Scientists are constantly searching for better ways to understand how cells change over time. Single-cell RNA sequencing allows researchers to look at which genes are active in individual cells, but analyzing data from experiments that track the same subjects across time has been a challenge.

A research team led by researchers from National Jewish Health has introduced a new tool called rescueSim. This method creates simulated data that closely mimics what scientists see when studying cells from the same individual across multiple time points. By doing this, rescueSim helps researchers plan experiments more effectively, decide how many samples are needed, and test whether their analysis methods will work before collecting real data.

Data/analysis and simulation workflows

(A) Summary of a typical data preparation workflow for paired/longitudinal scRNA-seq data. Paired/longitudinal samples are sequenced, the resulting count matrices are combined and the data are integrated, cell types are identified through clustering, and count data is separated by cell type. (B) Overview of the simulation framework. Parameters and estimates are shown in circles, with the colors indicating whether they are population, subject, sample, or cell-level values. Circles with bold outlines represent parameters estimated at a gene level. The left most values are estimated from empirical data.

This approach is especially important because real-life experiments must account for differences not just between cells, but also between people and across time. With rescueSim, scientists now have a way to capture these complexities, making it easier to design reliable studies that use single-cell RNA sequencing.

Availability – rescueSim is implemented as an R package and is available at https://github.com/ewynn610/rescueSim.