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 multi-dimensional 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 big challenge that researchers continue to struggle with. "People are good at recognizing patterns in two or three dimensions," says Prof. 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.
A tree instead of a flat map
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 artefacts 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 Daan de Groot. “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. Compared to existing methods, Bonsai reconstructs developmental pathways vastly more accurately, preserves the true relationships between cells, and identifies similar cells much more reliably.”
