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Machine learning is making it easier to identify cell types from single cell data
By Sara Moein  |  Sep 03, 2024
Machine learning is making it easier to identify cell types from single cell data
Image courtesy of and under license from Shutterstock.com
Single-cell RNA sequencing (scRNA-seq) is an advanced technique for measuring the transcriptome of every cell. After extracting scRNA-seq data, computational methods then detect cell types for each extracted sequence. In this article, Dr Sara Moein explains the role of machine learning in cell type identification.

NEW YORK - Single-cell RNA sequencing (scRNA-seq) measures the transcriptome - i.e., all the messenger RNA molecules found in a living organism - of every individual cell in that organism. An alternative technique for extracting cell transcriptomes is known as bulk sequencing, which is based on averaging the gene expression level in individual cells. 

ScRNA-seq has become a powerful technique for measuring the transcriptome of individual cells. Unlike bulk measurements that take averages of gene expressions over individual cells, gene measurements of single cells are used to study several different tissues and organs at different stages. Identifying the cell types present in samples taken from single-cell transcriptome data is a common goal of many single-cell experiments. Single-cell tech is also now becoming more widely used in cancer research and the mechanism identification of complex diseases.1

Cell type annotation methods

One of the most critical tasks following scRNA-seq is cell type detection or annotation. The task of cell type annotation consists of

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