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

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...

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Bibliographic Details
Published inNature communications Vol. 15; no. 1; pp. 760 - 17
Main Authors Piran, Zoe, Nitzan, Mor
Format Journal Article
LanguageEnglish
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
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Summary: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 biological processes from single-cell data. To address this, we present SiFT, a kernel-based projection method for filtering biological signals in single-cell data, thus uncovering underlying biological processes. SiFT applies to a wide range of tasks, from the removal of unwanted variation in the data to revealing hidden biological structures. We demonstrate how SiFT enhances the liver circadian signal by filtering spatial zonation, recovers regenerative cell subpopulations in spatially-resolved liver data, and exposes COVID-19 disease-related cells, pathways, and dynamics by filtering healthy reference signals. SiFT performs the correction at the gene expression level, can scale to large datasets, and compares favorably to state-of-the-art methods. Cells simultaneously encode multiple signals, some harder to recover. Here, authors introduce SiFT (Signal FilTering), a kernel-based projection method, revealing underlying biological processes in single-cell data.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-44757-7