Q:
Currently, I’m working on reconstructing gene regulatory network. It’s
really an interesting topic and I would like to estimate which tools is
suitable for our experimental data. I have read your published paper
"Improved Reconstruction of In Silico Gene Regulatory Networks by
Integrating Knockout and Perturbation
Data". In this paper, I can’t understand the section of learning noise from
deletion data.
Step 1: Calculate the probability of regulation Pb->a for each pair of genes
(b,a). I want to know how to calculate this probability, and this value of
probability can decide potential regulation or not?
I want to ask you that how to work in this section, and I’m appreciated if
you can help me to figure out.

A: Basically we used the expression levels currently believed to be
unaffected by a deletion to form a Gaussian background. Then if a gene
has an expression level far away from the mean of this Gaussian
distribution (by calculating the probability that the expression is as
extreme or more extreme than the observed one based on the Gaussian), we
consider the gene to be affected by the deletion.