Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference
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ABSTRACT:
PROVIDER: PRJNA902315 | ENA |
REPOSITORIES: ENA
Dataset's files
| 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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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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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.
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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.
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