Credit: MD Anderson Cancer Center HOUSTON — In an effort to address a major challenge when analyzing large single-cell RNA-sequencing datasets, researchers from The University of Texas MD Anderson Cancer Center have developed a new computational technique to accurately differentiate between data from cancer cells and the variety of normal cells found within tumor samples. The work was published today in Nature Biotechnology. 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, which deliver gene expression data from many thousands of individual cells. CopyKAT uses that gene expression data to look for aneuploidy, or the presence of abnormal chromosome numbers, which is common in most cancers, said study senior author Nicholas Navin, Ph.D., associate professor of Genetics and Bioinformatics & Computational Biology. The tool also helps to identify distinct subpopulations, or clones, within the cancer cells.