> For the complete documentation index, see [llms.txt](https://help.connected.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/batch-removal/general-linear-model.md).
# General linear model
This method is based on general linear model, much like ANOVA\[1] in reverse, 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 transform the original values to remove the batch effect using this tool.
We recommend normalizing your data to log normal distribution prior to removing batch effects using this method, but the task will run on any counts data node.
* Click the counts data node
* Click the **Batch removal** section of the toolbox
* Click **General linear model**
The batch effect removal dialog is similar to the dialog for ANOVA. To set up the model, you need to choose factors and interaction of factors based on your experiment design in addition to attribute(s) represent batch you would like to remove.
For example, in the case where you have samples from different cell types, treatment, they are from different batches, batch may have a different effect on different cell types and/or different treatment, you would need to include both treatment, cell type, batch and maybe the interaction between treatment and cell type in the model.
* Select the *Remove* checkbox for **batch**
* Click **Finish** to run
The output of is a new data node, contains the batch effect corrected values, but the treatment and cell type variation is still kept. It can be used as the input for downstream tasks.
## References
1. Fisher, R. A. (1925). *Statistical Methods for Research Workers.* \[Edinburgh: Oliver & Boyd]. The landmark text that originally introduced the statistical procedures and terminology for ANOVA to a broad research audience.
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