Last updated: 2024-06-25

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Knit directory: PPP/

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Untracked files:
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Unstaged changes:
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    Modified:   output/cnv/dset_hnsc.RDS
    Modified:   output/cnv/dset_kirc.RDS
    Modified:   output/cnv/dset_luad.RDS
    Modified:   output/cnv/dset_lusc.RDS
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    Modified:   output/cnv/regul_paad.RDS
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    Deleted:    output/pho/paad/kinase_regulators.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/KSEA.Rmd) and HTML (docs/KSEA.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	85fc7b1	Zhen Zuo	2024-06-24	.
html	85fc7b1	Zhen Zuo	2024-06-24	.
html	f0809ae	Zhen Zuo	2024-06-17	.
Rmd	9d7bb61	Zhen Zuo	2024-06-16	.
html	9d7bb61	Zhen Zuo	2024-06-16	.
html	e55118d	Zhen Zuo	2024-06-16	Build site.
html	7576f76	Zhen Zuo	2024-06-16	wflow_publish("*", all = TRUE)
Rmd	3477290	Zhen Zuo	2024-06-16	update workflow
html	3477290	Zhen Zuo	2024-06-16	update workflow
Rmd	8c84adb	Zhen Zuo	2024-06-16	.

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':

    filter

library(ggplot2)
library(EnsDb.Hsapiens.v86)

Loading required package: ensembldb

Loading required package: BiocGenerics


Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

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    anyDuplicated, aperm, append, as.data.frame, basename, cbind,
    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
    tapply, union, unique, unsplit, which.max, which.min

Loading required package: GenomicRanges

Loading required package: stats4

Loading required package: S4Vectors


Attaching package: 'S4Vectors'

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    rename

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    expand.grid, I, unname

Loading required package: IRanges


Attaching package: 'IRanges'

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Loading required package: GenomeInfoDb

Loading required package: GenomicFeatures

Loading required package: AnnotationDbi

Loading required package: Biobase

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'AnnotationDbi'

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    select

Loading required package: AnnotationFilter


Attaching package: 'ensembldb'

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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")
#meta <- read.csv("data/KSA_gold_standard.csv")
#meta <- meta[!duplicated(meta$seq_15),]
#geneID <- ensembldb::select(EnsDb.Hsapiens.v86, keys = meta$UniProt.ID.1, keytype = "UNIPROTID",
#        columns = c("SYMBOL", "UNIPROTID", "GENEID"))
#meta$UniProt.ID.1_SYMBOL <- plyr::mapvalues(meta$UniProt.ID.1, from = geneID$UNIPROTID,
#        to = geneID$SYMBOL, warn_missing = FALSE)
#meta$GS <- paste(meta$UniProt.ID.1_SYMBOL, meta$Position, sep="_")
#ksa$gene <- plyr::mapvalues(ksa$gene, from = meta$seq_15, to = meta$GS)

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

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
f0809ae	Zhen Zuo	2024-06-17
9d7bb61	Zhen Zuo	2024-06-16
e55118d	Zhen Zuo	2024-06-16
3477290	Zhen Zuo	2024-06-16

Negative

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Human Head and Neck Squamous Carcinoma

Positive

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
f0809ae	Zhen Zuo	2024-06-17
9d7bb61	Zhen Zuo	2024-06-16
e55118d	Zhen Zuo	2024-06-16
3477290	Zhen Zuo	2024-06-16

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Negative

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
f0809ae	Zhen Zuo	2024-06-17
9d7bb61	Zhen Zuo	2024-06-16
e55118d	Zhen Zuo	2024-06-16
3477290	Zhen Zuo	2024-06-16

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Human Lung Squamous Carcinoma

Positive

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
f0809ae	Zhen Zuo	2024-06-17
9d7bb61	Zhen Zuo	2024-06-16
e55118d	Zhen Zuo	2024-06-16
3477290	Zhen Zuo	2024-06-16

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Negative

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
f0809ae	Zhen Zuo	2024-06-17
9d7bb61	Zhen Zuo	2024-06-16
e55118d	Zhen Zuo	2024-06-16
3477290	Zhen Zuo	2024-06-16

Human Lung Adenocarcinoma

Positive

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24
3477290	Zhen Zuo	2024-06-16

