Robust network inference using response logic

Groß, Torsten; Wongchenko, Matthew J.; Yan, Yibing; Blüthgen, Nils

doi:10.1093/bioinformatics/btz326

Cited by 7 publications

(5 citation statements)

References 46 publications

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“…Often, experimental settings do not allow determining all unknown parameters ( De Smet and Marchal, 2010 ; Gross et al , 2019 ). Nevertheless, they constrain the solution space such that after fixing one or multiple parameters, others become identifiable.…”

Section: Resultsmentioning

confidence: 99%

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Identifiability and experimental design in perturbation studies

Groß

Blüthgen

2020

Bioinformatics

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Motivation A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network’s response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimizing the experimental design is essential to achieve a meaningful characterization of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters. Results We show analytically that the identifiability of network parameters can be determined by an intuitive maximum-flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved, on average, with less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments. Availability and implementation IdentiFlow is available at github.com/GrossTor/IdentiFlow. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

“…Often, network inference is an underdetermined problem ( De Smet and Marchal, 2010 ; Gross et al , 2019 ). Thus, to achieve identifiable effective network models, certain parameters have to be set to constant values, such that the remaining parameters become uniquely determinable.…”

Section: Methodsmentioning

confidence: 99%

Identifiability and experimental design in perturbation studies

Groß

Blüthgen

2020

Bioinformatics

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“…Large-scale single-cell perturbation-response screens enable exploration of complex cellular behavior not accessible from bulk observation. Directionality in regulatory network models cannot be inferred without interventional or time-series data about the system (Gross et al, 2019). Experiments with targeted perturbations can be modeled as affecting individual nodes of a regulatory network model, while the molecular readouts provide information on the state changes.…”

Section: [Uses Of Single-cell Perturbation Data]mentioning

confidence: 99%

scPerturb: Harmonized Single-Cell Perturbation Data

Peidli

Green

Shen

et al. 2022

Preprint

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Recent biotechnological advances led to growing numbers of single-cell studies, which reveal molecular and phenotypic responses to large numbers of perturbations. However, analysis across diverse datasets is typically hampered by differences in format, naming conventions, data filtering and normalization. In order to facilitate development and benchmarking of computational methods in systems biology, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform pre-processing and quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. Using these datasets, we demonstrate the application of E-distance for quantifying perturbation similarity and strength. This work provides an information resource and guide for researchers working with single-cell perturbation data and highlights conceptual considerations for new experiments. The data collection, scPerturb, is publicly available at scperturb.org.

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“…Often, network inference is an underdetermined problem (De Smet and Marchal, 2010;Gross et al, 2019). Thus, to achieve identifiable effective network models, certain parameters have to be set to constant values, such that the remaining parameters become uniquely determinable.…”

Section: Identifiability Relationshipsmentioning

confidence: 99%

Identifiability and experimental design in perturbation studies

Groß

Blüthgen

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Motivation:A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network's response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimising the experimental design is essential to achieve a meaningful characterisation of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters. Results: We show analytically that the identifiability of network parameters can be determined by an intuitive maximum flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved with on average less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments.

show abstract

Robust network inference using response logic

Cited by 7 publications

References 46 publications

Identifiability and experimental design in perturbation studies

Identifiability and experimental design in perturbation studies

scPerturb: Harmonized Single-Cell Perturbation Data

Identifiability and experimental design in perturbation studies

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