Researchers Develop a Better Method to Compare Gene Expression in Single Cells

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Computational biologist Xiang Chen, right, and his colleague Wenan Chen, developed the new algorithm.

Researchers at St. Jude Children’s Research Hospital have developed an algorithm to help identify biomarkers that differentiate between cell populations at the single-cell level.

Credit: St. Jude Children’s Research Hospital

St. Jude Children’s Research Hospital researchers have developed an algorithm to help identify biomarkers that differentiate between cell populations at the single-cell level, a development that could yield insights into cancer.

St. Jude will offer the algorithm, called negative binomial model with independent dispersions (NBID), free of charge to researchers worldwide.

St. Jude’s Xiang Chen and colleagues created NBID to take better advantage of single-cell RNA sequencing to track differences in gene expression in individual cells. This information could advance precision medicines and enable more sensitive diagnostic tests.

Chen and his colleagues used molecular “barcodes” to track gene expression by marking and counting messenger RNAs using a process called unique molecular identifier (UMI) counting.

Testing revealed NBID is more sensitive and more accurate than other methods in recognizing differences in gene expression between different groups of cells.

From St. Jude Children’s Research Hospital
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Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA


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