Prediction of the gene expressions after a knock-out experience predict
Prediction of the gene expressions after a knock-out experience
## S4 method for signature 'micro_array' predict(object, Omega, nv = 0, targets = NULL, adapt = TRUE)
object |
a micro_array object |
Omega |
a network object. |
nv |
[=0] numeric; the level of the cutoff |
targets |
[NULL] vector; which genes are knocked out? |
adapt |
[TRUE] boolean; do not raise an error if used with vectors instead of one column matrices. |
Nicolas Jung, Frédéric Bertrand , Myriam Maumy-Bertrand.
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014). Cascade: a R-package to study, predict and simulate the diffusion of a signal through a temporal gene network. Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram, S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
data(Selection) data(network) #A nv value can chosen using the cutoff function nv=.11 EGR1<-which(match(Selection@name,"EGR1")==1) P<-position(network,nv=nv) #We predict gene expression modulations within the network if EGR1 is experimentaly knocked-out. prediction_ko5<-predict(Selection,network,nv=nv,targets=EGR1) #Then we plot the results. Here for example we see changes at time point t2: plot(prediction_ko5,time=2,ini=P,label_v=Selection@name)
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