New algorithm enables data integration at single-cell resolution

A team of computational biologists has developed an algorithm that can 'align' multiple sequencing datasets with single-cell resolution. The new method, published today in the journal Nature Biotechnology, has implications for better understanding how different groups of cells change during disease progression, in response to drug treatment, or across evolution."This approach for data integration will enable the comparison of single-cell datasets and the ability to dissect the differences between them," explains Rahul Satija, the study's senior author, who is an assistant professor in NYU's Center for Genomics and Systems Biology and a core faculty member at the New York Genome Center. "Moreover, these methods will be valuable for the integration of diverse datasets produced across individuals and laboratories—and even for researchers studying the same tissue across different species."