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New Computational Tool Reliably Differentiates Between Cancer and Normal Cells From Single-Cell RNA-Sequencing Data

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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. Read more . . .


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