Hi,

I'm trying to analysis a time-series RNA-seq data using DESeq2, I have 15 time points, 2 conditions, and 2 biological repeat.

I perform LRT test first and the full model is ~batch + condition + time + condition:time, while the reduce model is ~batch + condition + time. All variances are recognized as factor. I choose the threshold fdr < 0.01 and get ~5000 different expressed genes. 

Then I want to map these different expressed genes at each time point and perform wald test of these different expressed genes at each time point. I use the threshold fdr < 0.01 and | log2FoldChange | > 1  but only ~2000 genes could be mapped in the time points.  Then I loose the delimitation and use the threshold only fdr < 0.01, there are ~3000 genes could be mappped in specific time points.

I want to make sure whether my method of process time-series data is right. If so, what is the biological meaning of these genes could not mapped in specific time points.

Thanks a lot!!

Fei

The LRT is looking for any differences at any time point and so has higher power compared to each individual time point comparison.

Below is a small example just using R's lm() function.

Notice that, while in fact all of the 10 groups have a different mean, none have a p-value < 0.0004. Meanwhile, the ANOVA has a p-value < 0.0004. So this difference in power means that you can't necessarily find which particular time point is different despite detecting it with the LRT.

> set.seed(2); y <- rnorm(50, rep(rnorm(10,0,1),each=5), 1)
> summary(lm(y ~ x))$coefficients
              Estimate Std. Error    t value     Pr(>|t|)
(Intercept) -0.5470609  0.5540166 -0.9874450 0.3293602263
x2           0.7415973  0.7834978  0.9465212 0.3495659760
x3           3.0229304  0.7834978  3.8582500 0.0004064738
x4          -0.8811522  0.7834978 -1.1246390 0.2674456775
x5           0.6814552  0.7834978  0.8697602 0.3896209633
x6          -0.1264786  0.7834978 -0.1614282 0.8725687700
x7           1.1233450  0.7834978  1.4337564 0.1594131044
x8           0.3496955  0.7834978  0.4463261 0.6577686361
x9           2.7103169  0.7834978  3.4592526 0.0013006822
x10          0.6998463  0.7834978  0.8932332 0.3770750836
> anova(lm(y ~ x))
Analysis of Variance Table

Response: y
          Df Sum Sq Mean Sq F value   Pr(>F)
x          9 66.199  7.3555  4.7929 0.000232 ***
Residuals 40 61.387  1.5347
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1