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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning.

Chia-Hung Yeh^1,2, Ze-Guang Chen¹, Cheng-Yue Liou¹

¹Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan.

Bioengineering (Basel, Switzerland)

|September 28, 2023

Summary

Predicting cellular responses to perturbations is challenging due to cell type differences. The proposed Information-Navigated Variational Autoencoder (INVAE) improves prediction accuracy by filtering irrelevant information and creating a unified feature space.

Keywords:

cell perturbation response prediction cell response homogeneous space construction deep learning disentangled representations interpretability

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Area of Science:

Computational Biology
Genomics
Machine Learning in Biology

Background:

Predicting cellular responses to perturbations is crucial but remains challenging.
Existing methods often fail due to the assumption of similar responses across cell types, neglecting cell-type-specific genomic interactions.
Deep learning models struggle with the heterogeneity of biological data, leading to poorly defined latent spaces and domain-specific mappings.

Purpose of the Study:

To develop a novel deep learning framework for accurate prediction of cellular responses to perturbations.
To address the challenge of data heterogeneity and domain shift in cell type-specific perturbation prediction.
To improve the interpretability of cellular response mechanisms by identifying conserved regulatory patterns.

Main Methods:

Introduction of the Information-Navigated Variational Autoencoder (INVAE), a deep neural network designed for perturbation response prediction.
INVAE employs an information-filtering mechanism to remove non-essential biological data.
The model constructs a homogeneous control condition space and maps it to the perturbation condition space, enabling cross-domain predictions.

Main Results:

INVAE demonstrated superior performance in cell state prediction compared to three state-of-the-art methods across three real-world datasets.
The information filtering approach significantly enhances prediction accuracy.
Analysis revealed that filtering irrelevant information highlights conserved gene regulation similarities across different cell types post-perturbation.

Conclusions:

INVAE offers a robust and accurate method for predicting cellular responses to perturbations, outperforming existing approaches.
The model's ability to handle data heterogeneity and domain shift makes it suitable for diverse biological applications.
INVAE provides insights into conserved gene regulatory mechanisms, advancing our understanding of cellular responses.