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- Tjärnberg, Andreas, et al.
(författare)
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Avoiding pitfalls in L-1-regularised inference of gene networks
- 2015
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Ingår i: Molecular Biosystems. - : Royal Society of Chemistry (RSC). - 1742-206X .- 1742-2051. ; 11:1, s. 287-296
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Tidskriftsartikel (refereegranskat)abstract
- Statistical regularisation methods such as LASSO and related L-1 regularised regression methods are commonly used to construct models of gene regulatory networks. Although they can theoretically infer the correct network structure, they have been shown in practice to make errors, i.e. leave out existing links and include non-existing links. We show that L-1 regularisation methods typically produce a poor network model when the analysed data are ill-conditioned, i.e. the gene expression data matrix has a high condition number, even if it contains enough information for correct network inference. However, the correct structure of network models can be obtained for informative data, data with such a signal to noise ratio that existing links can be proven to exist, when these methods fail, by using least-squares regression and setting small parameters to zero, or by using robust network inference, a recent method taking the intersection of all non-rejectable models. Since available experimental data sets are generally ill-conditioned, we recommend to check the condition number of the data matrix to avoid this pitfall of L-1 regularised inference, and to also consider alternative methods.
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| 2. |
- Tjärnberg, Andreas, 1981-
(författare)
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Exploring the Boundaries of Gene Regulatory Network Inference
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. The third investigation aimed to understand the influence of system and data properties on network inference accuracy. L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. In this study we elucidated some general principles for under what conditions we expect strongly degraded accuracy. Moreover, it allowed us to estimate expected accuracy from conditions of simulated data, which was used to predict the performance of inference algorithms on biological data. Finally, we built a software package GeneSPIDER for solving problems encountered during previous investigations. The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference.
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