[BioC] microarray analysis in R without replicates

Groot, Philip de philip.degroot at wur.nl
Wed Sep 23 13:32:55 CEST 2009

Hello all,
The puma package allows you to calculate Differentially Expressed genes (function"pumaDE") utilizing single .CEL-files (no biological replicates). Be careful with interpreting the resulting data, because it does not replace the statistically more sound inclusion of biological replicates!
Dr. Philip de Groot Ph.D.
Bioinformatics Researcher

Wageningen University / TIFN
Nutrigenomics Consortium
Nutrition, Metabolism & Genomics Group
Division of Human Nutrition
PO Box 8129, 6700 EV Wageningen
Visiting Address: Erfelijkheidsleer: De Valk, Building 304
Dreijenweg 2, 6703 HA  Wageningen
Room: 0052a
T: +31-317-485786
F: +31-317-483342
E-mail:   Philip.deGroot at wur.nl <mailto:Philip.deGroot at wur.nl> 
Internet: http://www.nutrigenomicsconsortium.nl <http://www.nutrigenomicsconsortium.nl/> 
             http://humannutrition.wur.nl <http://humannutrition.wur.nl/> 
             https://madmax.bioinformatics.nl <https://madmax.bioinformatics.nl/> 


Van: Naomi Altman [mailto:naomi at stat.psu.edu]
Verzonden: wo 23-9-2009 5:25
Aan: Rainer Tischler; bioconductor at stat.math.ethz.ch
Onderwerp: Re: [BioC] microarray analysis in R without replicates

You cannot control the error rates or assign any statistical
interpretation to the data.  But you can still look and see what
might be interesting.   However, you do have to ask the investigators
how much time and money they want to throw at an analysis that will
have a very high false positive and false negative rate.  Which
depends, I suppose on the relative cost of someone's time to do the
analyses and validation studies versus the cost of collecting some


At 03:16 PM 9/22/2009, Rainer Tischler wrote:
>Dear all,
>have received a microarray data set in standard Affymetrix CEL-format
>consisting of only six samples  without any replicates (same organism
>and cell type, but different individuals and different biological
>conditions for each individual; the same Affymetrix GeneChip platform
>was used for all samples). Moreover, the data was apparently collected
>without any a-priori biological hypothesis.
>I know that it is
>impossible to apply standard clustering, feature selection or
>classification techniques in this case. However, I am wondering whether
>anybody is aware of a method in R to extract meaningful biological
>information in this case (i.e. from single-sample microarray data or
>from multiple samples with different biological conditions and no
>replicates) - or is there nothing I can do given the above limitations?
>Many thanks,
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>Search the archives:

Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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