<!-- README.md is generated from README.Rmd. Please edit that file --> # drugfindR <!-- badges: start --> [](https://lifecycle.r-lib.org/articles/stages.html#experimental) [](https://github.com/CogDisResLab/drugfindR/actions/workflows/rworkflows.yml) [](https://github.com/CogDisResLab/drugfindR/releases/latest) [](https://github.com/CogDisResLab/drugfindR/blob/main/LICENSE) [](https://zenodo.org/badge/latestdoi/338354715) [](https://app.codecov.io/gh/CogDisResLab/drugfindR?branch=devel) <!-- badges: end --> drugfindR provides R-based programmatic access to the [iLINCS](http://www.ilincs.org) (Integrative LINCS) database for drug repurposing and functional genomics research. The package enables systematic analysis of gene expression signatures against comprehensive databases of: - **Gene Knockdown (KD)** signatures - **Gene Overexpression (OE)** signatures - **Chemical Perturbagen (CP)** signatures ## Overview drugfindR streamlines transcriptomic signature analysis by: - Querying LINCS signatures without web platform dependencies - Processing custom transcriptomic data for concordance analysis - Identifying candidate repurposable drugs based on signature similarity - Discovering functional relationships between genes and compounds - Enabling high-throughput batch processing of multiple signatures The package integrates with standard differential expression analysis outputs from tools like [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) and [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html). ## Bioconductor Acceptance 🎉 **drugfindR has been accepted into Bioconductor!** The package will be available in **Bioconductor Release 3.23** (accessible via the main release channel). Additionally, drugfindR is available now from the **Bioconductor devel channel** and will be fully integrated within one week. ## Installation ### From Bioconductor (Recommended) Install from the **Bioconductor devel channel** (available now): ``` r if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("drugfindR", version = "devel") ``` Or wait for **Bioconductor Release 3.23** and install from the stable release channel: ``` r if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("drugfindR") ``` ### Alternative Installation Methods Install from [r-universe](https://cogdisreslab.r-universe.dev/drugfindR): ``` r install.packages("drugfindR", repos = c( "https://cogdisreslab.r-universe.dev", "https://cran.r-project.org" ) ) ``` Or install the development version from [GitHub](https://github.com/CogDisResLab/drugfindR): ``` r if (!requireNamespace("devtools", quietly = TRUE)) { install.packages("devtools") } devtools::install_github("CogDisResLab/drugfindR") ``` ## Quick Start drugfindR offers two analysis approaches: ### 1. High-Level Convenience Functions For rapid analysis with sensible defaults, use the wrapper functions: ``` r library(drugfindR) # Investigate a transcriptomic signature for candidate drugs results <- investigateSignature( expr = my_diffexp_data, outputLib = "CP", # Query chemical perturbagens filterThreshold = 1.5, # Filter by absolute log fold change geneColumn = "gene_symbol", logfcColumn = "logFC", pvalColumn = "PValue" ) # Investigate a specific gene target target_results <- investigateTarget( target = "TP53", inputLib = "KD", # Use knockdown signatures outputLib = "CP", # Find related chemical perturbagens filterThreshold = 0.5 ) ``` ### 2. Modular Pipeline Functions For customized workflows and fine-grained control: ``` r # Step 1: Prepare your signature signature <- prepareSignature(diffexp_data, geneColumn = "gene", logfcColumn = "logFC", pvalColumn = "PValue" ) # Step 2: Filter by thresholds filtered_up <- filterSignature(signature, direction = "up", threshold = 1.5) filtered_dn <- filterSignature(signature, direction = "down", threshold = 1.5) # Step 3: Query concordant signatures concordants_up <- getConcordants(filtered_up, ilincsLibrary = "CP") concordants_dn <- getConcordants(filtered_dn, ilincsLibrary = "CP") # Step 4: Generate consensus results consensus <- consensusConcordants(concordants_up, concordants_dn, paired = TRUE, cutoff = 0.2 ) ``` ## Primary Use Cases ### Drug Repurposing from Transcriptomic Data Identify candidate drugs that reverse or mimic a disease signature by comparing your expression data against LINCS chemical perturbagen signatures. ### Target Validation and Functional Analysis Discover genes and compounds with similar or opposite functional effects to a gene of interest using knockdown and overexpression signatures. ## Documentation - **[Full Documentation Website](https://cogdisreslab.github.io/drugfindR/)** - **[Get Started Tutorial](https://cogdisreslab.github.io/drugfindR/articles/drugfindR.html)** - Comprehensive vignette with worked examples - **[Function Reference](https://cogdisreslab.github.io/drugfindR/reference/index.html)** - Complete API documentation ### Key Functions | Function | Purpose | Reference | | ---- | ---- | ---- | | [`investigateSignature()`](https://cogdisreslab.github.io/drugfindR/reference/investigateSignature.html) | All-in-one signature analysis | [docs](https://cogdisreslab.github.io/drugfindR/reference/investigateSignature.html) | | [`investigateTarget()`](https://cogdisreslab.github.io/drugfindR/reference/investigateTarget.html) | Analyze a specific gene target | [docs](https://cogdisreslab.github.io/drugfindR/reference/investigateTarget.html) | | [`prepareSignature()`](https://cogdisreslab.github.io/drugfindR/reference/prepareSignature.html) | Format signatures for iLINCS | [docs](https://cogdisreslab.github.io/drugfindR/reference/prepareSignature.html) | | [`filterSignature()`](https://cogdisreslab.github.io/drugfindR/reference/filterSignature.html) | Apply thresholds to signatures | [docs](https://cogdisreslab.github.io/drugfindR/reference/filterSignature.html) | | [`getConcordants()`](https://cogdisreslab.github.io/drugfindR/reference/getConcordants.html) | Query iLINCS for matches | [docs](https://cogdisreslab.github.io/drugfindR/reference/getConcordants.html) | | [`consensusConcordants()`](https://cogdisreslab.github.io/drugfindR/reference/consensusConcordants.html) | Generate consensus results | [docs](https://cogdisreslab.github.io/drugfindR/reference/consensusConcordants.html) | ## Citation If you use drugfindR in your research, please cite: > Imami AS, Sahay S, Creeden JF, McCullumsmith RE (2024). > drugfindR: Investigate iLINCS for candidate repurposable drugs. > R package version 0.99.1302. > doi:10.5281/zenodo.338354715 ## Getting Help - **Questions & Discussions**: Use [GitHub Discussions](https://github.com/CogDisResLab/drugfindR/discussions) - **Bug Reports**: Open an [issue](https://github.com/CogDisResLab/drugfindR/issues) - **Feature Requests**: Submit via [GitHub Issues](https://github.com/CogDisResLab/drugfindR/issues) ## Related Resources - [iLINCS Portal](http://www.ilincs.org) - Web-based platform for LINCS data - [LINCS Project](https://lincsproject.org/) - Library of Integrated Network-Based Cellular Signatures - [O’Donovan et al. 2021](https://www.nature.com/articles/s41598-021-84044-9) - Example application for COVID-19 drug repurposing ## License GPL-3 + file LICENSE