This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Modern biology research can now generate valuable data on an unprecedented scale. But there is one major obstacle: making sense of these complex high-dimensional datasets.
A team at the University of Basel, Switzerland, has developed a new tool that can provide accurate pictures of the structures hiding within highly complex datasets. By helping scientists uncover hidden patterns in the data, the software will help make new scientific discoveries. Over the past decade, biology has entered the era of "big data." Today's high-throughput technologies generate vast datasets that capture the identities and multidimensional characteristics of cells.
Using single-cell RNA sequencing, for example, researchers can measure the activity of tens of thousands of genes in hundreds of thousands or even millions of individual cells. Similar large-scale datasets are now produced in many fields of biology, from genetics and cancer research to neuroscience. These data promise to provide fundamental new insights into how cells develop, communicate and change during disease.
However, analyzing and interpreting such complex datasets remains a major challenge that researchers continue to struggle with. "People are good at recognizing patterns in two or three dimensions," says professor Erik van Nimwegen. "But we simply can't make a picture of a dataset that exists in 10,000 dimensions and lack intuition for what kind of structures can even exist in such high-dimensional spaces." In Nature Biotechnology, the researchers present their newly developed software tool, "Bonsai," which visualizes high-dimensional data on a tree.
They demonstrate that this visualization provides a faithful picture of the structure in the data, including how cells are related and how they may have developed from precursor cells. Most popular tools force data with thousands of dimensions into a two-dimensional map. Although these methods are used in virtually every study, researchers appreciate that such pictures invariably distort the data, making it impossible to tell whether the displayed relationships between cells are true or artifacts created by forcing the data into a two-dimensional visualization.
Bonsai overcomes this problem. "Instead of creating a flat map, our tool builds a branching tree, with individual cells at the leaves of the branches," says first author Dr. "Crucially, the distances along the branches accurately reflect how closely cells are related in the high-dimensional space." The team tested the software on both simulated and real single-cell RNA sequencing datasets.
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