New Computational Tool Improves Analysis of scRNA-seq Data

Although single-cell transcriptomics is a popular tool to study the tumor microenvironment, it remains difficult to differentiate between data from cancer cells and the variety of normal cells found within tumor samples. In an effort to address this challenge, researchers from The University of Texas MD Anderson Cancer Center have developed a new computational technique.

The new tool, dubbed CopyKAT (copy number karyotyping of aneuploid tumors), allows researchers to more easily examine the complex data obtained from large single-cell RNA-sequencing experiments. It is described in a Nature Biotechnology paper published yesterday.

CopyKAT uses gene expression data to look for aneuploidy, which is common in most cancers, explained study senior author Nicholas Navin. The tool also helps to identify distinct subpopulations, or clones, within the cancer cells.

"We developed CopyKAT as a tool to infer genetic information from the transcriptome data. By applying this tool to several datasets, we showed that we could unambiguously identify, with about 99% accuracy, tumor cells versus the other immune or stromal cells present in a mixed tumor sample," Navin said. "We could then go one step further to discover the subclones present and understand their genetic differences."

Historically, tumors have been studied as a mixture of all cells present, many of which are not cancerous. The advent of single-cell RNA sequencing in recent years has enabled researchers to analyze tumors in much greater resolution, examining the gene expression of each individual cell to develop a picture of the tumor landscape, including the surrounding microenvironment.

The tool is freely available to researchers. The authors note that the tool is not applicable to the study of all cancer types. Aneuploidy, for example, is relatively rare in pediatric and hematologic cancers.