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Negative

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Human Pancreas Carcinoma

Positive

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Negative

Barplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Dotplots

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Kinase-Concept Network

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

Enrichment Map

Version	Author	Date
85fc7b1	Zhen Zuo	2024-06-24

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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.28.0         
 [3] AnnotationFilter_1.28.0   GenomicFeatures_1.56.0   
 [5] AnnotationDbi_1.66.0      Biobase_2.64.0           
 [7] GenomicRanges_1.56.1      GenomeInfoDb_1.40.1      
 [9] IRanges_2.38.0            S4Vectors_0.42.0         
[11] BiocGenerics_0.50.0       ggplot2_3.5.1            
[13] clusterProfiler_4.12.0   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.16.0          
  [3] jsonlite_1.8.8              magrittr_2.0.3             
  [5] farver_2.1.2                rmarkdown_2.27             
  [7] BiocIO_1.14.0               fs_1.6.4                   
  [9] zlibbioc_1.50.0             vctrs_0.6.5                
 [11] Rsamtools_2.20.0            memoise_2.0.1              
 [13] RCurl_1.98-1.14             ggtree_3.12.0              
 [15] S4Arrays_1.4.1              htmltools_0.5.8.1          
 [17] curl_5.2.1                  SparseArray_1.4.8          
 [19] gridGraphics_0.5-1          sass_0.4.9                 
 [21] bslib_0.7.0                 plyr_1.8.9                 
 [23] cachem_1.1.0                GenomicAlignments_1.40.0   
 [25] whisker_0.4.1               igraph_2.0.3               
 [27] lifecycle_1.0.4             pkgconfig_2.0.3            
 [29] Matrix_1.7-0                R6_2.5.1                   
 [31] fastmap_1.2.0               gson_0.1.0                 
 [33] MatrixGenerics_1.16.0       GenomeInfoDbData_1.2.12    
 [35] digest_0.6.35               aplot_0.2.3                
 [37] enrichplot_1.24.0           ggnewscale_0.4.10          
 [39] colorspace_2.1-0            patchwork_1.2.0            
 [41] rprojroot_2.0.4             RSQLite_2.3.7              
 [43] labeling_0.4.3              fansi_1.0.6                
 [45] abind_1.4-5                 httr_1.4.7                 
 [47] polyclip_1.10-6             compiler_4.4.0             
 [49] bit64_4.0.5                 withr_3.0.0                
 [51] BiocParallel_1.38.0         viridis_0.6.5              
 [53] DBI_1.2.3                   highr_0.11                 
 [55] ggforce_0.4.2               MASS_7.3-61                
 [57] DelayedArray_0.30.1         rjson_0.2.21               
 [59] HDO.db_0.99.1               tools_4.4.0                
 [61] ape_5.8                     scatterpie_0.2.3           
 [63] httpuv_1.6.15               glue_1.7.0                 
 [65] restfulr_0.0.15             nlme_3.1-165               
 [67] GOSemSim_2.30.0             promises_1.3.0             
 [69] grid_4.4.0                  shadowtext_0.1.3           
 [71] reshape2_1.4.4              fgsea_1.30.0               
 [73] generics_0.1.3              gtable_0.3.5               
 [75] tidyr_1.3.1                 data.table_1.15.4          
 [77] tidygraph_1.3.1             utf8_1.2.4                 
 [79] XVector_0.44.0              ggrepel_0.9.5              
 [81] pillar_1.9.0                stringr_1.5.1              
 [83] yulab.utils_0.1.4           later_1.3.2                
 [85] splines_4.4.0               dplyr_1.1.4                
 [87] tweenr_2.0.3                treeio_1.28.0              
 [89] lattice_0.22-6              rtracklayer_1.64.0         
 [91] bit_4.0.5                   tidyselect_1.2.1           
 [93] GO.db_3.19.1                Biostrings_2.72.1          
 [95] knitr_1.47                  git2r_0.33.0               
 [97] gridExtra_2.3               ProtGenerics_1.36.0        
 [99] SummarizedExperiment_1.34.0 xfun_0.45                  
[101] graphlayouts_1.1.1          matrixStats_1.3.0          
[103] stringi_1.8.4               UCSC.utils_1.0.0           
[105] workflowr_1.7.1             lazyeval_0.2.2             
[107] ggfun_0.1.5                 yaml_2.3.8                 
[109] evaluate_0.24.0             codetools_0.2-20           
[111] ggraph_2.2.1                tibble_3.2.1               
[113] qvalue_2.36.0               ggplotify_0.1.2            
[115] cli_3.6.2                   munsell_0.5.1              
[117] jquerylib_0.1.4             Rcpp_1.0.12                
[119] png_0.1-8                   XML_3.99-0.16.1            
[121] parallel_4.4.0              blob_1.2.4                 
[123] DOSE_3.30.1                 bitops_1.0-7               
[125] viridisLite_0.4.2           tidytree_0.4.6             
[127] scales_1.3.0                purrr_1.0.2                
[129] crayon_1.5.2                rlang_1.1.4                
[131] cowplot_1.1.3               fastmatch_1.1-4            
[133] KEGGREST_1.44.0

Kinase Set Enrichment Analysis

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