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Question: Differential Expression Analysis with monocle and batch effect correction
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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 ScaleData function (vars.to.regress argument) to regress out the batch effect. However, the output of this function is in scale format which contains negative values in it.

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?

ADD COMMENTlink 21 months ago Poorya Parvizi • 40 • updated 21 months ago Kevin Blighe 43k
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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

ADD COMMENTlink 20 months ago Kevin Blighe 43k
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Thank you Kevin. I will try it.

ADD REPLYlink 21 months ago
Poorya Parvizi
• 40

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