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Hi,

I would like to run differential expression analysis on my single-cell data. My data contains of 5 groups which sequenced in 3 batches. Each batch contains all of the groups. Before the differential expression analysis with **monocle** (I should run it with monocle) I have tried to eliminate batch effect from the data. I used ** Seurat**'s

Can I use scale values in differential expression analysis, specifically monocle?

I also tried limma's **removeBatchEffect** function, which also gives negative values.
What is the best way to regress out the batch effect before the monocle differential expression?

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I do not believe negative values will help differential expression analysis in any way. Monocle actually provides functionality for dealing with things like batch. In many of the functions, there is a parameter called `residualModelFormulaStr`

, which allow you to list any covariates for which the statistical modelling should be adjusted.

residualModelFormulaStr

A model formula string specify effects you want to exclude when testing for cell type dependent expression

So, for example, for a differential expression analysis, use:

```
differentialGeneTest(cds, fullModelFormulaStr = " ~ condition",
reducedModelFormulaStr = " ~ Batch", relative_expr=TRUE, cores=4)
```

Take a look at the Manual and Tutorial.

Kevin

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Thank you Kevin. I will try it.