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BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research

View Article: PubMed Central - PubMed

ABSTRACT

Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.

No MeSH data available.


Workflow of the heuristic model search algorithm.On the first step the marginal likelihood of the  model is calculated. Then each TF is evaluated as a variable independently and only TFs whose marginal likelihood is higher than the  model’s are further expanded. The highest marginal likelihood of the single TF models is selected as a threshold or bound to evaluate the nested models with two TFs.
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f1: Workflow of the heuristic model search algorithm.On the first step the marginal likelihood of the model is calculated. Then each TF is evaluated as a variable independently and only TFs whose marginal likelihood is higher than the model’s are further expanded. The highest marginal likelihood of the single TF models is selected as a threshold or bound to evaluate the nested models with two TFs.

Mentions: We developed a heuristic algorithm to search for models with high posterior probabilities. The proposed algorithm (Fig. 1) is inspired by Occam’s Up23 and Branch and Bound algorithms24. The procedure starts by evaluating the posterior probability of the model (M0) which does not have any regulator except itself. In the next step, the model is expanded by adding one TF. Each candidate TF is added one by one and the posterior probabilities of the new models with a single TF (M1) are evaluated. The models that have higher posterior probabilities than the model are selected and their posterior probabilities are compared. The highest posterior probability is used as a cut-off for the next stage. The selected models are further expanded by adding a new TF. Each of the remaining TFs (the TFs other than the ones already in the model) is added one by one. The models which have higher posterior probability than the cut-off are then kept and compared, and the highest posterior probability is then selected as the new cut-off for the next stage. This process is repeated until adding a new TF does not improve the posterior probability any further. Below we provide a pseudocode for our algorithm.


BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research
Workflow of the heuristic model search algorithm.On the first step the marginal likelihood of the  model is calculated. Then each TF is evaluated as a variable independently and only TFs whose marginal likelihood is higher than the  model’s are further expanded. The highest marginal likelihood of the single TF models is selected as a threshold or bound to evaluate the nested models with two TFs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120305&req=5

f1: Workflow of the heuristic model search algorithm.On the first step the marginal likelihood of the model is calculated. Then each TF is evaluated as a variable independently and only TFs whose marginal likelihood is higher than the model’s are further expanded. The highest marginal likelihood of the single TF models is selected as a threshold or bound to evaluate the nested models with two TFs.
Mentions: We developed a heuristic algorithm to search for models with high posterior probabilities. The proposed algorithm (Fig. 1) is inspired by Occam’s Up23 and Branch and Bound algorithms24. The procedure starts by evaluating the posterior probability of the model (M0) which does not have any regulator except itself. In the next step, the model is expanded by adding one TF. Each candidate TF is added one by one and the posterior probabilities of the new models with a single TF (M1) are evaluated. The models that have higher posterior probabilities than the model are selected and their posterior probabilities are compared. The highest posterior probability is used as a cut-off for the next stage. The selected models are further expanded by adding a new TF. Each of the remaining TFs (the TFs other than the ones already in the model) is added one by one. The models which have higher posterior probability than the cut-off are then kept and compared, and the highest posterior probability is then selected as the new cut-off for the next stage. This process is repeated until adding a new TF does not improve the posterior probability any further. Below we provide a pseudocode for our algorithm.

View Article: PubMed Central - PubMed

ABSTRACT

Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.

No MeSH data available.