A Better Way to Analyze Single-Cell Sequencing Data

A Better Way to Analyze Single-Cell Sequencing Data

Single cell RNA-sequencing (scRNA-seq) is a major scientific advance, but researchers face a key question in analyzing the genomic data from thousands of single cells: how to compare the data across different studies and experimental conditions. Deyou Zheng, Ph.D., and colleagues have developed a new algorithm to help them. Their algorithm, called reference principal component integration (RPCI), excels at removing noise from batches of data across different samples or studies while preserving the biological signals in the data.

In a study published online on March 25 in Nature Biotechnology, the researchers compared RPCI with 11 other tools of similar scope, using simulated and real scRNA-seq datasets.  RPCI was found to outperform these other tools under various scenarios. The researchers concluded that RPCI provides a superior method for robustly analyzing large quantities of scRNA-seq data in both basic and clinical research.

Dr. Zheng is professor of bioinformatics, in the department of genetics, as well as professor in the Saul R. Korey Department of Neurology, and in the Dominick P. Purpura Department of Neuroscience at Einstein.

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