Welcome to the homepage of HTSanalyzeR2 package!
This package provides gene set over-representation, enrichment and network analyses for various preprocessed high-throughput data as well as corresponding time-series data including CRISPR, RNA-seq, micro-array and RNAi. It could also generate a dynamic shiny report encompassing all the results and visualizations, facilitating the users maximally for downloading, modifying the visualization parts with personal preference and sharing with others by publishing the report to Shinyapps.io.
This package is available under R(>= 3.4).
If you are a current bioconductor user and have
devtools package installed, you only need to call
install_github function in
devtools to install
HTSanalyzeR2. If you encountered errors, please refer to the section Potential Dependency Issues.
# Installation requires bioconductor and devtools, please use the following commands if you've not source("https://bioconductor.org/biocLite.R") biocLite("devtools", dependencies=TRUE) # Before installing HTSanalyzeR2, you need also to install the dependent package `GO.db` biocLite("GO.db", dependencies=TRUE) devtools::install_github("CityUHK-CompBio/HTSanalyzeR2", dependencies=TRUE)
HTSanalyzeR2 requires the following R/Bioconductor packages for its full function:
HTSanalyzeR2 also suggests the following R/Bioconductor packages for improved user experience:
If you are using ubuntu, common dependency issues should be solved using the following one line command:
sudo apt-get install -y libssl-dev libcurl4-openssl-dev libxml2-dev libgmp-dev libmpfr-dev
Details about this:
devtools need package
git2r, which requires openssl library. Please install
libssl-dev on Ubuntu or corresponding package on other OS.
devtools need package
httr, which requires curl library. Please install
libcurl4-openssl-dev on Ubuntu or corresponding package on other OS.
igraph requres xml library. Please install
libxml2-dev on Ubuntu or corresponding package on other OS.
RankProd need package
Rmpfr, which requires gmp and mpfr library. Please install
libmpfr-dev on Ubuntu or corresponding package on other OS.
Here is a simple but useful case study to use HTSanalyzeR2 to perform gene set enrichment analysis and further visualize all the results in an interactive html report.
The only required input for HTSanalyzeR2 is a list of interested genes with weight under a specific phenotype. For example, in the following case study, we take the output from limma by comparing CMS4 with non-CMS4 colon cancer samples. Details please refer to our vignette.
Before starting the demonstration, you need to load the following packages:
library(HTSanalyzeR2) library(org.Hs.eg.db) library(KEGGREST) library(igraph)
## prepare input for analysis data(GSE33113_limma) phenotype <- as.vector(GSE33113_limma$logFC) names(phenotype) <- rownames(GSE33113_limma) ## specify the gene sets type you want to analyze PW_KEGG <- KeggGeneSets(species="Hs") ListGSC <- list(PW_KEGG=PW_KEGG) ## iniate a *GSCA* object gsca <- GSCA(listOfGeneSetCollections=ListGSC, geneList=phenotype) ## preprocess gsca1 <- preprocess(gsca, species="Hs", initialIDs="SYMBOL", keepMultipleMappings=TRUE, duplicateRemoverMethod="max", orderAbsValue=FALSE) ## analysis gsca2 <- analyze(gsca1, para=list(pValueCutoff=0.05, pAdjustMethod="BH", nPermutations=100, minGeneSetSize=180, exponent=1), doGSOA = FALSE) ## append gene sets terms gsca3 <- appendGSTerms(gsca2, keggGSCs=c("PW_KEGG")) ## view enrichment Map viewEnrichMap(gsca3, gscs=c("PW_KEGG"), allSig = FALSE, gsNameType = "term", ntop = 5)
## visualize all results in an interactive report report(gsca3)
Should you have any questions about this package, you can either email to the developers listed in the DESCRIPTION part of this package or create an issue in the issue part.
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