Hello,

In RNA sequencing data, I want to adjust for the covariates such as age, BMI, sex etc. I have created colData with these covariates and factorised (for binary) and scaled (for continuous) them. My concern is I have normalised the data and then applying adjustment. However, during adjustment using both voom and removeBatchEffect, will they double adjust the data or is it the right way to perform this. Please see below the functions used.

```
design_matrix <- model.matrix(~Age + Gender + BMI +
Batch + Condition, data=colData)
v <- voom(raw_counts, design_matrix, plot=TRUE)
fit <- lmFit(v, design_matrix)
adjusted_counts <- removeBatchEffect(v$E, covariates=phenodata[, c("Age", "BMI", "Batch")]).
```

This is because I wish to use this matrix for correlation studies and not for differential gene expression analysis.

Is there any other way to adjust for covariates?

Thank you,

Cross-posted to Bioconductor https://support.bioconductor.org/p/9158852/