Data is not only the answer to numerous questions in the business world; the same applies to biomedical research. In order to develop new therapies or prevention strategies for diseases, scientists need more and better data, faster and faster. However, the quality is often very variable and the integration of different data sets often almost impossible.
To overcome this, scientists are using artificial intelligence and machine learning to discover solutions to precisely these problems, thus enabling medical innovations for a healthier society. In the latest issue of the journal Nature Methods, they present three articles with groundbreaking new solutions.
To see whether an observation one makes in a single dataset can be generalized, you can check whether the same can be observed in other datasets of the same system. In single-cell data, so-called batch effects complicate combining datasets in this manner. These are differences in the molecular profiles between samples as they were generated at a different time, in a different place, or from a different person.
Overcoming these effects is a central challenge in single-cell genomics with more than 50 proposed solutions. But which one is the best? Researchers curated 86 datasets and compared 16 of the most popular data integration methods on 13 tasks. After over 55,000 hours of computation time and a detailed evaluation of 590 results, they built a guide for optimized data integration. This allows for improved observations on disease processes across datasets at a population scale.
Many questions in biology revolve around continuous processes like development or regeneration. For any cell in such a process, single-cell RNA-sequencing measures gene expression. The method, however, is destructive to cells and scientists obtain only static snapshots. Thus, many algorithms have been developed to reconstruct continuous processes from snapshots of gene expression.
A common limitation: These algorithms cannot tell us anything about the direction of the process. To overcome this limitation, Marius Lange and colleagues developed a new algorithm called CellRank. It estimates directed cell-state trajectories by combining previous reconstruction approaches with RNA velocity, a concept to estimate gene up- or down-regulation. Across in-vitro and in-vivo applications, CellRank correctly inferred fate outcomes and recovered previously known genes. In a lung regeneration example, CellRank predicted novel intermediate cell states on a dedifferentiation trajectory whose existence was validated experimentally. CellRank is an open-source software package that is already used by biologists and bioinformaticians around the world to analyze complex cellular dynamics in situations like cancer, reprogramming or regeneration.
Recent years have seen a growing development of technologies to measure gene expression variation in tissue. The advantage of such technologies is that scientists can see cells in their context, thus being able to investigate principles of tissue organization and cellular communication. Researchers need flexible computational frameworks in order to store, integrate and visualize the growing diversity of such data. To tackle this challenge, Giovanni Palla, Hannah Spitzer, and colleagues developed a new computational framework, called Squidpy. It enables analysts and developers to handle spatial gene expression data. Squidpy integrates tools for gene expression and image analysis to efficiently manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of machine learning tools in the python ecosystem. Scientists around the world are already using it to analyze spatial molecular data.
Malte D. Luecken, et al. Cell Rank for directed single-cell fate mapping. Nature Methods, 2022; DOI: 10.1038/s41592-021-01346-6
Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)
