[Bioc-devel] Can I analyze with bioconductor a microarray experiement where the distribution of probes intesisties follow a bi modal distribution?

Miguel Moreno-Risueno miguelangel.moreno at upm.es
Fri Jun 7 17:57:59 CEST 2013

Hi James

Thank you very much for the answer. Yes I understand that it is across
experiments that a given probe should follow a normal distribution, but if
this is true shouldn't the population of those probes similarly follow a
normal distribution in turn, or not necessarily. I am concern that because
of this overall bimodal distribution of the population of probes for a given
sample (but that happens for every sample) the probes may follow a bimodal
distribution themselves. I noticed that for other microarray experiments in
the same (Nimblegen) and other platforms (Affymetrix) the distribution of
probes within every sample follow a normal or normal-like distribution. 

Thank you again,


-----Original Message-----
From: James W. MacDonald [mailto:jmacdon at uw.edu] 
Sent: Friday, June 07, 2013 5:27 PM
To: Miguel Moreno-Risueno
Cc: bioc-devel at r-project.org
Subject: Re: [Bioc-devel] Can I analyze with bioconductor a microarray
experiement where the distribution of probes intesisties follow a bi modal

Hi Miguel,

On 6/7/2013 5:11 AM, Miguel Moreno-Risueno wrote:
> Hello all,
> We have recently received a microarray experiment in the Nimblegen 
> platform where the intensity of the probe sets follow a bi-modal 
> distribution. We have been said from the facility that this is because 
> of the dynamic range of the Agilent scanner they use. We are concerned 
> about the statistical analysis with bioconductor as it is our 
> understanding that these statistical analyses are developed for normal 
> or normal-like distribution. We appreciate any information on this regard.

If I understand your question correctly, you are noting that the overall
distribution of probes within a sample has a bi-modal distribution. This
doesn't really have anything to do with any statistical tests you might be
computing, as you are not doing any statistics within a sample (e.g., one
usually doesn't test to see if probe X is differentially expressed as
compared to probe Z in sample Q).

Instead, what you should be concerned with are the distributions of the
individual probes across samples. With microarray data we usually don't have
enough data to even begin to assess the across-sample, within probe
distributions (e.g., if you have three replicates for two sample types, good
luck trying to discern if those probes follow a normal distribution, or are
even 'hump-shaped'). In addition, there are usually tens of thousands of
probes on a given chip. I have never heard of anybody looking at each probe,
trying to assess if it follows a reasonable distribution across samples. I
suppose you could do it, but to what point?

Instead we simply assume that the data follow a reasonable distribution and
then do the test. This is one of the reasons that it is imperative to follow
up promising leads with confirmatory testing, preferably with new samples.



> Thank you in advance for your help,
> Miguel
> 	[[alternative HTML version deleted]]
> _______________________________________________
> Bioc-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/bioc-devel

James W. MacDonald, M.S.
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099

More information about the Bioc-devel mailing list