trigger.net-methods {trigger} | R Documentation |
Network-Trigger analysis estimates the joint posterior probability of causal regulation for each pair of genes in the genome. These probabilities can further be used to construct a gene regulatory network.
## S4 method for signature 'trigger' trigger.net(triggerobj, gender = NULL, idx = NULL, Bsec = 100, prob.cut = 0.7, include.loc = TRUE, seed = 123, inputfile = NULL)
triggerobj |
An object of class |
gender |
Optional. When computing statistics involving markers on sex chromosome, |
idx |
Optional. One can specify the indices of selected genes as putative regulators. By default, all the genes will be selected as putative regulators. |
Bsec |
Number of iterations to perform when estimating null statistics for secondary-linkage and conditional independence. |
prob.cut |
Probability threshold. The joint regulatory probabilities of a regulator to all the other genes will be set to zero if the local-linkage probability of the regulator is below the threshold; default |
include.loc |
Logical. If |
seed |
Optional. A numeric seed for reproducible results. |
inputfile |
Optional. If provided, reads in the probability matrix from working directory. |
The option idx
contains the indices of putative regulator genes. When the data set is large, one can use this option by selecting a subset of genes as putative regulators in one computation and parallel-computes the genome-wide regulatory probability. If idx=NULL
, all the genes will be computed for probability of regulation to other genes in the data.
If include.loc = TRUE
, the joint posterior probability of regulation is the product of local-linkage, secondary-linkage and conditional independence. Otherwise, it is the product of secondary-linkage and conditional independence. The local-linkage is not a necessary condition for calculating regulation probability. If the probability of local-linkage is considered, the joint probability of regulation is more conservative. See references for details.
A matrix of genome-wide regulatory probabilities with putative regulators in rows and regulated genes in columns. Note that the matrix is not symmetric. If gene i is estimated to be causal for gene j with high probability, the reverse is not true.
Lin S. Chen lschen.stat@gmail.com, Dipen P. Sangurdekar dps@genomics.princeton.edu and John D. Storey jstorey@princeton.edu
Chen L.S., Emmert-Streib F., and Storey J.D. (2007) Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biology, 8: R219.
trigger.loclink
, trigger.netPlot2ps
and trigger.trait
## Not run: data(yeast) attach(yeast) triggerobj <- trigger.build(marker = marker, exp = exp, marker.pos = marker.pos, exp.pos = exp.pos) triggerobj <- nettrig.loc(triggerobj, window.size = 30000) trig.prob <- trigger.net(triggerobj, Bsec = 100) netPlot2ps(trig.prob) detach(yeast) ## End(Not run)