**10**wrote:

Hi

I'm using the limma package to obtain some linear models out of some arrays.

I have the transcription rate of the cell at 7 different growth rates (7 different arrays).

I did created the design matrix, and because I just want the linear model and not any contrast bewtween arrays I did it this way.

```
design <- matrix(ncol = 1, nrow = 7 )
rownames(design) <-colnames(set)[alk]
colnames(design) <- "GRI"
design[] <- alk_gri
design <- model.matrix(~ + design)
fit_alk <- lmFit(alk_set, design)
toptable(fit_alk)
```

But now I have several doubts as I'm not an expert in statistics and I have not used limma before.

The end result of all this should be an list order the slope of the linear models and do some gene ontology. The questions are, because I'm not doing contrasts between the models can I trust the p-value output of toptable or should I use adjusted p-value.

The other doubt is that I think I may want to filter the models by the correlation of predictor and response variable (which is the p-value I have talked before) and probably by the goodness of the linnear model (R square or ¿Residual error of each model?) and I'm not very sure how to do this last filtering.

Sorry for this messy post.

Thanks

**37k**• written 4.5 years ago by nonCodingGene •

**10**

I'm wondering whether if a could use the sigma output of lmFit as a goodness of the linear model.

10