Question: Differential Expression Analysis with monocle and batch effect correction
gravatar for Poorya Parvizi
15 months ago by
The University of Edinburgh
Poorya Parvizi40 wrote:


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 ( 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 modified 15 months ago by Kevin Blighe46k • written 15 months ago by Poorya Parvizi40
gravatar for Kevin Blighe
15 months ago by
Kevin Blighe46k
Kevin Blighe46k wrote:

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.


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.


ADD COMMENTlink modified 14 months ago • written 15 months ago by Kevin Blighe46k

Thank you Kevin. I will try it.

ADD REPLYlink written 15 months ago by Poorya Parvizi40
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