Single-cell transcriptomics is a powerful technique that allows scientists to study how individual cells behave in different diseases. By looking at the RNA within single cells, researchers can see how various cell types change and how these changes might contribute to the progression of diseases like COVID-19 or responses to treatments like immunotherapy. However, one of the big challenges with this approach is the variability between different people and groups, which can lead to inaccurate results.
Imagine you’re trying to find a specific change in a cell type that occurs in a disease, but every person’s cells are a bit different, even if they have the same disease. This variability can make it hard to tell if a change is truly related to the disease or just a result of natural differences between individuals.
To solve this problem, a team led by researchers at Harvard University developed a new computational tool called scDist. This tool uses a mixed-effects model, which is a statistical method that helps account for the natural differences between individuals and groups. By doing so, scDist can more accurately identify changes in cell types that are genuinely associated with a disease or condition.
Visual representation of the scDist method
A scDist estimates the distance between condition means in high-dimensional gene expression space for each cell type. B To improve efficiency, scDist calculates the distance in a low-dimensional embedding space (derived from PCA) and employs a linear mixed-effects model to account for sample-level and other technical variability. This figure is created with Biorender.com, was released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.
In their study, the researchers tested scDist on both simulated (computer-generated) data and real patient data, including data from COVID-19 patients and people undergoing immunotherapy. They found that scDist was better at detecting true changes in cell types compared to other methods, even when they had a small number of samples to work with. Specifically, scDist revealed important changes in specific immune cells, such as dendritic cells, that provided new insights into how these cells might be involved in disease mechanisms and treatment responses.
As more single-cell data becomes available, scDist offers a reliable and efficient way to study how different cells contribute to human health and disease. This tool can help researchers and clinicians uncover cellular changes that could lead to new treatments or a better understanding of how diseases progress. Whether it’s finding new ways to tackle COVID-19 or improving immunotherapy, scDist is a valuable addition to the toolkit for studying the complex world of cells in the human body.