# _signatureSearch_: Environment for Gene Expression Searching and Functional Enrichment Analysis [](http://www.bioconductor.org/packages/devel/bioc/html/signatureSearch.html#archives) [](http://bioconductor.org/packages/stats/bioc/signatureSearch/) [](https://support.bioconductor.org/t/signatureSearch/) [](http://www.bioconductor.org/packages/devel/bioc/html/signatureSearch.html#since) [](http://bioconductor.org/checkResults/devel/bioc-LATEST/signatureSearch/) [](http://bioconductor.org/checkResults/devel/bioc-LATEST/signatureSearch/) [](http://www.bioconductor.org/packages/devel/bioc/html/signatureSearch.html#since) # Introduction The _signatureSearch_ package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. These utilities are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems. Specifically, in drug discovery they can be used for identifying novel modes of action (MOA) of bioactive compounds from reference databases such as LINCS containing the genome-wide GESs from tens of thousands of drug and genetic perturbations (Subramanian et al. 2017). A typical GES search (GESS) workflow can be divided into two major steps. First, GESS methods are used to identify perturbagens such as drugs that induce GESs similar to a query GES of interest. The queries can be drug-, disease- or phenotype-related GESs. Since the MOAs of most drugs in the corresponding reference databases are known, the resulting associations are useful to gain insights into pharmacological and/or disease mechanisms, and to develop novel drug repurposing approaches. Second, specialized functional enrichment analysis (FEA) methods using annotations systems, such as Gene Ontologies (GO), pathways or Disease Ontologies (DO), have been developed and implemented in this package to efficiently interpret GESS results. The latter are usually composed of lists of perturbagens (_e.g._ drugs) ranked by the similarity metric of the corresponding GESS method. Finally, network reconstruction functionalities are integrated for visualizing the final results, _e.g._ in form of drug-target networks. For each GESS and FEA step, several alternative methods have been implemented in _signatureSearch_ to allow users to choose the best possible workflow configuration for their research application. # Vignette The vignette of this package is available at [here](https://www.bioconductor.org/packages/release/bioc/vignettes/signatureSearch/inst/doc/signatureSearch.html) # Installation and Loading `signatureSearch` is a R/Bioconductor package and can be installed using `BiocManager::install()`. ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("signatureSearch") ``` To obtain the most recent updates immediately, one can install it directly from GitHub as follows. ```r devtools::install_github("yduan004/signatureSearch", build_vignettes=TRUE) ``` After the package is installed, it can be loaded into an R session as follows. ```r library(signatureSearch) ``` For detailed description of the package, please refer to the vignette by running ```r browseVignettes("signatureSearch") ```