The researchers developed an automated single-cell multimodal sequencing clustering software tool that can profile what is happening within the cell across multiple biological processes simultaneously. The tool can also better characterize relationships between changes in a cell.
Researchers used the tool to conduct simulation and real-data experiments and found that it outperformed existing single-cell single-modal and multimodal clustering methods on single-cell multimodal data sets.
“With this tool, we can better understand a single cell as an entity and not just as a fragmented unit,” said Hakon Hakonarson, MD, PhD, director of the Center for Applied Genomics at CHOP and a senior author of the study. “This is a significant advancement and allows us to integrate and put all of this information into biological perspective, which is particularly important when considering information on different diseases.”
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