# SurfR Proteins at the cell surface connect intracellular and extracellular signaling networks and largely determine a cell’s capacity to communicate and interact with its environment. Importantly, variations in transcriptomic profiles are often observed between healthy and diseased cells, presenting distinct sets of cell-surface proteins. Indeed, cell surface proteins i) may act as biomarkers for the detection of diseased cells in tissues or body fluids and ii) are the most prevalent target of pharmaceutical agents: 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. The investigation of the cell surfaceome therefore could provide new possibilities for diagnosis, prognosis, treatment development, and therapy response evaluation. ## What is SurfR The **SurfR** package aims to provide a streamlined end-to-end workflow for identifying surface protein coding genes from expression data using computational prediction. **SurfR** : - Returns a list of of surface protein coding genes, starting from a list of genes of interest, the raw count matrix of your own RNA-seq experiment, or from bulk transcriptomic data automatically retrieved from public databases. Protein classification is based on a recently developed surfaceome predictor, called SURFY, based on machine learning. - Allows automatic data retrieval from public databases such as GEO and TCGA. GEO queries are based on the ArchS4 pipeline. TCGA repository is interrogated through TCGAbiolinks. - Provides a function for differential gene expression analysis. For this task it relies on DESeq2 package, starting from counts data. - Offers the opportunity to increase the sample size of a cohort by integrating related datasets, therefore enhancing the power to detect differentially expressed genes of interest. Meta-analysis can be performed through metaRNASeq, taking into account inter-study variability that may arise from technical differences among studies (e.g., sample preparation, library protocols, batch effects) as well as additional biological variability. - Gene ontology (GO) and pathway annotation can also be performed within **SurfR** to gain further insights about surface protein candidates. - Includes functions to visualize DEG and enrichment results, including BarPlots, Histograms, Venn diagrams, and PCA plots to help users achieve efficient data interpretation. ## Installation To install this package, start R (version "4.3") and enter:. ```{r install, eval = FALSE} if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") ``` When the package is available on *Bioconductor*, use ```{r install-Bioconductor, eval = FALSE} BiocManager::install("SurfR") ``` Development package version can be installed from GitHub using devtools: ``` devtools::install_github("auroramaurizio/SurfR") ``` ## Dependencies This package is supported for macOS, and Linux (Windows not tested). **SurfR** works with R v4.1 or greater (tested also on 4.2 and 4.3). Dependencies are indicated in the DESCRIPTION file, and can be automatically installed when installing the **SurfR** pacakge. ## Vignettes A comprehensive vignette provides an introduction to the **SurfR** package. Examples and use-cases are covered for each function. ## Documentation Instructions to run the main functions can be found consulting the vignette or by entering ?FunctionName (e.g. ?Splot) in the console after loading the package. ## Citation Maurizio, A., Tascini, A.S., Morelli, M. SurfR: Riding the wave of RNA-Seq data with a comprehensive Bioconductor package to identify Surface Protein Coding Genes. Bioinformatics Advances, 2024 (DOI: 10.1093/bioadv/vbae201) ## Authors Aurora Maurizio (auroramaurizio1@gmail.com), Anna Sofia Tascini (volpesofi@gmail.com), Marco Morelli (morelli.marco@hsr.it) ## Help, Suggestions, and Contributions Any contribution is highly appreciated! If you are interested in contributing to this project, please open an issue.