Recovering Time-Varying Networks From Single-Cell Data
Euxhen Hasanaj, Barnab\'as P\'oczos, Ziv Bar-Joseph
TL;DR
This paper introduces Marlene, a deep learning model that reconstructs dynamic gene regulatory networks from time series single-cell data, capturing temporal changes and biological relevance.
Contribution
Marlene is a novel deep neural network that uses self-attention and meta-learning to infer time-varying gene networks from single-cell data, outperforming traditional methods.
Findings
Accurately reconstructs temporal gene networks.
Identifies gene interactions related to specific biological responses.
Effective for rare cell types and complex biological processes.
Abstract
Gene regulation is a dynamic process that underlies all aspects of human development, disease response, and other key biological processes. The reconstruction of temporal gene regulatory networks has conventionally relied on regression analysis, graphical models, or other types of relevance networks. With the large increase in time series single-cell data, new approaches are needed to address the unique scale and nature of this data for reconstructing such networks. Here, we develop a deep neural network, Marlene, to infer dynamic graphs from time series single-cell gene expression data. Marlene constructs directed gene networks using a self-attention mechanism where the weights evolve over time using recurrent units. By employing meta learning, the model is able to recover accurate temporal networks even for rare cell types. In addition, Marlene can identify gene interactions relevant…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
The paper focuses on an important, though well-studied task of regulatory gene network inference. It focuses on time-series, single-cell data, an increasing available, more detailed view of gene expression. The approach goes beyond simple application of existing deep learning models by using an architecture in which the projection matrices for calculating attention are evolve as part of an RNN.
The architecture relies on interpreting the attention matrix A generated within the model as the adjacency matrix of the regulatory network, with the model itself being trained on a surrogate task of predicting cell type (y). The assumption that A, used in this surrogate task, will capture direct regulatory interactions is not very well justified in the manuscript. Would using a matrix with different dimensionality (e.g. having #columns the same, to match # of TFs, but with different number of r
This is a very solid piece of research. The motivation is well explained and attractive. The idea is novel and has potential to be practically useful. The description of the method is very clear and the logic flows very well. The experiment part is comprehensive. In terms of the method itself, using PMA or Set transformer to convert the expression to gene feature matrix eliminate the axis of cells so downstream analysis could focus on the attentions on genes. The use of GRU in the next step is
1. I would like to see a more clear explanation on Equation 3-6. What exactly are the rational of using GRU here beyond it was used in EvolveGCN? Do we have any physical meaning on this operation on Equation 3-6? 2. Algorithm stability is a key metric in BEELINE. Could you comment on the stability of Marlene? 3. The output of Equation 6 is the adjacency matrix at timepoint t. Then, at least for the evaluation you have performed in this study, you must have transformed multiple At into one At. C
Strengths: * Marlene effectively leverages recent advances in deep learning, such as self-attention mechanisms and recurrent units, to model dynamic GRNs from scRNA-seq data. The approach uses set-based architectures to handle multiple cells per time point. * Employing meta-learning (MAML) enables Marlene to reconstruct accurate networks even for rare cell types by treating cell types as tasks. This enhances the model's ability to handle heterogeneous cell populations. * The model demonstrates s
Weaknesses: * The paper primarily evaluates using overlap analysis with existing incomplete databases of static interactions. More direct experimental validation of novel predicted regulatory links would strengthen the findings. * Potential limitations in scaling Marlene to a very large number of genes are not thoroughly discussed. In experiments, the quadratic memory usage from adjacency matrices led to gene filtering. * The model currently lacks the ability to predict the effects of perturbat
1. propose a novel deep learning framework that employs self-attention mechanisms and GRUs to model the dynamics of gene regulatory networks. 2. The method is tested across three diverse datasets, which demonstrate the generalization ability of the model.
1. While the paper presents a technical approach, it could benefit from a deeper discussion on the biological implications of the findings and how they align with or differ from current scientific understanding. 2. In the experimental phase, mainly presents results based on metrics, demonstrating that it outperforms other methods. However, for solving a specific GRN problem, we are more concerned with whether the inferred GRN can identify some key genes or transcription factors (TFs) that can be
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Taxonomy
TopicsBioinformatics and Genomic Networks · Functional Brain Connectivity Studies