The Remove batch effect tool functions much This method is based on general linear model, much like ANOVA in reverse, calculating it calculates the variation attributed to the factor(s) being removed then adjusting the original values to remove the variation. 

By including batch in the differential analysis model, the variability due to the batch effect is accounted for when calculating p-values. In this sense, batch effects are best handled as part of the differential analysis model. However, clustering data or visualizing biological effects can be very difficult if batch effects are present in the original data. We can modify transform the original values to remove the batch effect using the Remove batch effect toolthis tool. 

Running Remove batch effect

We recommend normalizing your data prior to removing batch effects, but the task will run on any counts data node. 

• Click the counts data node
• Click the Normalization and scaling section of the toolbox
• Click Remove batch effectGeneral linear model

The batch effect removal dialog is similar to the dialog for ANOVA. To set up the model, you need to choose which attributes should be considered. Here, you should include the batch attribute, any attributes that interact with batch, and the interactions between these attributes. 

...

The output of Remove batch effect is of is a new data node, Batch effect adjusted counts. This data node contains the batch effect corrected values can be used as the input for downstream tasks such as clustering and UMAP.

...

...

Advanced options

The advanced options for Remove batch effect are shared by ANOVA. 

...