We clearly need more information about your experiment to help you with
removing batch effects. Judging from your original email, it sounded like
all of condition 1 was on batch 1 and all of condition 2 was run on batch 2
(ie this is perfect confounding). What exactly was run on Batch 1
(experiment 1) and what was run on Batch 2 (experiment 2)?

In your approach outlined below, you might still miss a lot, or have a lot
of false positives. But its hard to know without your experiment design.

Also depending on your experiment, if you're working in a well understood
system, like yeast or stem cells, biologists could probably give you a
lists genes that should change with a given treatment. you could see the
variation across batches at these genes to see if SVA or ComBat is removing
real biology

-Andrew



Message: 14
Date: Mon, 12 Mar 2012 10:09:54 +0000
From: tefina <tefina.paloma@gmail.com>
To: <bioconductor@stat.math.ethz.ch>
Subject: Re: [BioC] batch effect confounded with condition
Message-ID: <loom.20120312T104842-37@post.gmane.org>
Content-Type: text/plain; charset="us-ascii"

Thanks for all your answers.

I think I will pursue the following strategy:
I will analyse each experiment separately. Contrasts within each experiment
should be fine. (As batch 1 concerns only experiment 1 and batch 2 concerns
only
experiment 2).
Any comparisons between experiments ( = comparisons between batches) I will
only
do on the p value level. So I will only evaluate things like: do genes that
show
up in experiment 1 also show up in experiment 2?

Doing this, I should be more or less on the safe side.
What do you think?

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