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


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Cancer cells.

Researchers at The University of Texas MD Anderson Cancer Center have developed a computational technique to accurately differentiate between data from cancer cells and the variety of normal cells found within tumor samples.

Credit: Vitanovski/iStock

Researchers at The University of Texas MD Anderson Cancer Center have developed a computational tool to reliably distinguish between cancerous and normal cells in tumor samples.

Scientists can use the CopyKAT (copy number karyotyping of aneuploid tumors) tool to more easily analyze gene-expression data from large single-cell RNA-sequencing experiments.

MD Anderson's Nicholas Navin noted that CopyKAT mines the data to uncover abnormal chromosome numbers typical of most cancers, and identifies distinct subpopulations, or clones, within the cancer cells.

 Said Navin, "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."

Added former MD Anderson researcher Ruli Gao, "We hope this tool will be useful to the research community to make the most of their single-cell RNA-sequencing data and to drive new discoveries in cancer."

From The University of Texas MD Anderson Cancer Center
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


 

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