I have a dataset analyzing global transcription shifts. And the samples have been spiked with a known but unequal amount of ERCC; Such that each sample has constant ratio of biological RNA to Spike-in . It states in the RUVseq vignette:

Note that one can relax the negative control gene assumption by requiring instead the identification of a set of positive or negative controls, with a priori known expression fold-changes between samples, i.e., known β. One can then use the centered counts for these genes (logY−Xβ) for normalization purposes

If I understand correctly, to adjust for a sample in which B has four times as much spike in as A I use the equation:

Log2(Spike-in_mat) - MatX * Matbeta

**Matrix X**
|   | Intercept | ploidy |
| A |         1 | 1      |
| B |         3 | 2     |

**Matrix Beta**
|           | Spike 1 | Spike 2 | ... |
| Intercept |       1 |       1 |     |
| Ploidy    |       1 |       1 |     |

And I then feed RUVg this matrix as the spike.

Is this correct?