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Scientists wield single-cell sequencing, ML data reduction to combat cancer
By Sara Moein  |  Aug 03, 2023
Scientists wield single-cell sequencing, ML data reduction to combat cancer
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Single-cell RNA sequencing is a mighty weapon in cancer research that maps cell types and states, both individually and in clusters. ML then pares down the ensuing reams of data for ease of analysis. ML genetic and healthcare data specialist Dr Sara Moein explains their workings.

NEW YORK - Single-cell sequencing is a powerful technique for extracting cellular and molecular data at the level of individual cells. This encompasses all types of sequencing, including that in genomics, transcriptomics, epigenomics, proteomics and metabolomics. Unlike bulk sequencing, which generates data in an averaged form, single-cell sequencing provides details from each cell.

Applying this sequencing technique has revolutionized cancer research by providing insights from cells in cancerous lesions. It can also supply information about malignant and immune cells, the heterogeneity of tumors, and the mechanism of the cancer cells in a tumor. All of this helps healthcare providers with diagnosis, prognosis, and target therapy. Thanks to single-cell sequencing, these huge advances in the field of cancer research are improving medical professionals’ understanding of the biological behaviors of tumor cells and helping to detect therapeutic targets for each patient more precisely.

Single-cell sequencing has rapidly progressed in recent years. The first experiments involving single-cell messenger-RNA and DNA sequencing in humans were conducted in 2009 and 2011, respectively. Sequencing at the single-cell level also outperforms previous methods because it reveals the microenvironmental heterogeneity of each cell. Single-cell sequencing also helps to prepare a cell atlas for developmental research. Scientists apply this tool to detect significant markers that differentiate subgroups in multiple disease or treatment conditions.1

Single-cell data are high dimensional

Single-cell sequencing provides huge amounts of information for scientists, but this can also yield cumbersome quantities of data for visualization and downstream analysis. This is because each cell is profiled by its genes, and the corresponding data have thousands

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