Researchers from Yale University accelerated t-distributed stochastic neighbor embedding (t-SNE) for visualization of rare cell populations
A team of researchers from Yale University improved the mathematical formula for a bioinformatics data visualization method. The update may help to develop snapshots of single-cell gene expression several times faster and at much higher-resolution. The improved approach is expected to reduce the rendering time of a million-point single-cell RNA-sequencing (scRNA-seq) data set from over three hours down to fifteen minutes, according to the researchers. t-Distributed Stochastic Neighborhood Embedding (t-SNE) is used to represent patterns in RNA sequencing data that is collected at the single cell level—scRNA-seq data—in two dimensions. t-SNE organizes the cells according to the genes they express and is used to identify new cell types and cell states.
However, t-SNE is a slow process, therefore, scRNA-seq dataset is often reduced as a smaller sample is taken from the initial sample before applying t-SNE. This can compromise the results as the process may fail to capture rare cell populations. Another team of Yale University had previously developed the fast multipole method (FMM) — a numerical technique that accelerates the calculation of long-ranged forces in the n-body problem. The current research team recognized that the principles behind the FMM can be used to nonlinear dimensional reduction problems such as t-SNE, The team used the technique to accelerate t-SNE and renamed it as FIt-SNE, or fast interpolation-based t-SNE.
According to Yuval Kluger, senior author and Yale University professor of pathology, the approach can be used to rapidly analyze single cell RNA-sequencing data and to characterize rare cell subpopulations that cannot be detected when the data is subsampled prior to t-SNE. The team used a heatmap-style visualization for its FIt-SNE results. This can help to observe the expression patterns of thousands of genes at the level of single cells simultaneously. According to the researchers, FIt-SNE can accelerate further work in the field of developmental biology along with neuroscience and cancer research, which require single-cell sequencing for mapping the brain and analyzing tumors. The research was published in the journal Nature Methods on February 11, 2019.
Dennis Nordstrom was born and raised in Tampa. Dennis has worked as a freelance journalist for nearly a decade and written for Tribune Media, the AP and MSNBC. As a journalist for Gator Ledger, Dennis mostly covers community events and human interest stories.