normalization and batch correction across multiple project

We have had to regularly address the same issues that you are facing. 
There is no blanket answer -- every case needs to be considered on its own 
merits -- but you seem to be considering the right options.

In our work, we generally adjust for the batch in the limma linear model 
rather than trying to remove it up-front using combat.  Also consider 
removeBatchEffect().

As you say, analysing multiple projects together can help estimate a batch 
effect.  However this approach will come unstuck if the samples for the 
projects are very different.  There is another reason why we generally 
avoid analysing multiple projects together.  The projects will usually 
need to be submitted eventually to a public repository such as GEO, and 
the different projects generally have to be submitted independently. 
Users will not be able to reproduce our normalization and analysis unless 
the projects are analyzed separately.

Best wishes
Gordon

> On Mon, Aug 18, 2014 at 1:11 PM, Adaikalavan Ramasamy wrote:
>
> Dear all,
>
> I would like to appeal to the collective wisdom in this group on how best
> to solve this problem of normalization and batch correction.
>
> We are a service unit for an academic institute and we run several
> projects simultaneously. We use Illumina HT12-v4 microarrays which can
> take up to 12 different samples per chip. As we QC the data from one
> project, the RNA from failed samples can be repeated to include into chips
> from another project (rather than running partial chips to avoid wastage).
> Sometimes we include samples from other projects also. Here is a simple
> illustration
>
> Chip No       ScanDate    Contents
> 1                1st July        *12 samples from project A*
> 2                1st July          *8 samples from project A* + 4 from
> project B
> 3                1st August   12 samples from Project B
> 4                1st August     *1 sample from Project A* + 5 samples
> from B + 6 from project C
> ...
>
> What is the best way to prepare the final data for *project A*? One
> option is to do the following:
>
>    1. Pool chips 1, 2 and 4 together.
>    2. Remove failed samples
>    3. Remove samples from other projects.
>    4. Normalize using NEQC from limma
>    5. Correct for scan date using COMBAT from sva.
>
> The other option we considered is to omit step 3 (i.e. use other samples
> for normalization and COMBAT) and subset at the end.
>
> I feel this second option allows for better estimation of batch effects
> (especially in chip 4). However, sometimes project A and B can be quite
> different (e.g. samples derived from different tissues) which might mess up
> the normalization especially if we want to compare project A to B directly. We
> also considered nec() followed by normalizeBetweenArrays with "Tquantile"
> but I felt it was too complicated. Anything else to try?
>
> Thank you.
>
> --
>
> Adaikalavan Ramasamy
>
> Senior Leadership Fellow in Bioinformatics
>
> Head of the Transcriptomics Core Facility
>
>
>
> Email: [email protected]
>
> Office: 01865 287 710
>
> Mob: 07906 308 465
>
> http://www.jenner.ac.uk/transcriptomics-facility


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