Centrala begrepp
Kernel-based testing reveals hidden patterns in single-cell data, enabling a deeper understanding of cell population heterogeneities.
Sammanfattning
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.
Statistik
"The DE genes are equally separated into the four alternatives DE, DM, DP, and DB."
"The power of the Gauss-kernel test is superior to other methods on detecting the DB alternative."
"Only 14 regions were significantly differentially enriched between persister cells and untreated cells."
Citat
"Kernel testing emerges as a promising approach to overcome challenges in capturing subtle variations and accurately identifying meaningful differences in molecular patterns."
"Kernel testing offers a powerful tool for uncovering hidden patterns and gaining deeper insights into the intricate heterogeneities of cell populations."