Hi,

My goal is to normalized multiple GEO microarray datasets to make them comparable to each other and to future samples. I'm using UPC function in the SCAN.UPC package. I saw there's an option to provide batch information for each sample and the batch effect will be corrected. I read the following in the SCAN.UPC documentation which I don't really understand --

"Batch adjusting will be performed after values have been SCAN normalized and summarized at the gene/probeset level. This is also true when UPC and UPCfast are being used—the data will be SCAN normalized and summarized, then batch adjusting will be performed, and lastly UPC transformation will occur. This process is different from when UPC or UPCfast are invoked without batch information; in this scenario, no SCAN normalization will occur."

I have some questions regarding the batch effect:

1. Is it true that SCAN and UPC build models on each sample individually and are applied to each sample individually? If so, why is batch effect relevant? Why perform batch correction after SCAN normalization?

2. In the document it says that "no SCAN normalization will occur" when no batch information is provided. Can someone please elaborate what this implies? Can UPC skip the SCAN step? I guess the final normalized values will be different than those using batch information. But which one is better?

3. Lastly I'd like to know if I'll get the same results by the following three approaches --

    a) Process each sample (GSM) individually, without batch information obviously

    b) Process a dataset (GSE) at a time, with batch information

    c) Process a dataset (GSE) at a time, without batch information

Thanks a lot!

Yunlei

Thanks for your question and sorry for the confusion. I put an option to do batch normalization in SCAN.UPC, but most people don't use it. I did this before the sva package was available. Now I would just point people to the sva package.

First, I'll answer your questions:

1. Yes, that's a key aspect of SCAN and UPC. They only use data from within a single sample for normalization. This has many benefits (see our paper) and does a pretty good job of standardizing data across samples. However, even though multiple samples may have the same/similar mean and variance, there may still be batch effects (systematic variations associated with when/how each batch was processed). So it's still a good idea in many cases to correct for these effects (I typically use ComBat). Having said that, batch effects **should** affect UPC relatively little compared to SCAN.

2. It depends on what type of data you are using. If you are using Affymetrix microarrays, it will skip SCAN and go straight to UPC. However, there is an option to first SCAN normalize, then batch-adjust, then UPC normalize. The reason for this sequence of steps is that batch-adjusting expects the data to be normally distributed. SCAN values are normally distributed, whereas UPC values are not (by design). In theory, this sequence should work well, but I haven't done thorough comparisons between this approach and simply using UPC.

3. a and c should give you the same results. b will be different because it will batch adjust across the samples.

Let me know if that isn't clear.