Deep Learning for Single-Cell Sequencing: A Microscope to See the Diversity of Cells

Single-cell sequencing (sc-seq) technology enables the analysis of individual cells, revealing cellular heterogeneity. The human genome contains about 20,000-25,000 protein-coding genes, and sc-seq, especially single-cell RNA sequencing (scRNA-seq), allows measurement of gene expression at the single-cell level. Deep learning is increasingly applied to sc-seq data due to its ability to handle high-dimensional, non-linear, and heterogeneous data. Autoencoders, particularly denoising autoencoders, are commonly used for dimensionality reduction and cell clustering, outperforming traditional methods like PCA. The Human Cell Atlas Project (HCAP) aims to map all human cells, and spatially resolved transcriptomics (SRT) addresses the loss of spatial information in scRNA-seq. Deep learning helps capture complex patterns, identify novel cell types, and integrate multimodal data.