Deep Learning Methods to Integrate Biological Information for Analysis of Single-cell RNAseq Data

Project Summary The broad long-term objective of the project concerns the development of novel machine learning methods and computational tools for modeling genomic data motivated by important biological questions and experiments. The analysis of single-cell RNAseq (scRNAseq) data presents substantial computational and bioinformatics challenges. The specific aim of the project is to develop novel model-based deep learning methods with prior biological information considered for modelling scRNAseq data. These problems are all motivated by the PI?s close collaborations with biomedical investigators. The proposed approaches are designed to integrate biological information for improving both analytical performance and biological interpretability. The methods hinge on novel integration of biological insights and deep learning methods for analysis of the noisy, sparse, and over-dispersed scRNAseq data, including zero- inflated negative binominal model, autoencoder, deep embedding, hyperbolic embedding, and reversed graph embedding. The new methods can be applied to two important biological problems using the scRNAseq technologies: cell type identification and discovery via clustering analysis and cell developments via trajectory inference. They will facilitate effective analyses of the increasingly important scRNAseq data sets and contribute to the important on-going studies that the PI is currently collaborating on, Paneth cell regulation and regeneration of human hair follicles. The project will develop practical and feasible computer programs in order to implement the proposed methods, and to evaluate the performance of these methods through real applications. The work proposed here will contribute deep learning methods to modeling scRNAseq data and to studying complex phenotypes and biological systems and offer insights into each of the biological areas represented by the various data sets. All programs developed under this grant and detailed documentation will be made available free-of-charge to interested researchers. Undergraduates researchers from diverse backgrounds will be recruited as an integral part in the project for implementing most critical parts of the proposed aims. The research project will stimulate the interests of students so that they can consider a career in the biomedical sciences.