1. The Board of Trustees of the Georgetown University provides OrthologAL software and code (“Service”) free of charge for non-commercial use only. Use of the Service by any commercial entity for any purpose, including research, is prohibited.
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1. Introduction
2. Features
3. Prerequisites
4. Installation
5. OrthologAL App
6. Contributing
7. Contact
8. License

Researchers can input their single-cell or other high-dimensional gene expression data (i.e. spatial transcriptomics) from any species, and OrthologAL will output a human ortholog-converted dataset for download and use.

To demonstrate the utility of this application, in the OrthologAL manuscript we characterized orthologous conversion in single-cell, single-nuclei, and spatial transcriptomic data derived from common pre-clinical models, including genetically engineered mouse models of medulloblastoma, patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury.

We show that OrthologAL can convert these data efficiently while preserving the dimensional architecture of the transcriptomic expression data.

OrthologAL will be broadly useful for integrating pre-clinical, high-dimensional transcriptomics data with existing human-derived datasets such as the BROAD DepMap or the NIH LINCS L1000.

• User friendly
• Standard Datatype format (Seurat)
• Works with single-cell, single-nuclei and spatial data
• Ortholog expression data is stored in a separate 'assay' slot within the Seurat object
• PDX harmonization
• Quality control metrics

You will need to install the following dependencies.

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("biomaRt")
install.packages("shiny")
install.packages("dplyr")
install.packages("data.table")
install.packages('DT')
install.packages("ggplot2")
install.packages("bslib")
remotes::install_github("satijalab/seurat", "seurat5", quiet = TRUE)

You can clone the repository using the git clone in your terminal, or download the ZIP folder from GitHub.

git clone "https://github.com/AyadLab/OrthologAL.git" 
cd OrthologAL-main/ 

#package installation
setwd("~/OrthologAL-main")
devtools::install()

library(shiny)
library(OrthologAL)
#Run the app
OrthologAL::RunOrthologAL()

• Example data used here is rat SCI single-cell rnaseq (Assay : RNA and species : RAT )

• Select the species, by default OrthologAL supports conversion from mouse, rat, and zebrafish expression data.
• For alternate species, a custom option is available to access additional species' annotations using the species code. A custom species search initiates a query based on EnsemblID to identify matching human orthologs.

• From a dropdown of 'assays' present in your Seurat object, select the appropriate assay you wish to map to human orthologs (i.e. 'RNA'). The ortholog converted assay will use the notation InputAssay_ortho (i.e. 'RNA_ortho').

• If your input data contains both human and non-human cells/tissue, select 'PDX mode'.
• No selection required for single-species datasets.

• The conversion is complete when the status symbol is green.

• Once the conversion process is completed, a download button will appear, allowing you to download an ortholog converted Seurat object. The ortholog converted expression data will be stored in a new assay / slot / layer corresponding to the input assay / slot/ layer. i.e., RNA assay input, RNA_ortho output.

• OrthologAL displays pie charts, showing the biotype distribution of input species genes with human orthologous gene annotations in the BioMart DB, as well as in the context of the converted dataset.
• 
• Also shows protein coding genes overlap between BioMart Database and data uploaded
• The system generates a summary table displaying both successfully matched genes and those without positive matches. This table is available to download as a .CSV file from within the application.

Rishika Chowdary, MS, Robert K. Suter, Ph.D., Matthew D’Antuono, BS, Cynthia Gomes, Ph.D., Joshua Stein, BA, Ki-Bum Lee, Ph.D., Jae K Lee, Ph.D., Nagi G. Ayad, Ph.D.

Robert K. Suter, PhD rks82@georgetown.edu

Nagi G. Ayad, PhD na853@georgetown.edu