Extrapolating heterogeneous time-series gene expression data using Sagittarius

Woicik, Addie; Zhang, Mingxin; Chan, Janelle; Ma, Jianzhu; Wang, Sheng

doi:10.1038/s42256-023-00679-5

Cited by 1 publication

(3 citation statements)

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“…Single-cell gene expression prediction has been investigated in previous studies (Cao et al, 2021) Recently, a few generative methods have introduced procedures to understand cell differentiation based on physical time and predict unobserved timepoints. Sagittarius method (Woicik et al, 2023) predicts gene expressions at future timepoints but mainly focuses on bulk RNA-seq data that requires cell matching between timepoints. This matching is hard to obtain for real-world scRNA-seq as the cells are lysed during the experiment.…”

Section: S1 Related Workmentioning

confidence: 99%

“…Especially when extrapolating five timepoints, scNODE significantly outperforms the other two methods in DR and SC datasets, which implies that scNODE better generalizes to unobserved future timepoints that have shifted distributions. Therefore, although accurate extrapolation is a challenge (Woicik et al, 2023) in single-cell gene expression prediction, scNODE still demonstrates significant improvement over state-of-the-art methods in accurately extrapolating future timepoints.…”

Section: S6 More On Experiments S61 Scnode Can Accurately Predict Exp...mentioning

confidence: 99%

“…Hence, they do not accurately model how cells develop in real physical time. Sagittarius method (Woicik et al, 2023) predicts gene expressions at future timepoints but requires cell matching between timepoints. This matching is hard to obtain for real-world scRNA-seq as the cells are lysed during the experiment.…”

Section: Introductionmentioning

confidence: 99%

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scNODE : Generative Model for Temporal Single Cell Transcriptomic Data Prediction

Zhang,

Larschan,

Bigness

et al. 2023

Preprint

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Temporal measurements using single-cell RNA sequencing (scRNA-seq) technology enable the study of gene expression programs of individual cells for normal developmental processes or diseased physiological states. However, due to the labor, expense, and technical challenges associated with these experiments, researchers are only able to profile gene expression at discrete and sparsely spaced time points. This results in information loss between consecutive discrete time points and impedes the downstream developmental analysis. To address this problem, we proposescNODE, an end-to-end model that can simulate and predict realisticin silicosingle-cell samples at any time point to enable temporal downstream analyses. Our method integrates a variational autoencoder (VAE) with neural ordinary differential equations (ODEs) to predict gene expression in a continuous and non-linear latent space. Importantly,scNODEadds a regularization term to integrate overall dynamics of cell developments to the latent space, such that the learned latent representation is informative and interpretable. Our evaluations on six real-world scRNA-seq datasets show thatscNODEachieves higher predictive performance than state-of-the-art methods. We further demonstrate thatscNODElearns an interpretable latent space, and helps recapitulate cell-type developmental trajectories.AvailabilityThe data and code are publicly available athttps://github.com/rsinghlab/scNODE.

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Section: S1 Related Workmentioning

confidence: 99%

Section: S6 More On Experiments S61 Scnode Can Accurately Predict Exp...mentioning

confidence: 99%