<!-- README.md is generated from README.Rmd. Please edit that file --> # hypeR <img src="media/logo.png" height="100px" align="right"/>  [](https://doi.org/doi:10.18129/B9.bioc.hypeR) [](https://bioconductor.org/checkResults/3.9/bioc-LATEST/hypeR/) [](https://www.tidyverse.org/lifecycle/#stable) [](https://github.com/montilab/hypeR/commits/master) ## Documentation Please visit <https://montilab.github.io/hypeR-docs/> You can also try out our [web-application](https://hyper-shiny.shinyapps.io/wapp/) if you prefer an interface\! ## Requirements We recommend the latest version of R (\>= 4.0.0) but **hypeR** currently requires R (\>= 3.6.0) to be installed directly from Github or Bioconductor. To install with R (\>= 3.5.0) see below. Use with R (\< 3.5.0) is not recommended. ## Installation Install the development version of the package from Github. <span style="color:#0278ae">**\[Recommended\]**</span> ``` r devtools::install_github("montilab/hypeR") ``` Or install the development version of the package from Bioconductor. ``` r BiocManager::install("montilab/hypeR", version="devel") ``` Or install with Conda. ``` bash conda create --name hyper source activate hyper conda install -c r r-devtools R library(devtools) devtools::install_github("montilab/hypeR") ``` Or install with previous versions of R. ``` bash git clone https://github.com/montilab/hypeR nano hypeR/DESCRIPTION # Change Line 8 # Depends: R (>= 3.6.0) -> Depends: R (>= 3.5.0) R install.packages("path/to/hypeR", repos=NULL, type="source") ``` ## Usage ``` r library(hypeR) ``` ``` r data(wgcna) # Process many signatures signatures <- wgcna[[1]] str(signatures) ``` #> List of 21 #> $ turquoise : chr [1:1902] "CLEC3A" "KCNJ3" "SCGB2A2" "SERPINA6" ... #> $ blue : chr [1:1525] "GSTM1" "BMPR1B" "BMPR1B-DT" "PYDC1" ... #> $ magenta : chr [1:319] "DSCAM-AS1" "VSTM2A" "UGT2B11" "CYP4Z1" ... #> $ brown : chr [1:1944] "SLC25A24P1" "CPB1" "GRIA2" "CST9" ... #> $ pink : chr [1:578] "MUC6" "GLRA3" "OPRPN" "ARHGAP36" ... #> $ red : chr [1:681] "KCNC2" "SLC5A8" "HNRNPA1P57" "CBLN2" ... #> $ darkred : chr [1:43] "OR4K12P" "GRAMD4P7" "FAR2P3" "CXADRP3" ... #> $ tan : chr [1:161] "LEP" "SIK1" "TRARG1" "CIDEC" ... #> $ lightcyan : chr [1:82] "CDC20B" "FOXJ1" "CDHR4" "MCIDAS" ... #> $ purple : chr [1:308] "C10orf82" "GUSBP3" "IGLV10-54" "IGKV1D-13" ... #> $ lightyellow : chr [1:48] "SLC6A4" "ERICH3" "GP2" "TRIM72" ... #> $ cyan : chr [1:143] "NOP56P1" "FABP6" "GNAQP1" "ZNF725P" ... #> $ royalblue : chr [1:47] "PCDHA12" "PCDHA11" "PCDHA4" "PCDHA1" ... #> $ black : chr [1:864] "NSFP1" "USP32P2" "OCLNP1" "RN7SL314P" ... #> $ yellow : chr [1:904] "NPIPB15" "MAFA-AS1" "C1orf167" "NT5CP2" ... #> $ lightgreen : chr [1:60] "HIST1H2APS3" "HIST1H2AI" "HIST1H1PS1" "HIST1H3H" ... #> $ darkgrey : chr [1:34] "MTND4P12" "MTRNR2L1" "MT-TT" "MTCYBP18" ... #> $ darkgreen : chr [1:43] "STK19B" "SNCG" "ELANE" "TNXA" ... #> $ midnightblue: chr [1:92] "LRRC26" "ARHGDIG" "TGFBR3L" "HS6ST1P1" ... #> $ grey60 : chr [1:71] "KRT8P48" "KRT8P42" "KRT8P11" "CRIP1P4" ... #> $ salmon : chr [1:151] "UBA52P3" "NPM1P33" "MYL6P5" "RPL29P30" ... ``` r # Access to hundreds of genesets genesets <- msigdb_gsets("Homo sapiens", "C2", "CP:KEGG", clean=TRUE) print(genesets) ``` #> C2.CP:KEGG v7.4.1 #> Abc Transporters (44) #> Acute Myeloid Leukemia (57) #> Adherens Junction (73) #> Adipocytokine Signaling Pathway (67) #> Alanine Aspartate And Glutamate Metabolism (32) #> Aldosterone Regulated Sodium Reabsorption (42) ``` r mhyp <- hypeR(signatures, genesets, test="hypergeometric", background=30000) ``` ``` r hyp_dots(mhyp, merge=TRUE, fdr=0.05, title="Co-expression