A New York University (NYU) research team has been able to establish an algorithm to align multiple single-cell datasets for efficient comparison. “These methods,” explained first author Rahul Satija, “will be valuable for the integration of diverse datasets produced across individuals and laboratories.” Until now, despite the expeditious advancements in this biocomputational field, engineers have faced challenges when comparing multiple datasets, like those obtained from different labs. But through the adjustment of the analytical techniques for pattern-spotting amongst images, the NYU team has been able to establish consistency within multiple datasets.
The discovery will enhance the comprehension of cell behavior under various conditions, including during drug response and disease progression. The algorithm also functions successfully for tissues of the same kind across different species, which is expected to provide us a further insight into evolutionary changes.