[BioC] microarray analysis in R without replicates

Naomi Altman naomi at stat.psu.edu
Wed Sep 23 05:25:38 CEST 2009

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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