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

0

donnieDarko •

**0**wrote: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.