Abstract
Motivation
Accurately understanding immune cell heterogeneity and function in single-cell datasets requires protein-level information, which is often unavailable because of 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.
Since 2004, UT Health San Antonio, Greehey Children’s Cancer Research Institute’s (Greehey CCRI) mission has been to advance scientific knowledge relevant to childhood cancer, contribute to the understanding of its causes, and accelerate the translation of knowledge into novel therapies. Through the discovery, development, and dissemination of new scientific knowledge, Greehey CCRI strives to have a national and global impact on childhood cancer. Our mission consists of three key areas: research, clinical, and education.
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