Deep learning for single-cell sequencing: a microscope to see the diversity of cells

The Gradient · 2 years ago

Deep learning techniques, particularly autoencoders, are increasingly applied to analyze single-cell RNA sequencing data to handle high-dimensional gene expression measurements across thousands of genes and cells. The scRNA-tools database compiled over 1,000 analysis tools by 2021, with many leveraging deep learning methods to capture non-linear relationships and cellular heterogeneity that traditional statistical approaches cannot detect. These deep learning approaches enable researchers to identify and characterize distinct cell types and subpopulations more effectively than conventional methods like principal component analysis.