Deep learning advances single-cell sequencing analysis

Single-cell sequencing (sc-seq) technology allows researchers to analyze individual cells rather than bulk populations. The human genome contains approximately 20,000 to 25,000 protein-coding genes, which account for about 1% of the total DNA.

In 2013, Nature selected single-cell RNA sequencing as its Method of the Year. Since then, the scRNA-tools database has compiled over 1,000 software tools for analyzing single-cell data by 2021.

Single-cell sequencing data is structured as a matrix where rows represent cells and columns represent genes. The numerical entries indicate the expression levels of specific genes within individual cells.

The Human Cell Atlas Project aims to map all cells in the human body, functioning similarly to Google Maps but for cellular spatial information and internal attributes.

Source: https://thegradient.pub/deep-learning-for-single-cell-sequencing-a-microscope-to-uncover-the-rich-diversity-of-individual-cells/