How to calculate Log2FC for time series microarray gene expression data ?

Question

[donnieDarko]

Hello,

I have time series microarray data with 5 time points and 3 replicates at each time point :

data is like :

contr_30m.1   contr_30m.2 contr_30m.3 contr_4h.1  contr_4h.2  contr_4h.3  contr_12h.1 contr_12h.2 contr_12h.3 contr_24h.1 contr_24h.2 contr_24h.3 contr_168h.1    contr_168h.2    contr_168h.3    treat_30m.1 treat_30m.2 treat_30m.3 treat_4h.1  treat_4h.2  treat_4h.3  treat_12h.1 treat_12h.2 treat_12h.3 treat_24h.1 treat_24h.2 treat_24h.3 treat_168h.1    treat_168h.2    treat_168h.3
15E1.2    6.31830509001173    6.50910103511847    6.40042470226987    6.77260093984888    6.73877459097465    6.62447424457527    6.93719959744148    6.83700972810815    6.73433704523287    6.55774627748981    6.76806652193203    6.92725174160908    6.64310204026992    6.40308379426978    6.48380787868878    6.66861556151074    6.46243225264411    6.64702087453105    6.35550949784669    6.70583555980519    6.59717492579843    6.74637054832231    6.6426175373088 6.64549416114948    6.84560420751365    6.86702539628196    6.98023213569837    6.30737496960958    6.23307662275724    6.61855538519875
  

where all the data has been converted to their log2 values for better performance in finding DEG's.

I have tried the following code to get the log2foldchange :

log2fc<-data.frame()
for (i in (1:nrow(df))) {
  #temp<-as.numeric(df[i,c(16:30)]/df[i,c(1:15)])
  trt<-as.numeric(df[i,c(16:30)])
  ctrl<-as.numeric(df[i,c(1:15)])
  log2fc<-rbind(log2fc,(mean(trt)-mean(ctrl)))
}#calculates the fold change

but in this code the value ranges from -0.25 to +0.25 which is never considered a significant fold change.

I want to use volcanoplot with this data, but I cannot do so, because of this arbitrary log2fc values.

Can you guys suggest me how to perfectly calculate log2foldchange values for Time Series microarray data with replicates ? also is there any other plot rather than volcanoPlot to show data's of time series DEG?

Plz Help.