Modules") ``` <img src="README_files/figure-gfm/unnamed-chunk-5-1.png" style="display: block; margin: auto;" /> ## Terminology ### Signature **hypeR** employs multiple types of enrichment analyses (e.g. hypergeometric, kstest, gsea). Depending on the type, different kinds of signatures are expected. There are three types of signatures `hypeR()` expects. ``` r # Simply a character vector of symbols (hypergeometric) signature <- c("GENE1", "GENE2", "GENE3") # A ranked character vector of symbols (kstest) ranked.signature <- c("GENE2", "GENE1", "GENE3") # A ranked named numerical vector of symbols with ranking weights (gsea) weighted.signature <- c("GENE2"=1.22, "GENE1"=0.94, "GENE3"=0.77) ``` ### Geneset A geneset is simply a list of vectors, therefore, one can use any custom geneset in their analyses, as long as it’s appropriately defined. ``` r genesets <- list("GSET1" = c("GENE1", "GENE2", "GENE3"), "GSET2" = c("GENE4", "GENE5", "GENE6"), "GSET3" = c("GENE7", "GENE8", "GENE9")) ``` #### Hyper enrichment All workflows begin with performing hyper enrichment with `hyper()`. Often we are just interested in a single signature, as described above. In this case, `hyper()` will return a `hyp` object. This object contains relevant information to the enrichment results and is recognized by downstream methods. ``` r hyp_obj <- hypeR(signature, genesets) ``` #### Downstream methods Please visit the [documentation](https://montilab.github.io/hypeR-docs/) for detailed functionality. Below is a brief list of some methods. ##### Downloading genesets ``` r BIOCARTA <- msigdb_gsets(species="Homo sapiens", category="C2", subcategory="CP:BIOCARTA") KEGG <- msigdb_gsets(species="Homo sapiens", category="C2", subcategory="CP:KEGG") REACTOME <- msigdb_gsets(species="Homo sapiens", category="C2", subcategory="CP:REACTOME") ``` ##### Visualize results ``` r # Show interactive table hyp_show(hyp_obj) # Plot dots plot hyp_dots(hyp_obj) # Plot enrichment map hyp_emap(hyp_obj) # Plot hiearchy map (relational genesets) hyp_hmap(hyp_obj) ``` ##### Saving results ``` r # Map enrichment to an igraph object (relational genesets) hyp_to_graph(hyp_obj) # Save to excel hyp_to_excel(hyp_obj, file_path="hypeR.xlsx") # Save to table hyp_to_table(hyp_obj, file_path="hypeR.txt") # Generate markdown report hyp_to_rmd(hyp_obj, file_path="hypeR.rmd", title="Hyper Enrichment (hypeR)", subtitle="YAP, TNF, and TAZ Knockout Experiments", author="Anthony Federico, Stefano Monti") ``` ## Related Repositories - [hypeR-db](https://github.com/montilab/hypeR-db) *A repository for commonly used open source genesets used by hypeR* - [hypeR-shiny](https://github.com/montilab/hypeR-shiny) *Our Shiny web application built on hypeR* - [hypeR-modules](https://github.com/montilab/hypeR-modules) *Integration of hypeR modules in custom Shiny applications* - [hypeR-docs](https://github.com/montilab/hypeR-docs) *Landing site for hosting documentation for hypeR* - [hypeR-workshop](https://github.com/montilab/hypeR-workshop) *Materials for a hypeR tutorial workshop* ## Cite ``` r citation("hypeR") ``` #> #> To cite hypeR in publications use: #> #> Federico, A. & Monti, S. hypeR: an R package for geneset enrichment #> workflows. Bioinformatics 36, 1307–1308 (2020). #> #> A BibTeX entry for LaTeX users is #> #> @Article{, #> title = {hypeR: an R package for geneset enrichment workflows}, #> author = {Anthony Federico and Stefano Monti}, #> journal = {Bioinformatics}, #> year = {2020}, #> volume = {36}, #> number = {4}, #> pages = {1307-1308}, #> url = {https://doi.org/10.1093/bioinformatics/btz700}, #> }