SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data

• Published in: Nature communications Vol. 15; no. 1; pp. 760 - 17
• Main Authors: Piran, Zoe, Nitzan, Mor
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2164; 631/114/2397; 631/1647/48; 631/61/212/2019; Algorithms; Biological activity; Biological computing; Cell cycle; Cell interactions; Circadian rhythms; COVID-19; Filtration; Gene expression; Hepatocytes; Humanities and Social Sciences; Knowledge; Liver; multidisciplinary; Reference signals; Science; Science (multidisciplinary); Subpopulations; Zonation

Cellular populations simultaneously encode multiple biological attributes, including spatial configuration, temporal trajectories, and cell-cell interactions. Some of these signals may be overshadowed by others and harder to recover, despite the great progress made to computationally reconstruct bio...