showHp

Plot for the hub probabilities ; there is one probability for each node in the network.

We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.

Frederic Bertrand

networkABC

Network Reverse Engineering with Approximate Bayesian
Computation

Myriam Maumy-Bertrand

Khadija Musayeva

Nicolas Jung

 Université de Strasbourg

 CNRS

showHp function

<dl><dt>result</dt>
<dd>: The result of the abc algorithm.</dd></dl>

Arguments

Plot for the hub probabilities — showHp

<dl>

<dt>result</dt>
<dd>: The result of the abc algorithm.</dd>

</dl>

showHp: Plot for the hub probabilities

Description

Usage

Arguments

Examples