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Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

ABSTRACT: Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

PROVIDER: PRJNA902315 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR22311675.fastq.gz	Fastqsanger.gz
	SRR22311676_1.fastq.gz	Fastqsanger.gz
	SRR22311676_2.fastq.gz	Fastqsanger.gz
	SRR22311677_1.fastq.gz	Fastqsanger.gz
	SRR22311677_2.fastq.gz	Fastqsanger.gz

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Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

Project description:Regulation of gene expression in biological systems is a complex, nonlinear process composed of context specific interactions, from signaling and transcription to genome modification. Modeling gene regulatory networks (GRNs) can be limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are consequently forced to model covariance between regulatory genes and their targets as a proxy for causal interactions. This in turn complicates validation and reuse of predictive modeling frameworks. To disentangle covariance and casual influence require aggregation of independent and complementary sets of evidences, such as transcription factor (TF) binding and target gene expression. Common approaches include the overlap of evidence to infer causal relations. However the complete state of the system, e.g. TF activity (TFA) is unknown. Other methods tries to estimate these latent features. These models often use linear frameworks that are unable to account for non-linearities, TF-TF interactions, and other higher order features. Deep learning frameworks can be used to model complex interactions between features and capture latent features of higher order. However deep learning methods often discard central concepts in biological systems modeling such as sparsity and latent feature interpretability in favour of increased complexity of the model. In this work we demonstrate that gene regulatory network inference using latent features such as transcription factor activity can be built into a single framework. We present a novel deep learning approach (the Supirfactor framework) that incorporates multiple data-type orthogonal evidence of regulation and maintains interpretable parameter estimates.

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Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Project description:Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression, yet information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

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Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.

Project description:BackgroundModeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features.ResultsWe propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC).ConclusionHere we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.

| S-EPMC10797903 | biostudies-literature

Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.

Project description:The modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are therefore forced to model relationships between regulatory genes and their targets with expression as a proxy for the upstream independent features, complicating validation and predictions produced by modeling frameworks. Separating covariance and regulatory influence requires aggregation of independent and complementary sets of evidence, such as transcription factor (TF) binding and target gene expression. However, the complete regulatory state of the system, e.g. TF activity (TFA) is unknown due to a lack of experimental feasibility, making regulatory relations difficult to infer. Some methods attempt to account for this by modeling TFA as a latent feature, but these models often use linear frameworks that are unable to account for non-linearities such as saturation, TF-TF interactions, and other higher order features. Deep learning frameworks may offer a solution, as they are capable of modeling complex interactions and capturing higher-order latent features. However, these methods often discard central concepts in biological systems modeling, such as sparsity and latent feature interpretability, in favor of increased model complexity. We propose a novel deep learning autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor), that scales to single cell genomic data and maintains interpretability to perform GRN inference and estimate TFA as a latent feature. We demonstrate that SupirFactor outperforms current leading GRN inference methods, predicts biologically relevant TFA and elucidates functional regulatory pathways through aggregation of TFs.

| S-EPMC9915715 | biostudies-literature

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Project description:Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

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Project description:Embedding is the key step in single-cell Hi-C (scHi-C) analysis which relies on capturing biological meaningful heterogeneity at various levels of genome architecture. To understand the strength and limitations of existing tools in various applications, here we use ten scHi-C datasets to benchmark thirteen embedding tools including Va3DE, a new convolutional neural network model that can accommodate large cell numbers. We built a software framework to decouple the preprocessing options of existing tools and found that no single tool works best across all datasets under default settings. The difficulty levels and preferred resolutions are different between benchmark datasets, and the choice of data representation and preprocessing strongly impact the embedding performance. Embedding cells from early embryonic stages relies on long-range compartment-scale contacts, but resolving cell cycle phases and complex tissue requires short-range loop-scale contacts. Both random-walk and inverse document frequency (IDF) transformation prefers long-range “compartment-scale” over short-range “loop-scale” embedding, while deep-learning methods better overcome sparsity at both scales and are more versatile with different resolutions. Finally, “diagonal integration” with independent data modal is a promising approach to distinguish similar cell subpopulations. Our findings underscore the significance of appropriate priors for scHi-C embedding and offer new insights into genome architecture heterogeneity.

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