Is Regressing Covariates in Both ScaleData and RunHarmony Redundant and Potentially Problematic?

[vertesy]

Hello,

Thank you for this cool method, I had very nice results with it!

When I'm analyzing scRNA-seq data using Seurat and Harmony, I'm regressing out covariates (e.g., library, sample type) at two steps:

1. During Scaling:

# pseudocode
NormalizeData(obj) |> 
ScaleData(obj, vars.to.regress = c('library', 'sample_type')) |> 
PCA(obj) 

2. During Batch Correction with Harmony:

library_sample_type = paste0('library', 'sample_type')

RunHarmony(obj, group.by.vars = c('library_sample_type')) -> result

This means I'm addressing the covariates twice—once in ScaleData and again in RunHarmony. My questions are:

• Is this redundancy problematic?
  • Specifically, could correcting for covariates at both steps lead to overcorrection?
• What are the potential consequences of regressing out covariates in both steps?
  • Could it affect downstream analyses like clustering or differential expression by reducing variability too much?
• What would be the recommended best practice?
  • Should I regress out covariates only during RunHarmony or OK to use them in both?
  • E.g. marker heatmap plots are based on ScaleData, that is one reason to correct already at scaling (of course I could redo scaling and PCA after Harmony).

I'd appreciate any guidance on whether I should adjust my workflow to correct for covariates in just one of these steps to avoid problems that you can foresee.

Thank you for your help!