[BioC] normalization and batch correction across multiple project
adaikalavan.ramasamy at gmail.com
Wed Aug 27 17:13:03 CEST 2014
Thank you for the advice. I am happy to do it both ways but these are large
projects and also it would be difficult to quantify if the differences are
small enough. Which is why I wanted to get the opinion of others in this
And yes, you are right in that we need to include project and scan date
into the adjustment if I batch correct first. Thanks.
On Tue, Aug 26, 2014 at 6:31 PM, Ryan <rct at thompsonclan.org> wrote:
> Hi Adaikalavan,
> Why not try it both ways and see if it even makes a difference? If you get
> the same results either way, then just do whatever is easier.
> If you do batch correction before removing other projects' samples, I
> would think you would need to include the project identifier as a batch
> effect in addition to the scan date or chip number, right?
> On 8/18/14, 5:11 AM, 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
>> simultaneously. We use Illumina HT12-v4 microarrays which can take up to
>> 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
>> 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: adaikalavan.ramasamy at ndm.ox.ac.uk
>> Office: 01865 287 710
>> Mob: 07906 308 465
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