Deep learning for inferring gene relationships from single-cell expression data

Yuan, Ye; Bar‐Joseph, Ziv

doi:10.1101/365007

Cited by 16 publications

(26 citation statements)

References 39 publications

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“…Other methods-We now discuss other papers on this topic and our rationale for not including them in the comparison. We did not consider a method if it was supervised 38 or used additional information, e.g., a database of transcription factors and their targets 15 or a lineage tree 39 . We did not include methods that output a single GRN without any edge weights 21,24 , since any such approach would yield just a single point on a precision-recall curve.…”

Section: Grisli (Gene Regulationmentioning

confidence: 99%

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

et al. 2020

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We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell transcriptional data. As the ground truth for assessing accuracy, we use synthetic networks with predictable trajectories, literature-curated Boolean models, and diverse transcriptional regulatory networks. We develop a strategy to simulate single-cell transcriptional data from synthetic and Boolean networks that avoids pitfalls of previously-used methods. Furthermore, we collect networks from multiple experimental single-cell RNA-seq datasets. We develop an evaluation framework called BEELINE. We find that the AUPRC and early precision of the algorithms are moderate. The methods are better in recovering interactions in synthetic networks than Boolean models. The algorithms with the best early precision values for Boolean models also perform well on experimental datasets. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, we present recommendations to end users. BEELINE will aid the development of GRN inference algorithms.Single-cell RNA-sequencing technology has made it possible to trace cellular lineages during differentiation and to identify new cell types 1,2 . A central question that arises now is whether we can discover the gene regulatory networks (GRNs) that control cellular differentiation and drive transitions from one cell type to another. In such a GRN, each edge connects a transcription factor (TF) to a gene it regulates. Ideally, the edge is directed from the TF to the target gene, represents direct rather than indirect regulation, and corresponds to activation or inhibition.Single-cell expression data are especially promising for computing GRNs because, unlike bulk transcriptomic data, they do not obscure biological signals by averaging over all the cells in a sample. However, these data have features that pose significant difficulties, e.g., Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

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Section: Grisli (Gene Regulationmentioning

confidence: 99%

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

et al. 2020

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“…We now discuss other papers on this topic and our rationale for not including them in the comparison. We did not consider a method if it used additional information, e.g., a database of transcription factors and their targets [8], a lineage tree [17], or was supervised [35].…”

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confidence: 99%

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Pratapa

Jalihal

Law

et al. 2019

Preprint

133

285

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We present a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. Our contributions include a comprehensive evaluation pipeline based on simulated data from "toy", artificial networks with predictable cellular trajectories and on simulated data from carefully-curated Boolean models. We develop a strategy to simulate these two types of data that avoids the pitfalls of existing strategies that have been used to mimic bulk transcriptional data. We found that the accuracy of the algorithms measured in terms of AUROC and AUPRC was moderate, by and large, although the methods were better in recovering interactions in the artificial networks than the Boolean models. Techniques that did not require pseudotime-ordered cells were more accurate, in general. There were an excess of feedforward loops in predicted networks than in the Boolean models. The observation that the endpoints of many false positive edges were connected by paths of length two in the Boolean models suggested that indirect effects may be predominant in the outputs of these algorithms. The outputs of the methods were quite inconsistent with each other, indicating that combining these approaches using ensembles is likely to be challenging. We present recommendations on how to create simulated gene expression datasets for testing GRN inference algorithms. We suggest that new ideas for avoiding the prediction of indirect interactions appear to be necessary to improve the accuracy of GRN inference algorithms for single cell gene expression data. Simulated data from synthetic networks Simulated data from curated models LEAP PIDC GRN inference methods Predicted networks SCODE LEAP PIDC SCODE Run algorithms Parameter search Software run time Evaluate network motifs ROC Early Precision Stability of inferred networks

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“…Both methods have high computational requirements and are less suitable for the human genome that consists of more than 25,000 genes. An alternative approach is to treat ChIP-seq binding predictions as a supervised learning problem [17,33]. While such models can be quite accurate, they are limited to the small number of TFs for which ChIP-seq experiments are available and thus limited in their discovery of new gene regulatory relationships.…”

Section: Experiments On Cancer Datamentioning

confidence: 99%

Gene Regulatory Network Inference as Relaxed Graph Matching

Weighill

Guebila

Lopes‐Ramos

et al. 2020

Preprint

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Gene regulatory network inference is instrumental to the discovery of genetic mechanisms driving diverse diseases, including cancer. Here, we present a theoretical framework for PANDA, an established method for gene regulatory network inference. PANDA is based on iterative message passing updates that resemble the gradient descent of an optimization problem, OTTER, which can be interpreted as relaxed inexact graph matching between a gene-gene co-expression and a proteinprotein interaction matrix. The solutions of OTTER can be derived explicitly and inspire an alternative spectral algorithm, for which we can provide network recovery guarantees. We compare different solution approaches of OTTER to other inference methods using three biological data sets, which we make publicly available to offer a new application venue for relaxed graph matching in gene regulatory network inference. We find that using modern gradient descent methods Preprint. Under review.with superior convergence properties solving OTTER outperforms state-of-the-art gene regulatory network inference methods in predicting binding of transcription factors to regulatory regions.

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Deep learning for inferring gene relationships from single-cell expression data

Cited by 16 publications

References 39 publications

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Gene Regulatory Network Inference as Relaxed Graph Matching

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