Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data

Luo, Qi; Maity, Alok Kumar; Teschendorff, Andrew E.

doi:10.1016/j.isci.2022.105709

iScience

2022

DOI: 10.1016/j.isci.2022.105709

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Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data

Qi Luo

Alok Kumar Maity

Andrew E. Teschendorff

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2024

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FateNet: an integration of dynamical systems and deep learning for cell fate prediction

Sadria,

Bury

2024

Bioinformatics

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Motivation Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. Results By leveraging information about normal forms and scaling behavior near bifurcations common to many dynamical systems, FateNet predicts cell decision occurrence with higher accuracy than conventional methods and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories. Availability github.com/ThomasMBury/fatenet Supplementary information Supplementary data are available at Bioinformatics online.

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FateNet: an integration of dynamical systems and deep learning for cell fate prediction

Sadria,

Bury

2024

Bioinformatics

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Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data

Cited by 1 publication

References 59 publications

FateNet: an integration of dynamical systems and deep learning for cell fate prediction

FateNet: an integration of dynamical systems and deep learning for cell fate prediction

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