Hi, I am starting to think about grouping a series of microarray datasets into bioconductor objects so that I can quickly look to see how a gene behaves in each dataset. The two main options seem to be to use ExpressionSet or Limma's MAList. Has anyone got an opinion on which would be best to use or the advantages and disadvantages of both. To my mind MAList stores the annotation with the dataset which I feel is an advantage whereas ExpressionSet is the base implementation for many libraries. Dan -- ************************************************************** Daniel Brewer, Ph.D. Institute of Cancer Research Email: daniel.brewer at icr.ac.uk ************************************************************** The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP. This e-mail message is confidential and for use by the a...{{dropped:2}}

Hi Daniel -- Daniel Brewer <daniel.brewer at="" icr.ac.uk=""> writes: > Hi, > > I am starting to think about grouping a series of microarray datasets > into bioconductor objects so that I can quickly look to see how a gene > behaves in each dataset. The two main options seem to be to use > ExpressionSet or Limma's MAList. Has anyone got an opinion on which > would be best to use or the advantages and disadvantages of both. Some biases on my part, but... I guess either ExpressionSet or MAList is really meant to represent a single 'experiment'. Sounds like you're going to create a collection of experiments, so a collection of ExpressionSet or MAList objects (it would be a mistake, I think, to jam all your experiments into a single object of either of these classes). > To my mind MAList stores the annotation with the dataset which I feel is Storing annotations with the object can be a bad thing if the annotations are the same, because then there are effectively different variants of the same annotation, one for each object. These will inevitably drift apart, leading to confusion. There is also a memory use issue. That said, annotations can be added to ExpressionSet, specifically using featureData to store an AnnotatedDataFrame (data.frame + annotation on column labels). > an advantage whereas ExpressionSet is the base implementation for many > libraries. ExpressionSet is a little more tightly designed than MAList (MAList is essentially a list and so can contain (or not contain) any data; ExpressionSet is an S4 class that has to contain certain data. While you lose on freedom with ExpressionSet, the constriction probably comes with a benefit in terms of greater certainty about what the object actually contains. This imposed uniformity likely has benefits when the number of experiments you're managing increases. Many users probably view their MAList / ExpressionSet as 'read-only', so for these users the fact that you could do something to mess up an MAList really is only an academic possibility (you can also do things to mess up an ExpressionSet, again maybe just a bit harder to do that). ExpressionSet also contains an experimentData slot, which would be an ideal location to document which experiment the ExpressionSet represents. > Dan hope that helps, Martin > -- > ************************************************************** > Daniel Brewer, Ph.D. > Institute of Cancer Research > Email: daniel.brewer at icr.ac.uk > ************************************************************** > > The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP. > > This e-mail message is confidential and for use by the a...{{dropped:2}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Martin Morgan Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M2 B169 Phone: (206) 667-2793