Single-cell technologies offer insights into molecular feature distributions, proposing a kernel-testing framework for non-linear cell-wise distribution comparison. The method allows feature-wise and global transcriptome/epigenome comparisons, identifying transitions in cell states. Kernel testing uncovers subtle population variations missed by traditional methods, demonstrating effectiveness in uncovering persister cells resembling untreated breast cancer cells. The approach provides a robust and flexible framework for differential analysis of single-cell data.
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arxiv.org
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by Anth... kl. arxiv.org 03-14-2024
https://arxiv.org/pdf/2307.08509.pdfDybere Forespørgsler