shinyDeepGxP: a user-friendly R shiny app for predicting surface protein abundance from scRNA-seq expression using deep learning in blood cells
Year: 2026
Type: article
Abstract: Motivation: Understanding accurate immune cell heterogeneity and function in single-cell datasets requires access to protein-level information, which is often unavailable due to experimental limitations. Results: We present shinyDeepGxP, an interactive web application featuring our deep learning model, DeepGxP, for predicting surface protein abundance from single-cell RNA-sequencing (scRNA-seq) data. This platform makes DeepGxP accessible to researchers without programming skills. Users can upload scRNA-seq count matrices and use "Predict Protein" to predict the abundance of 224 biologically relevant surface proteins. shinyDeepGxP provides visualizations to help identify distinct cell populations based on predicted protein profiles. Moreover, users can choose "Explore Model" to reveal key RNA predictors and their associated biological pathways for each protein. Overall, shinyDeepGxP is a user-friendly, freely available web tool that provides protein-level detail for RNA-only single-cell datasets, enabling multimodal discovery without additional experiments. Availability and implementation: shinyDeepGxP can be launched on https://shiny.crc.pitt.edu/deepgxp/.
Source: Bioinformatics Advances
Institutions: The University of Texas at San Antonio Health Science Center, National Taiwan University, Taichung Veterans General Hospital, Fu Jen Catholic University, National Chung Hsing University, Johns Hopkins University, Johns Hopkins Medicine, University of Pittsburgh, UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Industrial Technology Research Institute
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,726
OpenAlex ID: W7171072283