Wisdom of crowds for robust gene network inference

By Daniel Marbach, James C. Costello, R. Küffner, N. Vega, Robert J. Prill, Diogo M. Camacho, K. Allison, Manolis Kellis, James J. Collins, Gustavo A. Stolovitzky. Nature Methods, 2012

1,736 citations recorded by the source corpus.

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ∼1,700 transcriptional interactions at a precision of ∼50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c27e16cf9b626e983ad6372a25e98e060053afcb

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Citations in the corpus

Estimating Mutual Information for Discrete-Continuous Mixtures (2017, methodology): “Instead we resorted to a challenge dataset for reconstructing regulatory networks, called the DREAM5 challenge [30].”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, methodology): “To get a systematic assessment of the performance of different tools and methods, projects such as DREAM4 and DREAM5 [146] have been invaluable.”
SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation (2016, neutral): “performance of GENIE3 was best in the DREAM5 Network Inference challenge for population data [27].”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “[139]) user specified adds edges with probability p = 2e d 2−d Linear, GP Add, GP Mix, Sigmoid Add and Sigmoid Mix – mixed graph data CausalWorld [1] – comprehensive robotics dataset MPI3D [70] – visual disentanglement dataset Pendulum-light-shadow [267] – image data Phase coupled oscillator [5] – p”
Quantitative and logic modelling of gene and molecular networks (2015, methodology): “Recent software tools can be used to help to implement such a multiple-prong strategy (82).”
Gene regulatory network inference in the era of single-cell multi-omics (2023, methodology): “These would also enable the evaluation of GRN inference methods in an unbiased manner through open competition, as was illustrated for GRN inference from bulk transcriptomics data by the DREAM challenges 33 .”
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, methodology): “GeneNetWeaver simulator - DREAM5 The largest three networks of the DREAM5 challenge (Marbach et al., 2012) are considered to assess the scalability of SAM.”
A Theory of Usable Information Under Computational Constraints (2020, methodology): “We evaluate V -information on the in-silico dataset from the DREAM5 challenge (Marbach et al., 2012) and use the setup of Gao et al. (2017), where 20 genes with 660 datapoints are utilized to evaluate all methods.”
A community effort to assess and improve drug sensitivity prediction algorithms (2014, methodology): “By training the model with all 22 views, predictive performance was improved by 9% over using only the six profiling data sets, yielding a final wpc -index = 0.583 (one-sided, t-test from random predictions, FDR = 2.5 × 10 −5 ; scaled wpc -index = 0.629).”
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection (2012, methodology): “The first three benchmarks are taken from the DREAM5 challenge [28].”