dotplots
Kinase-Concept Network
Enrichment Map
Negative
Barplots
Dotplots
Kinase-Concept Network
Enrichment Map
Human Pancreas Carcinoma
Positive
Barplots
Dotplots
Kinase-Concept Network
Enrichment Map
Negative
Barplots
Dotplots
Kinase-Concept Network
Enrichment Map
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
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: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.5.1 clusterProfiler_4.12.0
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8
[4] magrittr_2.0.3 farver_2.1.2 rmarkdown_2.27
[7] fs_1.6.4 zlibbioc_1.50.0 vctrs_0.6.5
[10] memoise_2.0.1 ggtree_3.12.0 htmltools_0.5.8.1
[13] gridGraphics_0.5-1 sass_0.4.9 bslib_0.7.0
[16] plyr_1.8.9 cachem_1.1.0 whisker_0.4.1
[19] igraph_2.0.3 lifecycle_1.0.4 pkgconfig_2.0.3
[22] Matrix_1.7-0 R6_2.5.1 fastmap_1.2.0
[25] gson_0.1.0 GenomeInfoDbData_1.2.12 digest_0.6.35
[28] aplot_0.2.3 enrichplot_1.24.0 colorspace_2.1-0
[31] ggnewscale_0.4.10 patchwork_1.2.0 AnnotationDbi_1.66.0
[34] S4Vectors_0.42.0 rprojroot_2.0.4 RSQLite_2.3.7
[37] labeling_0.4.3 fansi_1.0.6 httr_1.4.7
[40] polyclip_1.10-6 compiler_4.4.0 bit64_4.0.5
[43] withr_3.0.0 BiocParallel_1.38.0 viridis_0.6.5
[46] DBI_1.2.3 highr_0.11 ggforce_0.4.2
[49] MASS_7.3-61 HDO.db_0.99.1 tools_4.4.0
[52] ape_5.8 scatterpie_0.2.3 httpuv_1.6.15
[55] glue_1.7.0 nlme_3.1-165 GOSemSim_2.30.0
[58] promises_1.3.0 grid_4.4.0 shadowtext_0.1.3
[61] reshape2_1.4.4 fgsea_1.30.0 generics_0.1.3
[64] gtable_0.3.5 tidyr_1.3.1 data.table_1.15.4
[67] tidygraph_1.3.1 utf8_1.2.4 XVector_0.44.0
[70] BiocGenerics_0.50.0 ggrepel_0.9.5 pillar_1.9.0
[73] stringr_1.5.1 yulab.utils_0.1.4 later_1.3.2
[76] splines_4.4.0 dplyr_1.1.4 tweenr_2.0.3
[79] treeio_1.28.0 lattice_0.22-6 bit_4.0.5
[82] tidyselect_1.2.1 GO.db_3.19.1 Biostrings_2.72.1
[85] knitr_1.47 git2r_0.33.0 gridExtra_2.3
[88] IRanges_2.38.0 stats4_4.4.0 xfun_0.45
[91] graphlayouts_1.1.1 Biobase_2.64.0 stringi_1.8.4
[94] UCSC.utils_1.0.0 workflowr_1.7.1 lazyeval_0.2.2
[97] ggfun_0.1.5 yaml_2.3.8 evaluate_0.24.0
[100] codetools_0.2-20 ggraph_2.2.1 tibble_3.2.1
[103] qvalue_2.36.0 ggplotify_0.1.2 cli_3.6.2
[106] munsell_0.5.1 jquerylib_0.1.4 Rcpp_1.0.12
[109] GenomeInfoDb_1.40.1 png_0.1-8 parallel_4.4.0
[112] blob_1.2.4 DOSE_3.30.1 viridisLite_0.4.2
[115] tidytree_0.4.6 scales_1.3.0 purrr_1.0.2
[118] crayon_1.5.2 rlang_1.1.4 cowplot_1.1.3
[121] fastmatch_1.1-4 KEGGREST_1.44.0