Deep-Learning Methods for Single-cell Data Analyses

Single-cell data analysis plays a crucial role in uncovering the heterogeneity and dynamics of tissues, organisms, and complex diseases. Deep learning and statistical methods offer powerful tools for identifying intricate biological patterns within large-scale, noisy datasets. We introduced scGNN, a hypothesis-free graph neural network framework for single-cell RNA-Seq analysis, which integrates three iterative multi-modal autoencoders and outperforms existing tools in gene imputation and cell clustering. We also introduced TrimNN, a neural network-based approach for detecting network motifs within triangulated cell graphs from spatial omics data. We applied prompt-based learning on the large single-cell RNA-seq models for data analyses. These methods and tools provide critical insights into the underlying mechanisms driving the complex tissue heterogeneities in development and diseases.