Entering edit mode
Darlene Goldstein
▴
230
@darlene-goldstein-1004
Last seen 9.7 years ago
Hi, I just wanted to mention that even if you do normalize all the
chips
together, you are still likely to see the 'batch' (or 'block')
effects. To try
to assess the extent of the problem, you might cluster the samples and
see if
you get samples from the same batch clustering together.
Best regards, Darlene
-------------------
Hello,
the 11 tumour sampel are considered as biological replicates, or are
these split
into different tumour classes?
We've had a similar problem. Our data was generated in three different
laboratories, each having slightly different protocols, but within
each lab we
had the same factors (the same doses of a drug).
I guess, if the tumours are considered as replicates one could include
the batch
as a factor (as you suggest below), but if they contain different
tumour classes
one could not separate the dmso effect from the "tomour" class effect.
The tissues samples (normal and tumour) are probably from different
subjects and
will show strong differences per se. Maybe one get some estimates for
the impact
of the batch by using a mixed effects model with each sample as random
effect
and the batch as fixed effect.
something like lme(response ~ batch, data=d, rand = ~ 1|sample)
I'm not sure about this, it's just an idea ...
Anyway, I'd pre-process (normalize) all samples together, otherwise
there'll
certainly be a batch effect.
kind regards,
Arne
> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch
> [mailto:bioconductor-bounces at stat.math.ethz.ch]On Behalf Of
> Adaikalavan
> Ramasamy
> Sent: 30 November 2004 23:51
> To: BioConductor mailing list
> Cc: Andrea Pellagatti
> Subject: [BioC] normalisation or analysis with batch effects
>
>
> Dear list,
>
> If the following question has been asked before, I do apologise in
> advance and hope someone can point to the relevant thread. Otherwise
I
> would appreciate some thoughts and pointers to this problem.
> Thank you.
>
>
> Problem : My collaborator (cc-ed here) has performed hybridisation
for
> 11 tumour and 40 normal samples on Affymetrix HGU-133Av2
> (contains ~55k
> probesets) chips. He had hybridised about half of the samples when
he
> realised he needed more Affymetrix chips.
>
> The second batch of chips arrived with the instruction to add DMSO
in
> the hybridisation cocktail, which he followed. The first batch did
not
> have such instruction. Therefore we believe that the two
> batches are not
> directly comparable. A posting to GeneArray mailing list had a reply
> (http://bfx.kribb.re.kr/gene-array/1255.html) supporting this view.
A
> cross-table of batch and sample is given below :
>
> | normal tumour total
> batch 1 (with DMSO) | 17 6 23
> batch 2 (without DMSO) | 23 5 28
> -----------------------|---------------------
> total | 40 11 51
>
>
> Therefore I have considered the following possible solutions :
>
> 1) Preprocess all arrays and compare tumour vs. normal
>
> 2) Preprocess the two batches separately and cbind() them.
> Then compare
> tumour vs. normal
>
> 3) Preprocess all arrays but include a batch effect in analysis ( I
am
> not sure how to do this - perhaps using LIMMA)
>
> 4) Preprocess separately and proceed as 3)
>
> Here, I use RMA to preprocess the arrays. I have done 1) and
> 2) and the
> correlation of the two gene lists, as assessed by correlation of
gene
> ranks, is only 0.35. I think 4) is a bit of overkill.
>
> Any opinions or alternative suggestions are very welcomed. Thank
you.
>
> Regards,
> --
> Adaikalavan Ramasamy ramasamy at cancer.org.uk
> Centre for Statistics in Medicine http://www.ihs.ox.ac.uk/csm/
> Cancer Research UK Tel : 01865 226 677
> Old Road Campus, Headington, Oxford Fax : 01865 226 962
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
>
--
Darlene Goldstein
Institute of Mathematics, EPFL Tel: +41 21 693 2552
CH-1015 Lausanne Fax: +41 21 693 4303
SWITZERLAND
